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Blind source separation of single-sensor recordings : Application to ground reaction force signals

Abstract : The purpose of the presented work is to develop a customized Single-channel Blind Source Separation technique that aims to separate cyclostationary and transient pulse-like patterns/sources from a linear instantaneous mixture of unknown sources. For that endeavor, synthetic signals of the mentioned characteristic were created to confirm the separation success, in addition to real life signals acquired throughout an experiment in which experienced athletes were asked to participate in a 24-hour ultra-marathon in a lab environment on an instrumented treadmill through which their VGRF, which carries a cyclosparse Impact Peak, is continuously recorded with very short discontinuities during which blood is drawn for in-run testing, short enough not to provide rest to the athletes. The synthetic and VGRF signals were then pre-processed, processed for Impact Pattern extraction via a customized Single-channel Blind Source Separation technique that we termed Cyclo-sparse Non-negative Matrix Factorization and analyzed for fatigue assessment. As a result, the Impact Patterns for all of the participating athletes were extracted at 10 different time intervals indicating the progression of the ultra-marathon for 24 hours, and further analysis and comparison of the resulting signals proved major significance in the field of fatigue assessment; the Impact Pattern power monotonically increased for 90% of the subjects by an average of 24.4 15% with the progression of the ultra-marathon during the 24-hour period. Upon computation of the Impact Pattern separation algorithm, fatigue progression showed to be manifested by an increase in reliance on heel-strike impact to push to the bodyweight as a compensation for the decrease in muscle power during propulsion at toe-off. This study among other presented work in the field of VGRF processing forms methods that could be implemented in wearable devices to assess and track runners’ gait as a part of sports performance analysis, rehabilitation phase tracking and classification of healthy vs. unhealthy gait.
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Submitted on : Friday, October 4, 2019 - 9:58:08 AM
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  • HAL Id : tel-02305364, version 1



Ramzi El Halabi. Blind source separation of single-sensor recordings : Application to ground reaction force signals. Physics [physics]. Université de Lyon, 2018. English. ⟨NNT : 2018LYSES031⟩. ⟨tel-02305364⟩



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