Digital Sports Rehabilitation and Health Management: An IoT-Driven Multi-Sensor Fusion Approach

Abstract

The convergence of Internet-of-Things (IoT) architectures and wearable technologies offers transformative, albeit technically challenging, pathways for modernizing digital sports rehabilitation and long-term health monitoring frameworks. Traditional assessment methodologies frequently suffer from diagnostic subjectivity and data fragmentation; yet, developing a seamless system presents notable constraints regarding heterogeneous data synchronization and sensor signal noise. Addressing these adjustments, this paper proposes an integrated digital rehabilitation system leveraging a multi-layered IoT framework and multi-sensor data fusion. By implementing adaptive filtering and spatial alignment algorithms to synthesize inputs from inertial and physiological sensors, the system captures complex biomechanical trajectories during training. Empirical validations suggest that the fusion mechanism enhances motion tracking accuracy and posture evaluation to some extent, though unexplained variances across atypical movement profiles indicate potential biases that require multi-perspective calibration. Considering these factors, the collaborative architecture elevates quantitative clinical evaluation paradigms while establishing a proactive health management model, though further research is needed to evaluate its longitudinal efficacy across broader demographic cohorts.