Development of Synchronized Biomechanics Sensors Detection Software

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Abstract:

Biomechanics is a relatively new discipline where engineering and mechanics principles are applied to the understanding of biological organism. Biomechanics simply taking place to study the mechanics of tissues, joints, human movements, circulatory system and digestive tract. One of the most challenging applications of biomechanics is in the field of sports and sports medicine in which the prevention of sports injuries is highly required. To understand and to diagnosis tissues abnormalities, mechanics of neuromuscular control, mechanics of cardiovascular function, a synchronizing interface with multi biomechanics sensors is developed in this effort. The interface shows different data recorded from several sensors during a physical activity made by the subject. These synchronized and combined data will help the user to make a specific diagnosis of the subject health. Moreover, these data will give an insight understanding on the correlation between variety aspects of biomechanics.

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Periodical:

Advanced Materials Research (Volumes 706-708)

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771-775

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Online since:

June 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Joseph N.F. Mak, Yong Hu, Keith D.K. Luk (2010). An automated ECG-artifact removal method for trunk muscle surface EMG recordings, Medical Engineering & Physics 32 (2010) 840–848

DOI: 10.1016/j.medengphy.2010.05.007

Google Scholar

[2] C. Marque, C. Bisch, R. Dantas, S. Elayoubi, V. Brosse, C. Perot. Adaptive filtering for ECG rejection from surface EMG recordings Journal of Electromyography and Kinesiology 15 (2005) 310–315

DOI: 10.1016/j.jelekin.2004.10.001

Google Scholar

[3] Nienke W. Willigenburg, Andreas Daffertshofer, Idsart Kingma, Jaap H. van Dieën, Removing ECG contamination from EMG recordings: A comparison of ICA-based and other filtering procedures, Journal of Electromyography and Kinesiology 22 (2012) 485–493.

DOI: 10.1016/j.jelekin.2012.01.001

Google Scholar

[4] Alan J. Fredlund, and John. T Cacioppo, 1986. Guidelines for Human Electromyography Research. Psychophysiology, vol (23).no,05

Google Scholar

[5] Bruno Mambrito, Carlo J. De Luca, Acquisition Decomposition of the EMG signal, Prog. clin. Neurophysiol. Vol(10) 1983. pp: 52-72

Google Scholar

[6] Takahiro Kawaga, Yu Ohta, and Yoji Uno,. "State-dependent corrective reactions for backward balance losses during human walking". Human Movement Science, Vol(30). 2011. pp: 1210-1224

DOI: 10.1016/j.humov.2011.03.003

Google Scholar

[7] Khalid Bashir, Tao Xiang and Shaogang Gong, Gait recognition without subject cooperation. Pattern Recognition Letters. 31(2010) 2052-2060.

DOI: 10.1016/j.patrec.2010.05.027

Google Scholar

[8] Hyun Gu Kang, Jonathan B Dingwell, Dynamics and stability of muscles activations during walking in healthy young and older adults. Journal of Biomechanics. vol 42(2009).pp, 2231-2237.

DOI: 10.1016/j.jbiomech.2009.06.038

Google Scholar

[9] Jing Xian Li, Youlian Hong, Kinematic and Electromyographic Analysis of the Trunk and lower Limbs During Walking in Negative Heeled Shoes. Journal of American Pediatric Medical Association.vol 97(6).2007, pp: 447-456.

DOI: 10.7547/0970447

Google Scholar