ArticleNature communications2025
Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Inverse Design of Pre-Strain via a Predictive Electromechanical Model for Enhanced Strain Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Materials-Structure-Hardware-Algorithm Co-Driven Hierarchical Optimization for Flexible Sensors.Nano-micro letters · 2026Review
- Large-Area Noise-Resilient Multiplexing Triboelectric Biomechanical Sensing on Clothing for High-Precision Full-Body Motion Capture in Wearables.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Transforming surgical ward rounds: constructivist pedagogy, structured models, and intelligent technologies.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.