Roessingh Research & Development, Roessinghsbleekweg 33b, 7522 AH Enschede, The Netherlands.
Department of Biomedical Signals & Systems, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2022 Aug 24;22(17):6351. doi: 10.3390/s22176351.
Pattern recognition in EMG-based control systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. To circumvent concept drift, adaptation strategies could be used to retrain a pattern recognition system, which could lead to comparable error rates over multiple days. In this study, we investigated the error rate development over one week and compared three adaptation strategies to reduce the error rate increase. The three adaptation strategies were based on entropy, on backward prediction and a combination of backward prediction and entropy. Ten able-bodied subjects were measured on four measurement days while performing gait-related activities. During the measurement electromyography and kinematics were recorded. The three adaptation strategies were implemented and compared against the baseline error rate and against adaptation using the ground truth labels. It can be concluded that without adaptation the baseline error rate increases significantly from day 1 to 2, but plateaus on day 2, 3 and 7. Of the three tested adaptation strategies, entropy based adaptation showed the smallest increase in error rate over time. It can be concluded that entropy based adaptation is simple to implement and can be considered a feasible adaptation strategy for lower limb pattern recognition.
基于肌电图的控制系统中的模式识别会随着时间的推移而出现错误率增加的问题，这可能导致意外的行为。这种肌电控制系统中的所谓概念漂移可能是由疲劳、传感器更换和不同的皮肤状况引起的。为了规避概念漂移，可以使用自适应策略来重新训练模式识别系统，从而在多天内实现可比的错误率。在这项研究中，我们研究了一周内的错误率发展情况，并比较了三种自适应策略来降低错误率的增加。这三种自适应策略基于熵、后向预测以及后向预测和熵的组合。十名健全的受试者在进行与步态相关的活动时，在四天的测量日进行了测量。在测量过程中，记录了肌电图和运动学数据。实施了这三种自适应策略，并与基线错误率和使用地面真实标签的自适应进行了比较。可以得出结论，没有自适应，基线错误率从第 1 天到第 2 天显著增加，但在第 2、3 和 7 天趋于稳定。在测试的三种自适应策略中，基于熵的自适应策略随着时间的推移错误率增加最小。可以得出结论，基于熵的自适应策略简单易用，可被视为下肢模式识别的一种可行的自适应策略。
