METHODS AND APPARATUS FOR PREDICTING MUSCULO-SKELETAL POSITION INFORMATION USING WEARABLE AUTONOMOUS SENSORS

- CTRL-labs Corporation

Methods and apparatus for providing a dynamically-updated computerized musculo-skeletal representation comprising a plurality of rigid body segments connected by joints. The method comprises recording, using a plurality of autonomous sensors arranged on one or more wearable devices, a plurality of autonomous signals from a user, wherein the plurality of autonomous sensors include a plurality of neuromuscular sensors configured to record neuromuscular signals. The method further comprises providing as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals. The method further comprises determining, based on an output of the trained statistical model, musculo-skeletal position information describing a spatial relationship between two or more connected segments of the plurality of rigid body segments of the computerized musculo-skeletal representation, and updating the computerized musculo-skeletal representation based, at least in part, on the musculo-skeletal position information.

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Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation of U.S. application Ser. No. 15/659,072, filed Jul. 25, 2017, entitled “METHODS AND APPARATUS FOR PREDICTING MUSCULO-SKELETAL POSITION INFORMATION USING WEARABLE AUTONOMOUS SENSORS”, which is a Non-Provisional of Provisional (35 USC 119(e)) of U.S. Application Ser. No. 62/366,421, filed Jul. 25, 2016, entitled “VIRTUAL MUSCULO-SKELETAL REPRESENTATION USING WEARABLE SENSOR”, the entire contents of which applications are incorporated by reference herein in their entirety.

BACKGROUND

In some computer applications that generate musculo-skeletal representations of the human body, it is desirable for the application to know the spatial positioning, orientation and movement of a user's body to provide a realistic representation of body movement. For example, in a virtual reality (VR) environment, tracking the spatial position of the user's hand enables the application to represent the hand motion in the VR environment, which allows the user to interact with (e.g., by grasping or manipulating) virtual objects within the VR environment. Some existing techniques for tracking movements using wearable sensors include using information obtained from multiple Inertial Measurement Units (IMUs) affixed to different parts of the user's body, and using external imaging devices (e.g., fixed-position cameras) to reconstruct the position and orientation of parts of the user's body.

SUMMARY

Some embodiments are directed to predicting information about the positioning and movements of portions of a user's body represented as a multi-segment articulated rigid body system (e.g., a user's arm, hand, leg, etc.) in an autonomous manner, i.e., without requiring external sensors, such as cameras, lasers, or global positioning systems (GPS). Signals recorded by wearable autonomous sensors placed at locations on the user's body are provided as input to a statistical model trained to predict musculo-skeletal position information, such as joint angles between rigid segments of an articulated multi-segment rigid body model of the human body. As a result of the training, the statistical model implicitly represents the statistics of motion of the articulated rigid body under defined movement constraints. The output of the trained statistical model may be used to generate a computer-based musculo-skeletal representation of at least a portion of the user's body, which in turn can be used for applications such as rendering a representation of the user's body in a virtual environment, interaction with physical or virtual objects, and monitoring a user's movements as the user performs a physical activity to assess, for example, whether the user is providing the physical activity in a desired manner.

Some embodiments are directed to a system configured to aggregate measurements from a plurality of autonomous sensors placed at locations on a user's body. The aggregate measurements may be used to create a unified representation of muscle recruitment by superimposing the measurements onto a dynamically-posed skeleton. In some embodiments, muscle activity sensed by neuromuscular sensors and/or information derived from the muscle activity (e.g., force information) may be combined with the computer-generated musculo-skeletal representation in real time.

Some embodiments are directed to a computerized system for providing a dynamically-updated computerized musculo-skeletal representation comprising a plurality of rigid body segments connected by joints. The system comprises a plurality of autonomous sensors including a plurality of neuromuscular sensors, wherein the plurality of autonomous sensors are arranged on one or more wearable devices, wherein the plurality of neuromuscular sensors are configured to continuously record a plurality of neuromuscular signals from a user and at least one computer processor. The at least one computer processor is programmed to provide as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, determine, based on an output of the trained statistical model, musculo-skeletal position information describing a spatial relationship between two or more connected segments of the plurality of rigid body segments of the computerized musculo-skeletal representation, and update the computerized musculo-skeletal representation based, at least in part, on the musculo-skeletal position information.

Other embodiments are directed to a method of providing a dynamically-updated computerized musculo-skeletal representation comprising a plurality of rigid body segments connected by joints. The method comprises recording, using a plurality of autonomous sensors arranged on one or more wearable devices, a plurality of autonomous signals from a user, wherein the plurality of autonomous sensors comprise a plurality of neuromuscular sensors configured to record a plurality of neuromuscular signals, providing as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, determining, based on an output of the trained statistical model, musculo-skeletal position information describing a spatial relationship between two or more connected segments of the plurality of rigid body segments of the computerized musculo-skeletal representation, and updating the computerized musculo-skeletal representation based, at least in part, on the musculo-skeletal position information.

Other embodiments are directed to a computer-readable storage medium encoded with a plurality of instructions that, when executed by at least one computer processor, perform a method. The method comprises recording, using a plurality of autonomous sensors arranged on one or more wearable devices, a plurality of autonomous signals from a user, wherein the plurality of autonomous sensors comprise a plurality of neuromuscular sensors configured to record a plurality of neuromuscular signals, providing as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, determining, based on an output of the trained statistical model, musculo-skeletal position information describing a spatial relationship between two or more connected segments of the plurality of rigid body segments of the computerized musculo-skeletal representation, and updating the computerized musculo-skeletal representation based, at least in part, on the musculo-skeletal position information.

Other embodiments are directed to a computer system for training a statistical model to predict musculo-skeletal position information based, at least in part, on autonomous signals recorded by a plurality of autonomous sensors, wherein the plurality of autonomous sensors include a plurality of neuromuscular sensors configured to record a plurality of neuromuscular signals. The computer system comprises an input interface configured to receive the neuromuscular signals recorded during performance of a task performed by one or more users, receive position information indicating a position of a plurality of rigid body segments of a computerized musculo-skeletal representation during performance of the task performed by the one or more users; and at least one storage device configured to store a plurality of instructions that, when executed by at least one computer processor perform a method. The method comprises generating training data based, at least on part, on the received neuromuscular signals and the received position information, training the statistical model using at least some of the generated training data to output a trained statistical model, and storing, by the at least one storage device, the trained statistical model, wherein the trained statistical model is configured to predict musculo-skeletal position information based, at least in part on continuously recorded signals from the neuromuscular sensors.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

BRIEF DESCRIPTION OF DRAWINGS

Various non-limiting embodiments of the technology will be described with reference to the following figures. It should be appreciated that the figures are not necessarily drawn to scale.

FIG. 1 is a schematic diagram of a computer-based system for predicting musculo-skeletal position information in accordance with some embodiments of the technology described herein;

FIG. 2 illustrates a wristband having EMG sensors arranged circumferentially thereon, in accordance with some embodiments of the technology described herein; and

FIG. 3 illustrates a user wearing the wristband of FIG. 2 while typing on a keyboard, in accordance with some embodiments of the technology described herein;

FIG. 4 is a flowchart of an illustrative process for generating a statistical model for predicting musculo-skeletal position information using signals recorded from autonomous sensors, in accordance with some embodiments of the technology described herein;

FIG. 5 is a flowchart of an illustrative process for using a trained statistical model to predict musculo-skeletal position information, in accordance with some embodiments of the technology described herein; and

FIG. 6 is a flowchart of an illustrative process for combining neuromuscular signals with predicted musculo-skeletal position information in accordance with some embodiments of the technology described herein.

 DETAILED DESCRIPTION

The human musculo-skeletal system can be modeled as a multi-segment articulated rigid body system, with joints forming the interfaces between the different segments and joint angles defining the spatial relationships between connected segments in the model. Constraints on the movement at the joints are governed by the type of joint connecting the segments and the biological structures (e.g., muscles, tendons, ligaments) that restrict the range of movement at the joint. For example, the shoulder joint connecting the upper arm to the torso and the hip joint connecting the upper leg to the torso are ball and socket joints that permit extension and flexion movements as well as rotational movements. By contrast, the elbow joint connecting the upper arm and the forearm and the knee joint connecting the upper leg and the lower leg allow for a more limited range of motion. As described herein, a multi-segment articulated rigid body system is used to model the human musculo-skeletal system. However, it should be appreciated that some segments of the human musculo-skeletal system (e.g., the forearm), though approximated as a rigid body in the articulated rigid body system, may include multiple rigid structures (e.g., the ulna and radius bones of the forearm) that provide for more complex movement within the segment that is not explicitly considered by the rigid body model. Accordingly, a model of an articulated rigid body system for use with some embodiments of the technology described herein may include segments that represent a combination of body parts that are not strictly rigid bodies.

In kinematics, rigid bodies are objects that exhibit various attributes of motion (e.g., position, orientation, angular velocity, acceleration). Knowing the motion attributes of one segment of the rigid body enables the motion attributes for other segments of the rigid body to be determined based on constraints in how the segments are connected. For example, the arm may be modeled as a two-segment articulated rigid body with an upper portion corresponding to the upper arm connected at a shoulder joint to the torso of the body and a lower portion corresponding to the forearm, wherein the two segments are connected at the elbow joint. As another example, the hand may be modeled as a multi-segment articulated body with the joints in the wrist and each finger forming the interfaces between the multiple segments in the model. In some embodiments, movements of the segments in the rigid body model can be simulated as an articulated rigid body system in which orientation and position information of a segment relative to other segments in the model are predicted using a trained statistical model, as described in more detail below.

FIG. 1 illustrates a system 100 in accordance with some embodiments. The system includes a plurality of autonomous sensors 110 configured to record signals resulting from the movement of portions of a human body. As used herein, the term “autonomous sensors” refers to sensors configured to measure the movement of body segments without requiring the use of external sensors, examples of which include, but are not limited to, cameras or global positioning systems. Autonomous sensors 110 may include one or more Inertial Measurement Units (IMUs), which measure a combination of physical aspects of motion, using, for example, an accelerometer and a gyroscope. In some embodiments, IMUs may be used to sense information about the movement of the part of the body on which the IMU is attached and information derived from the sensed data (e.g., position and/or orientation information) may be tracked as the user moves over time. For example, one or more IMUs may be used to track movements of portions of a user's body proximal to the user's torso (e.g., arms, legs) as the user moves over time.

Autonomous sensors 110 may also include a plurality of neuromuscular sensors configured to record signals arising from neuromuscular activity in skeletal muscle of a human body. The term “neuromuscular activity” as used herein refers to neural activation of spinal motor neurons that innervate a muscle, muscle activation, muscle contraction, or any combination of the neural activation, muscle activation, and muscle contraction. Neuromuscular sensors may include one or more electromyography (EMG) sensors, one or more mechanomyography (MMG) sensors, one or more sonomyography (SMG) sensors, and/or one or more sensors of any suitable type that are configured to detect neuromuscular signals. In some embodiments, the plurality of neuromuscular sensors may be used to sense muscular activity related to a movement of the part of the body controlled by muscles from which the neuromuscular sensors are arranged to sense the muscle activity. Spatial information (e.g., position and/or orientation information) describing the movement (e.g., for portions of the user's body distal to the user's torso, such as hands and feet) may be predicted based on the sensed neuromuscular signals as the user moves over time.

In embodiments that include at least one IMU and a plurality of neuromuscular sensors, the IMU(s) and neuromuscular sensors may be arranged to detect movement of different parts of the human body. For example, the IMU(s) may be arranged to detect movements of one or more body segments proximal to the torso, whereas the neuromuscular sensors may be arranged to detect movements of one or more body segments distal to the torso. It should be appreciated, however, that autonomous sensors 110 may be arranged in any suitable way, and embodiments of the technology described herein are not limited based on the particular sensor arrangement. For example, in some embodiments, at least one IMU and a plurality of neuromuscular sensors may be co-located on a body segment to track movements of body segment using different types of measurements. In one implementation described in more detail below, an IMU sensor and a plurality of EMG sensors are arranged on a wearable device configured to be worn around the lower arm or wrist of a user. In such an arrangement, the IMU sensor may be configured to track movement information (e.g., positioning and/or orientation over time) associated with one or more arm segments, to determine, for example whether the user has raised or lowered their arm, whereas the EMG sensors may be configured to determine movement information associated with wrist or hand segments to determine, for example, whether the user has an open or closed hand configuration.

Each of autonomous sensors 110 include one or more movement sensing components configured to sense movement information. In the case of IMUs, the movement sensing components may include one or more accelerometers, gyroscopes, magnetometers, or any combination thereof to measure characteristics of body motion, examples of which include, but are not limited to, acceleration, angular velocity, and sensed magnetic field around the body. In the case of neuromuscular sensors, the movement sensing components may include, but are not limited to, electrodes configured to detect electric potentials on the surface of the body (e.g., for EMG sensors) vibration sensors configured to measure skin surface vibrations (e.g., for MMG sensors), and acoustic sensing components configured to measure ultrasound signals (e.g., for SMG sensors) arising from muscle activity.

In some embodiments, the output of one or more of the movement sensing components may be processed using hardware signal processing circuitry (e.g., to perform amplification, filtering, and/or rectification). In other embodiments, at least some signal processing of the output of the movement sensing components may be performed in software. Thus, signal processing of autonomous signals recorded by autonomous sensors 110 may be performed in hardware, software, or by any suitable combination of hardware and software, as aspects of the technology described herein are not limited in this respect.

In some embodiments, the recorded sensor data may be processed to compute additional derived measurements that are then provided as input to a statistical model, as described in more detail below. For example, recorded signals from an IMU sensor may be processed to derive an orientation signal that specifies the orientation of a rigid body segment over time. Autonomous sensors 110 may implement signal processing using components integrated with the movement sensing components, or at least a portion of the signal processing may be performed by one or more components in communication with, but not directly integrated with the movement sensing components of the autonomous sensors.

In some embodiments, at least some of the plurality of autonomous sensors 110 are arranged as a portion of a wearable device configured to be worn on or around part of a user's body. For example, in one non-limiting example, an IMU sensor and a plurality of neuromuscular sensors are arranged circumferentially around an adjustable and/or elastic band such as a wristband or armband configured to be worn around a user's wrist or arm. Alternatively, at least some of the autonomous sensors may be arranged on a wearable patch configured to be affixed to a portion of the user's body.

In one implementation, 16 EMG sensors are arranged circumferentially around an elastic band configured to be worn around a user's lower arm. For example, FIG. 2 shows EMG sensors 504 arranged circumferentially around elastic band 502. It should be appreciated that any suitable number of neuromuscular sensors may be used and the number and arrangement of neuromuscular sensors used may depend on the particular application for which the wearable device is used. For example, a wearable armband or wristband may be used to predict musculo-skeletal position information for hand-based motor tasks such as manipulating a virtual or physical object, whereas a wearable leg or ankle band may be used to predict musculo-skeletal position information for foot-based motor tasks such as kicking a virtual or physical ball. For example, as shown in FIG. 3, a user 506 may be wearing elastic band 502 on hand 508. In this way, EMG sensors 504 may be configured to record EMG signals as a user controls keyboard 530 using fingers 540. In some embodiments, elastic band 502 may also include one or more IMUs (not shown), configured to record movement information, as discussed above.

In some embodiments, multiple wearable devices, each having one or more IMUs and/or neuromuscular sensors included thereon may be used to predict musculo-skeletal position information for movements that involve multiple parts of the body.

System 100 also includes one or more computer processors 112 programmed to communicate with autonomous sensors 110. For example, signals recorded by one or more of the autonomous sensors 110 may be provided to processor(s) 112, which may be programmed to perform signal processing, non-limiting examples of which are described above. Processor(s) 112 may be implemented in hardware, firmware, software, or any combination thereof. Additionally, processor(s) 112 may be co-located on a same wearable device as one or more of the autonomous sensors or may be at least partially located remotely (e.g., processing may occur on one or more network-connected processors).

System 100 also includes datastore 114 in communication with processor(s) 112. Datastore 114 may include one or more storage devices configured to store information describing a statistical model used for predicting musculo-skeletal position information based on signals recorded by autonomous sensors 110 in accordance with some embodiments. Processor(s) 112 may be configured to execute one or more machine learning algorithms that process signals output by the autonomous sensors 110 to train a statistical model stored in datastore 114, and the trained (or retrained) statistical model may be stored in datastore 114 for later use in generating a musculo-skeletal representation. Non-limiting examples of statistical models that may be used in accordance with some embodiments to predict musculo-skeletal position information based on recorded signals from autonomous sensors are discussed in more detail below.

In some embodiments, processor(s) 112 may be configured to communicate with one or more of autonomous sensors 110, for example to calibrate the sensors prior to measurement of movement information. For example, a wearable device may be positioned in different orientations on or around a part of a user's body and calibration may be performed to determine the orientation of the wearable device and/or to perform any other suitable calibration tasks. Calibration of autonomous sensors 110 may be performed in any suitable way, and embodiments are not limited in this respect. For example, in some embodiments, a user may be instructed to perform a particular sequence of movements and the recorded movement information may be matched to a template by virtually rotating and/or scaling the signals detected by the sensors (e.g., by the electrodes on EMG sensors). In some embodiments, calibration may involve changing the gain(s) of one or more analog to digital converters (ADCs), for example, in the case that the signals detected by the sensors result in saturation of the ADCs.

System 100 also includes one or more controllers 116 configured receive a control signal based, at least in part, on processing by processor(s) 112. As discussed in more detail below, processor(s) 112 may implement one or more trained statistical models 114 configured to predict musculo-skeletal position information based, at least in part, on signals recorded by autonomous sensors 110 worn by a user. One or more control signals determined based on the output of the trained statistical model(s) may be sent to controller 116 to control one or more operations of a device associated with the controller. In some embodiments, controller 116 comprises a display controller configured to instruct a visual display to display a graphical representation of a computer-based musculo-skeletal representation (e.g., a graphical representation of the user's body or a graphical representation of a character (e.g., an avatar in a virtual reality environment)) based on the predicted musculo-skeletal information. For example, a computer application configured to simulate a virtual reality environment may be instructed to display a graphical representation of the user's body orientation, positioning and/or movement within the virtual reality environment based on the output of the trained statistical model(s). The positioning and orientation of different parts of the displayed graphical representation may be continuously updated as signals are recorded by the autonomous sensors 110 and processed by processor(s) 112 using the trained statistical model(s) 114 to provide a computer-generated representation of the user's movement that is dynamically updated in real-time. In other embodiments, controller 116 comprises a controller of a physical device, such as a robot. Control signals sent to the controller may be interpreted by the controller to operate one or more components of the robot to move in a manner that corresponds to the movements of the user as sensed using the autonomous sensors 110.

Controller 116 may be configured to control one or more physical or virtual devices, and embodiments of the technology described herein are not limited in this respect. Non-limiting examples of physical devices that may be controlled via controller 116 include consumer electronics devices (e.g., television, smartphone, computer, laptop, telephone, video camera, photo camera, video game system, appliance, etc.), vehicles (e.g., car, marine vessel, manned aircraft, unmanned aircraft, farm machinery, etc.), robots, weapons, or any other device that may receive control signals via controller 116.

In yet further embodiments, system 100 may not include one or more controllers configured to control a device. In such embodiments, data output as a result of processing by processor(s) 112 (e.g., using trained statistical model(s) 114) may be stored for future use (e.g., for analysis of a health condition of a user or performance analysis of an activity the user is performing).

In some embodiments, during real-time movement tracking, information sensed from a single armband/wristband wearable device that includes at least one IMU and a plurality of neuromuscular sensors is used to reconstruct body movements, such as reconstructing the position and orientation of both the forearm, upper arm, wrist and hand relative to a torso reference frame using a single arm/wrist-worn device, and without the use of external devices or position determining systems. For brevity, determining both position and orientation may also be referred to herein generally as determining movement.

As discussed above, some embodiments are directed to using a statistical model for predicting musculo-skeletal information based on signals recorded from wearable autonomous sensors. The statistical model may be used to predict the musculo-skeletal position information without having to place sensors on each segment of the rigid body that is to be represented in a computer-generated musculo-skeletal representation of user's body. As discussed briefly above, the types of joints between segments in a multi-segment articulated rigid body model constrain movement of the rigid body. Additionally, different individuals tend to move in characteristic ways when performing a task that can be captured in statistical patterns of individual user behavior. At least some of these constraints on human body movement may be explicitly incorporated into statistical models used for prediction in accordance with some embodiments. Additionally or alternatively, the constraints may be learned by the statistical model though training based on recorded sensor data. Constraints imposed in the construction of the statistical model are those set by anatomy and the physics of a user's body, while constraints derived from statistical patterns are those set by human behavior for one or more users from which sensor measurements are measured. As described in more detail below, the constraints may comprise part of the statistical model itself being represented by information (e.g., connection weights between nodes) in the model.

In some embodiments, system 100 may be trained to predict musculo-skeletal information as a user moves. In some embodiments, the system 100 may be trained by recording signals from autonomous sensors 110 (e.g., IMU sensors, EMG sensors) and position information recorded from position sensors worn by one or more users as the user(s) perform one or more movements. The position sensors, described in more detail below, may measure the position of each of a plurality of spatial locations on the user's body as the one or more movements are performed during training to determine the actual position of the body segments. After such training, the system 100 may be configured to predict, based on a particular user's autonomous sensor signals, musculo-skeletal position information (e.g., a set of joint angles) that enable the generation of a musculo-skeletal representation without the use of the position sensors.

In some embodiments, after system 100 is trained to predict, based on a particular user's autonomous sensor signals, the musculo-skeletal position information, a user may utilize the system 100 to perform a virtual or physical action without using position sensors. For example, when the system 100 is trained to predict with high accuracy (e.g., at least a threshold accuracy), the musculo-skeletal position information, the predictions themselves may be used to determine the musculo-skeletal position information used to generate a musculo-skeletal representation of the user's body.

As discussed above, some embodiments are directed to using a statistical model for predicting musculo-skeletal position information to enable the generation of a computer-based musculo-skeletal representation. The statistical model may be used to predict the musculo-skeletal position information based on IMU signals, neuromuscular signals (e.g., EMG, MMG, and SMG signals), or a combination of IMU signals and neuromuscular signals detected as a user performs one or more movements.

FIG. 4 describes a process 400 for generating (sometimes termed “training” herein) a statistical model using signals recorded from autonomous sensors worn by one or more users. Process 400 may be executed by any suitable computing device(s), as aspects of the technology described herein are not limited in this respect. For example, process 400 may be executed by processors 112 described with reference to FIG. 1. As another example, one or more acts of process 400 may be executed using one or more servers (e.g., servers included as a part of a cloud computing environment). For example, at least a portion of act 410 relating to training of a statistical model (e.g., a neural network) may be performed using a cloud computing environment.

Process 400 begins at act 402, where a plurality of sensor signals are obtained for one or multiple users performing one or more movements (e.g., typing on a keyboard). In some embodiments, the plurality of sensor signals may be recorded as part of process 400. In other embodiments, the plurality of sensor signals may have been recorded prior to the performance of process 400 and are accessed (rather than recorded) at act 402.

In some embodiments, the plurality of sensor signals may include sensor signals recorded for a single user performing a single movement or multiple movements. The user may be instructed to perform a sequence of movements for a particular task (e.g., opening a door) and sensor signals corresponding to the user's movements may be recorded as the user performs the task he/she was instructed to perform. The sensor signals may be recorded by any suitable number of autonomous sensors located in any suitable location(s) to detect the user's movements that are relevant to the task performed. For example, after a user is instructed to perform a task with the fingers of his/her right hand, the sensor signals may be recorded by multiple neuromuscular sensors circumferentially (or otherwise) arranged around the user's lower right arm to detect muscle activity in the lower right arm that give rise to the right hand movements and one or more IMU sensors arranged to predict the joint angle of the user's arm relative to the user's torso. As another example, after a user is instructed to perform a task with his/her leg (e.g., to kick an object), sensor signals may be recorded by multiple neuromuscular sensors circumferentially (or otherwise) arranged around the user's leg to detect muscle activity in the leg that give rise to the movements of the foot and one or more IMU sensors arranged to predict the joint angle of the user's leg relative to the user's torso.

In some embodiments, the sensor signals obtained in act 402 correspond to signals from one type of autonomous sensor (e.g., one or more IMU sensors or one or more neuromuscular sensors) and a statistical model may be trained based on the sensor signals recorded using the particular type of autonomous sensor, resulting in a sensor-type specific trained statistical model. For example, the obtained sensor signals may comprise a plurality of EMG sensor signals arranged around the lower arm or wrist of a user and the statistical model may be trained to predict musculo-skeletal position information for movements of the wrist and/or hand during performance of a task such as grasping and twisting an object such as a doorknob.

In embodiments that provide predictions based on multiple types of sensors (e.g., IMU sensors, EMG sensors, MMG sensors, SMG sensors), a separate statistical model may be trained for each of the types of sensors and the outputs of the sensor-type specific models may be combined to generate a musculo-skeletal representation of the user's body. In other embodiments, the sensor signals obtained in act 402 from two or more different types of sensors may be provided to a single statistical model that is trained based on the signals recorded from the different types of sensors. In one illustrative implementation, an IMU sensor and a plurality of EMG sensors are arranged on a wearable device configured to be worn around the forearm of a user, and signals recorded by the IMU and EMG sensors are collectively provided as inputs to a statistical model, as discussed in more detail below.

In some embodiments, the sensor signals obtained in act 402 are recorded at multiple time points as a user performs one or multiple movements. As a result, the recorded signal for each sensor may include data obtained at each of multiple time points. Assuming that n autonomous sensors are arranged to simultaneously measure the user's movement information during performance of a task, the recorded sensor signals for the user may comprise a time series of K n-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements.

In some embodiments, a user may be instructed to perform a task multiple times and the sensor signals and position information may be recorded for each of multiple repetitions of the task by the user. In some embodiments, the plurality of sensor signals may include signals recorded for multiple users, each of the multiple users performing the same task one or more times. Each of the multiple users may be instructed to perform the task and sensor signals and position information corresponding to that user's movements may be recorded as the user performs (once or repeatedly) the task he/she was instructed to perform. When sensor signals are collected by multiple users which are combined to generate a statistical model, an assumption is that different users employ similar musculo-skeletal positions to perform the same movements. Collecting sensor signals and position information from a single user performing the same task repeatedly and/or from multiple users performing the same task one or multiple times facilitates the collection of sufficient training data to generate a statistical model that can accurately predict musculo-skeletal position information associated with performance of the task.

In some embodiments, a user-independent statistical model may be generated based on training data corresponding to the recorded signals from multiple users, and as the system is used by a user, the statistical model is trained based on recorded sensor data such that the statistical model learns the user-dependent characteristics to refine the prediction capabilities of the system for the particular user.

In some embodiments, the plurality of sensor signals may include signals recorded for a user (or each of multiple users) performing each of multiple tasks one or multiple times. For example, a user may be instructed to perform each of multiple tasks (e.g., grasping an object, pushing an object, and pulling open a door) and signals corresponding to the user's movements may be recorded as the user performs each of the multiple tasks he/she was instructed to perform. Collecting such data may facilitate developing a statistical model for predicting musculo-skeletal position information associated with multiple different actions that may be taken by the user. For example, training data that incorporates musculo-skeletal position information for multiple actions may facilitate generating a statistical model for predicting which of multiple possible movements a user may be performing.

As discussed above, the sensor data obtained at act 402 may be obtained by recording sensor signals as each of one or multiple users performs each of one or more tasks one or more multiple times. As the user(s) perform the task(s), position information describing the spatial position of different body segments during performance of the task(s) may be obtained in act 404. In some embodiments, the position information is obtained using one or more external devices or systems that track the position of different points on the body during performance of a task. For example, a motion capture system, a laser scanner, a device to measure mutual magnetic induction, or some other system configured to capture position information may be used. As one non-limiting example, a plurality of position sensors may be placed on segments of the fingers of the right hand and a motion capture system may be used to determine the spatial location of each of the position sensors as the user performs a task such as grasping an object. The sensor data obtained at act 402 may be recorded simultaneously with recording of the position information obtained in act 404. In this example, position information indicating the position of each finger segment over time as the grasping motion is performed is obtained.

Next, process 400 proceeds to act 406, where the autonomous sensor signals obtained in act 402 and/or the position information obtained in act 404 are optionally processed. For example, the autonomous sensor signals or the position information signals may be processed using amplification, filtering, rectification, or other types of signal processing.

Next, process 400 proceeds to act 408, where musculo-skeletal position characteristics are determined based on the position information (as collected in act 404 or as processed in act 406). In some embodiments, rather than using recorded spatial (e.g., x, y, z) coordinates corresponding to the position sensors as training data to train the statistical model, a set of derived musculo-skeletal positon characteristic values are determined based on the recorded position information, and the derived values are used as training data for training the statistical model. For example, using information about the constraints between connected pairs of rigid segments in the articulated rigid body model, the position information may be used to determine joint angles that define angles between each connected pair of rigid segments at each of multiple time points during performance of a task. Accordingly, the position information obtained in act 404 may be represented by a vector of n joint angles at each of a plurality of time points, where n is the number of joints or connections between segments in the articulated rigid body model.

Next, process 400 proceeds to act 410, where the time series information obtained at acts 402 and 408 is combined to create training data used for training a statistical model at act 410. The obtained data may be combined in any suitable way. In some embodiments, each of the autonomous sensor signals obtained at act 402 may be associated with a task or movement within a task corresponding to the musculo-skeletal position characteristics (e.g., joint angles) determined based on the positional information recorded in act 404 as the user performed the task or movement. In this way, the sensor signals may be associated with musculo-skeletal position characteristics (e.g., joint angles) and the statistical model may be trained to predict that the musculo-skeletal representation will be characterized by particular musculo-skeletal position characteristics between different body segments when particular sensor signals are recorded during performance of a particular task.

In embodiments comprising autonomous sensors of different types (e.g., IMU sensors and neuromuscular sensors) configured to simultaneously record different types of movement information during performance of a task, the sensor data for the different types of sensors may be recorded using the same or different sampling rates. When the sensor data is recorded at different sampling rates, at least some of the sensor data may be resampled (e.g., up-sampled or down-sampled) such that all sensor data provided as input to the statistical model corresponds to time series data at the same time resolution. Resampling at least some of the sensor data may be performed in any suitable way including, but not limited to using interpolation for upsampling and using decimation for downsampling.

In addition to or as an alternative to resampling at least some of the sensor data when recorded at different sampling rates, some embodiments employ a statistical model configured to accept multiple inputs asynchronously. For example, the statistical model may be configured to model the distribution of the “missing” values in the input data having a lower sampling rate. Alternatively, the timing of training of the statistical model occur asynchronously as input from multiple sensor data measurements becomes available as training data.

Next, process 400 proceeds to act 412, where a statistical model for predicting musculo-skeletal position information is trained using the training data generated at act 410. The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of autonomous sensor data. The statistical model may provide output that indicates, for each of one or more tasks or movements that may be performed by a user, the likelihood that the musculo-skeletal representation of the user's body will be characterized by a set of musculo-skeletal position characteristics (e.g., a set of joint angles between segments in an articulated multi-segment body model). For example, the statistical model may take as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj is a value measured by the ith autonomous sensor at time tj and/or derived from the value measured by the ith autonomous sensor at time tj. Based on such input, the statistical model may provide output indicating, a probability that a musculo-skeletal representation of the user's body will be characterized by a set of musculo-skeletal position characteristics. As one non-limiting example, the statistical model may be trained to predict a set of joint angles for segments in the fingers in the hand over time as a user grasps an object. In this example, the trained statistical model may output, a set of predicted joint angles for joints in the hand corresponding to the sensor input.

In some embodiments, the statistical model may be a neural network and, for example, may be a recurrent neural network. In some embodiments, the recurrent neural network may be a long short-term memory (LSTM) neural network. It should be appreciated, however, that the recurrent neural network is not limited to being an LSTM neural network and may have any other suitable architecture. For example, in some embodiments, the recurrent neural network may be a fully recurrent neural network, a recursive neural network, a variational autoencoder, a Hopfield neural network, an associative memory neural network, an Elman neural network, a Jordan neural network, an echo state neural network, a second order recurrent neural network, and/or any other suitable type of recurrent neural network. In other embodiments, neural networks that are not recurrent neural networks may be used. For example, deep neural networks, convolutional neural networks, and/or feedforward neural networks, may be used.

In some of the embodiments in which the statistical model is a neural network, the output layer of the neural network may provide a set of output values corresponding to a respective set of possible musculo-skeletal position characteristics (e.g., joint angles). In this way, the neural network may operate as a non-linear regression model configured to predict musculo-skeletal position characteristics from raw or pre-processed sensor measurements. It should be appreciated that, in some embodiments, any other suitable non-linear regression model may be used instead of a neural network, as aspects of the technology described herein are not limited in this respect.

It should be appreciated that aspects of the technology described herein are not limited to using neural networks, as other types of statistical models may be employed in some embodiments. For example, in some embodiments, the statistical model may comprise a hidden Markov model, a Markov switching model with the switching allowing for toggling among different dynamic systems, dynamic Bayesian networks, and/or any other suitable graphical model having a temporal component. Any such statistical model may be trained at act 412 using the sensor data obtained at act 402.

As another example, in some embodiments, the statistical model may take as input, features derived from the sensor data obtained at act 402. In such embodiments, the statistical model may be trained at act 412 using features extracted from the sensor data obtained at act 402. The statistical model may be a support vector machine, a Gaussian mixture model, a regression based classifier, a decision tree classifier, a Bayesian classifier, and/or any other suitable classifier, as aspects of the technology described herein are not limited in this respect. Input features to be provided as training data to the statistical model may be derived from the sensor data obtained at act 402 in any suitable way. For example, the sensor data may be analyzed as time series data using wavelet analysis techniques (e.g., continuous wavelet transform, discrete-time wavelet transform, etc.), Fourier-analytic techniques (e.g., short-time Fourier transform, Fourier transform, etc.), and/or any other suitable type of time-frequency analysis technique. As one non-limiting example, the sensor data may be transformed using a wavelet transform and the resulting wavelet coefficients may be provided as inputs to the statistical model.

In some embodiments, at act 412, values for parameters of the statistical model may be estimated from the training data generated at act 410. For example, when the statistical model is a neural network, parameters of the neural network (e.g., weights) may be estimated from the training data. In some embodiments, parameters of the statistical model may be estimated using gradient descent, stochastic gradient descent, and/or any other suitable iterative optimization technique. In embodiments where the statistical model is a recurrent neural network (e.g., an LSTM), the statistical model may be trained using stochastic gradient descent and backpropagation through time. The training may employ a cross-entropy loss function and/or any other suitable loss function, as aspects of the technology described herein are not limited in this respect.

Next, process 400 proceeds to act 414, where the trained statistical model is stored (e.g., in datastore 114). The trained statistical model may be stored using any suitable format, as aspects of the technology described herein are not limited in this respect. In this way, the statistical model generated during execution of process 400 may be used at a later time, for example, in accordance with the process described with reference to FIG. 5.

FIG. 5 illustrates a process 500 for predicting musculo-skeletal position information based on recorded signals from a plurality of autonomous sensors and a trained statistical model in accordance with some embodiments. Although process 500 is described herein with respect to IMU and EMG signals, it should be appreciated that process 500 may be used to predict musculo-skeletal position information based on any recorded autonomous signals including, but not limited to, IMU signals, EMG signals, MMG signals, SMG signals, or any suitable combination thereof and a trained statistical model trained on such autonomous signals.

Process 500 begins in act 510, where signals are recorded from a plurality of autonomous sensors arranged on or near the surface of a user's body to record activity associated with movements of the body during performance of a task. In one example described above, the autonomous sensors comprise an IMU sensor and a plurality of EMG sensors arranged circumferentially (or otherwise oriented) on a wearable device configured to be worn on or around a part of the user's body, such as the user's arm. In some embodiments, the plurality of EMG signals are recorded continuously as a user wears the wearable device including the plurality of autonomous sensors. Process 500 then proceeds to act 512, where the signals recorded by the autonomous sensors are optionally processed. For example, the signals may be processed using amplification, filtering, rectification, or other types of signal processing. In some embodiments, filtering includes temporal filtering implemented using convolution operations and/or equivalent operations in the frequency domain (e.g., after the application of a discrete Fourier transform). In some embodiments, the signals are processed in the same or similar manner as the signals recorded in act 402 of process 400 described above and used as training data to train the statistical model.

Process 500 then proceeds to act 514, where the autonomous sensor signals are provided as input to a statistical model (e.g., a neural network) trained using one or more of the techniques described above in connection with process 400. In some embodiments that continuously record autonomous signals, the continuously recorded autonomous signals (raw or processed) may be continuously or periodically provided as input to the trained statistical model for prediction of musculo-skeletal position information (e.g., joint angles) for the given set of input sensor data. As discussed above, in some embodiments, the trained statistical model is a user-independent model trained based on autonomous sensor and position information measurements from a plurality of users. In other embodiments, the trained model is a user-dependent model trained on data recorded from the individual user from which the data recorded in act 510 is also acquired.

After the trained statistical model receives the sensor data as a set of input parameters, process 500 proceeds to act 516, where predicted musculo-skeletal position information is output from the trained statistical model. As discussed above, in some embodiments, the predicted musculo-skeletal position information may comprise a set of musculo-skeletal position information values (e.g., a set of joint angles) for a multi-segment articulated rigid body model representing at least a portion of the user's body. In other embodiments, the musculo-skeletal position information may comprises a set of probabilities that the user is performing one or more movements from a set of possible movements.

After musculo-skeletal position information is predicted in act 516, process 500 proceeds to act 518, where a computer-based musculo-skeletal representation of the user's body is generated based, at least in part, on the musculo-skeletal position information output from the trained statistical model. The computer-based musculo-skeletal representation may be generated in any suitable way. For example, a computer-based musculo-skeletal model of the human body may include multiple rigid body segments, each of which corresponds to one or more skeletal structures in the body. For example, the upper arm may be represented by a first rigid body segment, the lower arm may be represented by a second rigid body segment the palm of the hand may be represented by a third rigid body segment, and each of the fingers on the hand may be represented by at least one rigid body segment (e.g., at least fourth-eighth rigid body segments). A set of joint angles between connected rigid body segments in the musculo-skeletal model may define the orientation of each of the connected rigid body segments relative to each other and a reference frame, such as the torso of the body. As new sensor data is measured and processed by the statistical model to provide new predictions of the musculo-skeletal position information (e.g., an updated set of joint angles), the computer-based musculo-skeletal representation of the user's body may be updated based on the updated set of joint angles determined based on the output of the statistical model. In this way the computer-based musculo-skeletal representation is dynamically updated in real-time as autonomous sensor data is continuously recorded.

The computer-based musculo-skeletal representation may be represented and stored in any suitable way, as embodiments of the technology described herein are not limited with regard to the particular manner in which the representation is stored. Additionally, although referred to herein as a “musculo-skeletal” representation, to reflect that muscle activity may be associated with the representation in some embodiments, as discussed in more detail below, it should be appreciated that some musculo-skeletal representations used in accordance with some embodiments may correspond to skeletal structures, muscular structures or a combination of skeletal structures and muscular structures in the body.

As discussed above, in some embodiments, one or more control signals may be sent to a controller based on the musculo-skeletal representation generated in act 518 of process 500. For example, when the controller is a display controller, the control signal(s) may instruct a display in communication with the display controller to display a graphical rendering based on the generated muscular-skeletal representation. For a computer application that provides a virtual reality environment, the graphical rendering may be a rendering of the user's body or another computer-generated character (e.g., an avatar) based on a current state of the musculo-skeletal representation. As sensor data is collected, the rendered character may be dynamically updated to provide an animation of the rendered character that mimics the movements of the user wearing the wearable device including the autonomous sensors. In a virtual reality environment, a result of the character's animation may be the ability of the animated character to interact with objects in the virtual reality environment, examples of which include, but are not limited to, grasping a virtual object.

In embodiments, in which the controller is configured to control a physical device (e.g., a robot), the control signal(s) sent to the controller may instruct the physical device to perform one or more actions corresponding to the generated musculo-skeletal representation. For example, when the device being controlled is a robot, the control signal(s) may instruct the controller of the robot to mimic the movements of the user or otherwise control an operation of the robot based on the generated musculo-skeletal representation.

In yet further embodiments, the generated musculo-skeletal representation may be used to track the user's movements over time and provide a control signal to a controller that provides feedback to the user about the tracked movements. For example, the generated and dynamically updated musculo-skeletal representation may track the position of the user's hands as the user is typing on a keyboard and provide feedback to the user when it is determined that the user is likely to experience muscle fatigue due to the position of their hands as they type. Recordings of muscle activity when used in combination with the generated musculo-skeletal representation, as discussed in more detail below with regard to FIG. 6, may be used to track muscle performance (e.g., fatigue, activation) during performance of various tasks and feedback may be provided to instruct the user performance of the task may be improved using different musculo-skeletal positioning as the task is performed. The feedback may be provided in any suitable way using, for example, haptic feedback, audio feedback, and/or visual feedback as embodiments of the technology described herein are not limited based on how the feedback is provided.

In some embodiments at least some of the sensor data recorded during use of the system may be used as training data to train the statistical model to enable the model to continue to refine the statistical relationships between movement-based information recorded by the autonomous sensors and musculo-skeletal position information output by the statistical model. Continuous training of the statistical model may result in improved performance of the model in predicting musculo-skeletal positioning information for movements that are performed by the user in a consistent manner.

Although process 500 is described herein as being performed after process 400 has completed and a statistical model has been trained, in some embodiments, process 400 and 500 may be performed together. For example, the statistical model may be trained in real-time, as a user is performing movements to interact with a virtual or physical object, and the trained statistical model may be used as soon as the model has been trained sufficiently to provide reliable predictions. In some embodiments, this may be performed using a variational autoencoder.

In the embodiments described above, a times series of movement data recorded by autonomous sensors such as IMUs and neuromuscular sensors (EMG) is used to predict musculo-skeletal position information (e.g., a set of joint angles) that describe how the orientation of different segments of a computer-based musculo-skeletal representation change over time based on the user's movements. In this way, the neuromuscular activity recorded by neuromuscular sensors, which indirectly measures body motion through the activation of skeletal muscles, may nonetheless be used to predict how a user is moving through the use of trained statistical model that learns statistical relationships between the recorded neuromuscular signals and the user's movements.

The inventors have recognized and appreciated that in addition to being useful for predicting musculo-skeletal position information, as discussed above, the neuromuscular activity directly recorded by the neuromuscular sensors may be combined with the generated musculo-skeletal representation to provide a richer musculo-skeletal representation that represents additional biophysical underpinnings involved in the user's movements compared to embodiments where only musculo-skeletal positioning/orientation over time is represented. This dual use of the neuromuscular signals recorded by neuromuscular sensors on a wearable device—to directly measure neuromuscular activity and to indirectly predict musculo-skeletal position information, enables some embodiments to control virtual or physical devices in a manner that more closely resembles the movements of the user. For example, some embodiments are configured to use the neuromuscular activity information recorded by the neuromuscular sensors to modify the control signals used to control a virtual or physical device. As a non-limiting example, for an application that provides a virtual reality environment, the dynamically updated computer-based musculo-skeletal representation may track the movements of a user to grasp a virtual egg located within the virtual environment allowing a computer-generated character (e.g., an avatar) associated with the user to hold the virtual egg in its hand. As the user clenches their hand into a fist without substantially moving the spatial position of the their fingers, corresponding detected neuromuscular signals may be used to modify the control signals sent to the application such that rather than just holding the egg, the computer-generated character squeezes the egg with force and breaks the egg. For example, a force value may be derived based on the detected muscle activity sensed by the neuromuscular sensor, and the derived force value may be used to modify the control signals sent to the application. In this way, neuromuscular activity directly recorded by the EMG sensors and/or derived measurements based on the directly recorded EMG sensor data may be used to augment the generated musculo-skeletal representation predicted in accordance with some embodiments of the technology described herein. Further applications of combining neuromuscular activity and musculo-skeletal representations are discussed below in connection with FIG. 6.

FIG. 6 illustrates a process 600 for combining neuromuscular activity recorded with neuromuscular sensors with a musculo-skeletal representation generated, at least in part, from the neuromuscular activity, in accordance with some embodiments. In act 602, neuromuscular signals are recorded from a plurality of neuromuscular sensors arranged near or on the surface of a user's body. In some embodiments, examples of which are described above, the plurality of neuromuscular sensors are integrated with a wearable device such as a flexible or adjustable band that may be worn around a portion of a user's body.

The process 600 then proceeds to act 604, where musculo-skeletal position information is predicted based, at least in part, on the recorded neuromuscular signals or signals derived from the neuromuscular signals. For example, as discussed above in connection with process 500, the recorded neuromuscular signals recorded by the neuromuscular signals may be processed using amplification, filtering, rectification, or any other suitable signal processing technique and the processed neuromuscular signals may be provided as input to a statistical model trained to output musculo-skeletal position information predicted based on the input provided to the statistical model. In some embodiments, examples, of which are described above, IMU signals recorded by one or more IMU sensors are also provided as input to the trained statistical model and the predicted musculo-skeletal position information output from the trained statistical model is based on both the IMU signals and the neuromuscular signals provided as input.

The process 600 then proceeds to act 606, where a computer-based musculo-skeletal representation is generated based on the predicted musculo-skeletal position information output from the trained statistical model. The processes of predicting musculo-skeletal position information using a trained statistical model and generating a musculo-skeletal representation based on predicted musculo-skeletal position information in accordance with some embodiments is described above in connection with process 500 and is not repeated, for brevity. As should be appreciated from the foregoing, in acts 604 and 606 of process 600, recorded neuromuscular activity is used in combination with a trained statistical model as an indirect way to estimate the movements of portions of a user's body without requiring the use of external sensors, such as cameras or global positioning systems.

The inventors have recognized that the neuromuscular signals, which provide a direct measurement of neuromuscular activity underlying the user's movements may be combined with a generated musculo-skeletal representation to provide an enriched musculo-skeletal representation. Accordingly, process 600 proceeds to act 608, where the neuromuscular activity and/or muscle activity predicted from the recorded neuromuscular activity are combined with the generated musculo-skeletal representation.

The neuromuscular activity may be combined with the generated musculo-skeletal representation in any suitable way. For example, as discussed above, in some embodiments, the control signal(s) sent to a controller for interacting with a physical or virtual object may be modified based, at least in part, on the neuromuscular signals. In some embodiments, modifying the control signal(s) may be implemented by sending one or more additional control signals to the controller and/or by modifying one or more of the control signals generated based on the generated musculo-skeletal representation. In an application that provides a virtual reality environment, one or more characteristics of the neuromuscular signals may be used to determine how a character within the virtual reality environment interacts with objects in the virtual environment. In other embodiments, a visual representation of the neuromuscular activity may be displayed in combination with the character within the virtual reality environment. For example, muscle fatigue due to prolonged contraction of particular muscles and as sensed by the neuromuscular sensors may be shown on the rendering of the character in the virtual reality environment. Other visual representations of the neuromuscular activity may also be used, and embodiments of the technology described herein are not limited in this respect.

In yet other embodiments, feedback may be provided to the user based on a combination of the neuromuscular activity recorded by the neuromuscular sensors and the generated musculo-skeletal representation. For example, a system in accordance with some embodiments may track both the movements of a portion of the user's body as predicted using the generated musculo-skeletal representation and muscle activity that results in the movements to instruct the user about the proper way to perform a particular task. Applications in which such tracking of combined movement and muscle activity may be useful includes, but is not limited to, tracking performance of athletes to provide feedback on muscle engagement to reduce muscle fatigue, facilitating physical rehabilitation by providing instructional feedback to injured patients, and providing instructional feedback to users to teach desired ways of performing tasks that proactively prevent injuries.

As shown in FIG. 6, process 600 includes an optional act 608 of using the neuromuscular signals to predict muscle activity. The inventors have recognized and appreciated that recorded neuromuscular signals precede the performance of the corresponding motor movements by hundreds of milliseconds. Accordingly, the neuromuscular signals themselves may be used to predict the onset of movement prior the movement being performed. Examples of using a trained statistical model to predict the onset of a movement are described in the co-pending patent application entitled, “Methods and Apparatus for Inferring User Intention,” filed on the same day as the instant application, the entire contents of which is incorporated herein by reference. Accordingly, in some embodiments, muscle activity predicted based, at least in part, on the recorded neuromuscular signals in act 608 is combined with the generated musculo-skeletal representation in act 610. Due to the time delay between the recording of the neuromuscular signals and the performance of the actual movement, some embodiments are able to control a virtual or physical device with short latencies.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware or with one or more processors programmed using microcode or software to perform the functions recited above.

In this respect, it should be appreciated that one implementation of the embodiments of the present invention comprises at least one non-transitory computer-readable storage medium (e.g., a computer memory, a portable memory, a compact disk, etc.) encoded with a computer program (i.e., a plurality of instructions), which, when executed on a processor, performs the above-discussed functions of the embodiments of the present invention. The computer-readable storage medium can be transportable such that the program stored thereon can be loaded onto any computer resource to implement the aspects of the present invention discussed herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs the above-discussed functions, is not limited to an application program running on a host computer. Rather, the term computer program is used herein in a generic sense to reference any type of computer code (e.g., software or microcode) that can be employed to program a processor to implement the above-discussed aspects of the present invention.

Various aspects of the present invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and are therefore not limited in their application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

Also, embodiments of the invention may be implemented as one or more methods, of which an example has been provided. The acts performed as part of the method(s) may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Such terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term).

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing”, “involving”, and variations thereof, is meant to encompass the items listed thereafter and additional items.

Having described several embodiments of the invention in detail, various modifications and improvements will readily occur to those skilled in the art. Such modifications and improvements are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description is by way of example only, and is not intended as limiting. The invention is limited only as defined by the following claims and the equivalents thereto.

Claims

1. A system, comprising:

a wearable device, comprising a plurality of sensors arranged at different locations on the wearable device, each sensor configured to measure a plurality of electrical signals from a wrist or arm of a user; and
at least one computer processor programmed to:
provide as input to a machine learning model, the plurality of electrical signals from the wrist or arm of the user;
determine, based on an output of the machine learning model, musculoskeletal position information that estimates a position of a hand of the user.

2. The system according to claim 1, wherein the plurality of sensors includes one or more autonomous sensors arranged on the wearable device.

3. The system according to claim 1, wherein the system determines a computerized musculoskeletal representation of a hand of the user.

4. The system according to claim 3, wherein the musculoskeletal position information describes a spatial relationship between two or more connected segments of a plurality of rigid body segments of the computerized representation of the hand of the user.

5. The system according to claim 1, wherein the at least one computer processor is located within the wearable device.

6. The system according to claim 1, wherein each sensor comprises an electrode located Between a surface of the wearable device and the wrist or arm of the user.

7. The system according to claim 1, wherein the electrical signals are detected using electrodes configured to detect electric potentials on a surface of the body of the user.

8. The system according to claim 1, further comprising an element that is configured to determine a musculoskeletal representation comprising a plurality of rigid body segments connected by joints that represents the hand, wrist, and arm position of the user.

9. The system according to claim 1, further comprising a display controller configured to send a plurality of signals to a computer-based device, wherein the plurality of signals cause the computer-based device to display a graphical representation of the output that describes the hand position.

10. The system of claim 1, wherein the machine learning model is configured to derive an indication of force from the received values of the electrical signals.

11. The system of claim 1, wherein the machine learning model is configured to use information derived from the electrical signals to compute the output, the information comprising one or more of a frequency-domain representation of the electrical signals and a time-domain representation of the electrical signals.

12. The system of claim 1, wherein the computer processor is further configured to extract features from information derived from the electrical signals, the features comprising angles between joints defining the hand position of the user.

13. The system of claim 1, wherein the wearable device further comprises an inertial measurement unit configured to generate inertial signals corresponding to movement of the wearable device and the user's arm, wherein the computer processor is further configured to determine, using information derived from the inertial signals with the machine learning model, the output that describes the hand position.

14. The system of claim 1, wherein the at least one processor is further programmed to send based, at least in part, on the musculoskeletal position information, one or more control signals to a controller configured to instruct a device to perform an action based on the one or more control signals.

15. The system of claim 1, wherein the computed output further describes forces exerted by at least a portion of the hand of the user on one or more entities or objects.

16. A method comprising acts of:

providing for a wearable device, the wearable device comprising a plurality of sensors arranged at different locations on the wearable device, each sensor configured to measure a plurality of electrical signals from a wrist or arm of a user;
providing, as input to a machine learning model, the plurality of electrical signals from the wrist or arm of the user; and
determining, based on an output of the machine learning model, musculoskeletal position information that estimates a position of a hand of the user.

17. The method according to claim 16, further comprising an act of providing for, on the wearable device, one or more autonomous sensors.

18. The method according to claim 16, further comprising an act of determining a computerized musculoskeletal representation of a hand of the user.

19. The method according to claim 18, wherein the musculoskeletal position information describes a spatial relationship between two or more connected segments of a plurality of rigid body segments of the computerized representation of the hand of the user.

20. The method according to claim 16, further comprising executing the machine learning model within the wearable device.

21. The method according to claim 16, wherein each sensor comprises an electrode, and wherein the method comprises locating the electrode between a surface of the wearable device and the wrist or arm of the user.

22. The method according to claim 16, further comprising an act of detecting the electrical signals using electrodes configured to detect electric potentials on a surface of the body of the user.

23. The method according to claim 16, further comprising an act of determining a musculoskeletal representation comprising a plurality of rigid body segments connected by joints that represents the hand, wrist, and arm position of the user.

24. The method according to claim 16, further comprising an act of sending a plurality of signals to a computer-based device, wherein the plurality of signals are used for controlling the computer-based device to display a graphical representation of the output that describes the hand position.

25. The method according to claim 16, further comprising an act of deriving, by the machine learning model, an indication of force from the received values of the electrical signals.

26. The method according to claim 16, further comprising using, by the machine learning model, information derived from the electrical signals to compute the output, the information comprising one or more of a frequency-domain representation of the electrical signals and a time-domain representation of the electrical signals.

27. The method according to claim 16, further comprising an act of extracting features from information derived from the electrical signals, the features comprising angles between joints defining the hand position of the user.

28. The method according to claim 16, wherein the wearable device further comprises an inertial measurement unit configured to generate inertial signals corresponding to movement of the wearable device and the user's arm, and wherein the method further comprises an act of determining, using information derived from the inertial signals with the machine learning model, the output that describes the hand position.

29. The method according to claim 16, further comprising an act of sending based, at least in part, on the musculoskeletal position information, one or more control signals to a controller configured to instruct a device to perform an action based on the one or more control signals.

30. The method according to claim 16, further comprising an act of providing an output describing forces exerted by at least a portion of the hand of the user on one or more entities or objects.

Patent History
Publication number: 20190212817
Type: Application
Filed: Mar 14, 2019
Publication Date: Jul 11, 2019
Applicant: CTRL-labs Corporation (New York, NY)
Inventors: Patrick Kaifosh (New York, NY), Timothy Machado (Palo Alto, CA), Thomas Reardon (New York, NY), Erik Schomburg (Brooklyn, NY)
Application Number: 16/353,998
Classifications
International Classification: G06F 3/01 (20060101);