Wireless Motion Variability Analysis with Integrated Triboelectric Textiles via Displacement Current

Triboelectric textiles have recently garnered significant attention for their ability to detect and analyze body movements. However, the transmission of wireless signals from natural human motion via integrated triboelectric wearables has been hampered by the reliance on nontextile components, physical interface spacers, and wired connections. Here, we report a wireless biosensing triboelectric wearable system that exploits inherent microgaps within electrospun nanofibers to create a triboelectric textile, seamlessly woven into the garment at various positions. This design enables untethered continuous biosignal monitoring, including those indicative of stride-time variability and joint-time variability analysis from daily activities and exercise. Signals are wirelessly transmitted via displacement current─enhanced by a textile inductor─to an external reader. This innovation enables wireless signal transmission directly from garments without the need for external energy sources. This work advances the development of fully textile-based wireless wearable sensors contributing to next-generation motion tracking, health monitoring, and smart clothing technologies.

Authors

Institutions

Publication Details

Journal
ACS Nano
Published
2025-05-23
DOI
https://doi.org/10.1021/acsnano.4c18766
Citations
7
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
4.72
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Wireless Motion Variability Analysis with Integrated Triboelectric Textiles via Displacement Current

Yinghong Wu, Carlo Menon, Sunil Kumar Sailapu, Chiara Spasiano
7 citations
ACS Nano
Advanced Sensor and Energy Harvesting Materials
4.72
article

Wireless Motion Variability Analysis with Integrated Triboelectric Textiles via Displacement Current

Yinghong Wu, Carlo Menon, Sunil Kumar Sailapu, Chiara Spasiano
article en
7 citations

Abstract

Triboelectric textiles have recently garnered significant attention for their ability to detect and analyze body movements. However, the transmission of wireless signals from natural human motion via integrated triboelectric wearables has been hampered by the reliance on nontextile components, physical interface spacers, and wired connections. Here, we report a wireless biosensing triboelectric wearable system that exploits inherent microgaps within electrospun nanofibers to create a triboelectric textile, seamlessly woven into the garment at various positions. This design enables untethered continuous biosignal monitoring, including those indicative of stride-time variability and joint-time variability analysis from daily activities and exercise. Signals are wirelessly transmitted via displacement current─enhanced by a textile inductor─to an external reader. This innovation enables wireless signal transmission directly from garments without the need for external energy sources. This work advances the development of fully textile-based wireless wearable sensors contributing to next-generation motion tracking, health monitoring, and smart clothing technologies.

ACS NanoVol. 19(22)
ETH Zurich (CH), Institute of Process Engineering (CN), Politecnico di Milano (IT)
Affordable and clean energy
Openalex Percentile: Top 5%
Advanced Sensor and Energy Harvesting Materials
4.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Wireless Motion Variability Analysis with Integrated Triboelectric Textiles via Displacement Current — Yinghong Wu, Carlo Menon, et al. · ACS Nano (2025) | TGRS Research Map | TGRS