Intuitive Control of Upper-Extremity Prostheses Using Novel Ultrasonic Sensing of Residual Muscle Activity
Abstract
Rationale: Upper extremity amputations frequently affect the dominant extremity, leading to significant impacts on activities of daily living. Fortunately, in recent years there has been significant advances in technology that have led to the development of sophisticated prosthetic hands that are capable of a large number of different grasps. However, the challenge of controlling these sophisticated hands in an intuitive manner using the residual muscles in the amputated limb remains formidable. Commercially available devices utilize a small number of sensors attached to the skin surface that detect the electrical activity of muscles when they contract and the signals from these sensors are used to control the prosthesis. Typically, the user has to perform a sequence of non-intuitive muscle contractions to select among a number of different grip patterns. Such control strategies are cumbersome and limit functionality of these devices. Other more intuitive strategies are being researched using a larger number of sensors, but there are fundamental challenges in discriminating between the weak electrical activity of different deep-seated muscles using sensors at the skin surface. These challenges have led to a recent push to develop surgically implanted electrical sensors in the muscle. The premise for our research is that it is possible to develop intuitive control strategies, which do not require invasive interventions, by investigating alternative sensing methods. Objectives: Our research proposes a fundamentally different noninvasive method for sensing the activity of muscles using ultrasound waves. This method uses technology currently used clinically for noninvasive medical imaging to visualize the contraction of muscles deep underneath the skin surface overcoming many of the challenges associated with the skin-surface electrodes. This method is called sonomyography: the use of ultrasound to map the activity of muscles in the body. Computer algorithms can then analyze these images of muscle activity in the residual stump to automatically infer the users intended movement. Our research will leverage recent advances in electronics and computer technology that enable significant miniaturization of ultrasound imaging sensors and instrumentation, so that it can be incorporated into a prosthetic shell. We will evaluate this sonomyographic control strategies using amputees. We will first train amputees to control individual fingers in a virtual hand, and then in combination to form grasps. We will then evaluate whether this control strategy enables amputees to perform tasks that are relevant to activities of daily living in a virtual reality environment. Finally, we will test whether this new control strategy is more intuitive for amputees, by evaluating whether they are able to complete tasks with the virtual hand while performing other unrelated mental tasks. Applicability: This project relates to the Rehabilitation Focus Area: Prosthetic and/or Orthotic Device Function. This project will impact this focus area by developing and evaluating a novel and paradigm-changing upper extremity prosthetic technology capable of improved intuitive control. It is expected that this research can improve the functionality of advanced prostheses in the future, thereby improving quality of life. This approach is non-invasive and therefore has significantly lower risk compared to implanted sensors and has no greater risk than conventional electrical sensors currently in use. At the conclusion of this project, we will have significantly advanced the technology and miniaturized it to the point that it can be incorporated into a prosthetic shell and used to interact with a virtual reality environment. The next step would be to use this technology to control a physical prosthetic hand to perform activities of daily living. We anticipate that physical prototypes that amputees can use for research and clinical studies wou
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 31, 2017
- Source ID
- W81XWH1610722
Entities
People
- Siddhartha Sikdar
Organizations
- George Mason University
- United States Army