Systems, computer medium and methods for management training systems

A training system, including a plurality of sensors to obtain a plurality of biometrics from a first user. A stress level, a level of interest, a level of engagement, a level of alertness, and a level of excitement are determined responsive to analysis of ones of the plurality of biometrics. An indication is displayed of the obtained biometrics, the determined stress level, and the determined levels of interest, engagement, alertness, and excitement.

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No. 13/540,300 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH OF EMPLOYEES USING MOBILE DEVICES”, U.S. patent application Ser. No. 13/540,153 filed on Jul. 2, 2012 and titled “SYSTEMS AND METHOD TO MONITOR HEALTH OF EMPLOYEE WHEN POSITIONED IN ASSOCIATION WITH A WORKSTATION”, U.S. patent application Ser. No. 13/540,028 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING COGNITIVE AND EMOTIVE HEALTH OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,067 filed on Jul. 2, 2012 and titled “COMPUTER MOUSE SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,095 filed on Jul. 2, 2012 and titled “CHAIR PAD SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,124 filed on Jul. 2, 2012 and titled “FLOOR MAT SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,180 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING BIOMECHANICAL HEALTH OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,208 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR COACHING EMPLOYEES BASED UPON MONITORED HEALTH CONDITIONS USING AN AVATAR”, U.S. patent application Ser. No. 13/540,335 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR PROVIDING HEALTH INFORMATION TO EMPLOYEES VIA AUGMENTED REALITY DISPLAY”, U.S. patent application Ser. No. 13/540,374 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH AND ERGONOMIC STATUS OF DRIVERS OF VEHICLES” (now U.S. Pat. No. 8,872,640), and/or U.S. patent application Ser. No. 13/540,262 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, the disclosures of which are incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates generally to training systems and more particularly to systems, machines, non-transitory computer medium having computer program instructions stored thereon, and methods for providing training systems.

BACKGROUND OF THE INVENTION

Training systems can assist in the training of individuals. For example, Management Training Systems (MTSs) can aid users' training in the skills necessary for management and leadership. For example, such training may relate to resolving conflicts, negotiating, identifying and mitigating health and safety hazards, among other topics. Within the field of management development and training, use of technology is increasing, including the use of virtual reality simulations. Virtual reality simulations may be used by professional development trainers to provide a user with experiential training, rather than training that relies only on rote or didactic learning.

Experiential training enables users to develop leadership skills, competencies, experiences, and behaviors. During a virtual reality training session, a user may guide a digital avatar through a series of simulated scenarios and make decisions at various points during the virtual reality training session. Such virtual reality training sessions are most effective when the user is highly engaged. For this reason, post-training reviews often request that a user reports on a number of personal metrics (such as engagement, interest, etc.) to gauge the effectiveness of the virtual reality simulation.

SUMMARY OF THE INVENTION

The Applicant has recognized that self-reported and/or post-training measurements may not provide sufficient accuracy to determine the effects and efficacy of a training system. The Applicant has recognized the need for methods for providing a training systems and for determining the effectiveness of training provided through training systems.

Having recognized that, in some cases, biometrics offer a more precise gauge of user engagement during a virtual simulation than self-reports while advantageously avoiding expected population biases, embodiments of the invention include systems, methods, processor-readable media, and electronic interfaces to enhance use of virtual reality training systems by incorporating biometric feedback.

Where a conventional virtual reality simulation training method may provide a three-dimensional (3D) training environment, embodiments of the present invention can be considered to provide a four-dimensional (4-D) system using through the use of real-time, biometric feedback during the virtual simulation to better assess the user's response to the training being provided. For example, a user's engagement with the virtual simulation, as well as a variety of other information, such as the user's stress level and emotions during the virtual simulation may be recorded and used to tailor the virtual reality simulation itself, post-training actions, and/or further training. In addition to providing real-time feedback, the user's biometric feedback may be recorded and stored for later analysis, and the stored data may indicate points in time within the virtual simulation session at which the biometric data was recorded. The correlated, stored data may then be used by other users, such as a trainee's supervisor, for example, to provide recommended behavioral modification or coaching in the context of specific simulated scenarios.

Generally, a system according to an embodiment can include one or more processors and one or more input and output units in communication with the one or more processors. The one or more input and output units can further be in communication with one or more communication networks. A system can also include one or more sensors in communication with the one or more input and output units, for instance. For example, a system can include one or more heart rate sensors, one or more respiratory rate sensors, one or more skin conductivity sensors, one or more blood glucose sensors, and one or more blood pressure sensors. Further, a system can include one or more neural sensors (such as electrocephalography (EEG) sensors) in communication with the one or more input and output units. Each of the one or more EEG devices may include a plurality of EEG electrodes and be adapted to be positioned on a head of a user, for instance. A system also can include one or more facial recognition sensors in communication with the one or more input and output units. The facial recognition sensors can be positioned to capture images of physical facial features, for example. A system can still further include one or more databases in communication with the one or more processors, one or more displays in communication with the one or more processors, and non-transitory memory medium in communication with the one or more processors.

According to a first aspect described herein, there is provided a training system which includes one or more processors and one or more input and output units in communication with the one or more processors. The training system further includes one or more sensors in communication with the one or more input output units. For example, the sensors may include one or more heart rate sensors, one or more respiratory rate sensors, one or more skin conductance sensors, one or more blood glucose sensors, one or more blood pressure sensors, one or more neural sensors, and/or one or more facial recognition sensors. The facial recognition sensors may be positioned to capture images of physical facial features. The system may also include one or more displays in communication with the one or more processors, and one or more non-transitory processor-readable media in communication with the one or more processors having processor-readable instructions stored therein.

The processor-readable instructions are arranged to, when executed, cause the training system to provide a virtual reality training session and to obtain biometric data from a first user during the virtual reality training session. The obtaining may include converting measurements from the one or more heart rate sensors into electronic heart rate data. The obtaining may include converting respiratory rate measurements from the one or more respiratory rate sensors into electronic respiratory rate data. The obtaining may include converting skin conductance measurements from the one or more skin conductance sensors into electronic skin conductance data. The obtaining may include converting blood glucose measurements from the one or more blood glucose sensors into electronic blood glucose data. The obtaining may include converting blood pressure measurements from the one or more blood pressure sensors into electronic blood pressure data. The obtaining may include converting neural signals measured by the one or more neural sensors into electronic neural data. The obtaining may include converting physical facial features captured by the one or more facial recognition sensors into electronic facial data indicative of one or more of gender, age, and emotion of the first user. The obtaining may include determining a stress level of the first user responsive to analysis of at least the electronic heart rate data, the electronic respiratory rate data, the electronic skin conductance data, the electronic blood glucose data, and the electronic blood pressure data. The obtaining may include determining a level of interest, a level of engagement, a level of alertness, and a level of excitement responsive to analysis of at least the electronic neural data and the electronic facial data.

The processor-readable instructions are arranged to, when executed, cause the training system to display, in real time on the one or more displays, a first indication of one or more of the electronic heart rate data, the electronic respiratory data, the electronic skin conductance data, the electronic blood glucose data, the electronic blood pressure data, the electronic neural data, the electronic facial data, the determined stress level, and the determined levels of interest, engagement, alertness, and excitement.

The displaying step may include displaying the first indication within a virtual reality interface associated with the virtual reality training session. The virtual reality interface may be configured to include display of an avatar representing the first user. Displaying the first indication may include determining one or more graphical operation based upon at least a portion of the obtained biometric data and applying the one or more graphical operation to the displayed avatar.

The non-transitory processor-readable media may have processor-readable instructions stored therein that when executed cause the training system to monitor one or more of the one or more heart rate sensors, the one or more respiratory rate sensors, the one or more skin conductance sensors, the one or more blood glucose sensors, the one or more blood pressure sensors, the one or more neural sensors and the one or more facial recognition sensors for changes in the obtained biometric data, to determine one or more further graphical operation responsive to determining a change in the obtained biometric data and apply the one or more further graphical operation to the displayed avatar.

The non-transitory processor-readable media may have processor-readable instructions stored therein that when executed cause the training system to provide a second indication of one or more of the electronic heart rate data, the electronic respiratory data, the electronic skin conductance data, the electronic blood glucose data, the electronic blood pressure data, the electronic neural data, the electronic facial data, the determined stress level, and the determined levels of interest, engagement, alertness, and excitement to a second user.

Providing the second indication to a second user may include providing at least the second indication to the second user in real-time during the virtual reality training session.

The non-transitory processor-readable media may have processor-readable instructions stored therein that when executed cause the training system to store at least a portion of the obtained biometric data. Providing the second indication to the second user may include transmitting the stored at least a portion of the obtained biometric data to the second user for review.

The non-transitory processor-readable media may have processor-readable instructions stored therein that when executed cause the training system to generate one or more alerts responsive to obtaining the biometric data.

Providing an indication of obtained biometric data to a user may include providing the one or more alerts to the user.

The non-transitory processor-readable medium may have processor-readable instructions stored therein that when executed cause the system to monitor the obtained biometric data in real-time to determine whether one or more biometric boundary conditions are exceeded.

Generating one or more alerts may be responsive to determining that one or more biometric boundary conditions are exceeded.

Providing the virtual reality training session may include: receiving a receiving data indicating a selected training module from one of a plurality of training modules and determining biometric data required by the selected training module. Obtaining biometric data may be responsive to determining the biometric data required by the selected training module.

The non-transitory processor-readable media may have processor-readable instructions stored therein to cause the training system to provide a plurality of virtual reality training modules. For example, such training modules may include one or more of an empowerment training module, a conversations training module, a decision-making training module and a collaboration training module and wherein each of the plurality of training modules.

A virtual reality simulation of the virtual reality training session may include a plurality of paths, and the method further includes selecting one or more of the plurality of paths responsive to obtaining the biometric data. In this way, the training provided by the training system may be made more effective through dynamic adaptation in response to the biometric feedback provided by the sensors.

According to a second aspect described herein, there is provided a method of providing training in a training system. The method includes obtaining biometric data from a first user during a virtual reality training session. The obtaining may include converting measurements from one or more heart rate sensors into electronic heart rate data. The obtaining may include converting respiratory rate measurements from one or more respiratory rate sensors into electronic respiratory rate data. The obtaining may include converting skin conductance measurements from one or more skin conductance sensors into electronic skin conductance data. The obtaining may include converting blood glucose measurements from one or more blood glucose sensors into electronic blood glucose data. The obtaining may include converting blood pressure measurements from one or more blood pressure sensors into electronic blood pressure data. The obtaining may include converting neural signals measured by one or more neural sensors into electronic neural data. The obtaining may include converting physical facial features captured by one or more facial recognition sensors into electronic facial data indicative of one or more of gender, age, and emotion of the first user. The obtaining may include determining a stress level of the first user responsive to analysis of one or more of the electronic heart rate data, the electronic respiratory rate data, the electronic skin conductance data, the electronic blood glucose data, and the electronic blood pressure data. The obtaining may include determining a level of interest, a level of engagement, a level of alertness, and a level of excitement responsive to analysis of at least the electronic neural data and the electronic facial data.

The method may further include displaying, in real time on the one or more displays, a first indication of one or more of the electronic heart rate data, the electronic respiratory data, the electronic skin conductance data, the electronic blood glucose data, the electronic blood pressure data, the electronic neural data, the electronic facial data, the determined stress level, and the determined levels of interest, engagement, alertness, and excitement.

The method may include displaying an avatar representing the first user within a virtual reality interface associated with the virtual reality training session. The may also include determining on or more graphical operation based upon at least a portion of the obtained biometric data and applying the one or more graphical operation to the displayed avatar.

The method may include monitoring the one or more heart rate sensors, the one or more respiratory rate sensors, the one or more skin conductance sensors, the one or more blood glucose sensors, the one or more blood pressure sensors, the one or more neural sensors and the one or more facial recognition sensors for a change in the obtained biometric data. The method may further include determining one or more further graphical operation responsive to determining a change in the obtained biometric data and apply the one or more further graphical operation to the displayed avatar.

The method may include providing a second indication of one or more of the electronic heart rate data, the electronic respiratory data, the electronic skin conductance data, the electronic blood glucose data, the electronic blood pressure data, the electronic neural data, the electronic facial data, the determined stress level, and the determined levels of interest, engagement, alertness, and excitement to a second user.

The method may include monitoring the obtained biometric data in real-time to determine whether one or more biometric boundary conditions are exceeded. The method may include generating one or more alerts responsive to determining that one or more biometric boundary conditions are exceeded.

Providing an indication of obtained biometric data to a user may include providing the one or more alerts to the user.

According to a third aspect described herein, there is provided non-transitory processor-readable media having processor-readable instructions thereon arranged to cause a training system to carry out a method according to the second aspect.

DETAILED DESCRIPTION

Exemplary embodiments of the invention are now described with reference to the accompanying drawings. This invention may, however, be embodied in forms other than those shown in the drawings. As such, the invention should not be construed as limited to the illustrated embodiments described herein.

Certain embodiments provide training systems that allow real-time biometric feedback to be provided to the user during training. A training system according to some embodiments is operable to provide training by way of one or more virtual reality-based training sessions (virtual reality training sessions) with which a user interacts. During training, a plurality of the user's biometrics, for example, physiological and neurological attributes, are monitored in real-time. The monitored biometrics are used to provide feedback to the user. In some embodiments described herein, a virtual reality-based training session may present a user with a scenario that simulates a scenario that may be encountered in the “real world”. For example, where the user is to perform a task in a potentially hazardous environment, a virtual reality training session may simulate the environment and hazards that may be encountered. In this way, the user may become familiar with the potential hazards which he or she may encounter before encountering those hazards.

FIG. 1is a block diagram that illustrates an exemplary training system (“system”)100in accordance with one more embodiments of the present invention. As depicted, training system100may include one or more training stations such as a mobile training station102and a stationary training station103. The training stations102,103may be used by one or more first users126of which one is depicted inFIG. 1. The first users126may be users that are accessing training through the training system100. InFIG. 1the user126is depicted using the training station102, however it will be appreciated that this is merely exemplary. The training system100further includes one or more trainer computers, such as trainer computer105. The trainer computer105may be used by second users (not shown). The second users may use the trainer computer105for providing, overseeing, guiding, contributing to and/or reviewing real-time and/or completed training undertaken by the first user126using the training system100. It is to be understood that while referred to as trainer computers herein, the trainer computers may be used by users other than training providers, for example employers, where the first users126are trainees, employees or prospective employees, etc.

The depicted training system100further includes one or more servers104(of which one is depicted), one or more file servers106(of which one is depicted) coupled to one or more datastores108(of which one is depicted), and one or more web servers110(of which one is depicted) connected to one or more remote computers112(of which one is depicted). In some embodiments and as depicted, the entities of the training system100are communicatively coupled via a network118. Datastore108may store training information109(including e.g., personal profile information, health profile information, collected user biometrics associated with particular training sessions, and/or the like) for one or more users.

In some embodiments, the network118includes an element or system that facilitates communications between entities of training system100. For example, the network118may include an electronic communications network, such as the Internet, a local area network (“LAN”), a wide area (“WAN”), a wireless local area network (“WLAN”) a cellular communications network or the like. In some embodiments, the network118includes a single network or combination of networks. For example, the training stations102,103, the trainer computer105, the server104, the file server106, and/or the web server110, may be networked using a private/LAN, with the remote computers112(e.g., user home computers, external service provider computers and/or the like) connected to the web server110via a WAN.

As described in more detail below, the training stations102,103may include sensors120,128for monitoring and collecting user data for use during and after a training session. In some embodiments, the collected data may include data that can be used to assess various biometrics (e.g. physiological, neurological, etc.) of the user. By way of example, the collected data may include one or more of heart rate, respiratory rate, skin conductance, blood glucose, electrical activity (e.g. brain and nerve activity), blood pressure, and facial features (e.g. shapes, positions, sizes, etc.). It is to be understood that while the following description is particularly concerned with the aforementioned collected data, the sensors120,128may include sensors for monitoring and collecting data relating to other user biometrics, including but not limited to body temperature, body weight, body fat, blood oxygen saturation (e.g., blood oxygenation), and/or the like. It is to be understood that the term “biometric sensors” is used herein to refer to both sensors that are used to acquire measurements relating to any one or more of neurological, emotional, electrical, biomechanical, behavioral, etc. attributes of a user.

As discussed in more detail below, the training stations102,103may further include user computers, such as the computer130of the training station103and the user computer122of the training station102. The computers122,130may be operable to receive biometric data from the various sensors120,128and to use the received biometric data in the provision of training feedback and/or to forward received data to the server104for use in provision of training feedback. For example, in response to determining that biometric data needs to be collected (e.g., based on a request from the server104, based on a request from a user, a predetermined training schedule, and/or the like), the computer122may monitor sensors120to collect data (e.g., measurements) from the sensors120, and forward the data to server104for use in monitoring the user's biometrics during a training simulation.

Although certain embodiments are described herein with regard to the computers122,130forwarding biometric data to the server104, it will be appreciated that in other embodiments, some or all of the biometric data is provided directly to the server104(i.e., without having to pass the data through the user computer130). For example, the sensors120may be communicatively coupled to the server104via the network118(e.g., via a WLAN) such that they can transmit biometric data directly to the server104. In other embodiments, data is not passed to the server104, for example, where training and feedback is provided through a “standalone” training station.

FIG. 2is a block diagram that schematically illustrates the training station102connected to the server104via the network118in accordance with one or more exemplary embodiments. In some embodiments the training station102includes the user computer122communicatively coupled to the one or more sensors120for taking measurements to provide biometric data200. For example, the training station102may be communicatively coupled to one or more skin conductance (sometimes referred to as galvanic skin response (GSR)) sensors202, one or more blood glucose sensors204, one or more blood pressure sensors (e.g., a blood pressure cuff)206, one or more facial recognition sensors208, one or more respiration sensors210, one or more neural sensors212and one or more heart rate sensors214(e.g., a heart rate monitor). Measurements taken from the sensors are converted into electronic biometric data200for use by the training system100. For example, in the arrangement ofFIG. 2, measurements taken by the skin conductance sensor202are converted into electronic skin conductance data200a, measurements taken by the blood glucose sensor204are converted into electronic blood glucose data200b, measurements taken by the blood pressure sensor206are converted into electronic blood pressure data200c, measurements taken by the facial recognition sensor208are converted into electronic facial recognition data200d, measurements taken by the respiration sensor210are converted into electronic respiratory rate data200e, measurements taken by the neural sensor212are converted into electronic neural data200f(including, for example, data indicative of one or more brain signals such as alpha, beta, delta, gamma, etc.), and measurements taken by the heart rate sensor214are converted into electronic heart rate data200g. Measurements taken by respective sensors120may be converted into electronic biometric data by the sensor itself, by the user computer122, or by another entity within the training system100.

The sensors120may include other arrangements and may not necessarily contain all of the sensors indicated inFIG. 2. Additionally, the sensors120may include sensors other than those depicted inFIG. 2. By way of example only, the sensors120may further include one or more temperature sensors (e.g., thermocouples, IR sensors, etc.), one or more blood condition sensors (e.g., pulse oximeters), one or more force sensors (e.g., force transducers), one or more body fat sensors (e.g., conductive contacts), one or more body position sensors (e.g., three-dimensional (“3D”) image/video camera), one or more audio sensors (e.g., microphone) and/or the like for collecting biometric data.

In some embodiments, the user computer122may be communicatively coupled to the sensors120via a wired connection. For example, some or all of the sensors120may include a communication cable extending between each of the respective sensors120and the user computer122. In some embodiments, the user computer122may be communicatively coupled to the sensors120via a wireless connection. For example, some or all of the sensors120may communicate with the user computer122via a wireless connection (e.g., a Bluetooth connection, a WLAN of network118, and/or the like). In some embodiments, biometric data200(e.g.,200a-200g) may be transmitted from the sensors120to the user computer122via the wired or wireless connection. In some embodiments, some of the biometric data200may be transferred between devices of training system100via a non-transitory storage medium such as a universal serial bus (“USB”) memory stick (e.g., a flash drive). For example, the biometric data200acquired from the sensors120may be downloaded from the sensors120and/or the user computer122to a USB memory stick and may be uploaded from the USB memory stick to another device of training system100, such as the user computer122, the trainer computer105, the file server106, the remote workstation112, and/or the sever104.

FIG. 3is a block diagram that schematically illustrates components of the user computer122in accordance with one or more embodiments of the present invention. In some embodiments, the user computer122includes a mobile device controller300for controlling the operational aspects of the user computer122. For example, the mobile device controller300may provide for allocating power to integrated devices, collecting biometric data200from the various sensors120and/or transmitting the collected biometric data200to the server104. In some embodiments, the mobile device controller includes a memory301, a processor302and an input/output (I/O) interface304.

The memory301may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or the like. The memory301may include a non-transitory processor-readable storage medium having program instructions306stored thereon that are executable by a computer processor (e.g., the processor304) to cause the functional operations (e.g., methods/routines/processes) described herein with regard to the user computer122. The program instructions306may include a mobile device module308including program instructions that are executable by the processor302to provide some or all of the functionality described herein with regard to the user computer122.

The processor302may be any suitable processor capable of executing/performing program instructions. The processor302may include a central processing unit (CPU) that carries out program instructions (e.g., of the mobile device module308) to perform arithmetical, logical, and input/output operations of the user computer122, including those described herein.

The I/O interface304may provide an interface for connection of one or more I/O devices to the user computer122. I/O devices may include integrated I/O components (e.g., buttons, microphone, speaker, graphical display (e.g., a touch screen), cameras, and/or the like)310, a power source312(such as a battery), integrated sensors120a, external devices320(including, for example, external display devices, the server104), and/or the like. The integrated I/O components310and/or the external devices320facilitate interaction by the user with a training session provided on the training station102. For example, as will be described in more detail below, visuals may be displayed on a graphical display (e.g. of the training station102or an external device) to illustrate scenarios to which the user must respond. Keypads, touchscreens, microphones, buttons, etc. may be provided to allow the user to respond to scenarios presented to the user during a training session simulation.

The external devices320may be connected to I/O interface304via a wired or wireless connection. For example, the external devices320may be connected to the I/O interface via wireless connection to the network118. In some embodiments, the integrated sensors120ainclude sensors120that are physically integrated with the user computer122. For example, as described in more detail below, the integrated sensors120amay include conductive contacts integrated into the exterior of the user computer122such that a measurement (e.g., temperature measurement, a skin conductance measurement, and/or the like) can be acquired via the conductive contacts while the user is grasping the exterior of the user computer122. In some embodiments, the external sensors120binclude the sensors120that are remote from the user computer122. For example, external sensors120bmay include facial recognition sensors208, blood pressure sensors206, respiratory sensors206, or the like that are worn by the user to take measurements at various locations on the user's body. It is to be understood that any of the sensors120may be integrated sensors120aor external sensors120b.

The user computer122may be employed to collect biometric data200from the various sensors120(e.g., integrated sensors120aand/or external sensors120b) and/or forward corresponding biometric data200to the server104for use in monitoring the user's biometrics. For example, in response to determining that biometric data200(e.g., skin conductance data, blood glucose data, blood pressure data, facial recognition data, respiration data, electronic neural data200fand/or heart rate data) needs to be collected (e.g. upon initialization of, or preparation for, a training simulation), the user computer122may employ, or otherwise monitor, one or more of the particular sensors120capable of sensing/measuring the needed biometric data200. The user computer122may collect/store the biometric data200(e.g., store/queue the acquired biometric data200in memory301), and/or the user computer122may forward the biometric data200to another entity in the training system100(such as the server104) for use in monitoring the user's biometric state.

In some embodiments, the user computer122may process the raw/acquired biometric data to generate corresponding processed biometric data. For example, where the user computer122receives raw biometric data (e.g., electronic skin conductance data200aincluding a current indicative of a sensed skin conductance), the user computer122may process the raw biometric data to generate a corresponding value (e.g., using a look-up table, equation, and/or the like to identify a skin conductance value corresponding to the current) that may be included in any biometric data200transmitted to other entities of the training system100(such as the server104). Accordingly, in some embodiments, the biometric data200may include the raw/acquired biometric data (e.g., a current value) and/or the processed biometric data corresponding thereto (e.g., the skin conductance value corresponding to the voltage value). Similar processing may be provided for the other types of biometric data.

In some embodiments, the user computer122may forward the biometric data200as the corresponding biometric data is received. For example, the user computer122may receive biometric data200from sensors120and immediately forward the biometric data200with little to no delay such that a continuous stream of biometric data200is provided to the server104for use in monitoring the user's biometrics. In some embodiments, the user computer122may store (e.g., queue or buffer) at least some of the biometric data200for transmission at a later time. For example, where a training simulation requires that the user computer122transmit a batch of biometric data200at the end of the training simulation, transmit a batch of biometric data200at a regular interval (e.g., every ten minutes), or the like, the biometric data200received may be stored in memory301of the user computer122and may be queued-up or buffered in memory local to the user computer122for transmission, as a batch of biometric data200, to server104at the end of the training simulation, at the regular interval, or the like as required.

In some embodiments, a skin conductance sensor202may include any suitable skin conductance sensor. During use, the skin conductance sensor may transmit biometric data200indicative of a conductance sensed by the skin conductance sensor202. For example, where a skin conductance sensor202is positioned to acquire a user's skin conductance at a given location (e.g., a user's fingertips, wrist, etc.), the user computer122may receive, from the skin conductance sensor202, the electronic skin conductance data200aindicative of the skin conductance at the given location. Skin conductance is effected by an amount of sweat that produced by a user, which is governed by the sympathetic nervous system in response to stimuli. As such, the skin conductance measurement may be used in the determination of an emotional state of the user. For example, the electronic skin conductance data200amay be used in determining a stress level indicating a level of stress experienced by the user.

In some embodiments, the blood glucose sensor204may include any suitable blood glucose sensor. For example, the blood glucose sensor204may include one or both of a lancet/glucose-meter sensor system and a continuous blood-glucose monitoring sensor system (e.g. an embedded system). The blood glucose sensor204may further or alternatively include non-invasive blood glucose monitoring sensors using, for example, infrared, ultrasound, etc. to monitor a blood glucose level of a user. In some embodiments, a blood glucose sensor may use photonic glucose crystal sensing/photoplethysomography to detect blood glucose as will be understood by those skilled in the art. During use, the user computer122may receive biometric data200indicative of blood characteristics sensed by the blood glucose sensor204. For example, where a lancet is used to draw blood from a user's fingertip, the blood may be provided to a glucose meter. The user computer122may receive, from the glucose meter, electronic blood glucose data200bindicative of the level of glucose in the user's blood. As blood glucose may be effected by stress, the electronic blood glucose data200bmay be used in determining an emotional state of the user. For example, the electronic blood glucose data200bmay be used in determining a stress level indicating a level of stress experienced by the user.

In some embodiments, a blood pressure sensor206may include blood pressure cuffs and/or the like. By way of example only, the blood pressure sensor206may include the UA-789PC Extra Large Cuff sold by LifeSource™, the CMS-08A Professional Upper Arm Blood Pressure Monitor manufactured by CMS™, or similar. During use, the user computer122may receive biometric data200indicative of the user's blood pressure sensed by the blood pressure sensor206. For example, where a blood pressure cuff is positioned about the user's wrist/arm, the user computer122, may receive, from the blood pressure cuff, electronic blood pressure data200cindicative of the user' blood pressure sensed at the user's wrist/arm.

In some embodiments, a facial recognition sensor208may include image sensors (such as cameras) operable to record images of a user's face during a training simulation, in combination with facial recognition processing. For example, in some embodiments the facial recognition sensor208may utilize the SHORE™ system from Fraunhofer IIS to detect faces in images captured by an image sensor. In some embodiments, the facial recognition processing may be performed on the user computer122, or may be performed by a processor integral with the facial recognition sensor208. Alternatively, the facial recognition processing may be performed by another entity within the training system100. In some embodiments, therefore, the facial recognition sensor206may include a plurality of distributed components, including, for example, the user computer122. In some embodiments, during use, the user computer122may the received electronic facial recognition data200d(using, for example, the SHORE™ system) to determine one or more of a gender, age, and emotion of a user.

In some embodiments, respiration sensor210may include a device for sensing the user's respiration rate (e.g., number of breaths taken within a set amount of time, typically sixty seconds). During use, the user computer122may receive biometric data200indicative of the respiration rate (“RR”) of the user sensed by the respiration sensor210. For example, the user computer122may receive, from the respiration sensor210, electronic respiratory rate data200eindicative of number of breaths taken by the user over sixty seconds.

In some embodiments, neural sensor212may include a device (e.g., an electrode) for sensing neural activity (e.g., brain activity) of the user. In some embodiments, the neural sensors212may employ electroencephalography (“EEG”) to measure neuro-signal voltage fluctuations resulting from ionic current flows within the neurons of the brain. EEG may refer to recording of the brain's spontaneous electrical activity over a short period of time (e.g., twenty-forty minutes) from a plurality of neural sensors212disposed on the user's scalp. For example, the neural sensor212may include a plurality of electrodes (e.g., sixteen neural sensors/channels) to be disposed about the user's scalp to detect neuro-signals (e.g., such as alpha, beta, gamma, and delta waves) that can be used to determine information relating to, for example, the user's emotional state (e.g., happy, sad, excited, etc.), the user's thoughts (e.g., cognitive thoughts, subconscious thoughts, intent, etc.), the user's facial movements (e.g., facial expressions), motor functions and/or the like. During use, the user computer122may receive biometric data200indicative of the user's neural activity sensed by the neural sensor212. For example, the user computer122may receive, from the neural sensor212, electronic neural data200findicative of the sensed neuro-signals.

In some embodiments, a heart rate sensor214may include a heart rate monitor. During use, the user computer122may receive biometric data200indicative of the user's heart rate sensed by the heart rate sensor214. For example, where a heart rate monitor is positioned about the user's torso, the user computer122may receive, from the heart rate monitor, electronic heart rate data200gindicative of the user's hear rate (e.g., 80 beats per minute (“BPM”)).

In some embodiments, some or all of the sensors120may be located at or near the user126(e.g., worn by the user) and/or physically integrated with the user computer122. For example, various ones of the sensors120may be provided in the user's apparel, such as their clothing (e.g., shirt and pants, gloves, etc.), footwear (e.g., work boots), head wear (e.g., a safety helmet), and eyewear (e.g., safety glasses) and/or various ones of the sensors120may be located in the user computer122. In some embodiments one or more of the sensors may be provided by a multi-sensing device worn by the user. For example, in some embodiments, the skin conductance sensor202, respiratory sensor210, and the heart rate sensor214may include a Basis™, or a Basis Peak™ wrist-worn tracking device from Basis Science Inc. In some embodiments, the neural sensor212may include an Emotiv EPOC or EPOC+ from Emotiv Systems Inc.

The training station103may be arranged similarly to the training station102.FIG. 4is a block diagram that illustrates the training station103connected to the server104in accordance with one or more embodiments of the present invention. In some embodiments the training station103includes the training station103communicatively coupled to one or more of the sensors128for collecting biometric data400. For example, the training station103may be communicatively coupled to one or more skin conductance sensors (e.g. galvanic skin response sensors)402, one or more blood glucose sensors404, one or more blood pressure sensors (e.g., a blood pressure cuff)406, one or more facial recognition sensors408, one or more respiration sensors410, one or more neural sensors412and one or more heart rate sensors414(e.g., a heart rate monitor). In the arrangement ofFIG. 4, the biometric data400includes electronic skin conductance data400a, electronic blood glucose data400b, electronic blood pressure data400c, electronic facial recognition data400d, electronic respiratory rate data400e, electronic neural data400f(including, for example, alpha, beta, delta, gamma and theta brain signals), and electronic heart rate data400g, collected from the corresponding sensors128.

The sensors128may include other arrangements and may not necessarily contain all of the sensors indicated inFIG. 4. Additionally, the sensors128may include sensors other than those depicted inFIG. 4. By way of example only, the sensors128may further include one or more temperature sensors (e.g., thermocouples, IR sensors, etc.), one or more blood condition sensors (e.g., pulse oximeters), one or more force sensors (e.g., force transducers), one or more body fat sensors (e.g., conductive contacts), one or more body position sensors (e.g., three-dimensional (“3D”) image/video camera), one or more audio sensors (e.g., microphone) and/or the like for collecting biometric data.

In some embodiments, the training station103is communicatively coupled to the sensors128via a wired connection. For example, some or all of the sensors128may include a communication cable extending between the respective sensor128and the training station103. In some embodiments, training station103is communicatively coupled to the sensors128via a wireless connection. For example, some or all of the sensors128may communicate with the training station103via a wireless connection (e.g., a Bluetooth connection, a wireless connection to a WLAN of network118, and/or the like). In some embodiments, the biometric data400is transmitted from the sensors128to the training station103via the wired or wireless connection (e.g., a Bluetooth connection, a WLAN of network118, and/or the like). In some embodiments, the biometric data400is transferred between devices of the training system100via a physical memory medium such as a universal serial bus (“USB”) memory stick (e.g., a flash drive). For example, the biometric data400acquired from the sensors128may be downloaded from the sensors128and/or the training station103to a USB memory stick and may be uploaded from the USB memory stick to another device of the training system100, such as the training station103, the trainer computer105, and/or the sever104.

The sensors128may be provided by any configuration of suitable sensors, and may, by way of example, be as described above with reference to the sensors120of the training station102. For example, in some embodiments one or more of the sensors128may include a multi-sensing device worn by the user. For example, in some embodiments, the skin conductance sensor402, respiratory sensor410, and the heart rate sensor414may include a Basis™, or a Basis Peak™ wrist-worn tracking device from Basis Science Inc., or other similar biometric tracking device. In some embodiments, the neural sensor may include an Emotiv EPOC or EPOC+ from Emotiv Systems Inc., or similar.

FIG. 5is a block diagram that illustrates components of the user computer130in accordance with one or more embodiments of the present invention. In some embodiments, the user computer130includes a memory500, a processor502and an input/output (I/O) interface504.

The memory500may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or the like. The memory500may include a non-transitory processor-readable storage medium having program instructions506stored thereon that are executable by a computer processor (e.g., the processor502) to cause the functional operations (e.g., methods/routines/processes) described herein with regard to the user computer130. The program instructions506may include a computer module508including program instructions that are executable by the processor502to provide some or all of the functionality described herein with regard to the user computer130.

The processor502may be any suitable processor capable of executing/performing program instructions. The processor502may include a central processing unit (CPU) that carries out program instructions (e.g., program instruction of the computer module508) to perform arithmetical, logical, and input/output operations of the user computer130, including those described herein.

The I/O interface504may provide an interface for connection of one or more I/O devices to the user computer530. I/O devices may include peripherals510, the sensors128, the server104, and/or the like. The peripherals510may include, for example, graphical user interface displays (e.g., a virtual reality headset, a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, and/or the like. The I/O devices (e.g., the peripherals510, the sensors128, and the server104) may be connected to the I/O interface504via a wired or wireless connection. The peripherals510facilitate interaction by the user with a training session provided on the training station103. For example, as will be described in more detail below, visuals may be displayed on a display device to illustrate scenarios to which the user must respond. Keyboards, touchpads, mice, etc. may be provided to allow the user to respond to scenarios presented as part of a training session.

The user computer130may be employed to collect the biometric data400from the various sensors128and/or forward corresponding biometric data400to the server104for use during or after a training session. For example, in response to determining that biometric data400needs to be collected, the user computer130may employ one or more of the sensors128capable of sensing/acquiring the needed biometric data400to acquire the needed biometric data400. The user computer130may collect/store the acquired biometric data400(e.g., store/queue the acquired biometric data200in the memory500), may process the biometric data400(e.g. for use in providing training) and may forward the acquired biometric data400to the server104for use in monitoring the user's biometric state during a training session.

As described above with reference to the user computer122, the user computer130may process raw/acquired biometric data400to generate the corresponding processed biometric data400. Indeed, it is to be understood that the acquisition of user biometric data400from the training station103may be implemented in any appropriate way and may be generally equivalent to the described acquisition of biometric data200from the user station102.

In some embodiments, some or all of the sensors120,128may be located throughout the user's environment on and surrounding the training stations102,103. For example, various ones of the sensors128may be located at or near the user's desk, chair, computer, or the like, while various ones of the sensors120may be integrated into the user computer122or be arranged for placement around an area in which the user computer122is intended for use.FIG. 6is a diagram that illustrates the user126wearing various of the sensors120of the mobile training station102in accordance with one or more embodiments of the present invention. The user126holds the user computer122. In some embodiments, the mobile user computer122includes a screen610, which may be a touchscreen to allow the user to both view and interact with a virtual reality simulation. In some embodiments, a separate screen (not shown) may be provided which is in communication (e.g. wired or wireless) with the mobile user computer122for use instead or in combination with an integrated screen (where an integrated screen is provided). For example, in some embodiments, visuals of a virtual reality simulation may be provided on an external screen (for example, an LCD screen, a virtual reality headset, etc.), while the integrated touchscreen610is utilized for user input for interaction with the virtual reality simulation. In some embodiments, the mobile user computer122includes one or more buttons620to allow the user126to provide inputs to the mobile user computer122, such as for interaction with a provided virtual reality simulation.

In some embodiments of the user computer122, the facial recognition sensor208is provided in the form of an integrated camera605. In some embodiments, the integrated camera605of the mobile computer122may include a two-dimensional still/video camera, a three-dimensional (“3D”) still/video camera and/or the like that includes all or part of the facial recognition sensor208. For example, the camera605may be used to acquire images of the face of the user126and provide those images for processing on the user computer122to generate the electronic facial recognition data200d. In some embodiments, an external camera is provided instead of or in addition to the integrated camera605.

In some embodiments, the user computer122includes an integrated speaker630, which may be used in the provision of sound components of a virtual reality simulation and/or instructions from a second user (e.g., a training provider/overseer, etc.). In some embodiments, an external speaker may be provided instead of or in addition to the integrated speaker630. In some embodiments, the user computer122includes an integrated microphone640which may be employed as an audio sensor. For example, the microphone640may be used to acquire audio data (e.g., words spoken by the user126). In this way, for example, the user126may interact with a virtual reality simulation and/or a second user (e.g., a training provider/overseer, etc.) using audio input. In some embodiments, an external microphone may be provided in addition to or instead of the integrated microphone640.

In some embodiments, a multi-sensor device650is provided. In the depicted embodiment, the multi-sensor device650is worn around the wrist of the user126. For example, as described above, a multi-sensor device such as the Basis® or Basis Peak® from Basis Science Inc. may be provided. Additionally or alternatively, any other multi-sensor devices may be utilized, such as chest mounted multi-sensor devices. The multi-sensor device650may provide a plurality of the sensors120in a convenient and compact arrangement. For example, the multi-sensor device650may provide the skin conductance sensor202, the respiration sensor210and the heart rate sensor214. It will be appreciated, however, that the multi-sensor device650may provide any number of the sensors120and/or additional sensors. In other embodiments, a plurality of multi-sensor devices may be provided. Such an integration of a plurality of the sensors120within one or more multi-sensor devices, and within the training station environment may help to reduce the physical profile of the sensors120, reduce distractions to the user126that may otherwise be caused by the presence of the sensors120and/or enhance the ease of use of the training station102to the user126by allowing the biometric data200to be acquired while the user126is engaging in the training session. For example, at least some of the sensors120may be able to passively acquire biometric data200without requiring the user to take special efforts to facilitate the acquisition of the biometric data200. It will be apparent to one skilled in the art, however, that the sensors120,128may be implemented in any appropriate manner and need not be provided in a multi-sensor device.

In some embodiments, a blood glucose sensor204is disposed at the user's finger. For example, the blood glucose sensor204may include a lancet for extracting a small sample of blood from a finger of the user216coupled with a glucose meter disposed about the user's body or within the surrounding area of the mobile training station102. Other embodiments may include any number of blood glucose sensors provided in any suitable configuration and any number of suitable locations such as the user's earlobe, toe and/or the like. In some embodiments, other types of blood glucose sensor may be provided for use instead of or in addition to the depicted blood glucose sensor. For example, an infrared sensor (not shown) may be used to provide a blood glucose sensor. In some embodiments, a passive blood glucose sensor may be used in combination with the depicted lancet-based blood glucose sensor. For example, an initial reading may be provided using the lancet-based blood glucose sensor to calibrate a passive blood glucose sensor prior to initializing a training session, with in-training blood glucose measurements being taken by a passive blood glucose sensor.

In some embodiments, a blood pressure sensor206is disposed at the user's arm/wrist. For example, the blood pressure sensor206may include a blood pressure cuff410secured about the user's wrist. In some embodiments, the blood pressure cuff410may be integrated into a sleeve of the user's shirt. Other embodiments may include any number of blood pressure sensors provided in any number of suitable locations such as the user's upper-arm and/or the like.

In some embodiments, one or more neural sensors212are disposed about the user's head/scalp on a neuro-headset660. In some embodiments, the neuro-headset660includes a plurality of neural sensors212(e.g., sixteen neural sensors212) integrated therein. The neuro-headset660may provide for positioning of the neural sensors212in discrete neural sensor locations about the user's head. Where the display screen610includes a virtual reality headset, the neuro-headset660may from a part of the virtual reality headset. That is, in some embodiments, both the neuro-headset660and a display screen610in the form of a virtual reality headset may be provided in an integrated unit.

FIG. 7is a is a block diagram illustrating the exemplary embodiment ofFIG. 6. In the embodiment depicted inFIG. 6, the training station102includes the multi-sensing device650. The multi-sensing device650includes the skin conductance sensor202, the respiration sensor210and the heart rate sensor214. The training station102further includes the neuro-headset660including the neural sensor212. The training station102further includes the blood glucose sensor204and the blood pressure sensor206. Each of the multi-sensing device650, the neuro-headset660, the blood glucose sensor204and the blood pressure sensor206are connected to the mobile user computer122via a wireless antenna704of the user computer122. The user computer122includes the mobile device controller300coupled to the display screen610, the speaker630, the microphone640, the selection button620, the camera605, a battery702and the wireless antenna704.

In some embodiments, the mobile device controller300may employ one or more of the integrated sensors120a(e.g., the camera605as part of the facial recognition sensor208, and any other integrated sensors120anot depicted inFIG. 7) and/or one or more of the external sensors120b(e.g., one or more skin conductance sensors202, one or more blood glucose sensors204, one or more blood pressure sensors206, one or more facial recognition sensors208(where externally provided), one or more respiration sensors210, one or more neural sensors212, and/or one or more heart rate sensors214) to collect corresponding biometric data200. For example, the mobile device controller300may be operable to provide commands to the ones of the sensors120to cause measurements to be taken by the respective ones of the sensors120and for those measurements to be provided to the mobile device controller300for processing.

In some embodiments, the wireless antenna704may include a Bluetooth transceiver, a network transceiver (e.g., WLAN transceiver, cellular transceiver, and/or the like), and/or similar wireless transceiver to enable wireless communication between the mobile device controller300and the network118, between the mobile device controller300and the external sensors120b, and/or the like. For example, as will be understood by those skilled in the art, where external sensors120band the wireless antenna704include Bluetooth transceivers, the sensors120bmay communicate measurements to the mobile device controller300via the wireless antenna704using Bluetooth wireless communication protocol. As a further example, where the wireless antenna includes a cellular/WLAN transceiver, the mobile device controller300may be able to communicate with the server104via the wireless antenna704and the cellular/WLAN network118.

While one particular embodiment of the mobile training station102has been described above with reference toFIGS. 6 and 7, it is to be understood that other embodiments may be arranged in any appropriate manner. In some embodiments, for example, the mobile training station102may be arranged similarly to one or more of the mobile workstation arrangements described in U.S. patent application Ser. No. 13/540,300 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH OF EMPLOYEES USING MOBILE DEVICES”. In this way, training simulations may, for example, be provided to the user126at a remote work location, such as an oil-field or building site. Training simulations (such as safety training simulations) may therefore be conducted immediately prior to engaging in activities that will utilize the skills acquired during such training simulations (such as engaging in potentially hazardous activities).

FIG. 8is a diagram that illustrates one exemplary embodiment of the training station103. The training station103may include devices, furniture and the like that facilitate the user in undertaking a training session. In some embodiments, the training station103may include various peripherals, such as a computer mouse (“mouse”)808, a computer keyboard810, a display (e.g., computer monitor)812, an audio headset814(e.g., a Bluetooth headset including a speaker and/or a microphone), or the like, so that the user126is able to receive and interact with a virtual reality simulation. In some embodiments, the facial recognition sensor408may be provided by a camera connected to the computer130. In the depicted embodiment, the facial recognition sensor408includes a camera unit mounted atop the display812. In some embodiments, facial recognition sensor408may include a two-dimensional still/video camera, a three-dimensional (“3D”) still/video camera and/or the like that includes all or part of the facial recognition sensor408.

The training station103includes one or more of the sensors128for acquiring biometrics of a user. In some embodiments, the sensors128are arranged similarly to the sensors120described above in connection with the mobile training station102. For example, the user126may wear a wrist-mounted multi-sensor device850. Alternatively, other multi-sensor devices may be utilized, such as chest mounted multi-sensor devices. The multi-sensor device850may provide a plurality of the sensors128in a convenient and compact arrangement. For example, the multi-sensor device850may provide the skin conductance sensor402, the heart rate sensor414and the respiration sensor410. It will be appreciated, however, that the multi-sensor device850may provide any number of the sensors128(and/or additional sensors). In other embodiments, a plurality of multi-sensor devices may be provided.

In some embodiments, a blood glucose sensor404may include a lancet component804adisposed at the user's finger and a glucose meter804bprovided at the training station103. Other embodiments may include any number of blood glucose sensors provided in any suitable configuration and any number of suitable locations such as the user's earlobe, toe and/or the like. In some embodiments, other types of blood glucose sensor may be provided for use instead of or in addition to the depicted blood glucose sensor804a,804b. For example, an infrared sensor (not shown) may be used to provide a blood glucose sensor. In some embodiments, a passive blood glucose sensor may be used in combination with the blood glucose sensor804a,804b. For example, an initial reading may be provided using the lancet-based blood glucose sensor to calibrate a passive blood glucose sensor prior to initializing a training session, with in-training blood glucose measurements being taken by a passive blood glucose sensor.

In some embodiments, a blood pressure sensor406is disposed at the user's arm/wrist. For example, the blood pressure sensor406may include a blood pressure cuff secured about the user's arm. In some embodiments, the blood pressure cuff may be integrated into a sleeve of the user's shirt. Other embodiments may include any number of blood pressure sensors provided in any number of suitable locations such as the user's upper-arm and/or the like.

In some embodiments, one or more neural sensors412are disposed about the user's head/scalp on a neuro-headset860. In some embodiments, the neuro-headset860includes a plurality of neural sensors412(e.g., sixteen neural sensors412) integrated therein. The neuro-headset860may provide for positioning of the neural sensors412in discrete neural sensor locations about the user's head.

FIG. 9is a is a block diagram that illustrates the training station103in accordance with the particular embodiment depicted inFIG. 8, in which particular embodiment it can be seen that the computer130does not include integrated sensors. Rather, each of the multi-sensing device650, the neuro-headset660, the blood glucose sensor204and the blood pressure sensor206connect to an I/O interface504of the computer130. While a particular example embodiment of the training station103is illustrated inFIGS. 8 and 9, it is to be understood that in other embodiments, a training station may be arranged in any appropriate manner. For example, a training station may be arranged similarly to one or more of the workstation arrangements described in U.S. patent application Ser. No. 13/540,153 filed on Jul. 2, 2012 and titled “SYSTEMS AND METHOD TO MONITOR HEALTH OF EMPLOYEE WHEN POSITIONED IN ASSOCIATION WITH A WORKSTATION”. In this way, training simulations may, for example, be provided to the user126at their place of work. Such an arrangement may make regular and/or periodic training particularly efficient to arrange and complete.

It will be appreciated from the above that the while arranged differently, each of the training stations102,103allow a user126to interact with a training simulation while biometric information of the user may be monitored. In the example embodiments described above, the mobile training station102may be conveniently used where a user cannot attend a specific testing center. For example, a mobile training station such as the training station102may be used in a user's own home and may utilize a user's own mobile device. Stationary training stations, such as the training station103may, in some embodiments, be used in an office or a dedicated training center.

To aid clarity in the following description, reference is generally made to the training station102and the biometric data200. It is to be understood, however, that the following description applies equally to the training station103and the biometric data400.

FIG. 10is a flowchart that illustrates a method of collecting biometric data200that may be carried out by the training station102in accordance with one or more embodiments of the present invention. The method ofFIG. 10may, for example, be executed by the mobile device module308to provide for collecting biometric data200by the training station102. For example, where the method is carried out by the mobile computer122, the mobile computer122may execute a routine for collecting biometric data200upon the user126successfully logging into a training application, for example, and/or upon starting a training simulation. In some embodiments, biometric data200may be obtained before starting a training simulation in order to obtain “baseline” biometric data with which to compare biometric data obtained during a training simulation. Similarly, in some embodiments biometric data may continue to be obtained after completion of a training simulation.

The method ofFIG. 10may include monitoring, at block1002the need for biometric data200to be obtained. In some embodiments, the need for biometric data200to be obtained may be identified based on a request from another component of training system100. For example, where training is to take place using the training station102, the mobile computer122may determine that there is a need to collect biometric data200in response to initialization of a training application portion of the mobile device module308. Alternatively or additionally a request for biometric data may be received from the server104and/or the user126.

In some embodiments, the need for biometric data200may be identified based on a training schedule/routine. For example, where a training schedule requires collection of biometric data200at 12:00 pm, it may be determined that biometric data200is needed if the current time is 12:00 pm. In some embodiments, the need for biometric data200may be determined based upon receiving signals from one or more of the sensors120. For example, one or more of the sensors120may be periodically polled (or continuously monitored) to determine whether biometric data can be obtained from those one or more sensors (e.g., whether facial features are detected by a facial recognition sensor208, or whether a current is detected at the skin conductance sensor202). Where it is determined that biometric data can be obtained, the processing at block1002may determine that biometric data200should be obtained. It will be appreciated that in other embodiments, other criteria for determining whether biometric data200should be obtained may be used.

Where it is determined at block1004that biometric data200need not be obtained, the method may loop back to block1002. As such, processing may loop through blocks1002and1004until it is determined that biometric data200should be obtained.

Where it is determined, at block1004, that biometric data200should be obtained, the method may include proceeding to monitor one or more of the sensors120to collect the biometric data200, as depicted at block1006. In some embodiments, monitoring the sensors120to collect the biometric data200includes monitoring and/or querying the particular sensors120that provide the particular biometric data200needed. For example, different training simulations and/or different training providers, employers, users, etc., may require the collection of different biometric data200. For example, where a training simulation simulates a hazardous environment, it may be desirable to determine a stress level to indicate a level of stress experienced by the user126during the simulation. Determining such a stress level may, for example, utilize the one or more neural sensors212and/or the one or more facial recognition sensors208. The processing at block1006may therefore receive an indication as to which biometric data200is required for a particular training session.

In some embodiments, monitoring of the sensors120at block1006may include providing prompts to the user126to take any actions necessary in order to obtain particular desired biometric data200. For example, where it is desired to obtain electronic blood glucose data200b, and where the blood glucose sensor requires the user126to provide a blood sample, a prompt may be displayed (e.g., on the display screen610) or played (e.g., using the speaker630) requesting that the user126provide the required blood sample. Similarly, if it is detected that a particular one of the biometric data200cannot be obtained, a suitable prompt may be provided to the user. For example, if electronic blood pressure data200ccannot be obtained, a prompt may be displayed or played to assist the user in correctly utilizing the blood pressure sensor206.

The method ofFIG. 10may include storing the biometric data200, as depicted at block1008. In some embodiments, storing the biometric data200may include storing the collected biometric data200in local or remote memory. For example, the mobile computer122may store the collected biometric data200in local memory301. In some embodiments, storing the biometric data200may include buffering/queuing the biometric data200for transmission at a later time.

The method ofFIG. 10may include transmitting the biometric data200, as depicted at block1010. In some embodiments, transmitting the biometric data200may include transmitting the biometric data200to another component/entity of the training system100. For example, the mobile computer122may transmit the biometric data200(e.g., the biometric data200stored in memory301) to server104and/or to the trainer computer105for use in monitoring the biometric state of the user126. In some embodiments, the biometric data200may be transmitted from the mobile computer122to the server104or the trainer computer105via network118.

In some embodiments, after transmitting the biometric data200, the method may progress to block1004to determine whether or not the acquisition of biometric data200should continue. Accordingly, the mobile computer122may collect the biometric data200from the various sensors120as required for use in monitoring the biometric state of users as training sessions are undertaken. It may be determined that acquisition of biometric data200should not continue if, for example, a signal has been received that acquisition of biometric data200should cease. Such a signal may be received, for example, in the event that a user logs out of a training application, or a training session is ended.

It will be appreciated that the method ofFIG. 10is an exemplary embodiment of methods that may be employed in accordance with techniques described herein. The method may be may be modified to facilitate variations of its implementations and uses. The method may be implemented in software, hardware, or a combination thereof. Some or all of the method may be implemented by one or more of the modules/applications described herein, such as mobile device module308. The order of the method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc.

The server104(seeFIG. 1) may include a network entity that serves requests by other network entities. For example, the sever104may serve requests made by client entities, such as the user computer122, the user computer130, the trainer computer105and/or the like. The server104may host a content site, such as a website, a file transfer protocol (FTP) site, an Internet search website or other source of network content. In some embodiments, the server104may host one or more applications, such as a training simulation and monitoring application. Some or all of the training simulation and monitoring application may be executed locally on the server104and/or remotely by various other network entities, such as the user computers122,130and/or the trainer computer105. For example, the server104may cause or allow the execution of remote applications/processes on the user computers122,130to provide training simulations to, and to collect biometric data200,400from, one or more users. As a further example, the server104may cause or allow the execution of remote applications/processes on the trainer computer105to allow a user of the trainer computer105to monitor one or more training sessions underway on the training stations102,103. The server104may also execute one or more local applications (e.g., a monitoring application) to conduct processing of the collected biometric data200,400for use during and/or after the provided training session.

In some embodiments, the server104, is connected to one or more of the user computers122,130, one or more file servers106and associated databases108for accessing and storing user training information109, one or more user computers105, one or more web servers110for connecting the computer server104to remote computers112(e.g., to provide communication with an offsite computer112, for example to allow users to remotely access the training information109stored in database108, to allow the server104to obtain external information, and/or the like).

As shown, one or more file server106may be employed by the system to manage the training information109and/or to allow the server104, the user computers122,130, the trainer computer105and/or the remote workstation112to upload/download data (e.g., the training information109) via the file server106. The file server106may include or otherwise have access to the database108. The database108may include a user biometric database for storing the training information109and/or a user access database that stores credential data and permissions data for verifying user's right to access the training system100based on the credentials and/or restricting access to the training system100based on corresponding permissions. The file server106and/or the database109may include network attached storage (“NAS”), storage area networks (“SAN”), or direct access storage (“DAS”), or any combination thereof, including, e.g., multiple hard disk drives. The file server106may have stored thereon a database management system, e.g. a set of software programs that controls the organization, storage, management, and retrieval of the data in the database(s)108, such as the training information109.

The database108, and any other databases or files stored in the file server106, may be a database separate from other user databases or the same database as other user databases, e.g., commingled in a database containing, for example, employee information (where the training system100is operated for employees). The training information109can also be stored in a plurality of databases (e.g., distributed databases, tables, or fields in separate portions of the file server memory). As one skilled in the art will appreciate, the file server106may provide the server104, and the user computers122,130access to the database108through, e.g., database management software or another application. A database server may be used to store the database108instead of or in addition to the file server106.

In some embodiments, the computers122,130,105and/or112may include remote terminals that enable a user to interact with various processes being controlled by the server104. For example, the operations described herein with regard to the user computers122,130may be executed by the server104and the user computers122,130may include network terminals that provides for user interaction with the operations provided by the server104. Moreover, the computers122,130,105and/or112may provide access to computer program instructions stored on the server104. For example, an application for providing user data running on the server104may be accessible via the user computers122,130such that the user may provide access credentials to login into their account, the server104may verify their credentials/permissions, and the user may be able to enter, via the user computer122,130, any inputs may be required by the training system. Thus, for example, profile information provided via the user computers122,130can be forwarded via the server104to the file server106for use in updating the user's information109stored in the database108. In some embodiments, the computers122,105can interface with different servers (e.g., the web or network servers104,106or110) for accessing the information109via the communications network118.

The trainer computer105may provide a second user, such as a training provider, or an employer (e.g., the user's manager, the user's human resources manager, or the like) access to the training information109and/or corresponding reports for reviewing, in real-time or retrospect, the training sessions of one or more users. In some embodiments, the second user may use the trainer computer105to interact with a virtual reality simulation provided to a first user as part of a training session and/or to interact with a first user undertaking training with the training system100. The trainer computer105may therefore provide input and output devices appropriate to allow the second user to interact with both a virtual reality simulation and with the first users.

FIG. 11is a block diagram illustrating components of the server104in accordance with one or more embodiments of the present invention. In some embodiments, the server104includes a memory1102, a processor1104and an input/output (I/O) interface1106. The memory1102may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or the like. The memory1102may include a non-transitory processor-readable storage medium having program instructions1108stored thereon that are executable by a computer processor (e.g., the processor1104) to cause the functional operations described herein with regard to the server104. The program instructions1108may include a server module1110including program instructions that are executable by the processor1010to provide some or all of the functionality described herein with regard to the server104.

The processor1104may be any suitable processor capable of executing/performing program instructions. The processor1104may include a central processing unit (CPU) that carries out program instructions (e.g., of the server module1110) to perform arithmetical, logical, input/output and other operations of the server104. The processor1104can be any commercially available processor, or plurality of processors, adapted for use in the computer server104, such as those manufactured by Intel Corporation, AMD Corporation, or the like. As one skilled in the art will appreciate, the processor1104may also include components that allow the computer server104to be connected to peripherals (e.g., a display and keyboard that would allow direct access to the processor and the memory1102, and/or application executing via the server104).

The I/O interface1106may provide an interface for connection of one or more I/O devices to server104. The I/O devices may include other network devices, such as the file server106, the web server110, the user computers122,130, the trainer computer105, the sensors120,128and/or the like. The I/O devices may be communicatively coupled to the I/O interface1106via a wired or wireless connection.

In some embodiments, the server104uses the biometric data200,400collected by the sensors120,128to monitor a biometric state of a user126before, during and/or after a training session.FIG. 12is a flowchart that illustrates a method of monitoring the user's biometric state in accordance with one or more embodiments of the present invention. In other embodiments, monitoring of a user's biometric state is performed at the user computer122,130of the training station102,103from which the biometric data is acquired. In order to aid clarity, the following description refers, generally, to collecting biometric data200from the training station102. It will be understood, however, that the following description applies equally to collection of biometric data from other training stations, such biometric data400from the training station103.

The method ofFIG. 12may include collecting biometric data200, as depicted at block1002. In some embodiments, collecting biometric data may include collecting biometric data200from the training station102. In some embodiments, collecting biometric data200may include an initialization protocol between the server104and the user computer122. For example, suitable signals may be sent from the server104to the user computer122to indicate that biometric data200is required, thereby automatically causing the processing ofFIG. 10to progress from block1004to block1006. In some embodiments, collecting biometric data200may include sending a suitable signal to the user computer122to display a prompt to the user126to request that the user126take action to initiate the collection of biometric data200. In some embodiments, the collection of biometric data200by the server104may begin upon receiving a signal from user computer122. For example, a signal may be received at block1202indicating that the server104should begin processing required to collect biometric data200.

As described herein, the mobile computer122may collect the measurements from each of the sensors120of the training station102and transmit corresponding biometric data200to the server104for use in monitoring the biometric state of the user126. In some embodiments, the data is collected and provided to the server104in real-time (e.g., within about 1 minute of being collected by the sensors120). In some embodiments, the biometric data200for one or more users may be logged over time as part of the training information109. For example, biometric data200may be collected for each of a group of users as those users undertake training through the training system100. The training information109for each of the users may be updated to reflect the biometric data collected. Thus, a log of biometric data associated with training activity, may be generated for each of the users. In some embodiments, the log of biometric data for a given user may be used to generate a profile for the user. For example, the logged biometric data for the user126may be used to generate profiles and/or reports that are based on current/recent training that the user126has undertaken and the biometric data associated with that training. Additionally, or alternatively, the logged biometric data may be used to generate profiles and/or reports that are based on historical training that the user has undertaken and the biometric data200associated with that training. In this way, the effect, efficacy, etc., of training sessions may be monitored both for a particular individual user and between users.

The method ofFIG. 12may include processing the collected biometric data at block1204. Processing at block1204may include processing raw biometric data200to enable the biometric data200to be used in providing training. For example, the collected biometric data200may be processed to determine one or more of a stress level, an indication of a user's level of interest, an indication of a user's level of engagement, an indication of a user's level of alertness and/or an indication of a user's level of excitement. In some embodiments, a stress level may be determine responsive to analysis of one or more of electronic heart rate data200g, the electronic respiratory rate data200e, the electronic skin conductance data200a, the electronic blood glucose data200band the electronic blood pressure data200c. In some embodiments, the stress level may be determined based upon analysis of others of the electronic biometric data200, such as, for example, electronic facial recognition data200dand the electronic neural data200f. In some embodiments, the raw biometric data200is time-stamped, or otherwise associated with a time. In such an embodiment, the data (or at least a time-stamped segment of data) can be associated with one or more events during testing that occur at or near the time. For example, portions/segments of biometric data200with time stamps that fall within about 1:45:30 pm to about 1:46:00 pm may be associated with a stressful event that occurred during testing at about 1:45:30 pm. Thus, a response of the user126to the event can be determined using the portions of biometric data200that have time stamps that fall within about 1:45:30 pm to about 1:46:00 pm.

In some embodiments, a level of interest, engagement, alertness and/or excitement of the user126may be determined. For example, a level of interest, engagement, alertness and/or excitement may be determined responsive to analysis of the electronic neural data200f, and/or the electronic facial recognition data200d. In some embodiments, the a level of interest, engagement, alertness and/or excitement may be determined responsive to analysis of others of the electronic biometric data200, such as, for example, electronic heart rate data200g, the electronic respiratory rate data200e, the electronic skin conductance data200a, the electronic blood glucose data200band the electronic blood pressure data200c.

In some embodiments, the processing at block1204includes generating visual representations of the electronic biometric data for display in real-time during a training session. The visual representations may include numerical representations, graphical representations and any other form of visual representations. In some embodiments, as described in more detail below, visual representations generated at the block1204may include an avatar for display on the user computer122and/or the trainer computer105. For example, an avatar may provide a virtual representation of the user126, and be updated to reflect the biometric state of the user126in a way that may be readily interpreted by the user126. By providing feedback through an avatar, the skills and competencies that are being trained through the system100are better internalized by the user126such that training is more efficient and effective.

In some embodiments, the processing at block1204includes generating training reports109for storage in the database108. The training reports109generated at block1204may include indications as to the types of training that the user126has undertaken and their corresponding biometric data, such as the biometric data200, the determined stress levels, level of interest, engagement, alertness and/or excitement. In some embodiments, the training reports109can include the biometric data being time-aligned with events during the testing. This may enable a determination of the biometrics of the user126at specific times and events during the testing such that the biometric data, and corresponding responses, of the user126can be associated with specific times and events during the testing. In some embodiments, the reports allow the user, a training provider, an employer, etc., to review a training session undertaken by the user126and to determine how the user reacted, biometrically, to one or more scenarios presented during the training session. In this way, a user, training provider, employer, etc., may be able to determine further actions for that user. For some types of training, such as management training, for example, training reports may allow an employer to determine which users display particular qualities necessary for particular roles within an organization (such as management competencies, health and safety awareness, etc.).

In some embodiments, the server104may transmit processed biometric data, visual representations and/or reports, to other entities in the training system100as depicted at block1206. For example, as described above, the server104may transmit visual representations to the user computers122,130and/or trainer computer105for display to a user and/or a trainer. In some embodiments, the processed biometric data, visual representations and/or reports can be used to generate an overall profile of the user126. In some embodiments, processed biometric data, visual representations, reports and/or user profile may be transmitted to the file server106for storage in the database108as training reports109(e.g., updating information already stored in the database108). In some embodiments, a user profile can be generated and/or updated for the user(s)126. For example, periodic (e.g., weekly, monthly, yearly, etc.) testing may be conducted for some or all of the users126in an organization, and their respective user profiles can be updated to reflect the results of the periodic testing. Such profiles (e.g., leadership ability profiles) can be useful, for example, to assess the developments of users126overtime in various areas, including leadership.

It will be appreciated that the method ofFIG. 12is an exemplary embodiment of methods that may be employed in accordance with techniques described herein. The method depicted inFIG. 12may be may be modified to facilitate variations of its implementations and uses. The method may be implemented in software, hardware, or a combination thereof. Some or all of the method may be implemented by one or more of the modules/applications described herein, such as server module1110. The order of the method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc.

FIG. 13schematically depicts information flow between functional components of the training system100in accordance with some embodiments. In the embodiment ofFIG. 13, the user126is provided with access to one or more training modules1310. InFIG. 13, four training modules are depicted, although it will be understood that this is merely exemplary and that any number of training modules1310may be provided. In some embodiments, the training system100is a management training system and one or more of the modules1310may be directed towards training the user126in various skills and competencies applicable to the management of people, systems, processes and/or the like. By way of example, management training modules may include an empowerment module, a people conversations module, a decision making module, a collaboration module, etc. Each module may include one or more sub modules, lessons, etc. (all referred to as modules herein for clarity) with particular training aims, tasks and requirements, etc. Each module may include one or more virtual reality simulations with which the user126interacts in order to complete the training provided by that module.

Each module utilizes a human computer interface1320in order to access biometric data1330provided by measurements taken by one or more of the plurality of sensors120,128. As described above, the biometric data1330provided by the sensors may be used to determine one or more further biometric states1340. For example, one or more of the1330may be used to calculate a level of engagement1341, an level of alertness1342, a level of excitement1343, a level of interest1344, a gender indication1345, an age indication1346, an emotional state indication1347and a stress level1348. It will be appreciated that determination of each of the further biometric states1340may utilize any one or more of the biometric data1330. Each of the training modules1310may utilize different ones of the biometric data1330,1340depending on the training aims and requirements of that training module. The biometric data1330may be referred to as biometric sensor data and the biometric data1340may be referred to as derived biometric data for convenience. It will be appreciated, however, that as described above, processing may be performed on data received directly from the sensors120,128to obtain desired biometric data. As such, it will be understood that the terms sensor biometric data and derived biometric data do not indicate limitations as to the processing that is performed to obtain the respective biometric data.

The biometric data1330and/or the further biometric data1340may be used to provide the user126and/or a training provider, employer, etc., with a real-time view (e.g. in a dashboard1350displayed on a display of a user computer122,130) indicating one or more of the biometric states of the user126as the user126interacts with one of the virtual reality training simulation. By providing a real-time view of the biometric states of the user126, the user (and/or a training provider, employer, etc.) is provided with a visual representation of the user's biometric response to the training. This biometric feedback therefore allows the user126to monitor their performance during the virtual reality training simulation. For example, while a training goal may be to practice handling difficult situations calmly, a user126may not always be aware of when their stress level, level of anger, etc., is increasing. By providing a visual representation of the user's biometrics, the user can use that feedback to practice calming measures during the training simulation and directly observe the result of those measures.

In some embodiments, the training system100may be operable to detect when one or more of the biometric states1320,1330exceeds a boundary condition. Upon determining that one or more of the biometric states1320,1330has exceeded a boundary condition, assistive prompts (or alerts) may be provided to the user126. In some embodiments, a prompt may be automatically generated and provided directly to the user126. For example, upon determining that a stress level has been exceeded, a prompt may be provided to the user to regulate their breathing. In some embodiments upon determining that one or more of the biometric states1320has exceeded a boundary condition, prompts may also or alternatively be provided to a training provider (e.g. via the trainer computer105). For example, a training provider may be prompted to monitor the user126closely, or more specific prompts may be provided. For example, a prompt may be provided to a training provider to provide coaching for a specific training goal.

In some embodiments, one or more of the biometric boundary conditions (e.g., a maximum value of the stress level1348) may be pre-set. For example, a user's heart rate may be compared to a known safe or desirable heart rate or heart rate range. Similarly, responses by the training system100, such as particular prompts (or alerts) provided to the user126may be pre-set.

In some embodiments, one or more biometric boundary conditions, goals, and/or prompts to be provided to the user126may be dynamically determined. That is, by monitoring a user's interactions with the training system100over time, the training system100may automatically personalize the training that is provided to each individual user of the training system100. By way of example, where a user is new to a particular training module or training aim, boundary conditions for that training aim may be set relatively widely (e.g. relatively high and/or low, depending on the biometric states1330,1340being monitored). Where a user's training history indicates that the user is making progress with a particular training aim (e.g., displaying better regulation of their breathing, better stress management, etc.) boundary conditions for that training aim may be adjusted. In this way, the system100is able to adjust the biometric feedback provided to the user126in order to increase the effectiveness of ongoing training. Similarly, in some embodiments, particular training aims may be determined for the user126based on their biometric responses to previous training. For example, if a user performs particularly poorly on a training module designed to develop skills of stress management, additional stress management training may be suggested and/or provided. Similarly, in some embodiments, real-time prompts (e.g. textual, graphical, audible, etc.) may be selected in dependence upon a user's real-time biometric responses and/or their biometric response history.

In some embodiments, the system100may utilize other information about the user126to dynamically set biometric boundary conditions, to suggest training to be undertaken, to provide personalized in-training prompts, etc. For example, where one or more health profiles/reports are available for a user (such as, for example, described in U.S. patent application Ser. No. 13/540,300 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH OF EMPLOYEES USING MOBILE DEVICES”, and/or U.S. patent application Ser. No. 13/540,153 filed on Jul. 2, 2012 and titled “SYSTEMS AND METHOD TO MONITOR HEALTH OF EMPLOYEE WHEN POSITIONED IN ASSOCIATION WITH A WORKSTATION”), such health profiles/reports may be utilized in determining appropriate boundary conditions, feedback prompts, and training goals to be provided to the user. For example, where a training report indicates a particular health problem (e.g., persistently higher blood pressure), training may be suggested to help to improve that health problem (e.g., mindfulness training).

In some embodiments, the user's emotions, thoughts and facial movements may be determined based upon sensed brain signals (e.g., electronic neural data200f,400f). For example, a plurality of predetermined brain wave patterns may be associated with corresponding emotions, thoughts, facial movements and/or motor functions. During processing of the brain signals, the sensed/observed brain signals may be compared to the plurality of predetermined brain signal patterns to identify any matches or similarities. Upon detecting a match or similarity of the observed brain signals to one or more of the predetermined brain signal patterns, the user's emotion (e.g., happy, sad, excited, depressed, etc.), thoughts (e.g., engagement with the training, interest in the training, alertness, excitement, etc.), facial movements (e.g., facial gestures such as smiling) that correspond to the matching predetermined brain signal pattern may be recorded. In some embodiments an avatar module1360may be used to generate a real-time avatar which mimics the user's current emotional state and/or facial gesture. For example, when it is determined that the user is happy and/or smiling, a displayed avatar can be animated to smile, providing the user or other persons reviewing the user's biometric state (e.g., a training provider, an employer, etc.) with an indication of the user's current emotional state and/or facial expression. In some embodiments, the ability to determine the user's thoughts may be employed to assist the user with training, as described above.

In some embodiments, the avatar module1360may be operable to recreate an avatar after a training session from biometric states of the user126sampled during the training session. For example, the biometric states1330,1340may be sampled at predetermined intervals and stored (e.g. as training information109in the database108). The sampling and storage of one or more of the biometric states1330,1340allows for a comprehensive review of the user's biometric states during a training session. The stored biometric samples may additionally be used by the avatar module1360to recreate the avatar that was displayed to the user126at the time corresponding to the sampled data, in order to provide a visual representation of the development of the user's biometric state during a training session. In some embodiments, an image of the avatar may be sampled at predetermined intervals (e.g. every second, every two seconds, etc.) during a training session and each sampled avatar image stored (e.g. as training information109in the database108). The stored avatar image samples may then be played back as an animation during a post-training review, thereby providing a visual representation of the development of the user's biometric state during a training session. In this way, processing necessary to recreate an avatar may be reduced. Additionally, in some embodiments, storage of avatar image samples only, may allow for storage requirements to be reduced. This may be beneficial where a comprehensive review of one or more of the biometric states1330,1340during a training session is not required.

In some embodiments, the avatar module1360may be configured to generate a coaching avatar that provides instructions, suggestions, and/or demonstrations that are intended to help coach the user during training. For example, as described herein, the avatar module1360may provide an avatar for demonstration of training techniques, such as breathing, meditation, etc. In some embodiments, the avatar module1360may be operable to provide audio information (e.g., via speakers of the computer122,130).

As described above, each training module1310, sub module or lesson may include one or more virtual reality simulations. Each virtual reality simulation may present the user126with a simulated environment in which to undertake one or more training exercises. A user may interact with the virtual reality simulation in order to complete the training exercises.FIGS. 14A, 14Billustrate a virtual reality simulation which presents the user126with a driving simulation as part of a health and safety training module.FIG. 14Aillustrates an initial view1410in which a scene1412of a road is displayed from a first person perspective. That is, from the point of view of the user126, the user126views the scene1412through the eyes of an avatar (the user's manifestation within the virtual reality simulation) that is looking along the road. A dialogue box1414provides a textual indication (“It is time to drive to work”) of a task that the user126is to perform. A timer1416in a top portion of the view1410indicates an amount of time that has elapsed during the training session and is shown inFIG. 14Awith a value of “0”. In some embodiments, the timer1416may display, or may be configurable to display, an amount of remaining time available for the user126to complete a task within the virtual reality simulation. A counter1418displays a score that has been accrued by the user126during the virtual reality simulation and is shown inFIG. 14Awith a value of “0”.

FIG. 14Billustrates six scenes1420-1430that may be displayed to the user126as he interacts and engages with the virtual reality simulation. In the scenes1420-1430the user126is presented with a first-person view from within a car. The user may provide inputs to the user computer122(or user computer130) in order to simulate driving the car. It will be appreciated that any user input devices may be provided. In some embodiments, in order to accurately simulate a particular environment, input devices may be selected to match the tasks being simulated. For example, in the virtual reality simulation ofFIGS. 14A, 14B, a steering wheel input device may be provided. Realistic input devices may allow the system100to provide a more immersive training experience, thereby contributing to associations made within the user's brain and increasing the training's efficacy. The system100may score the user126based upon actions taken within the virtual reality simulation. For example, if the user accelerates too rapidly, brakes too suddenly, or corners too sharply, points may be deducted. It will be appreciated that any appropriate scoring mechanism may be used and that the exact scoring mechanism will, generally, depend upon the particular virtual reality simulation and training being provided.

In some embodiments, one or more virtual reality simulations (or parts of virtual reality simulations) may not require user input to control the user's avatar. For example, with reference toFIGS. 14A, 14B, the user126may observe the scenes that are displayed on the display screen of the user computer122(or130) without active control. In this case, the training may require the user to identify (e.g. by appropriate input such as touching the screen, pressing a key on a keyboard, etc.), when a health and safety issue arises. For example, points may be awarded for successful identification of valid health and safety issues, no points awarded for failure to identify a valid health and safety issue, and points deducted for identification of invalid health and safety issues. It will be appreciated that the example virtual reality simulations, user interfaces and scoring mechanisms shown inFIGS. 14A, 14Band described above are provided only by way of example and that embodiments any utilize any manner of virtual reality simulation, user interface and scoring mechanism as appropriate to one or more particular training aims. It is to be further understood that while the use of virtual reality simulations may provide for a particularly effective training method, other forms of training may be provided. For example, a conversational training module may be provided in a purely textual form.

One or more information dashboards may be displayed to the user126, or to a training provider (or employer, etc.) during a virtual reality simulation. Such an information dashboard may be displayed, for example, overlaid on a portion of the virtual reality simulation, or on a different screen to the screen on which the virtual reality simulation is displayed (e.g. a screen of a training provider, employer, etc.). In some embodiments, information dashboards may be displayed subsequent to completion of a training session rather than, or in addition to, being displayed simultaneously with a training session. In some embodiments, an information dashboard may only be displayed simultaneously with a training session upon detection that one or more of the biometric parameters of the user126have exceeded one or more bounds. For example, where it is determined that a user's heart rate has exceeded a maximum heart rate, a heart rate indicator (and/or other indicators) may be displayed to the user.

FIG. 15illustrates an example of an information dashboard1500that may be provided in some embodiments. The dashboard1500may include an avatar1502, a biometric summary1504, a stress indicator1506, a training response summary1508, and/or the like. In some embodiments, the avatar1502includes a graphical depiction of the user's current emotional state, facial expression, gestures, and/or the like. For example, in response to determining that the user is smiling and/or happy (e.g., from the electronic neural data200fand/or the electronic facial recognition data200d), the avatar1502may be dynamically updated to include a graphic illustration of a smile, as depicted, to mimic the current emotion and/or facial expression of the user. While the avatar1502is shown inFIG. 2as including a depiction of a face, it will be appreciated that the avatar1502may be more detailed, and may include a depictions of other parts, or a whole, of a human body. In some embodiments more than avatar may be provided.

In some embodiments, the biometric summary1504displays of some or all of the current biometric states of the user based on the biometric data200,400received from the sensors120,128. For example, in the illustrated embodiment, the biometric summary1504includes an indication of the user's heart rate (HR), respiratory rate (RR), skin conductance (GSR) and blood glucose (BG). In some embodiments, the stress indicator1506includes an indication of the current determined level of stress the user. In some embodiments, the training response summary1508displays some or all of a determined level engagement of the user126, a determined level of interest of the user126, a determined level of excitement of the user and a determined level of alertness. The levels of engagement, interest, excitement, and alertness are depicted as having a rating out of five (‘5’), however, it will be appreciated that this is merely exemplary. The levels of engagement, interest, excitement, and alertness may be determined from the biometric data200,400in any appropriate way as will be readily apparent to those skilled in the art. For example, level of engagement, interest, excitement and alertness may be determined at least in part from the neural data200f,400fand the electronic facial recognition data200d,400d. By way of further example, detection of an increase alpha waves and/or a relaxing of facial muscles, may indicate a reduction in engagement, interest, excitement and alertness.

In some embodiments, only portions of the dashboard1500may be displayed during a training session, for example, only the avatar1502. As described above, a virtual reality simulation generally provides a simulation avatar which is the user's manifestation in the simulation. Additionally, or alternatively, therefore, where a virtual reality simulation provides perspectives in which all or part of the simulation avatar is visible, the user's biometric states may be reflected directly in the visible simulation avatar (in addition to, or instead of, the avatar1502). For example, where the simulation avatar's face is visible, emotions of the user126may be reflected on the face of the simulation avatar within the virtual reality simulation.

As indicated above, an information dashboard (or other presentation of biometric information) may be provided to the user126(or a training provider, employer, etc.) during a training session for real-time monitoring of the user126, or after a training session for a post-training review of the user's performance during the training session. For example, as depicted inFIG. 16, a reviewer may be provided with a view1600including both an information dashboard1602and a recorded training session1604. In this way, a reviewer can view a user's recorded biometric states together with the activity of the user in the training session. A progress bar1606may be provided to allow a reviewer of the training session (e.g. the user or a provider, etc.) to control playback and to select specific times of the training. In some embodiments, interest points, for example times at which a user's biometric parameters meet some predetermined criteria, may be noted during the training session. Navigation means may be provided to allow efficient location and playback of interest points during review. For example, in the embodiment depicted inFIG. 16, a plurality of markers1608,1610,1612are provided on the progress bar1606to indicate positions in the recording of the training session and biometric states of interest. The markers1608-1612may be selectable to allow a user to accurately navigate to the indicated periods of interest. It will be appreciated that any other navigable indicators for periods of interest may be provided, such as one or more lists.

FIG. 17shows a flowchart depicting example processing that may be performed by the training system100while a user is executing a training module in some embodiments. At block1702a training module is initialized. For example, the user126may use an input device of the training station102to select one of the training modules1310thereby causing execution of the selected training module on the training station102(or on the server104, for example, where the selected training module is provided remotely over the network118). At block1704biometric monitoring is initialized. For example, block1704may cause the process ofFIG. 10(or similar) to be initiated. After block1704, one or more of blocks1706,1708,1710,1712,1714,1716,1718may be executed to determine one or more of the derived biometric states1340. In particular, at block1706, a gender of the user may be determined. For example, a gender of the user may be determined based on the electronic facial recognition data200d. At block1708an age of the user may be determined, again, for example based on the electronic facial recognition data200d. At block1710a stress level of the user may be determined based, for example, on one or more of the electronic heart rate data200g, the electronic respiratory rate data200e, the electronic skin conductance data200a, the electronic blood glucose data200band the electronic blood pressure data200c. At block1712an emotion of the user may be determined based, for example, on the electronic facial recognition data200d. At blocks1714,1716and1718a level of interest, engagement and excitement of the user, respectively, may be determined based, for example on the electronic neural data200fand/or the electronic facial recognition data200d. Where the derived biometric states1340are determined by another entity in the training system100(e.g. the server104during the processing ofFIG. 10), the processing at blocks1706-1718may include obtaining the derived biometric states1340from the appropriate entity. Whether or not a particular one of blocks1706to1718is processed may be based upon a number of factors, such as, requirements of the particular training module that has been initialized and which biometric data200has been received.

At block1720an avatar (such as the avatar1502, and/or a simulation avatar) may be updated based upon one or more of the determined gender, age, stress level, emotion, interest, engagement and excitement determined at blocks1706to1718or indeed based upon any of the biometric data1330,1340. Updating the avatar at block1720may include determining and applying one or more graphical update operations to be applied to the avatar based upon the biometric data1330,1340. For example, updating the avatar at block1720may include determining a current state of the avatar, determining a desired state of the avatar, and determining one or more graphical operations to transition the avatar from the current state to the desired state.

At block1722a determination may be made as to whether the avatar requires updating. For example, a determination may be made as to whether a predetermined length of time has elapsed since a last update to the avatar. A determination may be made as to whether new biometric data has been received since a last update of the avatar. A determination may be made as to whether any received biometric data differs from biometric data that was last used to update the avatar at step1720. Other criteria that may be used for determining whether an update to the avatar is required will be readily apparent to the skilled person. If it is determined that an update to the avatar is required, one or more of blocks1706to1718may again be processed. If it is determined that an update is not required, a determination may be made at block1724as to whether the training module has ended. If it is determined that the training module has not ended, processing may loop between blocks1722and1724until it is determined that an update to the avatar is required or the training module has ended.

In some embodiments, the virtual simulation itself may be updated in response to processing of the biometrics obtained from the user126. For example, one or more training virtual simulations may include one or more possible “paths”. Paths may be selected in dependence upon a user's biometric response to events that are presented to the user in the virtual simulation. For example, where if it is determined, during a training session, that a user is doing well at a particular task (e.g. regulate breathing, control stress levels, etc.), a path may be taken that will challenge that user (e.g. the selected path may present more challenging events than other possible paths). Similarly, if it is determined that a particular simulation is not adequately stimulating or maintaining the attention of a user (e.g. based upon the determined levels of interest, excitement, engagement, alertness, emotion, etc.), paths may be selected through a virtual reality simulation to encourage a desired response. For example, paths may be selected that are expected to increase alertness. By improving user alertness/engagement, for example, the training provided by the training system may be more effective at causing skills and lessons to be internalized by users.

In some embodiments, where multiple users undertake training using the training system100, scores obtained by each user during training sessions may be recorded and used to provide scoreboards to enable ranking of the users. Scoreboards may be provided to the first users (i.e. those undertaking training), and such scoreboards may serve to improve motivation and therefore efficacy of the training provided through the training system100. Scoreboards may be provided to second users and may serve as a way to monitor training across a plurality of first users to determine, for example, where to focus future training. Rankings may also be beneficial for employers seeking to rank employees, candidate employees, etc. Multiple users may participate in training simultaneously (or substantially simultaneously having regard to, for example, network latency). For example, multiple first users may undertake training simultaneously, and/or a first user may undertake training while a second user oversees, guides or manages the training. Where a virtual reality simulation is provided, the multiple users may be represented in the virtual reality simultaneously such that the avatar of one user can interact with the avatar of one or more other users.

In some embodiments, machine learning is used to determine a set of desirable biometric responses to one or more of the training modules and/or virtual reality simulations of training modules. For example, one or more individuals may be selected to provide benchmarks. For example, individuals considered to be effective managers may be selected to provide benchmarks for a management training module. The selected individuals may use the training stations to undertake training and their biometrics may be measured and stored. Users undertaking training may then compare their own biometrics to those of the selected individuals. Additionally, the stored biometrics of the selected individuals may be used to form a training set for a neural network, for example. Such a trained neural network may then be operable to automatically analyze the biometrics of users. It will be appreciated that neural networks are provided only as an example of machine learning techniques that may be utilized with the training systems and techniques described herein.

While particular exemplary arrangements of the training stations102,103, and other entities of the training system100are described above, it is to be understood that the training stations102,103, and the other entities of the training system100may be implemented in any appropriate manner. For example, the user computer130(and the computers105,106,110112) may include personal computers (PC) as is known in the art. The user computer122may include a smartphone, a tablet computer, etc., as is known in the art. Each of the entities of the training system100may utilize any operating system compatible with the networked systems discussed herein. For example, the computers described herein may run UNIX, Linux, Windows®, OS X®, Android®, iOS®, etc. In the depicted exemplary embodiments, the training station102includes a generally stationary computer130, while the training station102includes a mobile (or portable) computer122. It will be appreciated, however, that this is merely one possible arrangement. For example, the training station102may include a “laptop” computer which may be stationary for the duration of a training session, but which is or may be re-located between training sessions.

Further, it is to be understood that while embodiments of a training system100have been described herein as including a network of entities, this is merely one exemplary embodiment. In some embodiments, a training system may be provided, for example, by a single device, or by two devices connected in a peer-to-peer arrangement. For example, in some embodiments, a training station (such as the training stations102,103) may be directly connected to a trainer computer (such as the trainer computer105). In such an embodiment, processing that is described above as being performed by the server104may, for example, be performed by the user computer and/or by the trainer computer105. By way of further example, while the datastore108is depicted inFIG. 1as connected to a file server106, it will be understood that the datastore108may be local to one or more of the other entities within the training system100. For example, the datastore108may be local to the server104or the training station102.

More generally, in the drawings and specification, there have been disclosed typical embodiments of the invention, and although specific terms are employed, the terms are used in a descriptive sense only and not for purposes of limitation. The invention has been described in considerable detail with specific reference to these illustrated embodiments. It will be apparent, however, that various modifications and changes can be made within the spirit and scope of the invention as described in the foregoing specification.

As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” mean including, but not limited to. As used throughout this application, the singular forms “a”, “an” and “the” include plural referents unless the content clearly indicates otherwise. Thus, for example, reference to “an element” may include a combination of two or more elements. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic processing/computer. In the context of this specification, a special purpose computer or a similar special purpose electronic processing/computer is capable of manipulating or transforming signals, typically represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic processing/computer.

The techniques described herein may include or otherwise be used in conjunction with techniques described in U.S. patent application Ser. No. 13/540,300 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH OF EMPLOYEES USING MOBILE DEVICES”, U.S. patent application Ser. No. 13/540,153 filed on Jul. 2, 2012 and titled “SYSTEMS AND METHOD TO MONITOR HEALTH OF EMPLOYEE WHEN POSITIONED IN ASSOCIATION WITH A WORKSTATION”, U.S. patent application Ser. No. 13/540,028 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING COGNITIVE AND EMOTIVE HEALTH OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,067 filed on Jul. 2, 2012 and titled “COMPUTER MOUSE SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,095 filed on Jul. 2, 2012 and titled “CHAIR PAD SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,124 filed on Jul. 2, 2012 and titled “FLOOR MAT SYSTEM AND ASSOCIATED, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,180 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING BIOMECHANICAL HEALTH OF EMPLOYEES”, U.S. patent application Ser. No. 13/540,208 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR COACHING EMPLOYEES BASED UPON MONITORED HEALTH CONDITIONS USING AN AVATAR”, U.S. patent application Ser. No. 13/540,335 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR PROVIDING HEALTH INFORMATION TO EMPLOYEES VIA AUGMENTED REALITY DISPLAY”, U.S. patent application Ser. No. 13/540,374 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING HEALTH AND ERGONOMIC STATUS OF DRIVERS OF VEHICLES” (now U.S. Pat. No. 8,872,640), and/or U.S. patent application Ser. No. 13/540,262 filed on Jul. 2, 2012 and titled “SYSTEMS, COMPUTER MEDIUM AND COMPUTER-IMPLEMENTED METHODS FOR MONITORING AND IMPROVING HEALTH AND PRODUCTIVITY OF EMPLOYEES”, the disclosures of which are incorporated herein by reference in their entireties.