Classifying activities of daily living based on hybrid features from vertical ground reaction force data

Leg posture plays a crucial role in performing activities of daily living (ADL) by influencing balance, mobility, and overall quality of life. The objective of this study was to classify various instrumental activities of daily living (IADL) leg postures using 1D force data using machine learning techniques. The study collected data from a SENSIX force platform, which measured the vertical ground reaction force (VGRF) and center of pressure (COP) data from 25 participants performing four static postures. These postures included double-leg standing, toe standing, single-leg standing, and squat standing. The study used five statistical and six nonlinear time series features from the VGRF data and COP to classify IADL postures. The artificial neural network performed the best, with an accuracy of approximately 98%, in organizing postures for everyday life activities.