Bao Tianzhe, Xie Sheng Quan, Yang Pengfei, Zhou Ping, Zhang Zhi-Qiang
IEEE J Biomed Health Inform. 2022 Aug;26(8):3822-3835. doi: 10.1109/JBHI.2022.3159792. Epub 2022 Aug 11.
To develop multi-functionalhuman-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.
为了开发能够帮助残疾人重建上肢丧失功能的多功能人机接口，机器学习（ML）和深度学习（DL）技术已被广泛应用于从表面肌电图（sEMG）信号中解码人类运动意图。然而，由于上肢运动的高度复杂性以及sEMG固有的不稳定特性，基于ML/DL的控制方案在实际场景中的可用性仍然受到很大限制。为此，人们付出了巨大努力来提高模型的鲁棒性、适应性和可靠性。在本文中，我们对近期的成果进行了系统综述，主要分为三类：多模态传感融合以获取用户的额外信息、迁移学习（TL）方法以消除域转移对估计模型的影响，以及后处理方法以获得更可靠的结果。特别关注融合策略、深度TL框架和置信度估计。还分析了在硬件开发、公共资源和解码策略方面的研究挑战和新出现的机会，以为未来发展提供展望。
