Abstract
This article describes the design of a wearable, remote inserted framework for the Peripheral Myopathy (PM) estimation in customary dynamic movements. This unique circumstance, the electromyographic investigation can give data about the muscle and nerve condition by evaluating the connected MFCV. The framework works with synchronized and digitized information tests from four electromyographic channels, which are situated on both hands of the individual test, misusing the rules given by an inserted positional filtering calculation. This work displays a novel approach for the assessment of MFCV that relies upon the excellent anode relative estimation standard. The system uses dynamic bitstream change of Electromyographic signs and low computational retort for utilization of bit stream cross-correlation. The structure totally chips away at Cyclone-V altera field programmable gate array. The test outcomes on five subjects (Human Beings) show the limit of the proposed procedure for planning the physiological muscle fibre conduction velocity values, as nitty–gritty in a therapeutic composition. In explicit, differentiating the remedial characteristics, obtained in controlled conditions, with the system evacuated muscle fibre conduction velocity, the proportionate preliminary conditions: preeminent bungle is, overall, 0.3 m/s. The framework restores a likelihood of invalid constant measures underneath of 2% (assuming the worst possible scenario).
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23 May 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-03960-4
References
Ambikapathy B, Krishnamurthy K (2018) Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0811-6
Annese VF, De Venuto D (2015) The truth machine of involuntary movement: FPGA based cortico-muscular analysis for fall prevention. In: 2015 IEEE International Symposium on Signal Processing and Information Technology, pp 553–558. https://doi.org/10.1109/ISSPIT.2015.7394398
Annese VF, Mezzina G, Gallo VL, Scarola V, De Venuto D (2017) Wearable platform for automatic recognition of Parkinson Disease by muscular implication monitoring. In: 2017 7th IEEE International workshop on advances in sensors and interfaces, pp 150–154. https://doi.org/10.1109/IWASI.2017.7974236
Arendt-Nielsen L, Zwarts MJ (1989) Measurement of muscle fiber conduction velocity in humans: techniques and applications. J Clin Neurophysiol 6:173–190. https://doi.org/10.1097/00004691-198904000-00004
Bender LF (1967) Muscles alive: their functions revealed by electromyography. JAMA 201:277–277. https://doi.org/10.1001/jama.1967.03130040073037
Davis JL, Lewis SB, Gerich JE, Kaplan RA, Schultz TA, Wallin JD (1977) Peripheral diabetic neuropathy treated with amitriptyline and fluphenazine. JAMA 238:2291–2292. https://doi.org/10.1001/jama.1977.03280220059023
De Venuto D, Annese VF, Ruta M, Di Sciascio E, Vincentelli ALS (2015) Designing a cyber–physical system for fall prevention by cortico–muscular coupling detection. IEEE Des Test 33:66–76. https://doi.org/10.1109/MDAT.2015.2480707
Deny J, Sudharsan RR (2020) Block rearrangements and TSVs for a standard cell 3D IC placement. In: Intelligent computing and innovation on data science. Springer, Singapore, pp 207–214
Farina D, Arendt-Nielsen L, Merletti R, Graven-Nielsen T (2002) Assessment of single motor unit conduction velocity during sustained contractions of the tibialis anterior muscle with advanced spike triggered averaging. J Neurosci Methods 115:1–12. https://doi.org/10.1016/S0165-0270(01)00510-6
Farina D, Merletti R (2004) Methods for estimating muscle fibre conduction velocity from surface electromyographic signals. Med BioI Eng Comput 42:432–445. https://doi.org/10.1007/BF02350984
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13:891–906. https://doi.org/10.1109/34.93808
Hosseini-Motlagh SM, Samani MRG, Homaei S (2020) Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). J Ambient Intell Human Comput 11:1085–1104. https://doi.org/10.1007/s12652-019-01315-0
Huppertz HJ, Disselhorst-klug C, Silny J, Rau G, Heimann G (1997) Diagnostic yield of noninvasive high spatial resolution electromyography in neuromuscular diseases. Muscle Nerve 20:1360–1370. https://doi.org/10.1002/(SICI)1097-4598(199711)20:11<1360:AID-MUS3>3.0.CO;2-8
Linssen WH, Stegeman DF, Joosten EM, Notermans SL, van't Hof MA, Binkhorst RA (1993) Variability and interrelationships of surface EMG parameters during local muscle fatigue. Muscle Nerve 16:849–856. https://doi.org/10.1002/mus.880160808
Masuda K, Masuda T, Sadoyama T, Inaki M, Katsuta S (1999) Changes in surface EMG parameters during static and dynamic fatiguing contractions. J Electromyogr Kinesiol 9:39–46. https://doi.org/10.1016/S1050-6411(98)00021-2
McVicar GN, Parker PA (1988) Spectrum dip estimator of nerve conduction velocity. IEEE Trans Biomed Eng 35:1069–1076. https://doi.org/10.1109/10.8692
Muthukumaran E, Deny J, Perumal B, Suseendran G, Akila D (2019) A narrative non-invasive diagnostic loom based by the side of correlation of nasal set rhythm in addition to customary three radial pulses measurement. J Phys Conf Ser 1228:012075. https://doi.org/10.1088/1742-6596/1228/1/012075
Parker PA, Scott RN (1973) Statistics of the myoelectric signal from monopolar and bipolar electrodes. Med BioI Eng 11:591–596. https://doi.org/10.1007/BF02477404
Qidwai U, Chaudhry J, Jabbar S, Zeeshan HMA, Janjua N, Khalid S (2019) Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems. J Ambient Intell Human Comput 10:4085–4097. https://doi.org/10.1007/s12652-018-1091-x
Ramji N, Toth C, Kennedy J, Zochodne DW (2007) Does diabetes mellitus target motor neurons? Neurobiol Dis 26:301–311. https://doi.org/10.1016/j.nbd.2006.11.016
Shima K, Tsuji T (2007) FPGA implementation of a probabilistic neural network using delta-sigma modulation for pattern discrimination of EMG signals. In: 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, pp 402–407. https://doi.org/10.1109/ICCME.2007.4381765
Suda EY, Gomes AA, Butugan MK, Sacco IC (2016) Muscle fiber conduction velocity in different gait phases of early and late-stage diabetic neuropathy. J Electromyogr Kinesiol 30:263–271. https://doi.org/10.1016/j.jelekin.2016.08.006
Sudharsan RR, Deny J (2020) Field programmable gate array (FPGA)-based fast and low-pass finite impulse response (FIR) filter. Intelligent computing and innovation on data science. Springer, Singapore, pp 199–206
Sudharsan RR, Deny J, Kumaran EM, Geege AS (2020) An analysis of different biopotential electrodes used for electromyography. https://doi.org/10.21272/jnep.12(1).01020
ul Islam I, Ullah K, Afaq M, Chaudary MH, Hanif MK (2019) Spatio-temporal sEMG image enhancement and motor unit action potential (MUAP) detection: algorithms and their analysis. J Ambient Intell Human Comput 10:3809–3819. https://doi.org/10.1007/s12652-019-01411-1
Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2018) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput 21:681–690. https://doi.org/10.1007/s10586-017-0977-2
Watanabe K, Gazzoni M, Holobar A, Miyamoto T, Fukuda K, Merletti R, Moritani T (2013) Motor unit firing pattern of vastus lateralis muscle in type 2 diabetes mellitus patients. Muscle Nerve 48:806–813. https://doi.org/10.1002/mus.23828
Wöhrle H, Tabie M, Kim SK, Kirchner F, Kirchner EA (2017) A hybrid FPGA-based system for EEG-and EMG-based online movement prediction. Sensors Basel 17:1552. https://doi.org/10.3390/s17071552
Wan X et al (2019) A review on electroencephalogram based brain computer interface for elderly disabled. IEEE Access 7:36380–36387. https://doi.org/10.1109/ACCESS.2019.2903235
Zwarts MJ (1989) Evaluation of the estimation of muscle fiber conduction velocity. Surface versus needle method. Clin Neurophysiol 73:544–548. https://doi.org/10.1016/0013-4694(89)90263-0
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03960-4
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Sudharsan, R.R., Deny, J., Muthukumaran, E. et al. RETRACTED ARTICLE: FPGA based peripheral myopathy monitoring using MFCV at dynamic contractions. J Ambient Intell Human Comput 12, 7019–7027 (2021). https://doi.org/10.1007/s12652-020-02363-7
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DOI: https://doi.org/10.1007/s12652-020-02363-7