Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency

Abstract

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.

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

“…Many of the logged variables are consistent with other at-home prosthesis use studies, including the on-board wear time and active use rates of different movements [ 18 ]. However, because our pilot’s prosthesis did not have a pressure sensor, we were not able to log object interaction, which has previously been used to provide additional context for home use [ 16 ].…”

“…However, it should be noted that the pilot’s daily use of the prosthesis may not necessarily be representative of users of the same prosthetic system, as participants in our previous study showed daily use of up to 18 h per day [ 17 ]. Similar studies with different prosthetic systems have also reported average daily use of 4–8 h per day [ 16 , 18 ]. These differences in daily use could arise from multiple sources, including differences in job requirements, hobbies, battery life of the prostheses, and others, and identifying these differences is outside the scope of this study.…”

“…Specifically, we investigate these use changes in terms of active time while the prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate the speed of movements. Importantly, the investigation largely included home-use data logged on-board the prosthesis, which recorded information about how the pilot controlled his prosthesis in a manner similar to previous studies [ 15 – 18 ], possible because the pilot used the same prosthesis during the competition as he did at home. We show that the pilot’s use of the prosthesis at home increased even following the Cybathlon, compared to before his training period, and that his self-perception of his prosthetic arm and functionality has improved.…”

Decoding HD-EMG Signals for Myoelectric Control - How Small Can the Analysis Window Size be?

“…Research shows that providing natural EMG-based feedback with different approaches including grasp/wrist force and movement estimation [1][2][3][4], continuous finger trajectory decoding [5], and discrete movement classification [6,7] could achieve promising results for desirable control of the prosthesis. Machine learning is a candidate tool in mapping motor intent to prosthesis control [8][9][10]. Extracting reliable features from the EMG signals plays a vital role in the control of upper limb prostheses with pattern recognition [8,9,11].…”

Towards User-Centred Prosthetics Research Beyond the Laboratory

“…The lack of real-world data from research studies may contribute to the limitations of current prosthetic devices in addressing user needs. Studies outside of a laboratory have focussed on the functional performance of an upper limb prosthetic device, but have gathered limited contextual data ( Hargrove et al, 2017 ; Graczyk et al, 2018 ; Cuberovic et al, 2019 ; Brinton et al, 2020 ; Chadwell et al, 2020 ; Schofield et al, 2020 ; Osborn et al, 2021 ; Wu et al, 2022 ). The importance of gathering contextual data to inform decision- making has been highlighted within clinical practice and health policy sectors ( Langlois et al, 2018 ).…”