ABSTRACT
Muscle fatigue detection and tracking has gained significant attention as the sports science and rehabilitation technologies developed. It is known that muscle fatigue can be evaluated through surface Electromyography (sEMG) sensors, which are portable, non-invasive and applicable for real-time systems. There are plenty of fatigue tracking algorithms, many of which uses frequency, time and time-frequency behaviors of sEMG signals. An example to most commonly used sEMG-based fatigue detection methods can be mean frequency (MNF), median frequency (MDF), zero-crossing rate (ZCR) and continuous wavelet transform (CWT). However, all of these muscle fatigue calculation methods are adversely affected by the dynamically changing sEMG contraction amplitude, since EMG spectrum also demonstrates a shift with the changing signal RMS; powerful contractions lead a shift to high frequency bounds and the opposite happens for the weak. To overcome that, we propose an adaptive algorithm, which learns the effect of contraction power on sEMG power spectral density (PSD) and subtracts that amount of frequency shift from the PSD.
- biometricsltd. 2017. sEMG Sensor. http://www.biometricsltd.com/semg.htm. (2017).Google Scholar
- A. Van Boxtel and L. R. B. Schomaker. 1983. Motor Unit Firing Rate During Static Contraction Indicated by the Surface EMG Power Spectrum. IEEE Transactions on Biomedical Engineering BME-30, 9 (Sept 1983), 601--609.Google ScholarCross Ref
- A. B. M. S. U. Doulah and M. A. Iqbal. 2012. An approach to identify myopathy disease using different signal processing features with comparison. In 2012 15th International Conference on Computer and Information Technology (ICCIT). 155--158.Google Scholar
- M. Ergeneci, K. Gokcesu, E. Ertan, and P. Kosmas. 2018. An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection. IEEE Transactions on Biomedical Circuits and Systems 12, 1 (Feb 2018), 68--79.Google ScholarCross Ref
- Y. Fan and Y. Yin. 2013. Active and Progressive Exoskeleton Rehabilitation Using Multisource Information Fusion From EMG and Force-Position EPP. IEEE Transactions on Biomedical Engineering 60, 12 (Dec2013), 3314--3321.Google ScholarCross Ref
- E. Hazan, A. Agarwal, and S. Kale. 2007. Logarithmic regret algorithms for online convex optimization. Machine Learning 69, 2-3 (2007), 169--192. Google ScholarDigital Library
- E. Koutsos and P. Georgiou. 2014. An analogue instantaneous median frequency tracker for EMG fatigue monitoring. In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). 1388--1391.Google Scholar
- Michael I. Lindinger and George J. F. Heigenhauser. 1991. The roles of ion fluxes in skeletal muscle fatigue. Canadian Journal of Physiology and Pharmacology 69, 2 (1991), 246--253. PMID: 2054741.Google ScholarCross Ref
- H. Liu. 2011. Exploring Human Hand Capabilities Into Embedded Multifingered Object Manipulation. IEEE Transactions on Industrial Informatics 7, 3 (Aug 2011), 389--398.Google ScholarCross Ref
- M. M. Lowery and M. J. O'Malley. 2003. Analysis and Simulation of changes in EMG amplitude during high-level fatiguing contractions. IEEE Transactions on Biomedical Engineering 50, 9 (Sept 2003), 1052--1062.Google ScholarCross Ref
- R. B. R. Manero, A. Shafti, B. Michael, J. Grewal, J. L. R. FernÃąndez, K. Althoefer, and M. J. Howard. 2016. Wearable embroidered muscle activity sensing device for the human upper leg. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 6062--6065.Google Scholar
- R. I. Pettigrew, W. J. Heetderks, C. A. Kelley, G. C. Y. Peng, S. H. Krosnick, L. B. Jakeman, K. D. Egan, and M. Marge. 2017. Epidural Spinal Stimulation to Improve Bladder, Bowel, and Sexual Function in Individuals With Spinal Cord Injuries: A Framework for Clinical Research. IEEE Transactions on Biomedical Engineering 64, 2 (Feb 2017), 253--262.Google ScholarCross Ref
- C. U. Ranniger and D. L. Akin. 1997. EMG mean power frequency determination using wavelet analysis. In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, Vol. 4. 1589--1592 vol.4.Google Scholar
- SENIAM. 2017. Sensor Placement. http://www.seniam.org/bicepsbrachii.html. (2017).Google Scholar
- F. B. Stulen and C. J. De Luca. 1981. Frequency Parameters of the Myoelectric Signal as a Measure of Muscle Conduction Velocity. IEEE Transactions on Biomedical Engineering BME-28, 7 (July 1981), 515--523.Google ScholarCross Ref
- M. Yochum, T. Bakir, R. Lepers, and S. Binczak. 2012. Estimation of Muscular Fatigue Under Electromyostimulation Using CWT. IEEE Transactions on Biomedical Engineering 59, 12 (Dec 2012), 3372--3378.Google ScholarCross Ref
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