Analysis of Gait Dynamics using Motion and EMG Sensors

Abstract Gait analysis is crucial for understanding the biomechanical complexities of osteoarthritis and Total Hip Arthroplasty (THA). Traditional methods lack the comprehensive data needed for effective analysis. Integrating Internet of Things (IoT) technologies offers a promising solution to enhance gait analysis by utilizing multiple sensors to capture various aspects of movement dynamics. This study aims to innovate gait analysis for individuals with osteoarthritis by employing IoT technologies. The primary objective is to integrate data from 9-axis Inertial Measurement Unit (IMU) sensors, Force-Sensitive Resistor (FSR) sensors, and Electromyography (EMG) sensors to provide a detailed analysis of gait dynamics. By doing so, the study seeks to uncover intricate connections between kinematics, ground response forces, and muscle exertion during walking. The study utilizes IMU sensors to capture three-dimensional movement, FSR sensors to measure ground response forces, and EMG sensors to record muscle activation patterns. Advanced algorithms are employed to accompany and integrate the data from these sensors. Actors undergo gait analysis under various conditions, including normal walking and different perturbations, to simulate real-world scenarios. The integration of data from multiple sensors and the application of advanced algorithms reveals detailed insights into the biomechanical differences associated with osteoarthritis and THA. The analysis demonstrates the effectiveness of IoT-based gait analysis in providing valuable perceptivity into musculoskeletal conditions. The proposed methodology showcases the potential of IoT-based gait analysis in advancing our understanding of musculoskeletal conditions and enhancing patient care. By uncovering intricate connections between kinematics, ground response forces, and muscle exertion, this approach provides a foundation for further development of effective interventions and rehabilitative strategies.