# US 2025/0050188 A1 · Systems and methods for predictive shoulder kinematics of rehabilitation exercises through immersive virtual reality

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- Number: US 2025/0050188 A1
- Document ID: US20250050188A1
- Status: Published application
- Kind: A1
- Application number: 18/717,978
- Filed: 2022-12-08
- Published: 2025-02-13
- Earliest claimed: 2021-12-08
- Assignees: The Regents of the University of California
- Applicants: The Regents of the University of California
- Inventors: Michael Powell; Aviv Elor; Ash Robbins; Mircea Teodorescu; Sri Kurniawan
- Web page: https://patentgrep.com/patent/US20250050188A1

## CPC

- A63B 71/0622: {Visual, audio or audio-visual systems for entertaining, instructing or motivating the user}
- A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance}
- G02B 27/0093: {with means for monitoring data relating to the user, e.g. head-tracking, eye-tracking}
- G02B 27/017: {Head mounted}
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/09: Supervised learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/20: Ensemble learning
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/251: {involving models}
- G06T 7/292: Multi-camera tracking
- G06T 19/20: Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- A63B 2071/0638: {Displaying moving images of recorded environment, e.g. virtual environment}
- A63B 2071/0666: {worn on the head or face, e.g. combined with goggles or glasses}
- A63B 2220/24: Angular displacement
- A63B 2220/54: Torque
- A63B 2220/805: Optical or opto-electronic sensors
- A63B 2220/836: Sensors arranged on the body of the user
- G02B 2027/0138: {comprising image capture systems, e.g. camera}
- G02B 2027/014: {comprising information/image processing systems}
- G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)}
- G06T 2207/10048: Infrared image
- G06T 2207/20016: Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30008: Bone
- G06T 2210/41: Medical
- G06T 2219/024: Multi-user, collaborative environment
- G06T 2219/2016: Rotation, translation, scaling
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)
- G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Methods and systems are provided for predictive shoulder kinematics via immersive virtual reality. In one example, a system comprises an immersive virtual reality (iVR) system, the iVR system including a headset and a hand-held controller, and machine readable instructions executable to: predict joint kinematics using a machine learning model based on motion data received from the iVR system during gameplay of a virtual reality-guided exercise with the iVR system. In this way, physical rehabilitation may be performed remotely with increased evaluation accuracy.

## Claims

### Claim 1

A system, comprising:
an immersive virtual reality (iVR) system, the iVR system including a headset and a hand-held controller; and
machine readable instructions executable to:
predict joint kinematics using a machine learning model based on motion data received from the iVR system during gameplay of a virtual reality-guided exercise with the iVR system.

### Claim 2 (depends on 1)

The system of claim 1, wherein the machine learning model is trained using joint angles and joint torques determined via biomechanical simulation using data obtained via an optical motion tracking system.

### Claim 3 (depends on 1)

The system of claim 1, wherein the machine learning model comprises a plurality of separate models for different parameters of the joint kinematics.

### Claim 4 (depends on 3)

The system of claim 3, wherein the plurality of separate models for the different parameters of the joint kinematics comprise one or more of an elevation plane angle model, an elevation angle model, an elevation plane torque model, an elevation torque model, and a rotation torque model.

### Claim 5 (depends on 1)

The system of claim 1, wherein the machine learning model is trained using an extreme gradient boost algorithm, an artificial neural network, a convolutional neural network, a long short-term memory, and/or a random forest.

### Claim 6

A method, comprising:
training a machine learning model using biomechanical simulation parameters generated using data from a high-resolution motion capture system; and
predicting joint parameters via the machine learning model by inputting data from a low-resolution motion capture system into the machine learning model.

### Claim 7 (depends on 6)

The method of claim 6, wherein the low-resolution motion capture system includes an immersive virtual reality (iVR) headset and a hand-held controller, and where predicting the joint parameters via the machine learning model by inputting data from the low-resolution motion capture system into the machine learning model comprises inputting a rotation and a position of the hand-held controller into the machine learning model.

### Claim 8 (depends on 7)

The method of claim 7, wherein the data from the high-resolution motion capture system and the data from the low-resolution motion capture system are both obtained during a series of exercises guided by a game displayed via the iVR headset.

### Claim 9 (depends on 6)

The method of claim 6, wherein the joint parameters comprise a shoulder joint torque and a shoulder joint angle.

### Claim 10 (depends on 6)

The method of claim 6, wherein training the machine learning model comprises training the machine learning model using an extreme gradient boost algorithm.