Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany.
Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Bologna, Italy.
J Neural Eng. 2020 Nov 4;17(5):056047. doi: 10.1088/1741-2552/abbed0.
Pattern-recognition-based myocontrol can be unreliable, which may limit its use in the clinical practice and everyday activities. One cause for this is the poor generalization of the underlying machine learning models to untrained conditions. Acquiring the training data and building the model more interactively can reduce this problem. For example, the user could be encouraged to target the model's instabilities during the data acquisition supported by automatic feedback guidance. Interactivity is an emerging trend in myocontrol of upper-limb electric prostheses: the user should be actively involved throughout the training and usage of the device.
In this study, 18 non-disabled participants tested two novel feedback-aided acquisition protocols against a standard one that did not provide any guidance. All the protocols acquired data dynamically in multiple arm positions to counteract the limb position effect. During feedback-aided acquisition, an acoustic signal urged the participant to hover with the arm in specific regions of her peri-personal space, de facto acquiring more data where needed. The three protocols were compared on everyday manipulation tasks performed with a prosthetic hand.
Our results showed that feedback-aided data acquisition outperformed the acquisition routine without guidance, both objectively and subjectively.
This indicates that the interaction with the user during the data acquisition is fundamental to improve myocontrol.
基于模式识别的肌电控制可能不可靠，这可能限制其在临床实践和日常活动中的应用。其中一个原因是基础机器学习模型对未训练条件的泛化能力较差。通过更具交互性地获取训练数据和构建模型，可以减少这个问题。例如，可以鼓励用户在数据采集过程中针对模型的不稳定性，同时提供自动反馈指导。在上肢电动假肢的肌电控制中，交互性是一个新兴趋势：用户应该在设备的整个训练和使用过程中积极参与。
在这项研究中，18 名非残疾参与者对两种新的反馈辅助采集协议与一种不提供任何指导的标准协议进行了测试。所有协议都在多个手臂位置动态采集数据，以抵消肢体位置效应。在反馈辅助采集过程中，发出声音信号促使参与者将手臂悬停在其个人空间的特定区域，实际上在需要的地方采集了更多的数据。然后将这三种协议在使用假肢手进行的日常操作任务中进行了比较。
我们的结果表明，反馈辅助数据采集在客观和主观上都优于没有指导的采集常规。
这表明在数据采集过程中与用户的交互对于改善肌电控制至关重要。
