A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System
A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System
复制标题
一种低成本端到端基于表面肌电图的步态子阶段识别系统
DOI：
10.1109/tnsre.2019.2950096
复制
发表时间：
2020-01-01
影响因子：
4.9
通讯作者：
Wang, Weide
中科院分区：
文献类型：
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作者：
Luo, Ruiming;Sun, Shouqian;Wang, Weide
由于表面肌电信号具有检测人体运动意图的能力，因此通常用作控制输入。然而，步态子相位分类通常需要单调的人工标记过程，并且商业sEMG采集设备相当笨重和昂贵，因此当前基于sEMG的步态子相位识别系统复杂且具有较差的可移植性。提出了一种低成本但有效的端到端的基于表面肌电信号的步态子相位识别系统，该系统包括一个同时采集大腿肌肉表面肌电信号和足底压力信号的无线多通道信号采集装置，以及一种结合长短期记忆（LSTM）和多层感知器（MLP）的基于神经网络的表面肌电信号分类器。我们评估了受试者在五种条件下行走的系统：平坦地形以5 km/h，平坦地形以3 km/h，20 kg背包以5 km/h，20 kg单肩包以5 km/h和15°坡度以5 km/h。实验结果表明，该方法的平均分类准确率分别为94.10%、87.25%、90.71%、94.02%和87.87%，明显高于现有的识别方法。该系统具有较好的实时性，平均推理时间在3.25 ~ 3.31 ms之间。
As surface electromyogram (sEMG) signals have the ability to detect human movement intention, they are commonly used to be control inputs. However, gait sub-phase classification typically requires monotonous manual labeling process, and commercial sEMG acquisition devices are quite bulky and expensive, thus current sEMG-based gait sub-phase recognition systems are complex and have poor portability. This study presents a low-cost but effective end-to-end sEMG-based gait sub-phase recognition system, which contains a wireless multi-channel signal acquisition device simultaneously collecting sEMG of thigh muscles and plantar pressure signals, and a novel neural network-based sEMG signal classifier combining long-short term memory (LSTM) with multilayer perceptron (MLP). We evaluated the system with subjects walking under five conditions: flat terrain at 5 km/h, flat terrain at 3 km/h, 20 kg backpack at 5 km/h, 20 kg shoulder bag at 5 km/h and 15° slope at 5 km/h. Experimental results show that the proposed method achieved average classification accuracies of 94.10%, 87.25%, 90.71%, 94.02%, and 87.87%, respectively, which were significantly higher than existing recognition methods. Additionally, the proposed system had a good real-time performance with low average inference time in the range of 3.25 ~ 3.31 ms.