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Human Activity Recognition (HAR) is pivotal for automating the detection and classification of human movements. Technological advances in data collection from sensors and cameras is making automatic HAR increasingly available to support robotics-assisted rehabilitation, where the detection of abnormal patterns in movement and gait can aid clinical evaluation of patients with diverse disorders. HAR typically employs Machine Learning (ML) or Deep Learning (DL) techniques to analyze sensor data patterns for various activities. However, the generalizability of ML/DL models remains underexplored, particularly concerning the impact of different training and evaluation settings. Overfitting is a common challenge, especially in medical applications where user-dependent training data hinder generalization. Investigating the performance of ML and DL algorithms after a cross-subject (CS) and a noncross-subject (NCS) division of the dataset into training and test sets is crucial. This study aims to systematically compare various ML and DL algorithms for HAR, evaluating their performance across the CS and NCS settings to discern the impact of inter-subject variability on model generalizability.
Pe et al. (Wed,) studied this question.