@Article{jai.2026.080854,
AUTHOR = {Ashwin Sankaran},
TITLE = {Intra-Modal Feature Translation for Robust Kinematic Modeling in Wearable Health Applications},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {8},
YEAR = {2026},
NUMBER = {1},
PAGES = {403--423},
URL = {http://www.techscience.com/jai/v8n1/68238},
ISSN = {2579-003X},
ABSTRACT = {Human Motion Kinetics Recognition (HMKR) plays a pivotal role in advancing intelligent monitoring systems for public safety, machine interaction, and elderly-care technologies. Despite the increasing availability of affordable Inertial Measurement Units (IMUs), achieving accurate full-body motion analysis remains challenging due to sensor noise and variability across real-world environments. Addressing these limitations requires robust processing strategies capable of handling complex inertial time-series data. This research introduces an optimized HMKR system that leverages a comprehensive pipeline focusing on inertial sensor time-series data. In this research, we introduce an HMK-based recognition system that primarily focuses on six-degree-of-freedom (6-DoF) sensors that capture translational and rotational movements of the human body to improve health and safety applications. The study also performed a simulation study on two public benchmark datasets. The research’s main contribution lies in integrating a preprocessing approach, including state-of-the-art denoising and a multi-stream feature set (statistical, frequency-domain, and vector features), that effectively mitigates sensor limitations and high-dimensionality issues via principal component analysis. These results justify the proposed system’s reliability and its significance to substantially enhance health and safety monitoring applications, particularly in unconstrained public health contexts.},
DOI = {10.32604/jai.2026.080854}
}