Abstract
The estimation of joint angle finds wide application in fields such as robotics, prosthetics, ergonomics, etc. Various estimation techniques have been discussed in the literature. The estimation of joint angle using Surface Electromyogram (sEMG) signals has gained importance due to its ability to recognize continuous human motion. This requires feature extraction from the acquired sEMG signals in order to develop an estimation model. In this paper, an attempt has been made to extract a few significant time domain features from sEMG signals acquired from the biceps brachii for four different subjects. The feature signals obtained using sliding window technique is further used to develop an estimation model using a suitable training algorithm. The trained model is eventually used to control the elbow joint angle.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495
Shaw L, Bagha S (2012) Online EMG signal analysis for diagnosis of neuromuscular diseases by using PCA&PNN. Int J Eng Sci Technol 4:4453–4459
Choi Y, Lee AY, Yu HJ (2011) Human elbow joint angle estimation using elctromyogram signal processing. Inst Eng Technol 5:767–775
Hussain MS, Mohd-yasin F, Reaz MBI (2006) Technique of EMG signal analysis: detection processing, classification and applications, vol. 24, pp 1342–1350
Einzinger PD, Livshitz LM, Mizrabi J (2001) Interaction of array of finite electrodes with layered biological tissue: effect of electrode size configuration. IEEE Trans Neural Syst Rehabil Eng 9:355–361
Hanung Adi Nugroho H, Wahunggoro O, Triwiyanto (2017) An investigtion into time domain features of surface electromyography to estimate the elbow joint angle. Adv Electr Electron Eng 15:448–458
Al-Jumaily A, Aung YM (2013) Estimation of upper limb joint angle using surface EMG signal. Int J Adv Robot Syst 10:369–377
Sivanandan KS, Ramakrishna R, Raj R (2016) A real time surface electromyography signal driven prosthetic hand model using PID controlled DC motor. In: The Korean society of medical & biological engineering and Springer, vol 6, pp 276–286
Raj R, Sivanandan KS (2015) Estimation of elbow joint angle from Time domain features of SEMG signals using fuzzy logic for prosthetic control. Int J Current Eng Technol 5:2078–2081
Acknowledgements
The technical support and laboratory facilities provided by the parent institution for completing this project are gratefully acknowledged. All the staffs who guided to the best of their knowledge are gratefully acknowledged. The authors of all the journals listed in the reference section are also acknowledged gratefully.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rajalakshmy, P., Jacob, E., Joclyn Sharon, T. (2019). Estimation of Elbow Joint Angle from Surface Electromyogram Signals Using ANFIS. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-04061-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04060-4
Online ISBN: 978-3-030-04061-1
eBook Packages: EngineeringEngineering (R0)