Open Access

ARTICLE

Intra-Modal Feature Translation for Robust Kinematic Modeling in Wearable Health Applications

Ashwin Sankaran^*

MSA Department, Central Michigan University, Mount Pleasant, MA, USA

* Corresponding Author: Ashwin Sankaran. Email:

(This article belongs to the Special Issue: Advances in Artificial Intelligence for Engineering and Sciences)

Journal on Artificial Intelligence 2026, 8, 403-423. https://doi.org/10.32604/jai.2026.080854

Received 16 February 2026; Accepted 02 June 2026; Issue published 29 July 2026

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.

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Keywords

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