A Novel Multi-Modal Rehabilitation Monitoring Over Human Motion Intention Recognition

Saleha Kamal¹Mohammed Alshehri²Yahya Alqahtani²Abdulmonem Alshahrani²Nouf Abdullah Almujally³Ahmad Jalal^1*Hui Liu^4*

• ¹Air University, Islamabad, Pakistan
• ²King Khalid University, Abha, Saudi Arabia
• ³Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• ⁴University of Bremen, Bremen, Bremen, Germany

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.