Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America.
Department of Rehabilitation, Walter Reed National Military Medical Center, Bethesda, MD, United States of America.
J Neural Eng. 2021 Mar 8;18(2). doi: 10.1088/1741-2552/abe20d.
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.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).Throughout the study, continuous prosthesis usage increased (1% per week,< 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,< 0.001) and prosthesis control performance (0.5% every month,< 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (< 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.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.
使用假肢完全恢复手臂功能仍然是一个巨大的挑战；然而，机器人硬件、手术干预和机器学习的进步正在使无缝人机界面更接近现实。
通过 1 年多的大量数据记录，我们监测了一位肱骨截肢、靶向肌肉再神经支配和骨整合（OI）患者通过肌电图（EMG）模式识别控制的灵巧模块化假肢的家庭使用情况。
在整个研究过程中，连续使用假肢的次数增加（每周增加 1%，<0.001），功能指标在对照评估中提高了 26%，在感知工作负荷评估中提高了 76%。我们观察到 OI 植入物上的扭矩加载增加（每月增加 12.5%，<0.001）和假肢控制性能提高（每月增加 0.5%，<0.005），这表明用户的整合、接受和熟练程度有所提高。更重要的是，用于假肢控制的 EMG 信号幅度随着时间的推移而减小，最大可达 34.7%（<0.001），而不会降低性能，这表明基于机器学习的肌电模式识别算法提高了控制效率。参与者在无需更新模式识别算法的情况下控制假肢长达一个月。参与者定制了假肢运动来执行特定任务，例如钢琴演奏的单个手指控制和手势通信，这可能有助于持续使用。
这项工作在单个参与者中证明了在较长时间内不受限制地使用高度拟人化假肢的功能益处。虽然在广泛使用方面仍存在障碍，包括设备可靠性、结果复制以及超越原型的技术成熟度，但这项研究提供了一个实验室外先进假肢技术康复影响的范例。
