Publication

Abstract : Quantifying gait analysis helps to understand neurological gait disorders, balance and posture that may provide information to design and develop exoskeleton technologies. In this current study, we employed mobile phone accelerometers to understand the kinematic differences between male and female based on Froude (Fr) number as a combination of velocity and acceleration. The average Fr number of female subjects showed higher amplitude in hip and ankle whereas in male subjects' activity was higher in knee and shoulder suggesting a possible fall risk. The gait variability between the subject groups with different weights were analysed during stance and swing. Fr numbers of shoulder, elbow, wrist, pelvis, and ankle showed significant differences across groups which indicates that gait patterns can be classified according to weight and age. Also, how gait spatio-temporal parameters changed during activity of lower body joints in young adults while transporting backpack with multiple loads was assessed. For all the subjects, an instantaneous variation in pelvis was observed while carrying loads that can be attributed to result in a postural change. The extracted patterns may be used as potential markers in machine learning/deep learning to understand human gait which can be of great use to clinicians to predict the pathological conditions.