EMG pattern classification to control a hand orthosis for functional grasp assistance after stroke
Summary
This study shows electromyography (EMG) signals can intuitively control wearable assistive devices for daily tasks. This technology offers a feasible path for advanced rehabilitation and restoring limb function.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Neuroprosthetics
Background:
- Wearable orthoses serve as assistive and rehabilitation tools, enhancing independence and limb function recovery.
- Intuitive controls and enabling Activities of Daily Living (ADLs) are crucial for wearable device effectiveness and user quality of life.
Purpose of the Study:
- To investigate the feasibility of using electromyography (EMG) signals for controlling a wearable exotendon device.
- To enable pick and place tasks using EMG pattern classification for intuitive device operation.
Main Methods:
- Utilized a commodity forearm EMG band with 8 sensors for signal acquisition.
- Developed an EMG pattern classification control system for a wearable exotendon device.
- Tested control accuracy in stroke survivors during non-functional and functional pick and place tasks.
Main Results:
- Successfully detected user intent to open using EMG pattern classification.
- Demonstrated the capability to enable hand extension and pick and place tasks.
- Achieved promising accuracy in controlling the exotendon device for functional tasks in stroke survivors.
Conclusions:
- EMG-based control is feasible for wearable orthoses, offering intuitive operation.
- Wearable devices with EMG control can provide a functional context for rehabilitation.
- This approach supports the development of advanced assistive and rehabilitative technologies for impaired limb function.
