Siddhartha Sikdar, Professor, Bioengineering, Volgenau School of Engineering, Parag Chitnis, Assistant Professor, Bioengineering, and Guoqing Diao, Associate Professor, Statistics, are working to develop and evaluate a prototype prosthetic control system that uses wearable ultrasound imaging sensors to sense residual muscle activity rather than electromyography.
The vast majority of all trauma-related amputations in the United States involve the upper limbs. Approximately half of those individuals who receive an upper extremity myoelectric prosthesis eventually abandon use of the system, primarily because of their limited functionality.
This novel approach--of using ultrasound imaging--can better distinguish between different functional compartments in the forearm muscles, and provide robust control signals that are proportional to muscle activity. This improved sensing strategy has the potential to significantly improve functionality of upper extremity prostheses, and provide dexterous intuitive control that is a significant improvement over current state of the art noninvasive control methods.
This interdisciplinary project brings together investigators at Mason, commercial partners at Infinite Biomedical Technologies as well as clinicians at MedStar National Rehabilitation Hospital and Hanger Clinic.
The researchers have two aims for this project.
First, they aim to develop and test a compact, research-grade sonomyographic prosthetic system. As part of this step, they will develop a miniaturized low-power ultrasound imaging system that can be integrated into a prosthetic socket and algorithms for real-time classification and control with multiple degrees of freedom (DOF). They will integrate ultrasound imaging transducers within test prosthetic sockets and complete system integration and testing. They will then evaluate the sonomyographic signal quality with changes in arm position and socket loading with individuals with transradial limb loss in a laboratory setting.
The researchers' second aim for the project is to assess performance of sonomyographic control compared to myoelectric direct control and state-of-the-art myoelectric pattern recognition control. They will perform these evaluations with individuals with transradial limb loss using a virtual reality Fitts' law task as well as clinical outcome measures using a terminal device. Fitts' law is a predictive model of human movement commonly used in human-computer interaction and ergonomics. The primary clinical outcome measure will be the Southampton Hand Assessment Procedure and the secondary outcome measure will be the Clothespin Relocation Task. In addition to the clinical outcome measures, the investigators will assess intuitiveness of control using gaze tracking, quality of movement, cognitive load during task performance, and utilize questionnaire-based measures of prosthesis satisfaction.
The successful completion of this project will lead to the first human evaluation of an integrated prototype that uses low-power portable imaging sensors and real-time image analysis to sense residual muscle activity for prosthetic control. In the long term, the researchers anticipate that the improvements in functionality and intuitiveness of control will increase acceptance by prosthesis users.
The researchers received $759,638 out of an anticipated total funding of $3,633,133 from the United States Department of Health and Human Services for this work. Funding began in February 2020 and will end in late January 2025.