Evaluation of Functional Performance of Persons with Limb Difference to Optimize Pattern Recognition Control of Powered Upper Limb Prostheses
Abstract
In the U.S., approximately 41,000 individuals live with major upper-limb absence at the wrist or higher. This number continues to grow worldwide and includes Service Members from active duty and Veterans. Clinical treatment for people with major limb difference is the fabrication, fitting, and use of prosthetic arms. It has been estimated that approximately half of these individual are fit with prostheses controlled by sensors that measure muscle contractions. These muscle sensors on the surface of the skin sense electrical signals called electromyographic (EMG) signals. EMG signals are processed to control multiple joints like fingers, hands, wrists, and elbows. Traditional direct EMG control requires complex contractions to control multiple devices by switching between them. A more modern control method for EMG involves the use of multiple sensor channels and advanced pattern recognition (PR) software. PR systems allow intuitive control and seamless switching between prosthetic devices. PR has been able to show substantial functional improvements over direct control when controlling physical prostheses. This research project aims to improve pattern recognition control in upper limb prostheses by testing the way sensors are configured on the limb. This study will compare system configurations with different number of sensor channels and with various strategies for sensor placement on the limb. Unlike much of the exiting research, this study will test these configurations with subjects with limb absence controlling physical prosthetic arms to directly conduct activities of daily living. This research aims to produce an impact by improving the fitting and functional outcomes for people who use upper limb prostheses. Gaining a more realistic and representative understanding of how to configure EMG sensors channels has the potential to lead to more intuitive control so that people can do more with their prostheses with less fatigue and more accurate and precise movements. The project will span 3 years to develop the study design, expand hardware capabilities in existing commercial PR systems and to conduct the study with upper limb prosthesis users conducting activities of daily living. The knowledge gained will be used to continuously iterate PR control systems for optimal EMG sensor placement and channel count. The proposed project will be beneficial to Service Members and Veterans with limb loss or impairment, by advancing the state of the art for human-machine interfaces. The technologies being tested will improve control algorithms and pattern recognition systems for detecting muscle signals used for the control of prosthetic limbs. Creating more reliable and intuitive control leads to better adoption and use of myoelectric prosthetic limbs.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210431
Entities
People
- Todd Farrell
Organizations
- Liberating Technologies (United States)
- United States Army