Work

A Skin‐Adherent Magneto‐Inertial Wearable for Real‐Time Joint Motion Analysis

Year: 2025

Type: article

Abstract: Musculoskeletal physiotherapy is evolving with the integration of wearable technologies that enable continuous monitoring and personalized rehabilitation. This study presents a multimodal, wireless, and cost‐effective wearable system designed to assess and classify joint motion using magneto‐inertial sensing. The system incorporates a flexible PDMS‐NdFeB magnetic skin patch and a compact sensor module with a 9‐degree‐of‐freedom IMU. Real‐time joint kinematics are wirelessly transmitted to a custom mobile application, enabling interactive visualization and feedback. Biomechanical modeling is employed to evaluate muscle contributions and joint dynamics across the wrist, elbow, and knee. Processed magnetic signals are used to classify range of motion (ROM) into three categories—Limited, Normal, and Hypermobility—through traditional machine learning and deep learning models. A 1D convolutional neural network (1D‐CNN) achieves the highest classification accuracy (95.3%). The proposed system demonstrates strong potential for enhancing musculoskeletal rehabilitation by providing accurate, real‐time assessments and individualized treatment feedback.

Source: Advanced Sensor Research

DOI: 10.1002/adsr.202500087

Authors: Montserrat Ramirez‐De Angel, Eckaard le Roux, Khaled N. Salama

Institution: King Abdullah University of Science and Technology

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,750