Abstract
This study presents a real-time visualization system of gait patterns of knee injured subjects for biofeedback monitoring and classification. The developed system includes non-invasive wireless body-mounted motion sensors for kinematics measurements of lower extremities, surface electromyography (EMG) system for relevant specific muscle activity measurements, a motion capture system for recording trial activities and custom-developed intelligent system software implemented using LabVIEW and MATLAB. The real-time biofeedback system provides a visual monitoring of individual and superimposed signals (kinematics, EMG and video data) in order to identify the knee joint abnormality and muscles strength during various ambulation activities performed by the subjects. It can facilitate the clinicians, physiotherapists and physiatrists in determining the impairments in the gait patterns the knee injured based on the data collected and identifying the subjects lacking behind the desired level of recuperation.
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Wulandari, P., Arosha Senanayake, S.M.N., Malik, O.A. (2016). A Real-Time Intelligent Biofeedback Gait Patterns Analysis System for Knee Injured Subjects. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_68
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DOI: https://doi.org/10.1007/978-3-662-49390-8_68
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