Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1C 5S7, Canada.
Department of Surgery, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Biosensors (Basel). 2022 Jan 21;12(2):57. doi: 10.3390/bios12020057.
Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.
近年来，基于表面肌电信号（sEMG）的模式识别在假肢控制领域受到了广泛关注。在大多数现有的方法中，由于信号相对稳定，假肢控制通常使用在紧握阶段采集的 sEMG 信号进行抓握分类。然而，仅使用紧握阶段可能会导致控制假肢手的运动出现延迟。针对这个问题，我们探讨了抓握分类精度在伸手和抓握过程中的变化情况，并确定了可以提高抓握分类精度和更早进行抓握检测的时间段。我们发现，抓握分类精度随着手逐渐抓住物体而逐渐提高，并且在紧握之前有一个时间段，可以实现延迟较小的早期抓握分类。在此基础上，我们还探索了相应的训练策略，以实现实时应用中的更好的抓握分类。
