Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for those using a prosthesis.
A research team conducted a nine-week take-home case study with a single participant with an upper-limb amputation and osseointegration (OI) to better understand how an advanced prosthesis is used during daily activities. The participant, already an expert prosthesis user, 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 to measure daily prosthesis usage, sensor data, and EMG data.
The participant’s continuous prosthesis use steadily increased over time, and over 30 percent of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern-recognition training session also increased during the nine weeks, resulting in up to 5.4 hours of use before retraining the movement decoding algorithm. Pattern recognition control accuracy improved with a maximum number of ten classes trained at once, and the transitions between different degrees of freedom increased as the study progressed, which the study’s authors said indicated smooth and efficient control of the advanced prosthesis.
Variability of decoding accuracy also decreased with prosthesis use, and 30 percent 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 percent and 6.2 percent, respectively, demonstrating prosthesis functionality. The NASA Task Load Index scores decreased on average by 25 percent across assessments, indicating reduced cognitive workload while using the MPL over the course of the study.
The study “Monitoring at-home prosthesis control improvements through real-time data logging” was published in the Journal of Neural Engineering.
