Hardware Implementation for Lower Limb Surface EMG Measurement and Analysis Using Explainable AI for Activity Recognition

被引:37
作者
Vijayvargiya, Ankit [1 ,2 ]
Singh, Puneet [3 ]
Kumar, Rajesh [1 ]
Dey, Nilanjan [4 ]
机构
[1] Malaviya Natl Inst Technol, Dept Elect Engn, Jaipur 302017, Rajasthan, India
[2] Swami Keshvanand Inst Technol Management & Gramot, Dept Elect Engn, Jaipur 302017, Rajasthan, India
[3] Malaviya Natl Inst Technol, Dept Elect & Commun Engn, Jaipur 302017, Rajasthan, India
[4] JIS Univ, Dept Comp Sci & Engn, Kolkata 700109, India
关键词
Electromyography; Sensors; Feature extraction; Legged locomotion; Muscles; Hardware; Wearable sensors; Electromyography (EMG) signal acquisition system; explainable artificial intelligence (XAI); local interpretable model-agnostic explanations (LIME); lower limb activity recognition; machine learning (ML); signal processing; FEATURE-EXTRACTION; SEMG;
D O I
10.1109/TIM.2022.3198443
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human-machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for measuring EMG signals is proposed for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects (three males and two females) were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of Wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time-domain (TD) features were extracted using the overlapping windowing technique. An investigation into the comparative effectiveness of several classifiers is presented, although it was hard to distinguish how the classifiers predicted the activities. Having a trustworthy explanation for the outcomes of these classifiers would be quite beneficial overall. An approach known as explainable artificial intelligence (XAI) was introduced to produce trustworthy predictive modeling results and applied the XAI technique known as local interpretable model-agnostic explanations (LIME) to a straightforward human interpretation. LIME investigates how extracted features are anticipated and which features are most responsible for each action. The accuracy of the extra tree classifier gives the highest accuracy of the other studied algorithms for identifying different human lower limb activities from sEMG signals.
引用
收藏
页数:9
相关论文
共 38 条
[1]   Window Size Impact in Human Activity Recognition [J].
Banos, Oresti ;
Galvez, Juan-Manuel ;
Damas, Miguel ;
Pomares, Hector ;
Rojas, Ignacio .
SENSORS, 2014, 14 (04) :6474-6499
[2]  
Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350
[3]   Smartphone Sensor-Based Human Activity Recognition Using Feature Fusion and Maximum Full a Posteriori [J].
Chen, Zhenghua ;
Jiang, Chaoyang ;
Xiang, Shili ;
Ding, Jie ;
Wu, Min ;
Li, Xiaoli .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (07) :3992-4001
[4]  
Daud Wan Mohd Bukhari Wan, 2013, International Journal of Modeling and Optimization, V3, P515, DOI 10.7763/IJMO.2013.V3.332
[5]   An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection [J].
Ergeneci, Mert ;
Gokcesu, Kaan ;
Ertan, Erhan ;
Kosmas, Panagiotis .
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2018, 12 (01) :68-79
[6]   Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution [J].
Gao, Wenbin ;
Zhang, Lei ;
Huang, Wenbo ;
Min, Fuhong ;
He, Jun ;
Song, Aiguo .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[7]   MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG [J].
Gautam, Arvind ;
Panwar, Madhuri ;
Biswas, Dwaipayan ;
Acharyya, Amit .
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, 2020, 8
[8]   Extremely randomized trees [J].
Geurts, P ;
Ernst, D ;
Wehenkel, L .
MACHINE LEARNING, 2006, 63 (01) :3-42
[9]   Robotic Prosthetic Limbs [J].
Gopura, Ruwan ;
Kiguchi, Kazuo ;
Mann, George ;
Torricelli, Diego .
JOURNAL OF ROBOTICS, 2018, 2018
[10]   AN INTRODUCTION TO WAVELETS [J].
GRAPS, A .
IEEE COMPUTATIONAL SCIENCE & ENGINEERING, 1995, 2 (02) :50-61