Abstract
Surface electromyography (sEMG) signal classification has many applications such as human-machine interaction, diagnosis of kinesiological studies, and neuromuscular diseases. However, these signals are complicated because of different artifacts added to the sEMG signal during recording. In this study, a multi-stage classification technique is proposed for the identification of distinct movements of the lower limbs using sEMG signals acquired from leg muscles of healthy knee and abnormal knee subjects. This investigation involves 11 subjects with a knee abnormality and 11 without knee abnormality for three distinct activities viz. walking, leg extension from sitting position (sitting), and flexion of the leg (standing). Discrete wavelet denoising to fourth level decomposition has been implemented for the artifact reduction and the signal has been segmented using overlapping windowing technique. A study of four different architectures of 1D convolutional neural network models is undertaken for the prediction of lower limb activities and the final prediction is achieved via a voting mechanism of all four model results. The performance parameters of CNN models have been calculated for three different cases: (1) healthy subjects (2) subjects with knee abnormality (3) Pooled data (combination of abnormal knee and healthy knee subjects) using nested threefold cross-validation. It has been found that the voting mechanism yields an average classification accuracy as 99.35%, 97.63%, and 97.14% for healthy subjects, knee abnormal subjects, and pooled data, respectively. The result validates that the proposed voting-based 1D CNN model is efficient and useful in lower limb activity recognition using the sEMG signal.
Similar content being viewed by others
References
Ranasinghe S, Al Machot F, Mayr HC (2016) A review on applications of activity recognition systems with regard to performance and evaluation. Int J Distrib Sensor Netw 12(8):1550147716665520
Liu X, Liu L, Simske SJ, Liu J (2016) Human daily activity recognition for healthcare using wearable and visual sensing data. 24–31
Lara OD, Labrador MA (2012) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209
Yang B-S, Liao S-T (2012) Fall detecting using inertial and electromyographic sensors. In: Proceedings of the 36th annual meeting of the American Society of Biomechanics, Gainsville, FL, USA, pp 15–18
Cheng J, Chen X, Shen M (2012) A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J Biomed Health Inform 17(1):38–45
Farina D, Negro F (2012) Accessing the neural drive to muscle and translation to neurorehabilitation technologies. IEEE Rev Biomed Eng 5:3–14
Nazmi N, Rahman A, Azizi M, Yamamoto S-I, Ahmad SA, Zamzuri H, Mazlan SA (2016) A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 16(8):1304
Au SK, Bonato P, Herr H (2005) An EMG-position controlled system for an active ankle-foot prosthesis: an initial experimental study. In: 9th international conference on rehabilitation robotics, 2005 (ICORR 2005), pp 375–379. IEEE
Vijayvargiya A, Singh PL, Verma SM, Kumar R, Bansal S (2019) Performance comparison analysis of different classifier for early detection of knee osteoarthritis. Sensors Health Monitor. Elsevier, pp 243–257
Burkow-Heikkinen L (2011) Non-invasive physiological monitoring of exercise and fitness. Neurol Res 33(1):3–17
Kiguchi K, Tanaka T, Fukuda T (2004) Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Trans Fuzzy Syst 12(4):481–490
Krasin V, Gandhi V, Yang Z, Karamanoglu M (2015) EMG based elbow joint powered exoskeleton for biceps Brachii strength augmentation. pp 1–6
Sharmila K, Sarath TV, Ramachandran KI (2016) EMG controlled low cost prosthetic arm. pp 169–172
Cai S, Chen Y, Huang S, Yan W, Zheng H, Li X, Xie L (2019) SVM-based classification of sEMG signals for upper-limb self-rehabilitation training. Front Neurorobot 13:31
Pancholi S, Joshi AM (2019) Electromyography-based hand gesture recognition system for upper limb amputees. IEEE Sensors Lett. 3(3):1–4
Eisenberg GD, Fyvie KGHM, Mohamed A-K (2017) Real-time segmentation and feature extraction of electromyography: towards control of a prosthetic hand. IFAC-PapersOnLine 50(2):151–156
Mukhopadhyay AK, Samui S (2020) An experimental study on upper limb position invariant EMG signal classification based on deep neural network. Biomed Signal Process Control 55:101669
Karabulut D, Faruk O, Yunus ZA, Mehmet AA (2017) Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics. Biocybern Biomed Eng 37(2):326–335
Souit C, Coelho DS, Szylit M, Camargo-Junior F, Junior Milton PC, Forner-Cordero A (2016) Design of a lower limb exoskeleton for experimental research on gait control. In: 2016 6th IEEE international conference on biomedical robotics and biomechatronics (BioRob), pp 1098–1103. IEEE
Xie H, Li Z, Li F (2020) Bionics design of artificial leg and experimental modeling research of pneumatic artificial muscles. J Robot
Erkaymaz O, Şenyer İ, Uzun R (2017) Detection of knee abnormality from surface EMG signals by artificial neural networks. pp 1–4
Miller JD, Beazer MS, Hahn ME (2013) Myoelectric walking mode classification for transtibial amputees. IEEE Trans Biomed Eng 60(10):2745–2750
Naik GR, Easter Selvan S, Arjunan SP, Acharyya A, Kumar DK, Ramanujam A, Nguyen HT (2018) An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology. IEEE Trans Neural Syst Rehabil Eng 26(3):675–686
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp 1097–1105
LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw 3361(10):1995
Ullah I, Hussain M, Aboalsamh H et al (2018) An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl 107:61–71
Gautam A, Panwar M, Biswas D, Acharyya A (2020) Myonet: a transfer-learning-based lRCN for lower limb movement recognition and knee joint angle prediction for remote monitoring of rehabilitation progress from sEMG. IEEE J Transl Eng Health Med 8:1–10
Sanchez OFA, Sotelo JLR, Gonzales MH, Hernandez GAM (2014) EMG dataset in lower limb data set. UCI Mach Learn Repos pp 2014–02
Vijayvargiya A, Kumar R, Dey N, Tavares JMR. (2020) Comparative analysis of machine learning techniques for the classification of knee abnormality. pp 1–6
Chowdhury R, Reaz M, Ali M, Bakar A, Chellappan K, Chang T (2013) Surface electromyography signal processing and classification techniques. Sensors 13(9):12431–12466
Phinyomark A, Phukpattaranont P, Limsakul C (2011) Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluct Noise Lett 10(02):157–167
Graps A (1995) An introduction to wavelets. IEEE Comput Sci Eng 2(2):50–61
Jing-Yi L, Hong L, Dong Y, Yan-Sheng Z (2016) A new wavelet threshold function and denoising application. Math Prob Eng
Phinyomark A, Limsakul C, Phukpattaranont P (2011) Application of wavelet analysis in EMG feature extraction for pattern classification. Meas Sci Rev 11(2):45–52
Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55(8):1956–1965
Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1-d convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675
Ullah I, Petrosino A (2016) About pyramid structure in convolutional neural networks. pp 1318–1324
Kingma DP, Adam JB (2014) A method for stochastic optimization. arXiv:1412.6980
Mehta S, Paunwala C, Vaidya B (2019) CNN based traffic sign classification using Adam optimizer. pp 1293–1298
Vijayvargiya A, Gupta V, Kumar R, Dey N, Tavares JMR (2021) A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition. IEEE Sensors J 21:20431–20439
Herrera-González M, Martínez-Hernández GA, Rodríguez-Sotelo JL, Avilés-Sánchez OF (2015) Knee functional state classification using surface electromyographic and goniometric signals by means of artificial neural networks. Ing Univ 19(1):51–66
Zhang Y, Peng X, Li P, Duan K, Wen Y, Yang Q, Zhang T, Yao D (2017) Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals. Biomed Eng Online 16(1):107
Acknowledgements
This publication is supported by Visvesvaraya PhD Scheme, Meity, Govt. of India, MEITY-PHD-2942.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any study with human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Vijayvargiya, A., Khimraj, Kumar, R. et al. Voting-based 1D CNN model for human lower limb activity recognition using sEMG signal. Phys Eng Sci Med 44, 1297–1309 (2021). https://doi.org/10.1007/s13246-021-01071-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-021-01071-6