Abstract
Recent studies on myoelectric-based prosthetic control have shown that surface electromyography (sEMG) can enhance prosthetic intuitiveness by improving motion detection algorithms and continuous data processing. This study aims to use a combination of feature extraction techniques and machine learning approaches to map sEMG signals to 10 upper-limb motions for real-time control. The study implements four machine learning methods (i.e., k-nearest neighbours (k-NN), artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA)) as classifiers and six time-domain features (i.e., root mean square (RMS), integrated absolute value (IAV), mean absolute value (MAV), simple square integration (SSI), waveform length (WL), average amplitude change (AAC)) to extract sEMG features to differentiate six individual fingers and four-hand gri** patterns. Five subjects volunteered in the research and training datasets were recorded using seven sEMG electrodes for three static and three dynamic arm positions. The modalities were assessed with offline classification performance from the collected datasets and real-time evaluation metrics such as motion completion rate, motion detection accuracies and reach and grasp experiments. Based on the above, the control methodology differentiates independent finger motions with high accuracy, 94% completion rates with 0.23 s data processing and prediction time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Darnall, B.D., et al.: Depressive symptoms and mental health service utilization among persons with limb loss: results of a national survey. Arch. Phys. Med. Rehabil. 86(4), 650–658 (2005)
Ziegler-Graham, K., MacKenzie, E.J., Ephraim, P.L., Travison, T.G., Brookmeyer, R.: Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch. Phys. Med. Rehabil. 89(3), 422–429 (2008)
Resnik, L., et al.: Advanced upper limb prosthetic devices: implications for upper limb prosthetic rehabilitation. Arch. Phys. Med. Rehabil. 93(4), 710–717 (2012)
Furui, A., et al.: A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control. Sci. Robot. 4(31), eaaw6339 (2019)
Farrell, T.R., ff Weir, R.F.: A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE Trans. Biomed. Eng. 55(9), 2198–2211 (2008)
Roche, A.D., Rehbaum, H., Farina, D., Aszmann, O.C., Aszmann, O.C.: Prosthetic myoelectric control strategies: a clinical perspective. Curr. Surg. Reports 2(3) (2014)
Raspopovic, S., et al.: Restoring natural sensory feedback in real-time bidirectional hand prostheses: supplemental material. Sci. Transl. Med. 6(222), 222ra19 (2014)
Jarque-Bou, N.J., Sancho-Bru, J.L., Vergara, M.: A systematic review of EMG applications for the characterization of forearm and hand muscle activity during activities of daily living: results, challenges, and open issues. Sensors 21(9) (2021)
De Luca, C.J., Donald Gilmore, L., Kuznetsov, M., Roy, S.H.: Filtering the surface EMG signal: movement artifact and baseline noise contamination. J. Biomech. 43(8), 1573–1579 (2010)
Tenore, F.V.G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., Thakor, N.V.: Decoding of individuated finger movements using surface electromyography. IEEE Trans. Biomed. Eng. 56(5), 1427–1434 (2009)
Benatti, S., Milosevic, B., Farella, E., Gruppioni, E., Benini, L.: A prosthetic hand body area controller based on efficient pattern recognition control strategies. Sensors (Switzerland) 17(4), 869 (2017)
Vujaklija, I., et al.: Translating research on myoelectric control into clinics-are the performance assessment methods adequate? Front. Neurorobot. 11, 1–7 (2017)
Patel, G.K., Castellini, C., Hahne, J.M., Farina, D., Dosen, S.: A classification method for myoelectric control of hand prostheses inspired by muscle coordination. IEEE Trans. Neural Syst. Rehabil. Eng. 26(9), 1745–1755 (2018)
Jochumsen, M., Waris, A., Kamavuako, E.N.: The effect of arm position on classification of hand gestures with intramuscular EMG. Biomed. Signal Process. Control 43, 1–8 (2018)
Atzori, M., et al.: Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data 1, 1–13 (2014)
Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Caputo, B., Atzori, M.: Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. In: IEEE International Conference on Rehabilitation Robotics, pp. 1154–1159 (2017)
Yang, D., Gu, Y., Jiang, L., Osborn, L., Liu, H.: Dynamic training protocol improves the robustness of PR-based myoelectric control. Biomed. Signal Process. Control 31, 249–256 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Balandiz, K., Ren, L., Wei, G. (2022). Motor Learning-Based Real-Time Control for Dexterous Manipulation of Prosthetic Hands. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-13835-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13834-8
Online ISBN: 978-3-031-13835-5
eBook Packages: Computer ScienceComputer Science (R0)