Recognition of walking environments and gait period by surface electromyography

Seulki KYEONG; Wonseok SHIN; Minjin YANG; Ung HEO; Ji-rou FENG; Jung KIM

doi:10.1631/FITEE.1800601

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Recognition of walking environments and gait period by surface electromyography

Special Feature on Intelligent Robots | Updated：2022-05-19

- Recognition of walking environments and gait period by surface electromyography
- 基于表面肌电信号的行走环境与步态周期识别
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 3, Pages: 342-352(2019)
- Affiliations：
  
  Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Author bio：
  
  Jung KIM, E-mail: jungkim@kaist.ac.kr
- Funds：
- DOI：10.1631/FITEE.1800601
  CLC： TP242
- Received：27 September 2018，
  
  Revised：2019-;3-14，
  
  Published：2019-03
- Accepted：
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Seulki KYEONG, Wonseok SHIN, Minjin YANG, et al. Recognition of walking environments and gait period by surface electromyography[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 342-352.
DOI：

Seulki KYEONG, Wonseok SHIN, Minjin YANG, et al. Recognition of walking environments and gait period by surface electromyography[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 342-352. DOI： 10.1631/FITEE.1800601.

摘要

在外骨骼机器人控制研究中，识别及预测操作者运动意图是一项很大挑战。外骨骼机器人是一种检测和驱动人体运动的机械动力装置，因其复杂的人机交互技术，需要使用很多不同类型传感器。当使用各种传感器时，需要选择数据类型以进行有效传感。肌电信号能在实际运动做出之前识别预期动作，并且可以缩短外骨骼机器人控制中的时间延迟。为应用下肢外骨骼帮助行走，本文工作旨在使用不同类型传感器（包括表面肌电信号传感器）识别行走环境和区分步态周期。为此，将地面反作用力、人机相互作用力、位置传感器和表面肌电信号传感器相结合，对4个受试者进行步态实验。本文展示了在行走环境与步态周期的识别中使用表面肌电信号及其他类型传感器的实验成果。选择不同传感器类型组合、表面肌电信号传感器不同位置组合以及表面肌电信号不同特征值组合，作为有效数据类型。实验结果表明，与使用单个力学传感器相比，将表面肌电信号传感器与其结合使用，可使识别结果得到很大改善。此外，在3种不同类型传感器的不同组合中，使用表面肌电信号与位置传感器信息的组合得出最高分类准确率。当使用全部4种类型传感器时，行走环境与步态周期分类准确率分别为96.1%与97.8%。在腿部5个表面肌电传感器不同位置组合里，股内侧肌和腓肠肌组合是最有效的2种肌肉类型组合。当仅使用表面肌电信号进行分类时，该组合在行走环境识别实验中，足跟接地前与足趾离地前的情况下，分类准确率分别为74.4%与71.7%；步态周期分类准确率为68%。两组有效的表面肌电信号特征值组合分别是"平均绝对值、过零点数"与"平均绝对值、波形长度"。"平均绝对值、过零点书"特征组合在行走环境识别实验中，在足跟接地前与足趾离地前情况下，分类准确率分别为80%与77.1%；步态周期分类准确率为75.5%。上述结果表明，表面肌电信号可有效用于控制外骨骼机器人。

Abstract

Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots

which measure and drive human movements

to interact with humans. Therefore

many different types of sensors are needed. When using various sensors

a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement

and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking

the aim of this work is to distinguish the walking environment and gait period using various sensors

including the surface electromyography (sEMG) sensor. For this purpose

a gait experiment was performed on four subjects using the ground reaction force

human–robot interaction force

and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design

we used a combination of sensor types

sEMG sensor locations

and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor

and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used

the environment classification accuracy was 96.1%

and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs

and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification

and 68.0% for gait period classification

when using only the sEMG sensor. The two effective sEMG feature combinations were "mean absolute value (MAV)

zero crossings (ZC)" and "MAV

waveform length (WL)"

and the "MAV

ZC" results were 80.0%

77.1%

and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.

关键词

Keywords

references

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