Classification and Kinetic Analysis of Healthy Gait using Multiple Accelerometer Sensors

• Published in: Procedia computer science Vol. 171; pp. 395 - 402
• Main Authors: Nutakki, Chaitanya, Mathew, Reuben Jacob, Suresh, Amritha, Vijay, Anjitha R, Krishna, Swetha, Babu, Anandu S, Diwakar, Shyam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2020
• Subjects: Gait analysis; kinematics; kinetics; machine learning models; mobile phone sensors; multi joint; machine learning models; multi joint; kinematics; kinetics; Gait analysis; mobile phone sensors
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2020.04.041

Analysis of walking-related pathologies can be done using low-cost wearable sensors that could be perceived as efficient and plausible methods for identifying gait abnormalities and neurodegenerative disorders in low economy nations. In this study, we have used mobile phone sensors that contain both accelerometers as well as gyroscopes in order to extract kinematic information and analysis of physiologically-relevant parameters to classify stance and swing among healthy volunteering subjects. Sufficient joint torque required for each limb during stance and swing phases were estimated among the subjects with weights and relevant attributes analyzed using attribute evaluators. The validation of this data was performed by pattern reconstructions in a gait simulation platform and testing whether they reproduced gait phases. Gait data categorization allowed kinematics and dynamics to be mapped to male and female subjects allowing differentiation between the genders. By translating the joint-kinematic data classification and performing torque analysis real-time allows an extension in to gait-based reconstruction of human walking and may facilitate future prosthetic devices.