Objective. Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality. Approach. Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI). Main results. Throughout the study, continuous prosthesis usage increased (1% per week, p < 0.001) and functional metrics improved up to 26% on control assessments and 76% on perceived workload evaluations. We observed increases in torque loading on the OI implant (up to 12.5% every month, p < 0.001) and prosthesis control performance (0.5% every month, p < 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (p < 0.001), over time without degrading performance, demonstrating improved control efficiency with a machine learning-based myoelectric pattern recognition algorithm. The participant controlled the prosthesis up to one month without updating the pattern recognition algorithm. The participant customized prosthesis movements to perform specific tasks, such as individual finger control for piano playing and hand gestures for communication, which likely contributed to continued usage. Significance. This work demonstrates, in a single participant, the functional benefit of unconstrained use of a highly anthropomorphic prosthetic limb over an extended period. While hurdles remain for widespread use, including device reliability, results replication, and technical maturity beyond a prototype, this study offers insight as an example of the impact of advanced prosthesis technology for rehabilitation outside the laboratory.
Introduction Acquired Brain Injury, whether resulting from Traumatic brain injury (TBI) or Cerebral Vascular Accident (CVA), represent major health concerns for the Department of Defense and the nation. TBI has been referred to as the “signature” injury of recent U.S. military conflicts in Iraq and Afghanistan – affecting approximately 380,000 service members from 2000 to 2017; whereas CVA has been estimated to effect 795,000 individuals each year in the United States. TBI and CVA often present with similar motor, cognitive, and emotional deficits; therefore the treatment interventions for both often overlap. The Defense Health Agency and Veterans Health Administration would benefit from enhanced rehabilitation solutions to treat deficits resulting from acquired brain injuries (ABI), including both TBI and CVA. The purpose of this study was to evaluate the feasibility of implementing a novel, integrative, and intensive virtual rehabilitation system for treating symptoms of ABI in an outpatient clinic. The secondary aim was to evaluate the system’s clinical effectiveness. Materials and Methods Military healthcare beneficiaries with ABI diagnoses completed a 6-week randomized feasibility study of the BrightBrainer Virtual Rehabilitation (BBVR) system in an outpatient military hospital clinic. Twenty-six candidates were screened, consented and randomized, 21 of whom completed the study. The BBVR system is an experimental adjunct ABI therapy program which utilizes virtual reality and repetitive bilateral upper extremity training. Four self-report questionnaires measured participant and provider acceptance of the system. Seven clinical outcomes included the Fugl-Meyer Assessment of Upper Extremity, Box and Blocks Test, Jebsen-Taylor Hand Function Test, Automated Neuropsychological Assessment Metrics, Neurobehavioral Symptom Inventory, Quick Inventory of Depressive Symptomatology-Self-Report, and Post Traumatic Stress Disorder Checklist- Civilian Version. The statistical analyses used bootstrapping, non-parametric statistics, and multilevel/hierarchical modeling as appropriate. This research was approved by the Walter Reed National Military Medical Center and Uniformed Services University of the Health Sciences Institutional Review Boards. Results All of the participants and providers reported moderate to high levels of utility, ease of use and satisfaction with the BBVR system (x̄ = 73–86%). Adjunct therapy with the BBVR system trended towards statistical significance for the measure of cognitive function (ANAM [x̄ = −1.07, 95% CI −2.27 to 0.13, p = 0.074]); however, none of the other effects approached significance. Conclusion This research provides evidence for the feasibility of implementing the BBVR system into an outpatient military setting for treatment of ABI symptoms. It is believed these data justify conducting a larger, randomized trial of the clinical effectiveness of the BBVR system.
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