Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning

Abstract

The increasing demand for motion tracking systems has been accelerated by advancements in virtual reality (VR) and motion reconstruction technologies. Combined with emerging innovations in the Internet of Things (IoT), these systems have unlocked transformative applications, from immersive user experiences to personalized healthcare solutions. However, conventional motion tracking systems often fall short of delivering sophisticated tracking and feedback capabilities, while systems designed for detailed motion analysis are typically costly and limited to controlled environments. This study introduces a cost-effective motion tracking system that integrates full-body motion analysis with real-time, bidirectional haptic feedback. Utilizing flexible, patch-type epidermal haptic devices alongside a remote machine‑learning framework, the system captures full‑body motion and delivers personalized, time‑synchronized feedback. Its closed‑loop design lays the groundwork for real‑time bidirectional haptic cues that accommodate user responsiveness and engagement. This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine