Abstract
The surface electromyographic (sEMG) signal-based hand gesture recognition system has been widely adopted for the development of prosthetic control, robotics, and surgical systems. However, it is a challenging task to extract distinguishable features from the sEMG signal for accurate recognition of the gesture class. In this work, a set of time-domain features (SoTF) are extracted from each channel of the sEMG signal for effective recognition of the gesture class. The proposed SoTF is a combination of average, standard deviation, and waveform length features extracted from each channel. The classification accuracy using the SoTF is compared for three different classifiers such as k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) on 52 gesture classes of NinaPro DB1 dataset. Variations in parameters of the classifiers are also analyzed to obtain the best classifier. Experimental results show that the SoTF with RF classifier achieves superior performance compared to the state-of-the-art techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rodríguez-Tapia B, Soto I, Martínez DM, Arballo NC (2020) Myoelectric interfaces and related applications: current state of EMG signal processing-a systematic review. IEEE Access 8:7792–7805
Luo R, Sun S, Zhang X, Tang Z, Wang W (2019) A low-cost end-to-end sEMG-based gait sub-phase recognition system. IEEE Trans Neural Syst Rehabil Eng 28(1):267–276
Pancholi S, Joshi AM (2018) Portable EMG data acquisition module for upper limb prosthesis application. IEEE Sens J 18(8):3436–3443
Raurale SA, McAllister J, Del Rincón JM (2021) Emg biometric systems based on different wrist-hand movements. IEEE Access 9:12256–12266
Guo L, Lu Z, Yao L (2021) Human-machine interaction sensing technology based on hand gesture recognition: a review. IEEE Trans Hum-Mach Syst
Sahoo JP, Ari S, Ghosh DK (2018) Hand gesture recognition using DWT and F-ratio based feature descriptor. IET Image Process 12(10):1780–1787
Atzori M, Gijsberts A, Kuzborskij I, Elsig S, Hager AGM, Deriaz O, Castellini C, Müller H, Caputo B (2014) Characterization of a benchmark database for myoelectric movement classification. IEEE Trans Neural Syst Rehabil Eng 23(1):73–83
Cene VH, Tosin M, Machado J, Balbinot A (2019) Open database for accurate upper-limb intent detection using electromyography and reliable extreme learning machines. Sensors 19(8):1864
Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AGM, Elsig S, Giatsidis G, Bassetto F, Müller H (2014) Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci Data 1(1):1–13
Pizzolato S, Tagliapietra L, Cognolato M, Reggiani M, Müller H, Atzori M (2017) Comparison of six electromyography acquisition setups on hand movement classification tasks. PloS one 12(10), e0186,132 (2017)
He Y, Fukuda O, Bu N, Okumura H, Yamaguchi N (2018) Surface EMG pattern recognition using long short-term memory combined with multilayer perceptron. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 5636–5639. IEEE (2018)
Du Y, Jin W, Wei W, Hu Y, Geng W (2017) Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation. Sensors 17(3):458
Saeed B, Gilani SO, ur Rehman Z, Jamil M, Waris A, Khan MN (2019) Comparative analysis of classifiers for EMG signals. In: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE), pp 1–5. IEEE (2019)
Li Y, Zhang W, Zhang Q, Zheng N (2021) Transfer learning-based muscle activity decoding scheme by low-frequency sEMG for wearable low-cost application. IEEE Access 9:22804–22815
Zhou T, Omisore OM, Du W, Wang L, Zhang Y (2019) Adapting random forest classifier based on single and multiple features for surface electromyography signal recognition. In: 2019 12th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–6. IEEE (2019)
Paul Y, Goyal V, Jaswal RA (2017) Comparative analysis between SVM & KNN classifier for EMG signal classification on elementary time domain features. In: 2017 4th international conference on signal processing, computing and control (ISPCC), pp 169–175. IEEE (2017)
Xing W, Bei Y (2020) Medical health big data classification based on KNN classification algorithm. IEEE Access 8:28808–28819. https://doi.org/10.1109/ACCESS.2019.2955754
Chethana C (2021) Prediction of heart disease using different KNN classifier. In: 2021 5th international conference on intelligent computing and control systems (ICICCS), pp 1186–1194 (2021). 10.1109/ICICCS51141.2021.9432178
Apostolidis-Afentoulis V, Lioufi KI (2015) SVM classification with linear and RBF kernels. July): 0-7. Classification with Linear and RBF kernels [21] (2015). http://www.academia.edu/13811676/SVM
Javeed A, Zhou S, Yongjian L, Qasim I, Noor A, Nour R (2019) An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 7:180,235–180,243. 10.1109/ACCESS.2019.2952107
Padhy S (2020) A tensor-based approach using multilinear SVD for hand gesture recognition from sEMG signals. IEEE Sens J 21(5):6634–6642
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Krishnapriya, S., Sahoo, J.P., Ari, S. (2023). Surface Electromyographic Hand Gesture Signal Classification Using a Set of Time-Domain Features. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_40
Download citation
DOI: https://doi.org/10.1007/978-981-99-0047-3_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0046-6
Online ISBN: 978-981-99-0047-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)