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.
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, 2018.