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Torque Analysis of Male-Female Gait and Identification using Machine Learning

Publication Type : Conference Paper

Thematic Areas : Learning-Technologies, Medical Sciences, Biotech

Publisher : Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India.

Source : Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, Sept 19-22, 2018.

Url : https://scholar.google.com/scholar?oi=bibs&cluster=14938073353141413767&btnI=1&hl=en

Keywords : Accelerometers, Gait analysis, Machine learning, movement

Campus : Amritapuri

School : School of Biotechnology

Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology

Department : Computational Neuroscience Laboratory, biotechnology

Verified : Yes

Year : 2018

Abstract : Relevance of gait-attributed changes in male and female subjects could be a significant tool for clinicians to identify and diagnose movement disorders. In this paper, we used 6 low-cost wearable mobile phone sensors to extract gait data. Classification and inverse dynamic analysis were performed to identify gait changes for distinctly identifying gender-specific characteristics. Machine learning algorithms were used to classify the joint kinetic and kinematic parameters. Based on current analysis and in the context wearable low-cost sensors, the change in average torque amplitude and torque differences across right and left hip and ankle could be the relevant classification biomarker.

Cite this Research Publication : Nutakki C., Edakkepravan H., Gunasekaran S., Ramachandran L. P., Sasi V, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Torque Analysis of Male-Female Gait and Identification using Machine Learning”, in Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, Sept 19-22, 2018.

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