Wearable Sensors for Human Position, Attitude and Motion Tracking

This Reprint brings together a focused collection of peer-reviewed contributions addressing the design, implementation, and real-world deployment of wearable sensor systems for human position, attitude, and motion tracking. Across the contributions, several technical directions emerge. First, advances in sensor design-including soft materials, textile-integrated electrodes, MEMS devices, and frequency-output sensing-demonstrate how hardware is becoming lighter, more flexible, and more energy-efficient without sacrificing precision. Second, algorithmic innovation plays a central role: recurrent neural networks, LSTM architectures, clustering-based personalization, and physics-aware modeling approaches are leveraged to improve robustness under noise, sensor reduction, and user variability. Third, system-level integration is emphasized, with distributed wearable computing frameworks and multimodal fusion strategies enabling scalable and low-latency motion estimation. Importantly, the works do not treat motion tracking as an isolated technical problem. Instead, they position wearable sensing within broader application domains such as rehabilitation monitoring, driver attention assessment, sports biomechanics, collaborative robotics, elderly health assessment, and human-machine interaction. This application-driven perspective highlights current challenges-calibration, personalization, energy constraints, and real-time processing-and proposes practical solutions grounded in experimental validation.