AUTHOR=Kamal Saleha , Alshehri Mohammed , AlQahtani Yahya , Alshahrani Abdulmonem , Almujally Nouf Abdullah , Jalal Ahmad , Liu Hui 

TITLE=A novel multi-modal rehabilitation monitoring over human motion intention recognition

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1568690

DOI=10.3389/fbioe.2025.1568690

ISSN=2296-4185

ABSTRACT=Human Motion Intention Recognition (HMIR) plays a vital role in advancing medical rehabilitation and assistive technologies by enabling the early detection of pain-indicative actions such as sneezing, coughing, or back discomfort. However, existing systems struggle with recognizing such subtle movements due to complex postural variations and environmental noise. This paper presents a novel multi-modal framework that integrates RGB and depth data to extract high-resolution spatial-temporal and anatomical features for accurate HMIR. Our method combines kinetic energy, optical flow, angular geometry, and depth-based features (e.g., 2.5D point clouds and random occupancy patterns) to represent full-body dynamics robustly. Stochastic Gradient Descent (SGD) is employed to optimize the feature space, and a deep neuro-fuzzy classifier is proposed to balance interpretability and predictive accuracy. Evaluated on three benchmark datasets—NTU RGB + D 120, PKUMMD, and UWA3DII—our model achieves classification accuracies of 94.50%, 91.23%, and 88.60% respectively, significantly outperforming state-of-the-art methods. This research lays the groundwork for future real-time HMIR systems in smart rehabilitation and medical monitoring applications.