Closed-Loop Recording-Stimulation System for Accelerating Recovery After Musculoskeletal Injury
Abstract
Combat- and training-related arm injuries often paralyze one or more muscles and impair movement. Nerve damage leads to poorly functioning muscles, which degenerate overtime. In this proposal, we will develop noninvasive technology that will help prevent muscle wasting, exercise functioning muscles, and speed up recovery. This technology addresses the following Focus Areas: (1) accurate diagnosis of neuromusculoskeletal injuries; (2) developing objective support tools to enable assessment of function and performance during treatment; (3) optimization and acceleration of the recovery and restoring Warfighter performance after limb trauma or loss. Mathematical models of anatomy capture how muscles cause motion in the presence of external forces, such as gravity. Unfortunately, considering only motion without calculating the forces causing this motion, as often done in clinical practice, is insufficient for the understanding of how well the arm functions or what goes wrong when it does not function as needed. This is because even in a seemingly relaxed arm, muscles are often co-activated so that their forces counterbalance each other around the joints. Thus, muscles do a lot with no visible motion. Therefore, the critical need is to use the information from detailed mathematical models of muscle forces and of their interaction with the forces in the world in the rehabilitation of injured muscles. The long-term goal of this project is to develop automated diagnostic and treatment systems for the assessment and rehabilitation of movement deficits after arm injury. The focus of this proposal is to develop the technology for predicting intended motion from the activity of undamaged muscles and inducing contractions in paralyzed or underused muscles using a high-density electromyographic (hdEMG) technology for neuromuscular electrical stimulation (ES). The first aim is to improve existing algorithms and devices for hdEMG-ES to predict and intervene in the motion of the forearm using the activity of upper arm muscles and vice versa. The second aim is to evaluate the system performance in participants with amputations and denervated muscles. To achieve these aims, we will first improve the model of the arm anatomy to better represent the human shoulder joint. We will then develop virtual reality tasks to allow for the users to try movements with muscle stimulation controlled by the model arm. The virtual tasks can also be used to measure progress in the recovery of arm function. We will also build a system with a wearable sleeve that can both record ongoing muscle activity from the whole arm and stimulate muscles simultaneously under the control of the model arm. We will then stimulate shoulder muscles and recreate the forces experienced by the shoulder during motion of the forearm in amputees without the forearm using the motion of the virtual arm as a reference. This will show the feasibility of the approach to maintain shoulder muscle health in amputees by preventing shoulder muscle weakening. We will also stimulate paralyzed forearm muscles in people with nerve damage using the shoulder muscle activity as reference and predicting intended hand motion with the model. This will show the feasibility of the approach to maintain the health of denervated muscles. The outcomes of this research will translate current scientific knowledge into objective diagnostic tools. The new designs of an ES-based, exercise-based, or robot-assisted rehabilitation techniques will be enabled by the technology developed in this project. Patient progress and warfighter performance can be objectively monitored with the model and virtual tasks.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110138
Entities
People
- Sergiy Yakovenko
Organizations
- United States Army
- West Virginia University