Monitoring at-home prosthesis control improvements through real-time data logging

• Published in: Journal of neural engineering 2022-05, Vol.19 (3), p.36021
• Main Authors: Osborn, Luke E, Moran, Courtney W, Dodd, Lauren D, Sutton, Erin E, Norena Acosta, Nicolas, Wormley, Jared M, Pyles, Connor O, Gordge, Kelles D, Nordstrom, Michelle J, Butkus, Josef A, Forsberg, Jonathan A, Pasquina, Paul F, Fifer, Matthew S, Armiger, Robert S
• Format: Article
• Language: English
• Subjects: amputation; electromyography; osseointegration; prosthesis control; rehabilitation; translation
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/ac6d7b

Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users. A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data. The participant's continuous prosthesis usage steadily increased ( = 0.04, max = 5.5 h) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time ( = 0.04), resulting in up to 5.4 h of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week, < 0.001) with a maximum number of ten classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage ( < 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study. . In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.