Physiologic Signaling and Exoskeleton Control of the Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI) During Volitional Behavior
Abstract
We seek to restore lost motor function and quality of life to both soldiers and individuals from the general public who suffer from debilitating musculoskeletal injuries and/or disease. This proposal aligns with the specific PRMRP Topic Area of Musculoskeletal Health. Furthermore, this grant application addresses the Area of Encouragement of Effective Use of Exoskeleton Technology. Robotic exoskeletons have recently emerged as promising tools for restoration of functional independence for persons afflicted with debilitating musculoskeletal injuries such as traumatic brachial plexus nerve injuries, and loss of muscle function due to blast trauma, compartment syndrome (CS), volumetric muscle loss (VML), and advanced sarcoma resection. Current exoskeletons available on the market today are difficult to control, primarily because they cannot mimic volitional motor control initiated by the user. The primary reason for this is that there is a lack of an appropriate neural interface that provides stable, accurate, and reliable amplified signals for control of advanced exoskeletons. This often leads to frustration and an abandonment of the use of exoskeleton devices. In this grant proposal, we plan to utilize a novel biologic interface to control complex exoskeleton devices termed the muscle cuff regenerative peripheral nerve interface (MC-RPNI). The primary goal of this proposal is to utilize MC-RPNIs in rats to amplify intact peripheral efferent motor action potential signaling necessary for intuitive control of an exoskeleton device. Thus, the proposed research is relevant to the core mission of CDMRP to seek fundamental knowledge that will help to enhance health, lengthen life, and reduce illness and disability in both military and civilian populations. Musculoskeletal injuries represent more than half of all injuries in military populations, resulting in 2.4 million annual healthcare visits and 25 million limited-duty days. Furthermore, musculoskeletal disorders are associated with costs of more than $800 billion annually in the US. Motor extremity impairment affects approximately 80% of these individuals, and only half are able to regain useful limb function following injury. While rehabilitation therapy is often recommended, several longitudinal studies have suggested that even rigorous therapy leads to a significant recovery of motion function in only half of patients with significant upper extremity paresis. This reduced limb mobility negatively impacts the patient’s independence in performing normal activities of daily living, their capacity to participate in occupational and social activities, and is often associated with depression, anxiety, and an overall decreased quality of life. Functional deficits that dramatically affect the quality of life for individuals with musculoskeletal disorders may be attenuated with prosthetic rehabilitation with exoskeleton devices. Although numerous advances in both exoskeleton control and function have been achieved in the past decade, they are all still hampered by a lack of an ability to properly carry out the motor intent of an exoskeleton user. The current technology used to detect motor intention involves using noninvasive electrodes to record electrical activity of the brain (electroencephalography [EEG]) or surface electrodes (sEMG) placed on the skin over the muscles of the user. However, there are numerous technical challenges with both of these current methods of detecting motor intent. While these pattern classification systems have indeed been used to enable volitional control of assistive devices, this technology is limited due to its overreliance on precise sensor positioning, implementation of complex processing algorithms, poor movement classification accuracy, necessity for intensive user training, and the need for continual recalibration, which commonly results in user device abandonment. To date, the user-intention detection process remains inaccurate and over
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110429
Entities
People
- Stephen Kemp
Organizations
- United States Army
- University of Michigan