Human manner of walking characterized by kinematic parameters measure posture-gait control characterizing the dynamic changes in body parts with the involvement of multi-sensory patterns processed by different parts of the brain. In this study, low-cost sensors have been used to collect gait signals and identify the features responsible for differentiating the gait phases (swing/stance). Dataset was obtained for a total of 160 trails with 5 gait cycles per trail from healthy volunteers (n=20). Torque involved during progressive gait was also estimated to model regulation of the body for maintaining balance in gait and posture. Additionally, we also investigated EEG and gait correlates by identifying the brain regions that are active during movement initiation and during stance and swing (a progressive gait) using electroencephalography. While identifying key biomarkers relevant for posture control and gait, this could enhance low-cost detection of movement related diseases in technically challenged regions.
Balachandran A., Nutakki C., Sandeep Bodda, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Experimental Recording and Assessing Gait Phases Using Mobile Phone Sensors and EEG”, Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018). IEEE, Bangalore, India, 2018.