Augmented reality field of view based on sensed user data

User-specific augmentation of a real word field of view viewable through an augmented reality (AR) device is facilitated by a processor(s) receiving image data representative of a real world field of view viewable by a user through the AR device, and receiving sensor data indicative of the user's stress level, which is related, at least in part, to the user's real world field of view viewable through the AR device. The processor(s) processes the image data, based on the user's stress level, to identify one or more stress-inducing elements to be hidden in the real world field of view viewable through the AR device. Further, the processor(s) provides an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view.

BACKGROUND

Agoraphobia is a type of anxiety disorder in which an individual is anxious in situations or places where the individual perceives that their environment is unsafe, with no easy way to escape. For instance, the individual can fear situations such as using public transportation, being in open or enclosed spaces, standing in line, being in a crowd, or simply being outside their home. An individual with agoraphobia often has a hard time feeling safe in any public place, especially where crowds gather. The individual's anxiety can be so overwhelming that the individual may feel unable to leave their home. Agoraphobia treatment can be challenging, because it often involves confronting the patient's fears. Without treatment, it is uncommon for agoraphobia to resolve. Treatment is typically with a type of counseling referred to as cognitive behavioral therapy (CBT), which is helpful in resolving the disorder for only about half of the individuals counseled.

SUMMARY

Certain shortcomings of the prior art are overcome and additional advantages are provided through the provision, in one or more aspects, of a computer-implemented method, which includes receiving, by one or more processors, image data representative of a real world field of view viewable by a user through an augmented reality (AR) device, and receiving, by the one or more processors, sensor data indicative of a stress level of the user, where the user's stress level is related, at least in part, to the real world field of view viewable by the user through the AR device. Based on the user's stress level, the one or more processors process the image data to identify one or more stress-inducing elements for the user to be hidden in the real world field of view viewable through the AR device. The one or more processors further provide an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view viewable by the user through the AR device.

Systems and computer program products relating to one or more aspects are also described and claimed herein. Further, services relating to one or more aspects are also described and can be claimed herein.

Additional features and advantages are realized through the techniques described herein. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed aspects.

DETAILED DESCRIPTION

The accompanying figures, in which like reference numerals refer to identical or functionally similar elements throughout the separate views, and which are incorporated in and form a part of the specification, further illustrate the present invention and, together with the detailed description, serve to explain aspects of the present invention. Note in this regard that descriptions of well-known systems, devices, processing techniques, etc., are omitted so as not to obscure the invention in detail. It should be understood, however, that the detailed description and this specific example(s), while indicating aspects of the invention, are given by way of illustration only, and not limitation. Various substitutions, modifications, additions, and/or other arrangements, within the spirit or scope of the underlying inventive concepts will be apparent to those skilled in the art from this disclosure. Note further that numerous inventive aspects and features are disclosed herein, and unless inconsistent, each disclosed aspect or feature is combinable with any other disclosed aspect or feature as desired for a particular application of one or more of the concepts disclosed herein.

Note also that illustrative embodiments are described below using specific code, designs, architectures, protocols, layouts, schematics, or tools only as examples, and not by way of limitation. Furthermore, the illustrative embodiments are described in certain instances using particular software, tools, or data processing environments only as example for clarity of description. The illustrative embodiments can be used in conjunction with other comparable or similarly purposed structures, systems, applications, or architectures. One or more aspects of an illustrative embodiment can be implemented in hardware, software, or a combination thereof.

As understood by one skilled in the art, program code, as referred to in this application, can include both software and hardware. For example, program code in certain embodiments of the present invention can include fixed function hardware, but other embodiments can utilize a software-based implementation of the functionality described. Certain embodiments combine both types of program code.

One example of program code, also referred to as one or more programs or program instructions, is depicted inFIG. 8as program/utility840, having a set (at least one) of program modules842, which can be stored in memory823. As a further example,FIG. 8depicts additional, or alternative, program code implemented as a field of view processing and augmentation facility or module801. As a further example, inFIG. 3, program code implementing one or more aspects described herein could be stored or resident in main memory308, read-only memory324, disc storage326, CD-ROM330, and/or other peripheral devices of computing environment300.

As noted initially, agoraphobia is a type of disorder in which an individual can become stressed in one or more situations that might cause the individual to feel anxious and trapped. Depending on the individual disorder, stressful situations can include open spaces, public transport, shopping centers, standing in line, or being in a crowd. People with agoraphobia often have a hard time feeling safe in any public space, especially where crowds gather.

To assist in addressing this disorder, disclosed herein, in one or more aspects, is the use of augmented reality (AR) to dynamically modify a real world situational experience of the user by overlapping or hiding one or more stress-inducing elements to the user in the real world field of view viewed by the user through the AR device. For instance, where the user is an agoraphobic patient, or a patient with social anxiety disorder, large crowds are overlaid with other objects, or individuals within the crowd can be removed or hidden entirely from view using spatial mapping techniques, providing the user with a perception of a smaller gathering, personalizing the AR viewable image to reduce the user's anxiety, and making the space feel more open to the user, thereby reducing the user's stress or anxiety. Over time, as the user's condition improves, the field of view processing and augmentation facility can dynamically expose more the of real world field of view to the user based on the user's currently sensed health data to assist in the user's treatment plan.

An augmented reality (AR) device is, for instance, a wearable glass device, or headset-mounted device, with an incorporated, or associated, augmented reality system that provides an interactive experience of a real world environment to a user where objects or elements that reside in the real world can be enhanced or modified by computer-generated perceptual information, including across multiple sensory modalities, if desired. An augogram is a computer-generated image, in whole or in part, used to create an augmented reality field of view. An AR device or system can combine real and virtual worlds, is real-time interactive, and provides accurate 3-D registration of virtual and real objects. The overlaid sensory information can be constructive, that is, additive to the natural environment, or destructive, that is, masking of the natural environment. The experience can be seamlessly interwoven with the physical world such that it is perceived as an immersive aspect of the real environment. In this way, augmented reality can alter the user's ongoing perception of a real world environment.

Advantageously, through a combination of real-time stress level sensing or measurement, and diminished reality techniques using an augmented reality (AR) device/system, a computer-implemented method, system and computer program product are provided herein which allow an individual or user with, for instance, social anxiety or agoraphobia, to function in the real world without fully having addressed their disorder. Over time, the computer-implemented method, system and computer program product disclosed herein allow the individual to resolve their condition by gradually confronting their fears, dependent on the real-time stress level data obtained for the user as the user functions in the real world.

More particularly, embodiments of the present invention include a computer-implemented method, system, and computer program product, where program code executing on one or more processors receives image data representative of a real world field of view viewable by a user through an augmented reality (AR) device, and receives sensor data indicative of a current stress level of the user, where the user's stress level is related, at least in part, to the real world field of view viewable by the user through the AR device. Embodiments of the present invention also include program code executing on one or more processors which processes, based on the user's stress level, the image data to identify one or more stress-inducing elements for the user to be hidden in the real world field of view viewable by the user through the AR device. Further, embodiments of the present invention include program code executing on one or more processors that provides an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view viewable through the AR device.

In certain embodiments of the present invention, providing the augmented real world field of view for display to the user through the AR device includes selectively hiding, by the one or more processors, only the identified one or more stress-inducing elements in the augmented real world field of view for display to the user through the AR device.

In one or more embodiments of the present invention, the one or more stress-inducing elements include one or more people in the real world field of view viewable through the AR device. Further, in one embodiment, program code executing on the one or more processors determines that the user and the one or more people are in motion relative to each other, and the identifying includes predicting by the program code that the user and the one or more people will not intersect. In one or more embodiments of the present invention, the program code executing on the one or more processors receives location data for the user to predict whether the user is approaching or in a crowded area, and the processing of the image data is further based on the location data predicting that the user is approaching or in the crowded area.

In certain embodiments of the present invention, the real world field of view viewable by the user through the AR device includes multiple stress-inducing elements for the user, and the one or more stress-inducing elements identified to be hidden are only a portion of the multiple stress-inducing elements viewable by the user through the AR device, with the portion being less than all of the multiple stress-inducing elements.

In one or more embodiments of the present invention, providing the augmented real world field of view for display to the user includes, based on identifying the one or more stress-inducing elements for the user to be hidden, generating by the program code executing on the one or more processors, a spatial mapping of the image data around the one or more stress-inducing elements, and using the spatial mapping to provide the augmented real world field of view by selectively hiding the one or more stress-inducing elements.

In certain embodiments of the present invention, program code executing on one or more processors uses machine learning and the sensor data to classify the user's stress level, and the processing includes processing the image data to identify the one or more stress-inducing elements for the user based, at least in part, on the user's classified stress level.

In one embodiment, the sensor data includes data indicative of the user's heart rate.

Embodiments of the present invention are inextricably tied to computing and provide significantly more than existing approaches to addressing an individual's anxiety disorder. For instance, embodiments of the present invention provide program code executing on one or more processors to exploit the interconnectivity of various systems, as well as to utilize various computing-centric data analysis and handling techniques, in order to receive image data representative of a real world field of view viewable by a user through an augmented reality (AR) device, and receive sensor data indicative of a stress level of the user, where the user's stress level is related, at least in part, the real world field of view viewable by the user through the AR device, and based on the user's stress level (e.g., based on the user's stress level exceeding a threshold), process the image data to identify one or more stress-inducing elements to be hidden and provide an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view. Both the interconnectivity of the devices and/or computing systems utilized, and the computer-exclusive data processing techniques utilized by the program code, enable various aspects of the present invention. Further, embodiments of the present invention provide significantly more functionality than existing approaches to treating an individual with an anxiety disorder, by advantageously allowing the individual to continue to function in the real world, while simultaneously addressing the individual's anxiety disorder through conditioning.

In embodiments of the present invention, the program code provides significantly more functionality, including but not limited to: 1) program code that receives image data representative of a real world field of view viewable by a user through an augmented reality (AR) device; 2) program code that receives sensor data indicative of a stress level of the user, where the user's stress level is related, and least in part, to the real world field of view viewable by the user through the AR device; 3) program code that processes, based on the user's stress level, the image data to identify one or more stress-inducing elements for the user to be hidden in the real world field of view viewable through the AR device; and 4) program code that provides an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view viewable through the AR device.

By way of example,FIG. 1depicts one embodiment of a workflow or process illustrating one or more aspects of some embodiments of the present invention. In one or more embodiments of the present invention, program code executing on one or more processors receives sensor data indicative of a user's stress level, where the user's stress level is related, at least in part, the user's real world field of view100as viewed through an augmented reality (AR) device, such as AR glasses or an AR headset. For example, the sensor data can be from one or more health measurement or sensor devices worn by or associated with the user, such as a heart rate monitor or smart watch capable of measuring the user's heart rate. Program code executing on the one or more processors processes received image data representative of the user's real world field of view to identify one or more stress-inducing elements for the user to be hidden in the real world field of view viewable by the user through an augmented reality (AR) device102. In one or more implementations, based on the user's stress level, program code executing on one or more processors selectively hides or removes the one or more stress-inducing elements from the real world field of view viewable by the user through the AR device104. For instance, in one or more embodiments, only the identified one or more stress-inducing elements are removed from the modified or augmented real world field of view displayed to the user through the AR device.

Note that the particular stress-inducing element to be hidden is dependent on the individual user, and the user's condition being addressed. Agoraphobia, or a social anxiety disorder, is discussed herein in connection with one or more embodiments of the invention, by way of example only. For instance, in one or more implementations, the one or more stress-inducing elements could be one or more animals, such as one or more dogs, cats, etc., or any other stress-inducing element or object for the particular user. Advantageously, the computer-implemented method, system, and program product disclosed herein allow a user to continue to function in the real world by selectively hiding or blocking one or more stress-inducing elements from the augmented real world field of view display to the user through the AR device based on the received sensor data indicative of the user's current stress level. Note also, although described with reference to heart rate, the sensor data could measure other biological characteristics indicative of stress or anxiety, such as blood pressure, perspiration, or breathing.

FIG. 2depicts one embodiment of a system200, illustrating certain aspects of an embodiment of the present invention. System200includes various computing devices, including one or more mobile devices and one or more sensor(s)201, such as an augmented reality (AR) device210, one or more sensors220, such as sensors worn by or associated with the user of the system, and one or more mobile computing resources230, such as a smartphone or other mobile computing resource associated with the user. In the embodiment depicted, system200also includes one or more remote computing resources240in communication with AR device210, sensors220and/or mobile computing resource(s)230across one or more networks205. By way of example, in one or more embodiments, AR device210, sensor(s)220, mobile computing resource(s)230, and remote computing resource(s)240, can each have a wireless communication capability for communicating data to facilitate processing, as described herein. By way of example, network(s)205can be, for instance, a telecommunications network, a local-area network (LAN), a wide-area network (WAN), such as the Internet, or a combination thereof, and can include wired, wireless, fiber-optic connections, etc. The network(s) can include one or more wired and/or wireless networks that are capable of receiving and transmitting data, including image data, sensor data, and location data, such as discussed herein.

By way of example, AR device210can include or have associated therewith digital image capture components211, such as conventional image or video camera components and related sensors. Further, computing resource(s)210can include an image processing module212. Note in this regard that, in the embodiment ofFIG. 2, system200includes, by way of example, image processing module212associated with AR device210, as well as, or alternatively, image processing module231associated with mobile computing resource(s)230, and image processing module244associated with remote computing resource(s)240. This is one implementation only. In one or more other implementations, the image processing module (or program code) could be associated with only one of the computing resources or AR device, or otherwise located. In one embodiment, image processing module212can include image-video-based processing for, for instance, object detection or element detection using conventional detection algorithms. For instance, where people are the element to be detected in the image data, facial recognition code can be used to detect people in the user's field of view. Additionally, AR device210includes transmitter and/or receiver logic or circuitry213, and a display214for displaying, for instance, the real world field of view of the user of the system, or an augmented version of the real world field of view, such as disclosed herein. In one or more embodiments, display214of AR device210can include augmented reality glasses or an augmented reality headset worn by the user.

In the embodiment illustrated, sensors220include, by way of example, one or more stress-related sensors221, one or more vision sensors222, and one or more geolocation sensors223. Note that sensors220can be associated with or worn by the user, and can be separate from AR device210and mobile computing resource(s)230, or integrated within one or more both of AR device210and mobile computing resource(s)230. In one or more embodiments, stress-related sensor(s)221can be, or can include, for instance, a heart rate sensor, blood pressure sensor, perspiration sensor, etc., worn by the user, and which produces sensor data related to or indicative of the user's current level of stress or anxiety. Vision sensor(s)222can include, for instance, image capture components and/or object or element recognition software to, for instance, facilitate identifying one or more stress-inducing elements (e.g., people) within image data representative of a real world field of view viewable by the user through AR device210. Geolocation sensor(s)223can be, for instance, a global positioning sensor, to identify a geographic location of a user, and to facilitate correlating that geographic location to an area of historically high-traffic, such as an area that is typically crowded, such as an airport, train station, arena, etc. Further, geolocation sensor(s)223and related program code could assist in identifying a currently congested area, such as by identifying the presence of a large number of mobile devices in close proximity, where the devices are associated with different people.

Mobile computing resource(s)230can be, for instance, associated with AR device210, or separate from AR device210, in which case mobile computing resource(s)230can be in wireless communication with AR device210. By way of example, mobile computing resource(s)230can be a smartphone, wireless computer, tablet, personal digital assistant (PDA), a laptop computer, etc., owned by or associated with the user of system200. In the embodiment illustrated, mobile computing resource(s)230can further include an image processing module231with program code configured to perform one or more aspects of the image processing and augmentation facility disclosed herein. Mobile computing resource(s)230further includes transmitter and/or receiver logic or circuitry232for facilitating data transfer from or to AR device210and sensor(s)220, as well as remote computing resource(s)240.

Note that AR device210, sensor(s)220, and mobile computing resource(s)230can include additional and/or different components, modules, sensors, sub-systems, etc., without departing from the spirit of the present invention.

Remote computing resource(s)240can be, in one or more embodiments, a cloud-based computing resource which includes program code241executing on one or more processors to implement one or more aspects of the image processing and augmentation facility disclosed herein. In the embodiment illustrated, program code241includes, or has associated therewith, a learning agent242, such as a neural network, which uses one or more models to provide one or more functional aspects disclosed herein, and an image processing module244, again, to facilitate implementing one or more aspects of image processing and augmentation as disclosed herein.

Note again that although image processing module212is shown associated with AR device210, image processing module231is associated with mobile computing resource(s)230, and image processing module(s)244is associated with remote computing resource(s)240, this represents one distributed embodiment only of the concepts disclosed. For instance, in one or more other embodiments, AR device210may be in communication with mobile computing resource(s) which processes the image and provides the augmented real world field of view for display to the user, and/or can be in communication with remote computing resource(s)240for image processing module244to process the image data and provide the augmented real world field of view for display to the user through the AR device. Note that one or more of the image processing modules of AR device210, mobile computing resource(s)230, and/or remote computing resource(s)240can include program code to execute on one or more processors to implement processing as described herein to, for instance, allow an individual with an anxiety disorder to continue to function in the real world, while simultaneously helping the individual in addressing the disorder through conditioning tailored specifically to the user's current level of stress. This is accomplished by selectively overlaying or hiding one or more identified stress-inducing elements for the user within the user's augmented field of view as seen through the AR device.

By way of example,FIG. 3is one example of a processing or computing environment in which illustrative embodiments can be implemented.FIG. 3is only an example, and not intended to imply limitation with regard to the environment in which different embodiments can be implemented. A particular implementation can have any number of modifications to the depicted environment.

Referring toFIG. 3, a block diagram of a data processing system in which illustrative embodiments can be implemented is shown by way of further example. Data processing system300is an example of a computing system, such as AR device210, mobile computing resource(s)230, and/or remote computing resource(s)240inFIG. 2, in which computer-usable program code or instructions implementing processes such as disclosed herein can be located, in one or more embodiments.

In the depicted example, data processing system300includes a hub architecture including a north bridge and memory controller hub (NB/MCH)302and a south bridge and input/output (I/O) controller hub (SB/ICH)304. Processing unit306, main memory308, and graphics processor310are coupled to north bridge and memory controller hub302. Processing unit306can contain one or more processors and can even be implemented using one or more heterogeneous processor systems. Graphics processor310can be coupled to the NB/MCH through an accelerated graphics port (AGP), for example.

In the depicted example, a local area network (LAN) adapter312is coupled to south bridge and I/O controller hub304and audio adapter316, keyboard and mouse adapter320, modem322, read only memory (ROM)324, universal serial bus (USB) and other ports332, and PCI/PCIe devices334are coupled to south bridge and I/O controller hub304through bus338, and hard disk drive (HDD)326and CD-ROM330are coupled to south bridge and I/O controller hub304through bus340. PCI/PCIe devices can include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM324can be, for example, a flash binary input/output system (BIOS). Hard disk drive326and CD-ROM330can use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. A super I/O (SIO) device336can be coupled to south bridge and I/O controller hub304.

An operating system runs on processing unit306and coordinates and provides control of various components within data processing system300inFIG. 3. The operating system can be a commercially available operating system. An object oriented programming system can run in conjunction with the operating system and provide calls to the operating system from programs or applications executing on data processing system300.

Instructions for the operating system, the object-oriented programming system, and applications or programs can be located on storage devices, such as hard disk drive326, and can be loaded into main memory308for execution by processing unit306. The processes of the illustrative aspects discussed herein can be performed by processing unit306using computer implemented instructions, which can be located in a memory such as, for example, main memory308, read only memory324, or in one or more peripheral devices.

Note that the hardware embodiment depicted inFIG. 3can vary depending on the desired implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, can be used in addition to or in place of certain hardware depicted. Also, the processes of the illustrative aspects described herein can be applied to other hardware environments, such as to a multiprocessor data processing system.

In one or more implementations, data processing system300can be a mobile electronic device or a server computer resource, and can be generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data. A bus system can include one or more buses, such as a system bus, an I/O bus and a PCI bus. Of course the bus system can be implemented using any type of communications fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communications unit can include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory can be, for example, main memory308or a cache such as found in north bridge and memory controller hub302. A processing unit can include one or more processors or CPUs. Those skilled in the art should note that the depicted system example ofFIG. 3, as well as other examples referenced herein, are not meant to imply architectural limitations. As noted, data processing system300can be implemented as part of AR device210, mobile computer resource(s)230and/or remote computer resource(s)240inFIG. 2, and is presented by way of example only.

The illustrated systems ofFIGS. 2-3can vary depending on the implementation. Other components, hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, can be used in addition to or in place of certain components or hardware depicted inFIGS. 2-3. In addition, the processes of the illustrative embodiments can be applied to a multiprocessor data processing system. Examples of additional computing resource(s) or computer system(s) which can implement one or more aspects disclosed herein are also described below with references toFIGS. 8-10. Note also that, depending on the implementation, one or more aspects of the AR device and/or the computing resources can be associated with, licensed by, subscribed to by, etc., a company or organization operating, owning, etc., the AR device/system.

As illustrated inFIG. 2, and as noted above, program code241executing on computing resource(s)240can include a learning agent which continually learns (in one embodiment) and updates the patterns that form one or more models243used, for instance, by the image processing module244to, for instance, process sensor data indicative of a stress level of the user, identify one or more stress-inducing elements for a particular user to be hidden in the real world field of view viewable by the user through the AR device, as well as to provide an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view viewable through the AR device. In particular, the number of stress-inducing elements to be hidden, location of the stress-inducing elements to be hidden, type of stress-inducing elements to be hidden, etc., can all be customized to the particular user based on the user's disorder and health condition, including the user's current stress level dynamically monitored via the sensor data. Note that these aspects can change over time, for instance, as the user makes improvements to overcoming the disorder. Examples of how the process can be used in one or more applications are described further below, by way of example.

In one or more embodiments, program code241executing on remote computing resource(s)240applies machine learning algorithms of machine learning agent242to generate and train the one or more models243, which the program code then utilizes to process the sensor data and the image data, and to provide the augmented real world field of view for display to the user through the AR device, as described herein. In an initialization or learning stage, program code241can train the algorithm(s) based on patterns for the given user of the AR device/system. Note again that this is one embodiment only. In one or more other embodiments, the machine learning agent and models could run on or be associated with mobile computing resource(s)230and/or AR device210.

FIG. 4is an example machine-learning training system400that can be utilized to perform machine-learning, such as described herein. Training data410used to train the model in embodiments of the present invention can include a variety of types of data, such as data generated by the AR device and/or sensors. Program code, in embodiments of the present invention, can perform machine-learning analysis to generate data structures, including algorithms utilized by the program code to perform the image processing and augmentation facility, as disclosed herein. Machine-learning (ML) solves problems that cannot be solved by numerical means alone. In this ML-based example, program code extracts various features/attributes from training data410, which can be stored in memory or one or more databases420. The extracted features415are utilized to develop a predictor function, h(x), also referred to as a hypothesis, which the program code utilizes as a machine-learning model430. In identifying machine-learning model430, various techniques can be used to select features (elements, patterns, attributes, etc.), including but not limited to, diffusion mapping, principle component analysis, recursive feature elimination (a brute force approach to selecting features), and/or a random forest, to select the attributes related to the user's condition, and/or to the image processing and augmentation. Program code can utilize a machine-learning algorithm440to train machine-learning model430(e.g., the algorithms utilized by the program code), including providing weights for conclusions, so that the program code can train any predictor or performance functions included in the machine-learning model440, such as whether the user is likely to intersect with one or more stress-inducing element(s) based on determined trajectories. The conclusions can be evaluated by a quality metric450. By selecting a diverse set of training data410, the program code trains the machine-learning model(s)440to identify and weight various attributes (e.g., features, patterns) that correlate to enhance performance of the machine-learning implemented by the computing resource(s) and/or the AR device.

The model(s) used by each respective AR device and/or computing resource(s) can be self-learning, as program code updates the model(s) based on feedback received during performance of the stress level evaluation, image processing, and/or image augmentation, as described herein. For instance, as the user's condition improves, and the sensor data indicates that the user's stress level is lower, a fewer number of the stress-inducing elements, such as a fewer number of people, can be hidden from the user in the augmented real world field of view presented to the user through the AR device.

In some embodiments of the present invention, the program code executing on the respective computing resource(s) of system200(FIG. 2) utilizes existing machine-learning analysis tools or agents to create, and tune, each respective model, based, for instance, on data obtained, for instance, from the AR device, or the sensors.

Some embodiments of the present invention can utilize IBM Watson® as learning agent. IBM Watson® is a registered trademark of International Business Machines Corporation, Armonk, N.Y., USA. In embodiments of the present invention, the respective program code can interface with IBM Watson application programming interfaces (APIs) to perform machine-learning analysis of obtained data. In some embodiments of the present invention, the respective program code can interface with the application programming interfaces (APIs) that are part of a known machine-learning agent, such as the IBM Watson® application programming interface (API), a product of International Business Machines Corporation, to determine impacts of data on an operational model, and to update the respective model, accordingly.

In some embodiments of the present invention, certain of the APIs of the IBM Watson API include a machine-learning agent (e.g., learning agent) that includes one or more programs, including, but not limited to, natural language classifiers, Retrieve-and-Rank (i.e., a service available through the IBM Watson® developer cloud that can surface most-relevant information from a collection of documents), concepts/visualization insights, tradeoff analytics, document conversion, natural language processing, and/or relationship extraction. In an embodiment of the present invention, one or more programs can be provided to analyze data obtained by the program code across various sources utilizing one or more of, for instance, a natural language classifier, Retrieve-and-Rank APIs, and tradeoff analytics APIs. In operation, the program code can collect and save machine-learned data used by the machine-learning agent.

In some embodiments of the present invention, the program code utilizes a neural network to analyze collected data relative to a user to generate the operational model(s). Neural networks are a programming paradigm which enable a computer to learn from observational data. This learning is referred to as deep learning, which is a set of techniques for learning in neural networks. Neural networks, including modular neural networks, are capable of pattern (e.g., state) recognition with speed, accuracy, and efficiency, in situations where data sets are multiple and expansive, including across a distributed network, including but not limited to, cloud computing systems. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, or to identify patterns (e.g., states) in data (i.e., neural networks are non-linear statistical data modeling or decision making tools). In general, program code utilizing neural networks can model complex relationships between inputs and outputs and identify patterns in data. Because of the speed and efficiency of neural networks, especially when parsing multiple complex data sets, neural networks and deep learning provide solutions to many problems in multi-source processing, which the program code, in embodiments of the present invention, can accomplish when managing machine-learned data sets between devices.

In general, the image processing and augmentation facilities disclosed herein use augmented reality to simplify, via an augmented reality device, the real world field of view viewable by a user so that, for instance, a user with social anxiety disorder, sees fewer people within the user's viewable environment than are actually there. In one or more embodiments, any person within the field of view of the user whose path is unlikely to cross the user's path can be digitally edited out in the augmented real world field of view displayed to the user through the AR device, or can be overlaid with another object, creating a more calming environment for the user to see. This process is implemented dynamically, using the systems disclosed herein, in response to sensor data from sensors worn by the user. In this manner, the amount of alternation to the user's viewable environment is increased or decreased according to the user's current level of stress or anxiety. Advantageously, real-time anxiety-reducing content is delivered to the user through the AR device using the image processing and augmentation facility disclosed herein. For instance, a real-time reduction in the number of people in the user's field of view can be achieved where the individual user has a fear of large crowds. Further, displayed content can be modified dynamically whenever sensor data indicates a change in stress or anxiety in the user, all while allowing the user to operate in the real world. In this manner, the user is able to gradually be reintroduced to the real environment as their stress levels drop. In one or more embodiments, sensor data and machine learning are used to detect an elevated stress level, for instance, above one or more predetermined thresholds, and to take action to alter or augment the user's real world field of view viewable through the AR device. Through the combination of real-time stress level measurement and diminished reality AR techniques, the system allows the user with social anxiety or agoraphobia to function in public, while also conditioning the user to overcome the disorder.

As a specific example, an individual user may suffer from a social anxiety disorder, but need to go out in public to run errands. Public shopping centers present a challenge for the individual due to the large number of people present, and the individual's fear of having to interact with strangers or casual acquaintances. The individual makes use of the system disclosed herein, which combines one or more sensors to dynamically measure the user's level of stress, and an augmented reality headset to provide assistance to the user. As the user enters the shopping center, sensors in the system track people within the user's field of view through the AR device, and calculate their trajectory, along with the user's trajectory, to determine probability that trajectories might intersect. If the system detects that the user is experiencing an elevated level of stress, and the probability that one or more people within the shopping center have a trajectory unlikely to intersect with the user's, for instance, below a configurable threshold, then the system automatically removes those people from the user's augmented field of view viewable through the AR device. People whose trajectory might intersect with the user's trajectory would remain in view to avoid potential collision. The system can further make use of the user's measured stress levels to determine how many people to remove. Thus, as the user becomes more used to crowds, and the user's stress level lowers in the presence of crowds, the system will make fewer adjustments in the augmented real world field of view that the user sees, for instance, removing fewer people as the user's anxiety level lowers, or more people as the user's anxiety level increases.

FIG. 5depicts one detailed implementation of image processing and augmentation, in accordance with one or more aspects of the present invention. InFIG. 5, a user goes out in public wearing the AR device and associated sensors500, with the AR device being switched on502, and the sensors collecting data. In the embodiment ofFIG. 5, the sensors include sensor data to measure the user's stress level504, as well as vision sensors to identify stress-inducing elements in the user's path506, and a geolocation sensor to provide data to assist in identifying crowded areas508. The sensor data can be provided as streaming sensor data510to one or more computing resources522. In one or more embodiments, the image processing and augmentation facility520includes one or more machine learning algorithms to, for instance, classify a stress-inducing event of the user, determine and identify one or more stress-inducing elements in the user's field of view, and determine trajectories of the one or more stress-inducing elements524. For instance, streaming sensor data is passed to one or more machine learning algorithms to, for instance, predict potentially anxiety-causing situations for the user. Heart rate monitoring, geolocation data, traffic data, and vision sensor data, can all be used as time-series features in an LSTM or RNN, which continuously updates predictions of a user's propensity to have a high-stress or anxiety event. Continuous determination of stress or anxiety levels can be used, and the number of stress-inducing elements can be reduced, as the user's stress level reduces. For instance, LSTM predictions can be gathered throughout the use of the augmented reality device/system, and as the user's level of anxiety is reduced, the amount of diminished reality displayed to the user by the AR device can also be reduced.

As illustrated inFIG. 5, processing determines whether there are one or more candidate stress-inducing elements for removal526. If “no”, then there is no modification, or no further modification, to the real world field of view viewable by the user through the augmented reality device529. Assuming that there are one or more stress-inducing elements in the user's field of view to be removed, then spatial mapping of the environment surrounding the one or more stress-inducing elements to be removed can be employed530. This can be done through a mesh-mapping system that is in-built in standard AR devices. Region tracking with 3-D positions can then be performed through simultaneous localization and mapping (SLAM) techniques532. Existing APIs for augmented reality systems help manage spatial mapping of an environment, as well as performing post-processing operations on the spatial mapping. In one or more implementations, stress-inducing element tracking can be automatic due to the SLAM being performed during the spatial mapping stage. SLAM continually determines, for instance, the camera's position relative to the origin of the environment, and all 3-D locations are mapped relative to the origin as well. Once an object or element has been selected, its 3-D location within the spatial mapping is all that is needed to locate the object per frame. Processing can then perform element removal or diminishing534. For instance, in-painting can be used, which relies on the idea that patterns are common in nature and often repeated. By repeating nearby patterns in front of the selected region, the element will appear to vanish. A neural network can also be used to learn and repeat patterns from similar images to provide a realistic diminished result. Post-processing536can then be performed to provide the augmented real world field of view image to the user's AR display536. The augmented real world field of view is then displayed to the user via the AR device538.

FIGS. 6A & 6Bdepict one embodiment of a real world field of view600viewable through an AR device602which includes one or more stress-inducing elements601, that is, one or more people in the case of a user with a social anxiety disorder. In these figures,FIG. 6Arepresents the actual real world field of view without any augmentation, whileFIG. 6Bdepicts an augmented real world field of view seen by the user through the augmented reality device, where multiple stress-inducing elements (multiple people in this example) have been removed or hidden from the user's view, while others remain. The ones remaining may be selected or identified to remain, since machine learning predicts there is a possibility or probability that the user's path may intersect with, or come close to the paths of, those remaining individuals.

FIGS. 7A-7Bdepict a further embodiment of program code processing, in accordance with one or aspects of the present invention.

Referring collectively toFIGS. 7A & 7B, program code executing on one or more processors implements a process700which includes receiving, by one or more processors, image data representative of a real world field of view viewable by a user through an augmented reality (AR) device702, and receiving, by the one or more processors, sensor data indicative of a stress level of a user, where the user's stress level is related, at least in part, to the real world field of view viewable by the user through the AR device704. Based on the user's stress level, the one or more processors process the image data to identify one or more stress-inducing elements for the user to be hidden in the real world field of view viewable through the AR device706. The one or more processors provide an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view viewable through the AR device708.

In one or more embodiments, providing the augmented real world field of view for display includes selectively hiding, by the one or more processors, only the identified one or more stress-inducing elements in the augmented real world field of view for display to the user through the AR device710.

In certain embodiments, the one or more stress-inducing elements include one or more people in the real world field of view viewable through the AR device712. In one embodiment, where the user and one or more people are in motion relative to each other, the identifying includes predicting, by the one or more processors, that the user and the one or more people will not intersect714. In one or more embodiments, the process also includes receiving, by the one or more processors, location data for the user to predict whether the user is approaching a crowded area, and the processing is further based on the location data resulting in a prediction that the user is approaching a crowded area716.

In one or more implementations, the real world field of view viewable by the user through the AR device includes multiple stress-inducing elements for the user, with the one or more stress-inducing elements being only a portion of the multiple stress-inducing elements, the portion being less than all of the multiple stress-inducing elements718.

In one or more embodiments, providing the augmented real world field of view for display further includes a process720, which includes generating, based on identifying the one or more stress-inducing elements to be hidden, a spatial mapping of the image data around the one or more stress-inducing elements722, and using the spatial mapping to provide the augmented real world field of view by selectively hiding the one or more stress-inducing elements724.

In one or more embodiments, the process further includes using machine learning and the sensor data to classify the user's stress level, and the processing includes processing the image data to identify the one or more stress-inducing elements for the user based, at least in part, on the user's classified stress level726.

In one or more embodiments, the user and the one or more stress-inducing elements are in motion relative to each other, and the identifying includes predicting, by the one or more processors, that the user and the one or more stress-inducing elements will not intersect728.

In one embodiment, the sensor data includes data indicative of the user's heart rate730.

Further exemplary embodiments of a computing environment to implement one or more aspects of the present invention are described below with reference toFIGS. 8-10.

By way of further example,FIG. 8depicts one embodiment of a computing environment800, which includes a computing system812. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system812include, but are not limited to, a server, a desktop computer, a work station, a wireless computer, a handheld or laptop computer or device, a mobile phone, a programmable consumer electronic device, a tablet, a personal digital assistant (PDA), and the like.

Computing system812can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types.

As depicted inFIG. 8, computing system812, is shown in the form of a general-purpose computing device. The components of computing system812can include, but are not limited to, one or more processors or processing units816, a system memory823, and a bus818that couples various system components including system memory823to processor816.

In one embodiment, processor816may be based on the z/Architecture offered by International Business Machines Corporation, or other architectures offered by International Business Machines Corporation or other companies.

Computing system812can include a variety of computer system readable media. Such media may be any available media that is accessible by computing system812, and it includes both volatile and non-volatile media, removable and non-removable media.

Program/utility840, having a set (at least one) of program modules842, can be stored in memory832by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, can include an implementation of a networking environment. Program modules842generally carry out the functions and/or methodologies of embodiments of the invention as described herein. Alternatively, a field of view processing and augmentation facility, module, logic, etc.,801can be provided within computing environment812, as disclosed herein.

Computing system812can also communicate with one or more external devices814such as a keyboard, a pointing device, a display824, etc.; one or more devices that enable a user to interact with computing system812; and/or any devices (e.g., network card, modem, etc.) that enable computing system812to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces822. Still yet, computing system812can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter820. As depicted, network adapter820communicates with the other components of computing system,812, via bus818. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computing system812. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

One or more aspects may relate to or use cloud computing.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

A cloud computing node can include a computer system/server, such as the one depicted inFIG. 8. Computer system/server812ofFIG. 8can be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. Computer system/server812is capable of being implemented and/or performing any of the functionality set forth hereinabove.

Although various embodiments are described above, these are only examples. For example, computing environments of other architectures can be used to incorporate and use one or more embodiments. Further, different instructions, instruction formats, instruction fields and/or instruction values may be used. Many variations are possible.