Predictive Shoulder Kinematics via Immersive Virtual Reality
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Solution Overview
Problem
Current telehealth-mediated physical rehabilitation lacks established metrics for remote evaluation, particularly for upper limb kinematics, due to the complexity of the shoulder joint's tri-planar movement, which cannot be accurately estimated by simple single-plane joint models.
Innovation Solution
A system utilizing an immersive virtual reality (iVR) system with a headset and hand-held controller, paired with a machine learning model trained on biomechanical simulation data from an optical motion tracking system, to predict joint kinematics during virtual reality-guided exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple single-plane joint models are used to estimate shoulder movement, then the evaluation process is simplified, but the measurement precision of tri-planar shoulder movement is insufficient
Solution Approach 1:
The patent replaces complex mechanical motion capture systems with a virtual reality system using hand-held controllers and head-mounted displays. The VR system captures six-degree-of-freedom motion data (position and orientation) which is then processed through machine learning models to predict shoulder joint kinematics, substituting traditional mechanical measurement approaches with a combination of sensor-based tracking and computational prediction
Solution Approach 2:
The patent introduces machine learning prediction models as intermediaries between the VR motion capture data and the final shoulder kinematics evaluation. These models translate controller motion data into predicted joint angles and torques, serving as a computational mediator that bridges the gap between simple VR tracking and complex shoulder movement analysis
2Measurement precision
If immersive virtual reality systems with machine learning models are used, then the measurement precision of shoulder kinematics is improved, but the device complexity increases
Solution Approach 1:
The patent uses virtual copies and simulations to represent complex physical systems. Instead of directly measuring shoulder joint mechanics with complex equipment, the system creates virtual replicas of shoulder movement through machine learning models trained on biomechanical simulation data, allowing accurate prediction without direct complex measurement
Solution Approach 2:
The patent transforms the measurement approach by changing from direct mechanical measurement parameters to virtual reality sensor parameters (controller position and orientation). The machine learning models then transform these VR parameters into clinically relevant shoulder kinematics parameters, effectively changing the parameter space to avoid direct complex measurement
3Ease of operation
If traditional video conferencing is used for telehealth, then accessibility is improved, but the ability to perform hands-on evaluation is lost
Solution Approach 1:
The patent substitutes traditional video conferencing with a virtual reality telehealth system that provides immersive three-dimensional interaction. This replacement maintains the accessibility benefits of remote care while restoring evaluation capabilities through spatially aware motion tracking and virtual environment interaction, allowing therapists to assess patient movement patterns that were previously only detectable through hands-on evaluation
Data Source
AI summary
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.