Meng Zixia, Kang Jiyeon
Mechanical and Aerospace Engineering, School of Engineering and Applied Sciences, University at Buffalo, Buffalo, NY, United States.
Electrical Engineering, School of Engineering and Applied Sciences, University at Buffalo, Buffalo, NY, United States.
Front Neurorobot. 2023 Sep 7;17:1185052. doi: 10.3389/fnbot.2023.1185052. eCollection 2023.
Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks.
This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation.
The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks.
假肢的肌电控制是一项成熟的技术，利用表面肌电图（sEMG）来检测用户意图并执行后续机械动作。控制系统中使用的大多数机器学习模型都是通过孤立动作进行训练的，这些动作并不能反映日常活动中发生的自然动作。此外，动作往往会受到手臂姿势、活动持续时间和个人习惯的影响。拥有一个用于多自由度（DoF）假肢手臂的控制系统至关重要，该系统应使用从日常生活（ADL）任务中收集的sEMG数据进行训练。
这项工作聚焦于两种主要的功能性手腕动作：旋前 - 旋后和投镖动作（DTM），并引入了一种新的手腕控制系统，该系统可将sEMG信号直接映射到多自由度手腕的关节速度。此外，还提出了一种特定的训练策略（快速训练），使控制器能够应用于新的受试者，并处理日常生活中传感器可能移位、肌肉可能疲劳或传感器可能被污染（例如由于出汗）的情况。假肢手腕控制器基于24名参与者的数据进行设计，并使用均方根误差（RMSE）和皮尔逊相关性来评估其性能。
结果发现取决于任务的特征。例如，与旋前 - 旋后任务（灯泡：43.98度/秒和螺丝：53.64度/秒）相比，具有投镖动作的任务显示出较小的RSME值（锤子：6.68度/秒和杯子：7.92度/秒）。利用快速训练提出的控制技术在所有四个ADL任务中平均均方根误差（RMSE）值降低了35%，平均皮尔逊相关值提高了40%。
