AUTHOR=Ashraf Muhammad Abrar , Wu Yanfeng , Najam Shaheryar , Alshehri Mohammed , AlQahtani Yahya , Aljuaid Hanan , Jalal Ahmad , Liu Hui 

TITLE=Deep multimodal biomechanical analysis for lower back pain rehabilitation to improve patients stability

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.2025.1631910

DOI=10.3389/fbioe.2025.1631910

ISSN=2296-4185

ABSTRACT=IntroductionAdvancements in artificial intelligence are transforming rehabilitation by enabling scalable, patient-centric solutions within modern healthcare systems. This study introduces 3D-PoseFormer, a deep multimodal framework designed for the telerehabilitation of individuals with lower back pain (LBP).MethodsThe proposed system performs automated data acquisition using synchronized RGB and depth video streams to enable real-time, markerless, and sensor-free analysis of physiotherapy exercises. From the depth sensing module, 3D body joint positions are extracted and used to generate SMPL-based mesh vertices for detailed biomechanical analysis and postural representation. Simultaneously, RGB frames are processed using keypoint detection algorithms—Shi-Tomasi, AKAZE, BRISK, SIFT, and Harris corner detection. Extracted features are enhanced through semantic contour analysis of segmented body parts to capture localized appearance-based information relevant to LBP therapy. The fused multimodal features are then passed to a Transformer-based machine learning model that captures temporal motion patterns for accurate exercise classification and human intention recognition.ResultsThe system removes the need for wearable sensors and supports autonomous, continuous monitoring in home-based rehabilitation. Validation on the KIMORE dataset (baseline, including rehabilitation exercises by patients with lower back pain), mRI dataset (rehabilitation exercises), and UTKinect-Action3D dataset (comprising diverse subjects and activity scenarios) achieved state-of-the-art accuracies of 94.73%, 91%, and 94.2%, respectively.DiscussionResults demonstrate the robustness, generalizability, and clinical potential of 3D-PoseFormer in AI-assisted rehabilitation, offering a scalable and intelligent healthcare system for remote physiotherapy and patient monitoring.