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摘要:
在基于表面肌电信号(Surface electromyography, sEMG)的意图识别研究领域, 目前大多数的研究主要集中在提高肌电识别的准确性方面. 然而, 在实际应用中, 基于sEMG识别的交互系统往往受到诸多非理想因素干扰, 肌电识别的准确性大大降低. 本文主要关注在非理想条件下肌电识别的鲁棒性研究, 首先详细归纳了肌电识别方法受到的非理想干扰因素(如电极偏移、个体性差异、肌肉疲劳、肢体姿态或其他综合性干扰), 总结了当前研究的抗干扰方法; 随后讨论了非理想干扰因素研究现状中的主要问题; 最后在构建肌电数据集、探索深度学习和迁移学习以及肌电分解研究等方面, 对未来的关键技术进行了展望.
Abstract:In sEMG (surface electromyography)-based recognition, most studies are currently focusing on improving recognition accuracies. While in real applications, sEMG-based recognition systems are limited by many disturbances in non-ideal conditions, and recognition accuracies are worsen greatly. This paper is focusing on the robustness of sEMG-based recognition. Many disturbances in non-ideal conditions are detailed and summarized, including electrode shifts, individual differences, muscle fatigue, limb postures and others. Also, many novel methods that are proposed to remove or reduce the impact of these disturbances are summarized. Furthermore, main problems in these current studies are discussed. Finally, prospections for the future development are proposed, including building sEMG-based datasets, exploiting deep learning based and transfer learning-based recognition, and sEMG decomposition.
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图 1 基于监督学习的sEMG识别模型训练与测试框架
Fig. 1 The training and testing framework of sEMG recognition model based on supervised learning
图 8 基于迁移学习的理想场景与非理想场景之间肌电识别模型的更新与适应
Fig. 8 Update and adaptation of sEMG-based recognition model between ideal and non-ideal scenarios based on transfer learning
表 1 非理想因素及解决方案
Table 1 Non-ideal factors and solutions
非理想因素 数据扩增 鲁棒特征 模型更新 电极偏移 1) 多位置数据扩增[13, 16]
2) 多通道数据扩增[12]
3) 惯导等数据融合[17-18]1) AR、TDAR特征[16, 19]
2) 倒频谱特征[20]
3) Variogram 特征[10]
4) 结构相似性特征[21]
5) 共空间模式特征[22]
6) 灰度共生矩阵特征[14]1) 模型修正与协方差偏移适应[24-25]
2) 期望最大化迁移学习[15, 26-27]
3) 自适应增量式混合分类器[28-29]
4) 偏移估计与模型更新[30]
5) 骨骼位置估计与校正[31]个体性差异 1) 多人数据扩增[36]
2) 惯导等数据融合[37]
3) 个体形态参数归一化[38]1) 肌电分解特征[39]
2) 多分辨率肌肉协同特征[40]
3) 共同空间映射[41]1) SVM权重更新策略[42-43, 91]
2) 典型相关分析低维共空间映射[44]
3) 用户、动作依赖的双线性模型[45]
4) 基于最大收缩力的模型泛化[46]
5) 基于卷积神经网络的模型迁移[47]肢体姿态 1) 多姿态下的数据扩增[75-77]
2) “动态训练”数据采集[70, 78]
3) 惯导等数据融合[17, 75-76, 79]1) 谱矩等频域特征[80-82]
2) 稀疏表达特征[83-84]— 肌肉疲劳 1) 频域特征归一化 (MDF, MNF[52, 92],
STFT, WT[93-94], iMDF, iMNF[56], 一维
频谱—标准差[59]等)
2) 疲劳状态的分类识别[59, 62–64]
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表 2 sEMG数据集
Table 2 Surface EMG signal datasets
数据集 传感器 参与人数 动作类别 NinaPro[4] DB1 1) Otto Bock MyoBock 13E200 电极,
(10 通道, 肌电 RMS 特征)
2) 数据手套 Cyberglove (22 通道)27 名健康人 52 类手部动作重复 10 次 DB2/3 1) Delsys Trigno Wireless 肌电系统,
(12 通道双差分 EMG, 36 通道 ACC)
2) 数据手套 Cyberglove (22 通道)
3) 手指力传感器 (6 通道)
4) 腕部倾角传感器 (2 通道)40 名健康人 49 个手部动作重复 6 次 DB4 1) Cometa 单差分无线肌电电极 (12
通道单差分)10 名健康人 52 类手部动作重复 6 次 DB5 1) Thalmic Myo 肌电臂环 (2 套, 共计
2×8 通道单差分)
2) 数据手套 Cyberglove (22 通道)10 名健康人 52 类手部动作重复 6 次 DB6 1) Delsys Trigno Wireless 肌电系统
(14 通道, 42 通道 ACC)
2) Tobii Pro Glasses II (追踪眼动和视野)10 名健康人 7 个抓握动作重复 12 次
重复 5 天DB7 1) Delsys Trigno Wireless 肌电系统
(12 通道 EMG, 9 轴 IMU)
2) 数据手套 Cyberglove (22 通道)20 名健康人
2 名截肢者40 个手部动作重复 6 次 DB8 1) Delsys Trigno Wireless 肌电系统
(16通道 EMG, 9 轴 IMU, 采样至 2 kHz)
2) 数据手套 Cyberglove (22 通道)10 名健康人
2 名截肢者9 个手部动作 CSL-HDEMG[31] 1) HD-EMG (8×24 = 192通道) 5 名健康人 27 个手部/手指动作 CapgMyo[95] DB-a 1) HD-EMG (8×16 = 128 通道) 18 名健康人 8 个手指动作
8 个手指动作 (同上)
不同时间段重复两次
12 个手指动作DB-b 10 名健康人 DB-c 10 名健康人 UCI 等 Myo[96] 1) Thalmic Myo 肌电臂环 (8 通道) 36 名健康人 8 个抓握动作 Christos-Delsys[97] 1) Delsys Trigno Wireless (2 通道) 6 名健康人 6 个抓握动作
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