Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America.
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America.
J Neural Eng. 2022 May 30;19(3). 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.
验证高级假肢在实验室之外改善功能的能力仍然是使假肢使用者长期受益的关键步骤。一项为期九周的居家案例研究完成了，参与者是一名上肢截肢并接受骨整合的患者，目的是更好地了解高级假肢在日常活动中的使用情况。该参与者已经是一位熟练的假肢使用者，并且在研究期间在家中使用模块化假肢（MPL）。MPL 使用基于无线肌电图（EMG）模式识别的运动解码进行控制。在居家研究部分之前和之后进行了临床评估。为了测量日常假肢使用情况、传感器数据和 EMG 数据，使用板载数据记录器进行数据记录。参与者持续使用假肢的时间稳步增加（= 0.04，最大值为 5.5 小时），超过 30%的时间用于主动控制假肢。随着时间的推移，每次模式识别训练后使用假肢的时间也在增加（= 0.04），最多可以在重新训练运动解码算法之前使用 5.4 小时。模式识别控制精度随着时间的推移而提高（每周提高 1.2%，< 0.001），同时可以同时训练最多十个类，并且随着研究的进展，不同自由度之间的转换增加，表明对高级假肢的控制平稳且高效。随着假肢使用量的增加，解码精度的可变性也降低（< 0.001），并且 30%的时间用于进行假肢运动。在临床评估中，Box 和 Blocks 以及肌电控制能力评估分数分别提高了 43%和 6.2%，表明假肢的功能，而 NASA 任务负荷指数分数在整个评估过程中平均降低了 25%，表明在使用 MPL 时认知工作量减少，在为期九周的研究中。在这项案例研究中，我们证明了一种用于监测假肢使用情况的板载系统可以更好地了解假肢如何融入日常生活。这些知识可以支持为上肢截肢患者恢复完全独立和生活质量的长期目标。
