Skip to main content
SpringerLink
Log in

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview

Biomedical Engineering Letters volume 12, pages 343–358 (2022)Cite this article

Abstract

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Ranasinghe S, Al Machot F, Mayr HC. A review on applications of activity recognition systems with regard to performance and evaluation. Int J Distrib Sens Netw. 2016;12(8):1550147716665520.

    Article  Google Scholar 

  2. Jamieson A, Murray L, Stankovic L, Stankovic V, Buis A. Human activity recognition of individuals with lower limb amputation in free-living conditions: a pilot study. Sensors. 2021;21(24):8377.

    Article  Google Scholar 

  3. Aggarwal J, Ryoo M. Human activity analysis: a review. ACM Comput Surv. 2011;43(3):1–43.

    Article  Google Scholar 

  4. Dang LM, Min K, Wang H, Piran MJ, Lee CH, Moon H. Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 2020;108:107561.

    Article  Google Scholar 

  5. Hussain Z, Sheng M, Zhang WE. Different approaches for human activity recognition: A survey (2019). arXiv preprint arXiv:1906.05074

  6. Lara OD, Labrador MA. A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor. 2012;15(3):1192–209.

    Article  Google Scholar 

  7. Beddiar DR, Nini B, Sabokrou M, Hadid A. Vision-based human activity recognition: a survey. Multimed Tools Appl. 2020;79(41):30509–55.

    Article  Google Scholar 

  8. Cheng J, Chen X, Shen M. A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J Biomed Health Inform. 2012;17(1):38–45.

    Article  Google Scholar 

  9. Yang B-S, Liao S-T. Fall detecting using inertial and electromyographic sensors. In: Proceedings of the 36th Annual Meeting of the American Society of Biomechanics, Gainsville, FL, USA; 2012. p. 15–18.

  10. Englehart K, Hudgin B, Parker PA. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2001;48(3):302–11.

    Article  Google Scholar 

  11. Pawar T, Chaudhuri S, Duttagupta SP. Body movement activity recognition for ambulatory cardiac monitoring. IEEE Trans Biomed Eng. 2007;54(5):874–82.

    Article  Google Scholar 

  12. Tunçel O, Altun K, Barshan B. Classifying human leg motions with uniaxial piezoelectric gyroscopes. Sensors. 2009;9(11):8508–46.

    Article  Google Scholar 

  13. Khan AM, Lee Y-K, Lee SY, Kim T-S. A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed. 2010;14(5):1166–72.

    Article  Google Scholar 

  14. Wang Z, Jiang M, Hu Y, Li H. An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans Inf Technol Biomed. 2012;16(4):691–9.

    Article  Google Scholar 

  15. Zhang M, Sawchuk AA. Human daily activity recognition with sparse representation using wearable sensors. IEEE J Biomed Health Inform. 2013;17(3):553–60.

    Article  Google Scholar 

  16. Park S, Park J, Al-Masni M, Al-Antari M, Uddin MZ, Kim T-S. A depth camera-based human activity recognition via deep learning recurrent neural network for health and social care services. Proc Comput Sci. 2016;100:78–84.

    Article  Google Scholar 

  17. Chen Z, Zhu Q, Soh YC, Zhang L. Robust human activity recognition using smartphone sensors via ct-pca and online svm. IEEE Trans Industr Inf. 2017;13(6):3070–80.

    Article  Google Scholar 

  18. Naik GR, Selvan SE, Arjunan SP, Acharyya A, Kumar DK, Ramanujam A, Nguyen HT. An ica-ebm-based semg classifier for recognizing lower limb movements in individuals with and without knee pathology. IEEE Trans Neural Syst Rehabil Eng. 2018;26(3):675–86.

    Article  Google Scholar 

  19. Hussain T, Maqbool HF, Iqbal N, Khan M, Salman, Dehghani-Sanij AA. Computational model for the recognition of lower limb movement using wearable gyroscope sensor. Int J Sens Netw. 2019;30(1):35–45.

    Article  Google Scholar 

  20. Vijayvargiya A, Gupta V, Kumar R, Dey N, Tavares JMR. A hybrid wd-eemd semg feature extraction technique for lower limb activity recognition. IEEE Sens J. 2021.

  21. Jamal MZ. Signal acquisition using surface emg and circuit design considerations for robotic prosthesis. Comput Intell Electromyograp Analysis-A Perspect Curr Appl Fut Chall. 2012;18:427–48.

    Google Scholar 

  22. Fang C, He B, Wang Y, Cao J, Gao S. Emg-centered multisensory based technologies for pattern recognition in rehabilitation: state of the art and challenges. Biosensors. 2020;10(8):85.

    Article  Google Scholar 

  23. Hendry D, Chai K, Campbell A, Hopper L, O’Sullivan P, Straker L. Development of a human activity recognition system for ballet tasks. Sports Medicine-Open. 2020;6(1):10.

    Article  Google Scholar 

  24. Gopura R, Kiguchi K, Mann G, Torricelli D. Robotic Prosthetic Limbs. Hindawi; 2018.

  25. Luan Y, Shi Y, Wu W, Liu Z, Chang H, Cheng J. Har-semg: A dataset for human activity recognition on lower-limb semg. Knowl Inf Syst. 2021;63(10):2791–814.

    Article  Google Scholar 

  26. Shi X, Qin P, Zhu J, Zhai M, Shi W. Feature extraction and classification of lower limb motion based on semg signals. IEEE Access. 2020;8:132882–92.

    Article  Google Scholar 

  27. Park S, Lee D, Chung WK, Kim K. Hierarchical motion segmentation through semg for continuous lower limb motions. IEEE Robot Autom Lett. 2019;4(4):4402–9.

    Article  Google Scholar 

  28. Hu B, Rouse E, Hargrove L. Benchmark datasets for bilateral lower-limb neuromechanical signals from wearable sensors during unassisted locomotion in able-bodied individuals. Front Robot AI. 2018;5:14.

    Article  Google Scholar 

  29. Ryu J, Lee B-H, Kim D-H. semg signal-based lower limb human motion detection using a top and slope feature extraction algorithm. IEEE Signal Process Lett. 2016;24(7):929–32.

    Article  Google Scholar 

  30. Yu Y, Fan L, Kuang S, Sun L, Zhang F. The research of semg movement pattern classification based on multiple fused wavelet function. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE; 2015. p. 487–491.

  31. Sanchez O, Sotelo J, Gonzales M, Hernandez G. Emg dataset in lower limb data set. UCI machine learning repository. 2014;2.

  32. Nazmi N, Abdul Rahman MA, Yamamoto S-I, Ahmad SA, Zamzuri H, Mazlan SA. A review of classification techniques of emg signals during isotonic and isometric contractions. Sensors. 2016;16(8):1304.

    Article  Google Scholar 

  33. Reaz MBI, Hussain MS, Mohd-Yasin F. Techniques of emg signal analysis: detection, processing, classification and applications. Biol Proc Online. 2006;8(1):11–35.

    Article  Google Scholar 

  34. Chowdhury RH, Reaz MB, Ali MABM, Bakar AA, Chellappan K, Chang TG. Surface electromyography signal processing and classification techniques. Sensors. 2013;13(9):12431–66.

    Article  Google Scholar 

  35. Clancy EA, Morin EL, Merletti R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol. 2002;12(1):1–16.

    Article  Google Scholar 

  36. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface emg signal: Movement artifact and baseline noise contamination. J Biomech. 2010;43(8):1573–9.

    Article  Google Scholar 

  37. Romero FP, Romaguera LV, Vázquez-Seisdedos CR, Costa MGF, Neto JE, et al. Baseline wander removal methods for ecg signals: A comparative study; 2018. arXiv preprint arXiv:1807.11359

  38. Amrutha N, Arul V. A review on noises in emg signal and its removal. Int J Sci Res Publ. 2017;7(5):23–7.

    Google Scholar 

  39. Van Vugt J, Van Dijk J. A convenient method to reduce crosstalk in surface emg. Clin Neurophysiol. 2001;112(4):583–92.

    Article  Google Scholar 

  40. Andrade AO, Nasuto S, Kyberd P, Sweeney-Reed CM, Van Kanijn F. Emg signal filtering based on empirical mode decomposition. Biomed Signal Process Control. 2006;1(1):44–55.

    Article  Google Scholar 

  41. Jiang C-F, Kuo S-L. A comparative study of wavelet denoising of surface electromyographic signals. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE; 2007. p. 1868–1871.

  42. Phinyomark A, Phukpattaranont P, Limsakul C. Wavelet-based denoising algorithm for robust emg pattern recognition. Fluct Noise Lett. 2011;10(02):157–67.

    Article  Google Scholar 

  43. Phinyomark A, Limsakul C, Phukpattaranont P. A comparative study of wavelet denoising for multifunction myoelectric control. In: 2009 International Conference on Computer and Automation Engineering, IEEE; 2009. p. 21–25.

  44. Phinyomark A, Limsakul C, Phukpattaranont P. An optimal wavelet function based on wavelet denoising for multifunction myoelectric control. In: 2009 6th International Conference on Electrical Engineering/electronics, Computer, Telecommunications and Information Technology, vol. 2, IEEE; 2009. p. 1098–1101.

  45. Kumar DK, Pah ND, Bradley A. Wavelet analysis of surface electromyography. IEEE Trans Neural Syst Rehabil Eng. 2003;11(4):400–6.

    Article  Google Scholar 

  46. Hussain M, Mamun M. Effectiveness of the wavelet transform on the surface emg to understand the muscle fatigue during walk. Meas Sci Rev. 2012;12(1):28.

    Article  Google Scholar 

  47. Vijayvargiya A, Prakash C, Kumar R, Bansal S, Tavares JMR. Human knee abnormality detection from imbalanced semg data. Biomed Signal Process Control. 2021;66:102406.

    Article  Google Scholar 

  48. Vijayvargiya A, Khimraj, Kumar R, Dey N. Tavares: Voting-based 1d cnn model for human lower limb activity recognition using semg signal. Phys Eng Sci Med. 2021;44.

  49. Dutta S, Basu B, Talukdar FA. Classification of lower limb activities based on discrete wavelet transform using on-body creeping wave propagation. IEEE Trans Instrum Meas. 2020;70:1–7.

    Google Scholar 

  50. Comon P. Independent component analysis, a new concept? Signal Process. 1994;36(3):287–314.

    Article  MATH  Google Scholar 

  51. Nakamura H, Yoshida M, Kotani M, Akazawa K, Moritani T. The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: Part i–measuring techniques. J Electromyogr Kinesiol. 2004;14(4):423–32.

    Article  Google Scholar 

  52. Acharya D, Panda G. A review of independent component analysis techniques and their applications. IETE Tech Rev. 2008;25(6):320–32.

    Article  Google Scholar 

  53. Zhou P, Lowery MM, Rymer WZ. Extracting motor unit firing information by independent component analysis of surface electromyogram: A preliminary study using a simulation approach. Int J Comput Syst Signals. 2006;7(1):19–28.

    Google Scholar 

  54. Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7(6):1129–59.

    Article  Google Scholar 

  55. Sapsanis C, Georgoulas G, Tzes A. Emg based classification of basic hand movements based on time-frequency features. In: 21st Mediterranean Conference on Control and Automation, IEEE; 2013. p. 716–722.

  56. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci. 1998;454(1971):903–95.

    Article  MathSciNet  MATH  Google Scholar 

  57. Gaci S. A new ensemble empirical mode decomposition (eemd) denoising method for seismic signals. Energy Proc. 2016;97:84–91.

    Article  Google Scholar 

  58. Naik GR, Selvan SE, Nguyen HT. Single-channel emg classification with ensemble-empirical-mode-decomposition-based ica for diagnosing neuromuscular disorders. IEEE Trans Neural Syst Rehabil Eng. 2015;24(7):734–43.

    Article  Google Scholar 

  59. Lei Y, He Z, Zi Y. Eemd method and wnn for fault diagnosis of locomotive roller bearings. Expert Syst Appl. 2011;38(6):7334–41.

    Article  Google Scholar 

  60. Zhang Y, Xu P, Li P, Duan K, Wen Y, Yang Q, Zhang T, Yao D. Noise-assisted multivariate empirical mode decomposition for multichannel emg signals. Biomed Eng Online. 2017;16(1):1–17.

    Article  Google Scholar 

  61. Banos O, Galvez J-M, Damas M, Pomares H, Rojas I. Window size impact in human activity recognition. Sensors. 2014;14(4).

  62. Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003;50(7):848–54.

    Article  Google Scholar 

  63. Farina D, Merletti R. Comparison of algorithms for estimation of emg variables during voluntary isometric contractions. J Electromyogr Kinesiol. 2000;10(5):337–49.

    Article  Google Scholar 

  64. Daud WMBW, Yahya AB, Horng CS, Sulaima MF, Sudirman R. Features extraction of electromyography signals in time domain on biceps brachii muscle. Int J Model Optim. 2013;3(6):515.

    Article  Google Scholar 

  65. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for emg signal classification. Expert Syst Appl. 2012;39(8):7420–31.

    Article  Google Scholar 

  66. Spiewak C, Islam M, Zaman A, Rahman MH. A comprehensive study on emg feature extraction and classifiers. Open Access J Biomed Eng Biosci. 2018;1(1):1–10.

    Article  Google Scholar 

  67. Vijayvargiya A, Kumar R, Dey N, Tavares JMR. Comparative analysis of machine learning techniques for the classification of knee abnormality. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), IEEE; 2020. p. 1–6.

  68. Too J, Abdullah AR, Mohd Saad N, Tee W. Emg feature selection and classification using a pbest-guide binary particle swarm optimization. Computation. 2019;7(1):12.

    Article  Google Scholar 

  69. Javaid HA, Rashid N, Tiwana MI, Anwar MW. Comparative analysis of emg signal features in time-domain and frequency-domain using myo gesture control. In: Proceedings of the 2018 4th International Conference on Mechatronics and Robotics Engineering; 2018. p. 157–162.

  70. Too J, Abdullah A, Zawawi TT, Saad NM, Musa H. Classification of emg signal based on time domain and frequency domain features. Int J Human Technol Interact (IJHaTI). 2017;1(1):25–30.

    Google Scholar 

  71. Oladazimi M, Molaei-Vaneghi F, Safari M, Asadi H, Aghay Kaboli S. A review for feature extraction of emg signal processing. In: 4th International Conference on Computer and Automation Engineering (ICCAE 2012), ASME Press; 2012. p. 85–94.

  72. Ismail AR, Asfour SS. Continuous wavelet transform application to emg signals during human gait. In: Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No. 98CH36284), vol. 1, IEEE; 1998. p. 325–329.

  73. Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D. Improving emg based classification of basic hand movements using emd. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE; 2013. p. 5754–5757.

  74. Lee J-Y. Variable short-time fourier transform for vibration signals with transients. J Vib Control. 2015;21(7):1383–97.

    Article  Google Scholar 

  75. Xing K, Yang P, Huang J, Wang Y, Zhu Q. A real-time emg pattern recognition method for virtual myoelectric hand control. Neurocomputing. 2014;136:345–55.

    Article  Google Scholar 

  76. Vijayvargiya A, Singh PL, Verma SM, Kumar R, Bansal S. Performance comparison analysis of different classifier for early detection of knee osteoarthritis. In: Sensors for Health Monitoring; Elsevier; 2019. p. 243–257.

  77. Toledo-Pérez DC, Martínez-Prado MA, Gómez-Loenzo RA, Paredes-García WJ, Rodríguez-Reséndiz J. A study of movement classification of the lower limb based on up to 4-emg channels. Electronics. 2019;8(3):259.

    Article  Google Scholar 

  78. Phinyomark A, Hu H, Phukpattaranont P, Limsakul C. Application of linear discriminant analysis in dimensionality reduction for hand motion classification. Meas Sci Rev. 2012;12(3):82–9.

    Article  Google Scholar 

  79. Velliangiri S, Alagumuthukrishnan S, et al. A review of dimensionality reduction techniques for efficient computation. Proc Comput Sci. 2019;165:104–11.

    Article  Google Scholar 

  80. Chan FH, Yang Y-S, Lam F, Zhang Y-T, Parker PA. Fuzzy emg classification for prosthesis control. IEEE Trans Rehabil Eng. 2000;8(3):305–11.

    Article  Google Scholar 

  81. Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehab Res Dev. 2011;48(6).

  82. Herrera-González M, Martínez-Hernández GA, Rodríguez-Sotelo JL, Avilés-Sánchez ÓF. Knee functional state classification using surface electromyographic and goniometric signals by means of artificial neural networks. Ing Univ. 2015;19(1):51–66.

    Google Scholar 

  83. Qin P, Shi X. Evaluation of feature extraction and classification for lower limb motion based on semg signal. Entropy. 2020;22(8):852.

    Article  Google Scholar 

  84. Meng L, Pang J, Wang Z, Xu R, Ming D. The role of surface electromyography in data fusion with inertial sensors to enhance locomotion recognition and prediction. Sensors. 2021;21(18):6291.

    Article  Google Scholar 

  85. Zhang Y, Li P, Zhu X, Su SW, Guo Q, Xu P, Yao D. Extracting time-frequency feature of single-channel vastus medialis emg signals for knee exercise pattern recognition. PLoS ONE. 2017;12(7):0180526.

    Google Scholar 

  86. Gautam A, Panwar M, Biswas D, Acharyya A. 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 Trans Eng Health Med. 2020;8:1–10.

    Article  Google Scholar 

  87. Zhang J, Ling C, Li S. Emg signals based human action recognition via deep belief networks. IFAC-PapersOnLine. 2019;52(19):271–6.

    Article  Google Scholar 

  88. Swaroop R, Kaur M, Suresh P, Sadhu PK. Classification of myopathy and neuropathy emg signals using neural network. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), IEEE; 2017. p. 1–5

  89. Meng W, Liu Q, Zhou Z, Ai Q, Sheng B, Xie SS. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics. 2015;31:132–45.

    Article  Google Scholar 

  90. Wolf EJ, Cruz TH, Emondi AA, Langhals NB, Naufel S, Peng GC, Schulz BW, Wolfson M. Advanced technologies for intuitive control and sensation of prosthetics. Biomed Eng Lett. 2020;10(1):119–28.

    Article  Google Scholar 

  91. Khoshdel V, Akbarzadeh A, Naghavi N, Sharifnezhad A, Souzanchi-Kashani M. semg-based impedance control for lower-limb rehabilitation robot. Intel Serv Robot. 2018;11(1):97–108.

    Article  Google Scholar 

  92. Singh RM, Chatterji S, Kumar A. Trends and challenges in emg based control scheme of exoskeleton robots-a review. Int J Sci Eng Res. 2012;3(9):933–40.

    Google Scholar 

Download references

Acknowledgements

This work is supported by Visvesvaraya Ph.D. Scheme, Meity, Govt. of India, MEITY-PHD-2942.

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Ankit Vijayvargiya, Bharat Singh & Rajesh Kumar

  2. Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur, India

    Ankit Vijayvargiya

  3. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

Authors
  1. Ankit Vijayvargiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bharat Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. João Manuel R. S. Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Vijayvargiya.

Ethics declarations

Conflict of interest

The authors report no declarations 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

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vijayvargiya, A., Singh, B., Kumar, R. et al. Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview. Biomed. Eng. Lett. 12, 343–358 (2022). https://doi.org/10.1007/s13534-022-00236-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13534-022-00236-w

Keywords

search

Navigation