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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Sang Uk Park,
  • Hee Kyu Lee,
  • Hyun Bin Kim,
  • Doyoung Kim,
  • Wooseok Kim,
  • Janghoon Joo,
  • Bogeun Kim,
  • Byeong Woon Lee,
  • Yei Hwan Jung,
  • Sungjun Park,
  • Il Yong Chun,
  • Hyoyoung Jeong,
  • Joohoon Kang,
  • Jae-Young Yoo,
  • Sang Min Won

摘要

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.