VR Prosthetic Training via EMG Gesture Recognition
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Solution Overview
Problem
Individuals with limb loss or neurological disorders face challenges in effectively training and utilizing myoelectric prostheses due to the initial frustration of the prosthesis not functioning well during training, requiring specialized rehabilitation therapies and technologies.
Innovation Solution
A virtual reality (VR) system incorporating an electromyographic (EMG) control module that receives and processes EMG signals to determine intended gestures, allowing users to train and control assistive devices through a pattern recognition classifier, enabling self-administered rehabilitation and efficient training of myoelectric prostheses in a home or clinical setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prosthesis training is used, then users can learn to control the prosthesis, but the training process is frustrating and ineffective because the prosthesis does not work well at the start of training
Solution Approach 1:
The patent creates a virtual copy of the prosthesis and training environment in VR, allowing users to practice control without the limitations of physical hardware. The virtual prosthesis responds perfectly to EMG signals, providing immediate feedback and successful control experiences during training, while the actual prosthesis remains unavailable or non-functional.
Solution Approach 2:
The VR system acts as an intermediary between the user and the actual prosthesis. Users train by controlling their virtual avatar through EMG signals in a virtual environment, bridging the gap between their intent and the physical prosthesis that cannot yet respond reliably to their control attempts.
2Measurement precision
If specialized rehabilitation therapies are provided, then users receive targeted training, but the process requires extensive clinical resources and professional supervision
Solution Approach 1:
The VR rehabilitation system enables users to perform targeted training independently at home without requiring continuous professional supervision. The system automatically provides task-specific rehabilitation protocols, real-time feedback, and progress tracking, allowing users to self-administer precise rehabilitation therapy that would otherwise require clinical resources.
3Adaptability or versatility
If myoelectric prostheses with advanced technology are provided, then users gain functional capabilities, but extensive training is required which is frustrating and time-consuming
Solution Approach 1:
The VR system allows users to perform preliminary training in a virtual environment where the prosthesis control is perfectly responsive and predictable. Users can practice and master control techniques beforehand in VR, building confidence and skill before transitioning to actual prosthesis use, thereby reducing the time and frustration associated with initial physical training.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The VR system provides enjoyable and targeted rehabilitation, enhancing the functional utilization of myoelectric prostheses by simulating muscle movements and providing feedback, thus improving the user's ability to control and operate the prostheses effectively.
Implementation Method 1
Electromyographic (EMG) signals are electrophysiological signals generated by a muscular contraction which propagates spatially through the body. The origin of the signal is the depolarization and repolarization of the muscle fiber cell membrane during a contraction which causes ionic currents to circulate creating measurable action potentials in the body.
Data Source
AI summary
An apparatus comprising an arm band and an electromyographic (EMG) control module is disclosed. The apparatus includes an electromygraphic (EMG) control module configured to receive EMG information generated by an individual; identify a gesture class based on the EMG information, and train using the received EMG information and the gesture class. The gesture class corresponds to an intended gesture made by the individual.