Patent Publication Number: US-2021166511-A1

Title: Gesture access control system utilizing a device gesture performed by a user of a mobile device

Description:
BACKGROUND 
     The present disclosure relates to access control systems, and more particularly, to gesture access control systems utilizing a device gesture performed by a user of a mobile device. 
     Access control systems are used in a variety of applications including structures, buildings and or components including safes, subway turnstiles, child proof storage containers, and many other applications. In the, non-limiting, example of buildings, many such structures must be secured in the sense that the identification and number of people entering and exiting a building at any given moment in time should be known. One known way in achieving this task is to assign a badge to all individuals requiring access. Each human is then required to perform a hard badge-in task at a reader located proximate to any entry point. In one example, the badge may be identified by the reader via a magnetic strip. Another example is reading a badge using RFID. Unfortunately, such a process requires each human to, for example, swipe their badge separately before entry is allowed. This task can be time consuming. 
     More current access control systems utilize smartphones in place of badges. A key technology behind such use of smartphones is Near Field Communications (NFC) which allows short range communication. With this application, both the smartphone and the local access control reader must have NFC hardware. Other options may include a Human Interface Device (HID) of a reader capable of detecting, for example, a twisting of a smartphone in front of the reader in a controlled fashion to show intent. However, both the smartphone and the reader must be capable of independently detecting the intent. Moreover, current methods still require the user to retrieve the smartphone and perform specific acts with the smartphone. Such retrieval and/or action can be frustrating for the user and time consuming. 
     Improvements in access systems that may further optimize ease of operation with, or without, reduced components is desirable. 
     BRIEF DESCRIPTION 
     A gesture access system according to one, non-limiting, embodiment of the present disclosure includes a local access assembly adapted to operate between an access state and a no-access state, the local access assembly including a controller to effect actuation between the access state and the no-access state and a signal receiver; a mobile device including an inertial measurement unit (IMU) sensor system configured to measure a motion of the mobile device to conditionally detect a device gesture indicative of an intent of a user of the mobile device to gain access; one or more electronic storage mediums configured to store an application and a preprogrammed device gesture; and one or more processors configured to receive the measured motion from the IMU sensor system, and execute the application to compare the measured motion to the preprogrammed device gesture, and depending upon the comparison output a command signal to the controller of the local access assembly via the receiver to effect actuation from the no-access state to the access state. 
     Additionally to the foregoing embodiment, the measured motion is a compound motion including the device gesture and an independent motion made by the user. 
     In the alternative or additionally thereto, in the foregoing embodiment, the independent motion is walking by the user and the device gesture is waving of a hand of the user with the mobile device in the hand. 
     In the alternative or additionally thereto, in the foregoing embodiment, the application includes an RSSI module configured to detect periodic variations of signal strength of a beacon signal broadcasted from the local access assembly and compare the periodic variations of signal strength to a preprogrammed RSSI data indicative of the device gesture being performed. 
     In the alternative or additionally thereto, in the foregoing embodiment, the device gesture is an imitation of an access card swiping. 
     In the alternative or additionally thereto, in the foregoing embodiment, the mobile device includes the one or more electronic storage mediums and the one or more processors. 
     In the alternative or additionally thereto, in the foregoing embodiment, at least one of the one or more electronic storage mediums and at least one of the one or more processors are part of a cloud-based server configured for bi-directional communication with the mobile device. 
     A gesture access system according to another, non-limiting, embodiment includes a local access assembly adapted to operate between an access state and a no-access state, the local access assembly including a controller to effect actuation between the access state and the no-access state and a signal receiver, wherein the local access assembly is configured to broadcast a beacon signal; and a mobile device configured to be carried by a user and receive the beacon signal, the mobile device including a storage medium configured to store an application including an RSSI module and a preprogrammed device gesture, a processor configured to execute the application and associate the preprogrammed device gesture to measured periodic variation of signal strength of the beacon signal to at least in-part determine if a device gesture was performed by the user. 
     Additionally to the forgoing embodiment, the mobile device includes an Inertial Measurement Unit (IMU) sensor system configured to measure a motion of the mobile device to conditionally determine if the device gesture was performed. 
     A method of operating a gesture access system according to another, non-limiting, embodiment includes the steps of broadcasting a beacon signal by a local access assembly; detecting the beacon signal by a mobile device; waving the mobile device proximate to the local access assembly; sensing a periodic variation of signal strength of the beacon signal by the mobile device; comparing the sensed periodic variation of signal strength, to preprogrammed RSSI data indicative of the mobile device being waved; and sending an access command signal from the mobile device and to the local access assembly if the sensed periodic variation of signal strength compares to the preprogrammed RSSI data. 
     The foregoing features and elements may be combined in various combinations without exclusivity, unless expressly indicated otherwise. These features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings. However, it should be understood that the following description and drawings are intended to be exemplary in nature and non-limiting. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various features will become apparent to those skilled in the art from the following detailed description of the disclosed non-limiting embodiments. The drawings that accompany the detailed description can be briefly described as follows: 
         FIG. 1  is a schematic of an access control system utilizing a device-free gesture and applied to a door; 
         FIG. 2  is another schematic of the access control system; 
         FIG. 3  is a flow chart of a method of operating the access control system; 
         FIG. 4  is a flow chart of a method of determining motion, location and position of a mobile device of the access control system; 
         FIG. 5  is a schematic of another embodiment of the access control system applying a device gesture; 
         FIG. 6  is a schematic of first example of a device gesture; 
         FIG. 7  is a schematic of a second example of a device gesture; 
         FIG. 8  is a schematic of a third example of a device gesture; 
         FIG. 9  is a schematic of a fourth example of a device gesture; 
         FIG. 10  is a schematic of a user carrying a first type of containment containing the mobile device of the access control system; 
         FIG. 11  is a schematic of the access control system relative to  FIG. 10  and performing a first device-free gesture; 
         FIG. 12  is a schematic of the access control system relative to  FIG. 10  and performing a second device-free gesture; 
         FIG. 13  is a schematic of a user carrying a second type of containment containing the mobile device of the access control system and performing a first containment gesture; 
         FIG. 14  is a schematic of a user carrying the second type of containment containing the mobile device of the access control system and performing a second containment gesture; 
         FIG. 15  is a schematic of a user carrying the second type of containment containing the mobile device of the access control system and performing a third containment gesture; 
         FIG. 16  is a schematic of the user illustrating various positions, locations, and uses of the mobile device  26  relative to an adaptive intent mode detection feature of the gesture-based access control system; 
         FIG. 17  is a schematic of the gesture-based access control system illustrating the adaptive intent mode detection feature; 
         FIG. 18  is a flow chart illustrating a sequential portions of an inherent gesture of a seamless access control system as one embodiment of the gesture-based access control system; 
         FIG. 19  is a schematic illustrating a cloud-based embodiment of the gesture-based access control system; 
         FIG. 20  is a schematic of the application of another embodiment of the gesture-based access control system being a knocking gesture access control system; 
         FIG. 21  is a perspective view of the mobile device  26 ; 
         FIG. 22  is a flow chart of a method of operating a prestaging, gesture-based access control system as another embodiment of the gesture-based access control system; 
         FIG. 23  is a flow chart of a method of training the gesture-based access control system; and 
         FIG. 24  is a graph illustrating a user specific model as part of preprogrammed scenario data of a software-based application of the gesture-based access control system. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , a gesture-based access control system  20  is illustrated in one, non-limiting application, of a door  22  providing user access into, and out of, a building, structure, room, or the like. In this embodiment, the access control system  20  is adapted to unlock the door upon a detected, intentional, gesture made by a user  23  (e.g., human) desiring access. Although the present application is applied to the door  22 , it is contemplated and understood that the access control system  20  may also apply to anything requiring access control including, for example, computers, subway turnstiles, safes, child proof storage compartments, and others. As will become more apparent, the intentional gesture may be a device-free gesture (see arrow  25  in  FIG. 1 ) in some embodiments, or a device gesture (see arrow  94  in  FIG. 6 ) in other embodiments. 
     Referring to  FIGS. 1 and 2 , and in one embodiment, the access control system  20  includes a lock, or access, assembly  24 , a mobile device  26  carried by the user  23 , and a wireless interface  28 . The mobile device  26  is adapted to wirelessly communicate with the lock assembly  24  over the wireless interface  28 . The lock assembly  24  may include a latch  30  (e.g., deadbolt), a driver  32 , a controller  34 , and a receiver  36  that may be a transceiver with bi-directional communication capability, and that includes an antenna. The receiver  36  is configured to receive a wireless access, or command, signal (see arrow  38 ) over the wireless interface  28  and from the mobile device  26 . The access signal  38  is sent to the controller  34 . The controller  34  may process the signal  38 , and based on the signal, initiate the driver  32  to move the latch  30  from a no-access state to an access state (i.e., locked and unlocked positions). In one embodiment, the access assembly  24  is an access reader (e.g., RFID reader). Examples of the signal  38  may be Bluetooth, Wifi, or other communication signals that may be short range. The access assembly  24  may be a local access assembly  24 , and is generally located proximate to the door, or other component, whose access the assembly  24  is adapted to control. 
     The controller  34  may be any combination of one or more of a central processing unit (CPU), multiprocessor, microcontroller unit (MCU), digital signal process (DSP), application specific integrated circuit, and others capable of executing software instructions, or otherwise controllable to behave according to predetermined logic. In one example, the driver  32  is an electric motor with a relay operated by the controller. In another example, the driver  32  is an electromagnetic driver. The wireless interface  28  is any current or future wireless interface allowing communication between the mobile device  26  and the lock assembly  24 . Non-limiting examples of the wireless interface  28  include Bluetooth, Bluetooth Low Energy (BLE), Radio Frequency Identification (RFID), Near Field Communication (NFC), any of the IEEE 802.11 standards, and others. 
     In one embodiment, the mobile device  26  includes a transmitter  40  that may be a transceiver having an antenna, a controller  42 , and at least one detection system (i.e., three illustrated as  46 ,  48 ,  50 ). The at least one detection system may include an inertial measurement unit (IMU) sensor system  46 , an environment detection system  48 , an internal activity (i.e., usage) notification module  50 , and others for generally determining motion, position, location, and usage of the mobile device  26  relative to the user  23 . Non-limiting examples of the mobile device  26  include a smartphone, a mobile phone, a key fob, a wristwatch (i.e., smart watch), and other similar devices typically carried by the user  23 . 
     The controller  42  of the mobile device  26  includes a processor  56  and a storage medium  58 . Optionally, the processor  56  is any combination of one or more of a central processing unit (CPU), multiprocessor, microcontroller unit (MCU), digital signal processor (DSP), application specific integrated circuit, and others capable of executing software instructions or otherwise controllable to behave according to predetermined logic. The storage medium  58  is, optionally, any combination of read and write memory (RAM) and read only memory (ROM). The storage medium  58  may also include persistent storage, which can be any single one or combination of solid state memory, magnetic memory, or optical memory storing a computer program (i.e., application) with software instructions. 
     In one embodiment, and similar to the controller  42  of the mobile device  26 , the controller  34  of the lock assembly  24  may include a processor  70  and a storage medium  72 . Optionally, the processor  70  is any combination of one or more of a central processing unit (CPU), multiprocessor, microcontroller unit (MCU), digital signal processor (DSP), application specific integrated circuit, and others capable of executing software instructions or otherwise controllable to behave according to predetermined logic. The storage medium  72  is, optionally, any combination of read and write memory (RAM) and read only memory (ROM). The storage medium  72  may also include persistent storage, which can be any single one or combination of solid state memory, magnetic memory, or optical memory storing a computer program (i.e., application) with software instructions. It is contemplated and understood that in one embodiment, the controller  42  may not include a storage medium  72 , and may only include control circuitry capable of receiving the signal  38  from the mobile device  26  as a command signal that initiates actuation of the lock assembly  24 . 
     The gesture-based access control system  20  may further include an application  60 . In one embodiment, the application  60  is software-based and is stored, at least in-part, in the storage medium  58  for retrieval and execution by the processor  56  of the controller  42 . The application  60  may include computer instructions  62 , and a database of preprogrammed data. For example, the preprogrammed data includes credential data  64 , and scenario data  66 . In one embodiment, the scenario data  66  is indicative of a ‘compound’ motion by the user  23  that may not necessarily include the gesture, but is dependent upon (i.e., a function of) the carrying location of the mobile device  26  on the user  23 . 
     In another embodiment, the application  60  may at least in-part be stored in at least one storage medium contained in a cloud (i.e., remote server) and executed at least in-part by at least one processor of the cloud. 
     For reasons of clarity, the term “intentional gesture” as used herein is an act (e.g., physical motion) performed by the user  23  to gain access. In one example, the access gained may be through a door  22  (see  FIG. 1 ), but may also be access into any physical structure and/or electronic systems (e.g., computer). For purposed of this disclosure, examples of an intentional gesture may include a device-free gesture, a device gesture, and an inherent gesture. 
     The term “device-free gesture,” refers to an intentional gesture that generally does not physically include the mobile device  26  (see gesture  25  in  FIG. 1 ). For example, if the device-free  25  made by the user  23  is the waving of a right hand  74 , the mobile device  26  is not in the right hand  74  but may be located anywhere else on the person of the user  23 . In contrast, the term “device gesture,” (see gesture  94  in  FIG. 6 ) means the mobile device  23 , itself, is being used as part of the intentional gesture. In the present example, the device gesture  94  would include the waving of the mobile device  26 . More specifically and in line with the present example, the mobile device  26  would be in the right hand  74  being waved (see  FIGS. 5 and 6 ). Lastly, the term “inherent gesture” (see gesture  341  in  FIG. 18 ) is the gesture applied as part of a seamless access control system. That is, the typical act of, for example, opening a door (or typical motion(s) made toward the preparation of opening the door) is the gesture. The inherent gesture is “intentional” in the sense that the user  23  intends to gain access. Specific examples of the inherent gesture may be reaching for a door handle, or pulling upon a door handle. 
     Determination of Mobile Device Motion, Position, and Location Relative to User: 
     Determination of motion (i.e., the compound motion) of the mobile device  26  is needed to recognize an intentional gesture made by the user  23  through differentiation of one or more motions made by the user simultaneously. The determination of the position and/or location of the mobile device  26  relative to the user  23  may assist in the differentiation of multiple motions made by the user  23  from the measured compound motion of the mobile device  26 . Alternatively, or in addition to, determining the location of a mobile device  26  with respect to the user  23  may be advantageous when two access assemblies  24  if respective doors  22  are positioned closely together. In this scenario, knowing the location of the mobile device  26  would prevent, or reduce the chances, of the user  23 , via the device-free intentional gesture, gaining access through the wrong door. 
     The inertial measurement unit (IMU) sensor system  46  may include one or more of an accelerometer  80 , a gyroscope  82 , and others adapted to detect acceleration, and thus movement, in at least one dimension, and optionally three dimensions. The environment detection system  48  may include one or more of a visual camera  84  (i.e., computer-vision system), a temperature sensor,  86 , a light sensor  88 , and a proximity sensor  90  adapted to at least improve a level of confidence when differentiating the compound motion to determine if a device-free intentional gesture is being made by the user  23 . 
     The internal activity notification module  50  may also contribute toward the optimization of confidence levels, and may be part of the application  60  or may be a separate computer software instruction. For example, the activity notification module  50  may notify the application  60  that the user  23  is texting via the mobile device  26 , or is conducting a phone conversation. When differentiating the compound motion, the application  60  may then attribute part of the motion toward, for example, the texting activity. In one embodiment, and depending upon how the information data is processed by the application  60 , the visual camera  84  may be part of the IMU sensor system  46  (i.e., taking multiple pictures to determine motion), and/or may be part of the internal activity notification module  50  (i.e., the user  23  is undergoing the activity of taking photographs for pleasure). 
     In one embodiment, the visual camera  84  is adapted to detect movement via the capturing of images of surroundings and analyzing differences in the images over time. The temperature sensor  86  is adapted to measure temperature. In one embodiment, temperature data is indicative of, at least in-part, the body temperature of the user  23 . For example, if the mobile device  26  is in a rear pocket  56  (see  FIG. 1 ) of clothing worn by the user  23 , the temperature data may be associated with a temperature that is higher than if the mobile device  26  were located in a purse or backpack worn by the user  23 . The proximity sensor  90  is adapted to determine how close the mobile device  26  is to the user  23 . For example, the mobile device  26  may be resting on a desk, may be in a back pocket  56 , may be in a purse, or may be in a backpack. The proximity sensor  90  may also be used to determine if a substantial portion of the user  23  is located between the sensor  90  and the access assembly  24 , which may cause a degree of attenuation of signals between the assembly  24  and the mobile device  26 . 
     The light sensor  88  is adapted to measure the level of light adjacent to the mobile device  26 . Light data sent to the processor  42  from the light sensor  88  may be indicative of the location of the mobile device  26  at the time of gesturing by the user  23 . For example, the mobile device  26  may be in the rear pocket  56  of clothing worn by the user  23 . 
     In operation, the IMU sensor system  46  enables the identification of gesture based intent, and the environment detection system  48 , and optionally the activity notification module  50  function to boost the reliability of the intentional gesture identification. In one example, this is achieved by the fusion of information gained from the systems  46 ,  48 , and module  50  by the application  60  and use of machine learning algorithm(s) and/or the preprogrammed scenario data  66 . Referring to  FIG. 4 , a method of determining a location and/or position of a mobile device  26  with respect to the user  23  includes, at block  200 , the motion device  26  activity being in standby, or otherwise blocked. 
     At block  202 , the IMU sensor system  46  detects a periodic movement (i.e., the compound motion) and sends the information to the controller  42 . At block  204 , the application  60  determines that at least a portion of the compound motion is characteristic of walking via at least one algorithm, and at least a portion of the preprogrammed scenario data  66 . At block  206 , the temperature sensor  86  and/or the light sensor  88  of the environment detection system  48  sends information (i.e., confirmation parameter data) to the controller  42  that is used by the application  60 , to determine that the mobile device  26  is in, for example, a back pocket or a backpack (i.e., the light sensor  88  detects a dark environment). Moreover, the IMU sensor system  46  may also assist in detecting the relative position of the mobile device  26 . For example, the angle of the mobile device  26  with respect to the ground, or floor surface, may be indicative front pocket verse back pocket location, etc. At block  208 , the activity notification module  50  may provide information to the application  60  indicative of the current use (e.g., texting) of the mobile device  26  by the user  23 . Such current use may provide indications of the likely position of the mobile device  23  (i.e., vertical, horizontal, or positions there-between) and/or mobile device motions that are part of the compound motion which may ultimately be differentiated from the intentional gesture. To accomplish blocks  206  and  208 , the application  60  may apply an algorithm and/or the preprogrammed scenario data  66 . 
     Training of Software-Based Application: 
     Referring to  FIG. 2  and in operation, the application  60  may include training instructions (i.e., setup or calibration instructions) communicated to the user  23  via a human interface device (HID)  91  (see  FIG. 2 ) of the mobile device  26 . The training instructions may instruct the user  23  to perform a variety of motions with the mobile device  26  carried by the user  23  in various locations (e.g., back pocket, front pocket, left hand while right hand is gesturing, and others), or ways (e.g., backpack, purse, and others), and/or while performing certain activities with the mobile device  26  (e.g., texting, conversing, and others). While the user  23  performs the various motions and/or routines, the application  60  may build, and thus preprogram, the scenario data  66  utilizing information received from the at least one of the IMU sensor system  46 , the environment detection system  48 , and the internal activity notification module  50 . 
     For example, the application  60  may instruct the user  23  to walk with the mobile device  26  in the rear pocket  56 . The motion and other parameters are then detected by at least one of the systems  46 ,  48 , and the module  50 , and the resulting information is preprogrammed as part of the scenario data  66 . As part of another event, the application  60  may then instruct the user  23  to perform the same walk with the mobile device  26  in the same location, but while performing a chosen gesture intended to cause the access assembly  24  to respond (i.e., unlock). Again, the resulting motion detected by one or more of the systems  46 ,  48  and module  50  is recorded as part of the scenario data  66 . Similar instructions may progress with the user  23  relocating the mobile device  26  on his or her person and performing various movements with and without the gesturing. Upon completion of the training instructions, the scenario data  66  may generally resemble a matrix or array of data. 
     In one embodiment, the application  60  may include machine learning techniques and/or algorithms (e.g., deep learning). With machine learning algorithms, gesture recognition can be trained more and more to a given user&#39;s particular interactions. Moreover, by conducting a form of ‘continuous’ training, the application  60  has the ability to conform to a user&#39;s changing habits (i.e., possibly caused by an injury) over a period of time. 
     In one example, the application  60  may include machine learning algorithm(s) configured to determine, or confirm, user intent from explicit intent signal(s) generated by one or more of the detection systems  46   48 ,  50 , and determine user authentication (i.e., the mobile device  26  actually belongs to the user  23 ) by matching the intent signals against a user specific, pre-defined, pattern. The user intent and user authentication may be inferred from IMU signals, audio signals, RSSI (e.g., Bluetooth), and other data from, for example, from wearable mobile devices  26 . In another embodiment, while user intent may be confirmed by a number or pattern of knocks, user authorization may be confirmed by the intensity of the knocks, a delay between knocks, and/or a change of intensity from one knock to the next. 
     Referring to  FIG. 23  and in one embodiment, the application  60  may include a training mode of operation. At block  500 , and via the HID  91 , the user  23  may select the training mode. In this mode, and at block  502 , the user  23  is prompted by the application  60  via the HID  91 , and may select, an intentional gesture type from a library of supported gesture types as part of the scenario data  66 . At block  504 , the user  23  is prompted by the application  60 , and the user  23  may perform, repetitions of the selected gesture type for intent. At block  506 , machine learning algorithm(s) are collecting and analyzing data from the repetitious performance of the selected gesture type to build a user specific model associated with selected gesture type and as part of the scenario data  66 . At block  508 , the machine learning algorithm(s) determine that that the user specific model is of sufficiently high quality and confidence, and the application  60  via the HID  91 , notifies the user  91  of model completion. Non-limiting examples of gesture types may include tapping by the user  23  on the mobile device  26  for a fixed number of times (i.e., a prescribed pattern, see  FIG. 20 ), a knock on the door  22 , a user specific voice command made into a microphone  130  of the mobile device  26  (see  FIG. 2 ), and other gesture types. 
     After the training mode of operation, the application  60  may enter into a deployment mode. In this mode, statistical machine learning techniques are deployed, via algorithms, which may be in, and supported by, a cloud  360  (i.e., a remote server, see  FIG. 19 ). In this example, at least a portion of the application  60  may be in the cloud  360 , and the cloud functions to build the user specific model. In one embodiment, the user specific model may be improved over time via the use of machine learning algorithms. In this way, specific users  23  become easier to identify over time. At block  510 , the user  23  may then perform a list of pre-trained gestures (i.e., preprogrammed into the application  60 ) to signal intent and authenticate them. 
     More specifically, in the training mode of operation, data is collected reflective of specific actions enforced upon the user  23  for purposes of training. This may be considered as defining the ground truth of the ‘right way’ of performing a gesture. Optionally, the application  60  may also collect data on how the specific actions is not to be performed to further enhance the learning. 
     Once the training mode is complete and the data is collected, algorithms are then trained with the data to extract the relevant information/features that detect if the specific action, or gesture, was performed and in the right way. The result is a trained model (i.e., the user specific model) that is then deployed. 
     Referring to  FIG. 24 , a graph  118  having three portions  118 A,  118 B,  118 C is illustrated that generally reflects one example of a modeling process wherein the gesture type may be tapping on the mobile device  26 . The X-axis of each graph portion  118 A,  118 B,  118 C is over a common time duration. Graph portion  118 A illustrates raw accelerometer data caused by movement of the mobile device  26  incurred during tapping. Graph portion  118 B illustrates corresponding audio data. Graph portion  118 B illustrates extracted features with the tapping confirmation highlighted with star symbols. The spike patterns and the time intervals between spikes are unique to the user  23  and may be used as the authentication (i.e., code). 
     Completion of the training and deployment modes produces the user specific detection model that serves both as gesture confirmation and a user authentication based on the observed signals from one or more of the detection systems  46 ,  48 ,  50 . The model also provides a confidence level in user authentication that may improve with further usage. This confidence level may be used to allow or deny access to, for example, building areas. 
     Distinguishing Separate User Movements from a Measured Compound Motion by the Mobile Device: 
     In one embodiment, the application  60  may rely on the observation that the device-free gesture (e.g., hand waving) produces minute periodic motion of the human body (i.e., a part of the compound motion) that can be captured using the IMU sensor system  46 , the environment detection system  48 , and/or the internal activity notification module  50  of the mobile device  26 . Machine learning algorithms are trained to distinguish the associated minute motion, indicative of the gesture, from other and more prominent body movements that may be observed during walking or conversing. 
     Optionally, the controller  42  of the mobile device  26  may receive data from the light system  54 . In one example, the light data may be applied to determine if the mobile device  26  is carried in a hand, or alternatively, in a pocket, backpack, or purse. The temperature sensor  86  of the environment detection system  48  may output temperature data to the controller  42  to determine if, for example, the mobile device  26  is in a hand or pocket, as oppose to in a backpack or purse. The temperature and/or light data may be applied as additional data toward the compound motion to increase matching confidence levels when the application  60  compares, or attempts to match, the compound motion to the preprogrammed scenario data  66 . 
     In one embodiment, the chosen device-free intentional gesture may be the waving of a hand  74  (see  FIG. 1 ) that is free of the mobile device  26 . That is, the mobile device  26  is located elsewhere on, or near, the user  23 . In other words, the user  23  is not required to retrieve his/her mobile device  26  to perform any device function or input. The user  23  need only perform the correct intentional gesture to gain access through, for example, the door  22 . Examples of other intentional gestures may include left-to-right motions of a human arm, up-to-down motions of the human hand  74 , a motion of the head and/or shoulders, or any other distinctive motion. 
     In one embodiment, the intentional gesture may be a secret gesture, thus further authentication between the mobile device  26  and the access assembly  24  is not needed. In this example, the access assembly  24  may be relatively simple, and need not be preprogrammed. 
     In another embodiment, the access assembly  24  may be preprogrammed to only accept command signals  38  that are entrained, or accompanied, with an authentication code generally preprogrammed into both controllers  34 ,  42 . Thus the controller  34  is capable of matching a received authentication code from the mobile device  26  (i.e., part of signal  38 ) to a code  76  preprogrammed into the storage medium  72 . 
     Referring to  FIGS. 2 and 3 , and during normal operation of the gesture access control system  20 ; at block  100 , the controller  34  of the access assembly  24  may broadcast a beacon signal (see arrow  78  in  FIG. 2 ) via the transceiver  36 . In one example, the beacon signal  78  may be encoded as part of the authentication process between the mobile device  26  and the access assembly  24 . In one example, the broadcast beacon signals  78  may be of a Bluetooth radio type. In other examples, the signal  78  may be Wifi/cell radio or may be an audible frequency spectrum. It is further contemplated and understood that other ways of authenticating the mobile device  26  with the access assembly  24 , which are known by thus skilled in the art, may be applied while the novelty of the gesturing process is maintained. 
     At block  102 , the transceiver  40  of the mobile device  26  may receive the beacon signal  78  when generally within a prescribed range. Once received, at block  104 , the mobile device  26  generally initiates the application  60 . In another embodiment, the application  60  may not need to be initiated by a beacon signal. Therefore, in some applications, the access assembly  24  may not be adapted to broadcast a beacon signal. 
     At block  106 , when within a general vicinity of the access assembly  24 , and/or with the application  60  active, the application  60  may be accepting and processing compound motion data from the IMU sensor system  46  of the mobile device  26  to determine the activity of the user  23  (i.e., walking, conversing, standing still, and others), and other influencing data or information from the environment detection system  48 , and/or the internal activity notification module  50  to determine influential parameters such as the mobile device location, position and/or usage. At block  108 , the application  60  matches the compound motion data and influencing parameter data to the preprogrammed scenario data  66 , with a predetermined level of confidence, to determine if the user  23  is performing an intentional gesture (e.g., device-free intentional gesture) indicative of an intent to access. 
     At block  110 , and in one example, the user  23  may be walking with the mobile device  26  in a rear pocket, and while performing a device-free intentional gesture with the right hand  74 . At block  112 , the application  60  determines where the mobile device  26  is located on the user  23 , determines that the user  23  is walking, and determines that the device-free intentional gesture is being performed by comparing the compound motion and other influencing parameter data (e.g., light, temperature, and others) to the scenario data  66 . At block  114 , and after recognition of the device-free intentional gesture by the controller  42  of the mobile device  26 , the mobile device  26  broadcasts a command signal  38  to the access assembly  24 . At block  116 , the access assembly  24  actuates from a no-access state and to an access state, whereupon the door  22  may be opened by the user  23 . 
     In one embodiment, it may be a pre-condition that the user  23  is not walking before a gesture may be recognized or accepted by the mobile device  26 . In this embodiment, the accelerometer system and/or the gyroscope system of the mobile device  26  may be applied to confirm the user  23  is generally motionless except for the motion of the gesture itself. 
     Detecting and/or Confirming an Intentional Gesture Through RSSI: 
     Referring again to  FIG. 2 , the beacon signal  78  broadcasted by the access assembly  24  via the transceiver  36  may be received by the controller  42 , via the transceiver  40 , and generally as a received signal strength indicator (RSSI). More specifically and as an optional embodiment, the gesture-based access control system  20  may further include an RSSI module  92  that may be software-based and part of the application  60 . In other embodiments, the RSSI module  92  may by a separate sensor system of the mobile device  26  that may include software and hardware. 
     In operation, the gesture-based access control system  20  may perform as described in blocks  100 - 116  (see  FIG. 3 ), except with the additional feature provided by the RSSI module  92 . More specifically, the beacon signal  78  received by the mobile device  26  at block  102  is also processed by the RSSI module  92  that is configured to detect periodic variations in signal strength indicative of the intentional gesture crossing through the signal  78  (i.e., near to and repetitiously crossing in front of the access assembly  24 ). In one example, it may be an arm of the user  23  crossing back-and-forth in front of the access assembly  26 . In another embodiment, the placement of a hand of the user  23  on the access assembly  24  may also effect RSSI. 
     As described in block  110  above, the scenario data  66  may further include preprogrammed RSSI data indicative of the detected periodic variation in signal strength expected when the device-free gesture is performed. The RSSI module  92  may compare the measured periodic variation in signal strength to the preprogrammed RSSI data to further confirm, or increase a level of confidence, that the device-free gesture occurred. 
     In another embodiment, the scenario data  66  may only include the preprogrammed RSSI data. In this embodiment, the determination by the application  60  that the device-free gesture was performed may be based solely on the preprogrammed RSSI data. Therefore, the IMU sensor system  46  may not be required. 
     Mobile Device Disposed in User Carried Containment: 
     As previously described, the mobile device  26  may be located remotely from the immediate vicinity of the intentional gesture (i.e., device-free gesture  25 ) being performed. For example, the mobile device  26  may be carried generally against the body of a user  23  (e.g., rear pocket) but not in the hand  74  performing the device-free gesture (see  FIG. 1 ). 
     Referring to  FIGS. 10 and 11 , a generally device-free gesture  25  may be performed by the user  23 , but with the mobile device  26  located in a user-carried containment  95 . Non-limiting examples of the containment  95  include a handbag (see  FIGS. 10-12 ), a backpack (see  FIGS. 13-15 ), and other containments adapted to store and/or carry personal items for the user  23  including the mobile device  26 . 
     In one embodiment, the containment  95  is adapted to be carried by a specific body component of the user  23 . For example, the handbag is carried by the hand  74  of the user  23  and the backpack is carried by the back, or torso,  96  of the user  23 . For high confidence detections of the device-free gesture  25 , the containment  95  is carried by the body component performing the device-free gesture  25  (i.e., intentional body gesture). For example, if the containment  95  is a handbag or purse, the hand  74  that grasps the handbag may perform the device-free gesture  25  thus carrying the handbag along with the gesturing hand. 
     The motion of the mobile device  26  is generally measured as previously described using at least the IMU sensor system  46 . In one scenario, the measured motion of the mobile device  26  may be a compound motion dynamically created by the user  23  walking as the user performs the intentional body gesture  25  (i.e., device-free gesture). In this scenario, the act of walking may cause the user  23  to swing the arm and hand  74  (i.e., a routine body motion, see arrow  97  in  FIG. 10 ) in forward and rearward directions. The swinging of the hand  74  carries the handbag  95  with it causing the mobile device to experience an associated routine containment motion (see arrow  98  in  FIG. 10 ). 
     Referring to  FIG. 11  and in a continuation of the containment  95  example of a handbag, the intentional body gesture  25  may be the twisting of a wrist associated with the hand  74  of the user  23  that is grasping the handbag  95 . The intentional body gesture  25  creates an associated containment gesture (see arrow  99 ). In one embodiment, the containment gesture  99  may be an amplification of the intentional body gesture  25 . In other embodiments, gesture  99  may be about the same as gesture  25  or may be different but expected. 
     The measured motion of the mobile device  26  is thus a compound motion that includes the containment gesture  99 , which is directly affiliated with the intentional body gesture  25 , and the routine containment motion  98  that is affiliated with the routine body motion  97 . Therefore, the compound motion is indicative of the routine body motion  97  and the intentional body gesture  25  multiplied by a parameter factor. The parameter factor may represent the type of containment  95  (i.e., backpack or handbag) and the position and location of the mobile device  26  with respect to the user  23  and the containment  95 . The parameter factor may be part of the scenario data  66 , and the environment detection system  48  may assist in determining the position and location of the mobile device  26  and the type of containment  95 . 
     In one embodiment, the intentional body gesture  25  is such that the associated containment gesture  99  is contrary to the routine containment motion  98 . For example, the direction of gesture  99  is traverse, or orthogonal to the direction of motion  98 . This will assist in higher levels of confidence through improved motion differentiation by the application  60 . 
     Referring to  FIG. 12 , another example of a containment gesture  99  is illustrated wherein a handbag is shaken vertically. In this example, the intentional body gesture may be the repetitious lifting and lowering of the hand  74 . 
     Referring to  FIGS. 13-15 , another example of a containment  95  is illustrated as a backpack worn on the back, or torso,  101  of the user  23 . In  FIG. 13  the containment gesture  99  may be caused by a twisting (i.e., the intentional body gesture  25 ) of the torso  101 . In  FIG. 14 , the containment gesture  99  may be caused by a bending at the waist of the user  23 . In  FIG. 15 , the containment gesture  99  may be caused by a flexing left-to-right of the torso  101  or waist of the user  23 . 
     Detecting Device Gesture: 
     As previously described, determining the occurrence of a device-free gesture can be accomplished through the analysis of a measured compound motion of the mobile device  26  and other influencing parameters. For example, if the mobile device  26  is in a back pocket  56 , and a right hand  74  is performing the device-free gesture, the compound motion undergone by the mobile device  26  is analyzed as an indirect indication of the device-free gesture occurrence. 
     Referring to  FIGS. 2 and 5 , and in another embodiment, the mobile device  26  may be used to perform the gesture (i.e., a device gesture). In this example, the device gesture is generally measured directly as the motion of the mobile device  26 . However, it is still appreciated that the motion measured by the mobile device  26  may still be a type of compound motion. 
     For example, the device gesture (see arrow  94  in  FIG. 6 ) may generally be a generally horizontal waving of the mobile device  26 . If the user  23  remains perfectly still, other than performing the device gesture  94 , the mobile device  26  can measure the device gesture  94  directly and no motion differentiation of a compound motion is needed. However, if the user  23  is walking while performing the device gesture  94 , the walking motion will also be measured with the device gesture  94  thus producing a measured compound motion. That is, the walking motion creates a kind of noise that may interfere with a reliable interpretation of access intent. 
     The compound motion in this example may be analyzed as previously described with proper scenario data  66  established with the prescribed condition that the intentional gesture is a device gesture  94 . Other, non-limiting, examples of device gestures  94  may include waving the mobile device  26  in a substantially vertical direction in front of the access assembly  24  (i.e., an imitated swiping of an imaginary access card, see  FIG. 7 ), repeatedly moving the mobile device  26  toward and away from the access assembly  24  (see  FIG. 8 ), generally twisting the mobile device  26  by about ninety degrees in front of the access assembly (see  FIG. 9 ), and others gestures. 
     Like the example of a device-free gesture, in the example of the device gesture  94 , the access assembly  24  may not perform the motion detection or measurement. All such analysis may remain with the application  60  as part of the mobile device  26 . Optionally, the mobile device  26  may include the RSSI module  92  which can measure periodic variation signal strength of a beacon signal  78  as a result of the mobile device  26 , repetitiously, moving across the beacon signal path, or wireless interface  28 . 
     Knocking/Tapping Gesture Access Control System: 
     Referring to  FIGS. 2 and 20 , the gesture-based access control system  20 , in one embodiment, may be a knocking gesture access control system. In this embodiment, the user  23  of the mobile device  26  performs a knock that may be a predefined frequency of knocks. The term “knock” in the present embodiment would include the act of tapping. The knocking may be performed on the mobile device  26 , the access assembly  24 , the door  22  (see  FIG. 1 ), a wall area proximate to the access assembly  24  and/or door  22 , or any other surface conveniently located near the access point. 
     The mobile device  26  of the knocking gesture access control system  20  may further include a microphone  130 , and a knock module  132  of the application  60 . The microphone  130  may be sensitive enough to detect a wide range of frequencies and magnitudes (i.e., loudness) to track the sound originated by repetitious knocking on, for example, a surface (e.g., front surface) of the mobile device  26 , a surface of the door  22 , a surface of the door frame  136 , a surface of the access device  24 , a surface of a wall  138  through which the door  22  provides access, or other surfaces. The knocking is an intentional gesture performed by the user  23  (see knocking gesture  140  in  FIG. 20 . Knocking or tapping on the mobile device  26  may be considered to be a device gesture as a type of intentional gesture, and knocking on any other surface may be considered to be a device-free gesture as a type of intentional gesture. 
     In one embodiment, the knock module  132  of the application  60  is configured to receive the signature of, or information relative to, the audible sound created by the knocking gesture  140 . The knock module  132  may then compare a measured frequency pattern of the audible sound (i.e., frequency of knocks or taps) to a preprogrammed frequency pattern. In one embodiment, if the measured frequency pattern sufficiently compares to, or substantially matches, the preprogrammed frequency pattern, the knock module  132  may determine that the knocking gesture  140  was performed by the user  23 , and effect the sending of the command signal  38  to the access assembly  24 . 
     In another embodiment, the knocking gesture access control system  20  may be configured to further confirm (e.g., independently confirm) performance of the knocking gesture to enhance reliability and reduce or eliminate false gesture confirmations. One such confirmation may include use of the IMU sensor system  46  similar to that previously described. For example, if the mobile device  26  is in a back pocket  56  (see  FIG. 1 ) and the user  23  performs the knocking gesture  140  upon the door  22 , the mobile device  23  may still measure a motion (i.e., of the mobile device) attributable to the act of knocking. In certain scenarios (e.g., user walking), the actual motion measured may be a compound motion, and the application  60  is configured to decipher multiple motions from the compound motion. Once deciphered, the frequency pattern of the motion attributable by the knocking is compared to a preprogrammed motion frequency pattern (i.e., may be the same as the audible frequency pattern), if the motion frequency pattern compares to, or substantially matches, the preprogrammed frequency pattern, the confirmation that the knocking gesture was performed is re-affirmed. 
     In another embodiment, the knocking gesture access control system  20  may use other sensory data to re-affirm gesture confirmation. For example, light sensor data from the environment detecting system  48  and/or RSSI data produced by fluctuations of the beacon signal  78  and produced by the RSSI module  92  as previously described. In one embodiment, the knocking gesture  140  may be a device-free gesture. In this example and if the IMU sensing system  46  is applied, the location of the mobile device  26  may also be determined in ways previously described. The detection process applied to detect the knocking gesture  140  may fuse the various methods described and optionally, the mobile device location method, to provide good intent markers as part of the application  60 . 
     Referring to  FIGS. 2, 20 and 21 , and in another embodiment, the knocking gesture  140  may be performed upon a front surface  148  of the mobile device  26 . The mobile device  26  is associated with the X-Y-Z coordinates illustrated in  FIG. 21 . If the knocking gesture  140  is performed against the surface  148 , the audible knocking sound is evaluated as previously described. The re-confirmation of the detection utilizing the IMU sensing system  46  and conducted by the knock module  132 , may evaluate the motion along the Z-axis only to mask-off motion noise produced along other coordinates. That is, the knocking is performed against the front surface  148 , and the direction of the knocking is substantially normal to the front surface  148 . 
     It is understood and contemplated that the knocking on the mobile device  26  instead of the door  22  may prevent disturbing a person on the other side of the door  22 , where access is intended by the user  23 . It is further understood, that preconditions may apply before the knocking gesture  140  is accepted. Such a pre-condition may be a requirement that the user  23  is within a pre-defined proximity of the access assembly  24 , or door  22 . Moreover, the knocking on the mobile device  26  can be done before the uses 23 reaches the door. In contrast, the example of knocking on the door is when the user  23  has already arrived. Therefore, in the example of knocking on the mobile device  26  enables the user  23  to perform an action as the user walks up to the door  22 . The door  22  may then be unlocked when the user  23  arrives. 
     Adaptive Intent Mode Detection 
     Referring to  FIGS. 16 and 17 , the gesture-based access control system  20  may be flexible and capable of automatically adjusting for different intentional gestures including the device gesture  94  (see  FIG. 6 ) and the device-free gesture  25  (see  FIG. 1 ). In addition, the access control system  20  may adjust for the array of motions (i.e., compound motions), locations, and positions of the mobile device  26  when determining if an intentional gesture  25 ,  94  is being performed by the user  23 . 
       FIG. 16  illustrates a non-limiting plurality of mobile device  26  locations and uses, wherein the application  60  is capable of adapting to in order to determine if an intentional gesture  25 ,  94  is being performed. Accordingly, with the determination of mobile device motion, location, position, and/or usage, the application  60  may be further capable of selecting an appropriate preprogrammed gesture from a plurality of preprogrammed gestures. 
     As previously described, the inertial measurement unit (IMU) sensor system  46 , the environment detection system  48 , and the internal activity notification module  50 , together, are capable of providing information used by the application  60  to determine if an intentional gesture  25 ,  94  is being performed. 
     Examples of the potential multitude of mobile device  26  locations, positions, and uses are illustrated in  FIG. 16  and may include depiction  300  representative of the mobile device  26  located at an ear  302  of the user  23  with a usage of conversing or calling, and a substantially vertical position. Depiction  304  represents the mobile device  26  being in a front shirt pocket  306  thus having a substantially vertical position and in a relatively dark environment. Depiction  308  is representative of the mobile device  26  in the hand  74  of the user  23 , positioned at about thirty degrees for texting, and with a usage of texting. Depiction  310  is representative of the mobile device  26  being in a front pants pocket  312 , thus having a substantially vertical position and being in a relatively dark environment. Depiction  314  is representative of the mobile device  26  being located in the rear pants pocket  56  (also see  FIG. 1 ) thus having a substantially vertical position and being in a relatively dark environment. Depiction  316  is representative of the mobile device  26  hanging. For example, the user  23  may simply be carrying the mobile device  26  in the hand  74 . Depiction  318  is of the mobile device  26  in a handbag (i.e., containment  95 , also see  FIG. 10 ), thus in a dark environment, and depiction  320  is of the mobile device  26  in a backpack (i.e., containment  95 , also see  FIG. 13 ). 
     Referring to  FIG. 17 , the application  60  of the access control system  20  may include the activity notification module  50 , an environment module  322 , a motion module  324 , a selection module  326 , and a plurality of mode modules (i.e., five illustrated as  328 A,  328 B,  328 C,  328 D,  328 E). The activity notification module  50  is configured to determine and/or categorized current usage of the mobile device  26 . Examples of usage include texting, conversing, standby, and others. The environment module  322  is configured to receive and categorize environment information (see arrow  330 ) from the environment detection system  48 . As previously described, environment information  330  may include light level data, temperature data, position data, location data, photographic data, sound data, and other data. The motion module  324  is configured to receive and categorize motion information (see arrow  332 ) from the IMU sensor system  46 . Non-limiting examples of motion information include the compound motion previously describe, and which may occur in a variety of scenarios including when the user  23  is walking, standing still, carrying the containment  95 , performing a usage, and a wide variety of other events that may produce motion. One or more of the modules  50 ,  322 ,  324  may include algorithms, which may be self-learning algorithms, and preprogrammed data (i.e., portions of the scenario data  66 ) to refine and/or categorize the information  330 ,  332 , and other data for use by the selection module  326 . 
     The selection module  326  is configured to apply the information outputs from the modules  50 ,  322 ,  324  and thereby select one of the mode modules  328 . In one embodiment, each of the mode modules  328  may be, at least in-part, associated with a respective depiction  300 ,  304 ,  308 ,  310 ,  318 ,  320 . The selection module  326  may include a preprogrammed matrix of data  334  and algorithm(s). The preprogrammed matrix of data  334  may be representative of the motion and parameter (i.e., environment and usage) data received from the modules  50 ,  322 ,  324 . At least from the matrix of data  334 , the selection module is capable of selecting the appropriate mode module  328 . This selection may occur prior to, or during, the performance of an intentional gesture  25 ,  94 . 
     Each mode module  328 A,  328 B,  328 C,  328 D,  328 E may include a respective, preprogrammed, scenario data  66 A,  66 B,  66 C,  66 D,  66 E of the scenario data  66  previously. Each of the plurality of mode modules  328  may also include a respective one of a suite of intent detection algorithms  336  (i.e., see  336 A,  336 B,  336 C,  336 D,  336 E) for each respective mode module illustrated. In operation, the selection module  326  is configured to generally activate the appropriate algorithm  336 A,  336 B,  336 C,  336 D,  336 E by selecting the appropriate module  328 A,  328 B,  328 C,  328 D,  328 E. Each algorithm  336 A,  336 B,  336 C,  336 D,  336 E is characterized in accordance with the context where it is applied. For example, algorithm  336 A may be suitable when the user  23  has the mobile device  26  in the hand  74 , but may be less suitable when the mobile device  26  is in the rear pants pocket  56 . Therefore, different mode modules  328  are enabled and disabled in real time by the selection module  326 . 
     In operation, when the appropriate, selected, mode module  328  conditionally detects the intentional gesture  25 ,  94 , the mode module may output the command signal  38  to the access assembly  24 . 
     Seamless Access Control System: 
     Referring to  FIGS. 2 and 18 , and in one embodiment, the gesture-based access control system  20  may be a seamless access control system adapted to allow access to a user  23  after the user provides an inherent gesture  334  (see  FIG. 18 ) signifying the intentional desire and initial act of, for example, opening the door  22 . More specifically, the inherent gesture  334  is the initial part of a typical user exercise  336  conducted to gain entry. 
     The mobile device  26  for the seamless access control system  20  may be a wearable mobile device. Examples of the wearable mobile device  26  include a smart watch, smart glasses, and smart shoe(s). The term “smart” is meant to indicate that the wearable mobile device  26  includes the processor  56  and other features/components previously described. 
     The access assembly  26  may further include a short range communication device  337  (e.g., Near Field Communication (NFC)) for generating the beacon signal  78 . In one example, the short range communication device  337  may be a Bluetooth device, the beacon signal  78  is a Bluetooth signal, and the wearable mobile device  26  is configured to process the Bluetooth signal. In one example, the proximity sensor  90  of the environment detection system  48  may be used to measure the strength of the beacon signal  78 , and through this measurement, the application may determine the proximity of the wearable mobile device  26  to the access assembly  24 . 
     The mobile device  26  may further include a magnetometer  338  and a confirm ground truth module  340  as part of the application  60  (see  FIG. 2 ). The magnetometer  338  may be leveraged to confirm, for example, the grabbing of a handle  342  of the door  22  as part of the inherent gesture  334 . As best illustrated in  FIG. 18 , the inherent gesture  334  portion of the user exercise  336  may be a sequential set of motions made by the user. The sequential set of motions may be dependent upon the type of wearable mobile device  26  and the type of entry desired. 
     For simplicity of explanation and understanding that this is only one, non-limiting, embodiment of an application, the entry type to be gained will be described as entry through a door  22  (see  FIG. 1 ). Also in the present embodiment, the type of mobile device  26  is the smartwatch. In this example, the inherent gesture  334  of the user exercise  336  may begin with, at block  342 , a deceleration of walking and/or stopping completely. At block  344 , the user  23  may lift the hand  74 , carrying the smartwatch  26  with the hand, in order to reach a handle  346  of the door  22 . At block  348 , the hand  74  may grab the handle  346  preparing to pull or push the door  22  open. This grabbing action of the inherent gesture  334  may be sensed by the magnetometer  338  of the wearable mobile device  26 . 
     In operation, and after the inherent gesture  334  is performed and confirmed by the application  60 , the wearable mobile device  26  sends the command signal  38  to the access assembly  24  to effect actuation from the no-access state to the access state, and as previously described. With the access assembly  24  in the access state, and at block  350 , the user  23  may complete the entry exercise  336  by pulling (see arrow  352 ) the door  22  open. 
     The confirm ground truth module  340  (see  FIG. 2 ) of the application  60  is configured to receive information from the IMU sensing system  46  indicative of the pulling  352  that designates the final step of the entry exercise  336 . This confirmed pulling  352  may be verified by a preprogrammed confirmation pull which may be part of the scenario data  66  previously described. By confirming that the user  23  did indeed conduct the pulling  352 , the module  340  is able to further confirm an accurate determination of the inherent gesture. This confirmation may then be used to further improve the machine learning algorithm(s)  336  (see  FIG. 17 ) and/or other applied algorithms executed by the application  60 . 
     In the example of the wearable mobile device  26  being smart glasses, the smart glasses may be worn about the head of the user  23 , and parts of the inherent gesture  334  may include the user gaze when proximate to the access assembly  24 , and tilting of the head when approaching the handle  346  of the door  22 . 
     In the example of the wearable mobile device  26  being smart shoes, the smart shoes may be worn on the feet of the user  23 , and part of the inherent gesture  334  may include the tapping of a foot of the user  23 . 
     Prestaging, Gesture-Based, Access Control System: 
     Referring to  FIGS. 2 and 22 , the gesture-based access control system  20  may be a prestaging, gesture-based access control system. In this embodiment, the mobile device  26  is configured to pre-stage itself prior to the user performing a device, or device-free, gesture (i.e., a primary gesture). That is, the system applies implicit behavior detection in combination with an explicit gesture from a plurality of gestures. The prestaging event, or process, may be, or may include the performance of an inherent gesture  334  (see  FIG. 18 ). After performance of the inherent gesture  334  by the user  23 , the user  23  needs to perform the primary gesture within a prescribed duration of time. One, non-limiting, example of the inherent gesture  334  may be the act of slowing down a walk as the user  23  approaches the access assembly  24 . 
     Referring to  FIG. 2 , the application  60 , with any relevant hardware, may further include a timer or clock  142  and a satellite-based location module  144  (e.g., global positioning system (GPS). In another embodiment, the satellite-based location module  144  may be a separate device from the application  60 , which is configured to send pertinent location information to the application  60 . 
     In order to detect the prestaging event (i.e. inherent gesture  334 ), the IMU sensing system  46  may be active. The activation of the IMU sensing system  46  may be triggered when the user  23  is within a prescribed vicinity of the access assembly  24 . Establishing a user  23  presence within the vicinity may be established in any one of a variety of ways. For example, any one or more of the following may be used: the satellite-based location module  144 , the proximity sensor  90  of the environment detecting system  48 , detection of the beacon signal  78  generated from the short range communication device  337  of the access assembly  24 , and others. 
     In one, non-limiting, embodiment the implicit detection of an access intent of the user  23  may rely on the intuition that the user will slow down, and stop, as the user approaches a destination door  22  associated with the access assembly  24 , and perform a primary, intentional gesture, to indicate the intent. This intuition may be leveraged to improve the reliability of gesture detection. 
     Referring to  FIG. 22 , a method of operating the prestaging, gesture-based, access control system  20  is illustrated. At block  400 , the IMU sensing system  46  is initiated, wherein the IMU analytics performed by the motion module  324  of the application  60  are started. At block  402 , the motion module  324  determines if, for example, the user  23  is walking. At block  404 , and if the user  23  is walking, the motion module  324  determines if the user  23  is slowing down the walk (i.e., the inherent gesture  334 ). If the walking is slowing down, the inherent gesture  334  (in this example) is detected. 
     At block  406 , and after the user  23  is detected, or confirmed, via the inherent gesture  334 , the application  60  may start a timer  142  thereby running a prescribed time duration. At block  408 , and during the prescribed time duration, the mobile device  26  monitors for the occurrence of a primary, intentional, gesture. If the primary, intentional, gesture is detected and at block  410 , the application  60  effects the output of the command signal  38  to the access assembly  24  (e.g., open door  22 ). It is contemplated and understood that the primary, intentional, gesture may be a device gesture, a device-free gesture, and/or another inherent gesture. 
     At block  412 , as an optional step, and if the primary intentional gesture has yet to be detected, the motion module  324  of the application (or by other means) may determine if the user  23  has, for example, stopped walking altogether. If no, the application  60  continues to monitor for the performance of the primary, intentional, gesture. This optional step may assist when the gesture detection is not at a high confidence level. If the user  23  has stopped walking and at block  414 , the application  60  determines if the time duration has expired. If the time duration has not expired, the application  60  continues to monitor for the performance of the primary, intentional, gesture. If the time duration has expired, the process is deactivated, or the motion module  324  is re-initiated for detection of the prestaging, inherent, gesture (i.e., prestaging event performed by the user  23 ) if the user  23  remains in the vicinity of the access assembly  24 . 
     It is contemplated and understood, that at any stage during the process (e.g., at block  408 ), the mobile device  26  may provide audible and/or visual notifications to the user  23 . For example, the mobile device  26  may notify the user  23  that the mobile device is waiting upon the performance of the primary, intentional, gesture. As another example and upon expiration of the time duration, the mobile device  26  may inform the user  23  that detection of the primary, intentional, gesture has failed. 
     In one embodiment, the prestaging event may be preprogrammed, and the primary intentional gesture may be pre-selected from a plurality of preprogramed gestures by the user  23 . Non-limiting examples of the primary, intentional, gesture may include: the waving of the hand  74  near the access assembly  24  (i.e., a type of device-free or body gesture  25 , see  FIG. 1 ); tapping on the door  22  or the access assembly  24  (a type of device-free or body gesture  25 , see  FIG. 20 ); a specific body gesture triggering inertial motion, wherein the mobile device is attached to the body of the user (also see  FIG. 1 ); applying a body motion to a containment  95  containing the mobile device  26  and carried by the user  23  (i.e., a containment motion  99 , see  FIGS. 12-15 ); the waving of the mobile device  26  near the access assembly  24  (i.e., a type of device gesture  94 , see  FIGS. 6-9 ). 
     Cloud-Based, Gesture-Based, Access Control System: 
     Referring to  FIG. 19 , the gesture-based access control system  20  may include use of a cloud  360  (i.e., remote server). In this embodiment, the application  60  may be in the cloud  360 , thus information  330 ,  332  gathered by the IMU sensing system  46 , the environment detecting system  48 , and other components may be wirelessly sent from the mobile device  26  and to the cloud  360  for processing. The command signal  38  may be sent directly from the cloud  360  and to the access assembly  24 , or back to the mobile device  26  that then sends the signal  38  to the access assembly  24 . 
     Benefits of a cloud-based architecture include the performance of some or all computations and the storage of data in the cloud. This permits use of what may be more powerful algorithms, but at the potential expense of delay in communication. Another advantage may be that the mobile device  26  does not need to communicate directly with the access assembly  24 , and instead, the cloud  360  communicates a command signal directly to the access assembly  24  for access granting. 
     Advantages and benefits of the present disclosure include enablement of gesture detection without the need to hold a mobile device  26  in the hand. Another advantage includes the ability to identify, for example, a door  22  that a user  23  intends to enter as part of the intent detection. Yet other advantages include reliable intent detection, and a relatively inexpensive and robust design. 
     The various functions described above may be implemented or supported by a computer program that is formed from computer readable program codes, and that is embodied in a computer readable medium. Computer readable program codes may include source codes, object codes, executable codes, and others. Computer readable mediums may be any type of media capable of being accessed by a computer, and may include Read Only Memory (ROM), Random Access Memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or other non-transitory forms. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     Terms used herein such as component, application, module, system, and the like are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, or software execution. By way of example, an application may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. An application running on a server and the server, may be a component. One or more applications may reside within a process and/or thread of execution and an application may be localized on one computer and/or distributed between two or more computers. 
     While the present disclosure has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this present disclosure, but that the present disclosure will include all embodiments falling within the scope of the claims.