Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses
Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses
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DOI：
10.1088/1741-2552/aa7e82
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发表时间：
2017-10-01
影响因子：
4
通讯作者：
Dosen, Strahinja
中科院分区：
文献类型：
--
作者：
Patel, Gauravkumar K.;Hahne, Janne M.;Dosen, Strahinja
Objective.如今，灵巧的上肢假肢可用于恢复抓握，但仍然缺少有效和可靠的前馈控制。这项工作的目的是提高肌电控制的鲁棒性和可靠性，通过使用嵌入在假肢内的传感器的上下文信息。Approach.我们开发了一种上下文驱动的肌电控制方案（cxMYO），它结合了本体感受（惯性测量单元）和外感（力和抓地力孔径）传感器的上下文信息的推理，以调制肌电控制的输出。此外，使用三个功能任务在健全受试者中在线进行cxMYO的现实评估，在此期间，将cxMYO与纯粹基于机器学习的肌电控制（MYO）进行比较。主要结果。结果表明，利用上下文信息减少了不需要的命令的数量，提高了所有三个功能任务的性能（成功率和丢弃的对象）。具体而言，在所有三个任务中，cxMYO每轮丢弃的对象的中位数为零，并且在三个功能任务中的两个中观察到成功转移的数量显著增加。此外，受试者报告了更好的用户体验。意义这是对一种方法的首次在线评估，该方法整合了来自多个板载假肢传感器的信息，以调节基于机器学习的肌电控制器的输出。该方案是通用的，并提出了一个简单的，非侵入性和成本效益的方法，以提高肌电控制的鲁棒性。
Objective. Dexterous upper-limb prostheses are available today to restore grasping, but an effective and reliable feed-forward control is still missing. The aim of this work was to improve the robustness and reliability of myoelectric control by using context information from sensors embedded within the prosthesis. Approach. We developed a context-driven myoelectric control scheme (cxMYO) that incorporates the inference of context information from proprioception (inertial measurement unit) and exteroception (force and grip aperture) sensors to modulate the outputs of myoelectric control. Further, a realistic evaluation of the cxMYO was performed online in able-bodied subjects using three functional tasks, during which the cxMYO was compared to a purely machine-learning-based myoelectric control (MYO). Main results. The results demonstrated that utilizing context information decreased the number of unwanted commands, improving the performance (success rate and dropped objects) in all three functional tasks. Specifically, the median number of objects dropped per round with cxMYO was zero in all three tasks and a significant increase in the number of successful transfers was seen in two out of three functional tasks. Additionally, the subjects reported better user experience. Significance. This is the first online evaluation of a method integrating information from multiple on-board prosthesis sensors to modulate the output of a machine-learning-based myoelectric controller. The proposed scheme is general and presents a simple, non-invasive and cost-effective approach for improving the robustness of myoelectric control.