Evidence map›Paper›PMID 38982105›Full record

ArticleScientific reports2024

Embedding textile capacitive sensing into smart wearables as a versatile solution for human motion capturing.

Daniel Geißler, Bo Zhou, Hymalai Bello, Joanna Sorysz, Lala Ray, Hamraz Javaheri, Matthias Rüb, Jan Herbst, Esther Zahn, Emil Woop and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Daniel GeißlerEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany. daniel.geissler@dfki.de.ORCID 0000-0003-2643-4504
Bo ZhouEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Hymalai BelloEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Joanna SoryszEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Lala RayEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.ORCID 0000-0002-7133-0205
Hamraz JavaheriEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Matthias RübIntelligent Networks, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Jan HerbstIntelligent Networks, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Esther ZahnDesign Research eXplorations, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.
Emil WoopDesign Research eXplorations, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.
Sizhen BianInformation Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland.
Hans D SchottenIntelligent Networks, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Gesche JoostDesign Research eXplorations, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.
Paul LukowiczEmbedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01IW20002
6 · The paper itself

Abstract

This work presents a novel and versatile approach to employ textile capacitive sensing as an effective solution for capturing human body movement through fashionable and everyday-life garments. Conductive textile patches are utilized for sensing the movement, working without the need for strain or direct body contact, wherefore the patches can sense only from their deformation within the garment. This principle allows the sensing area to be decoupled from the wearer's body for improved wearing comfort and more pleasant integration. We demonstrate our technology based on multiple prototypes which have been developed by an interdisciplinary team of electrical engineers, computer scientists, digital artists, and smart fashion designers through several iterations to seamlessly incorporate the technology of capacitive sensing with corresponding design considerations into textile materials. The resulting accumulation of textile capacitive sensing wearables showcases the versatile application possibilities of our technology from single-joint angle measurements towards multi-joint body part tracking.

Indexed as

MovementTextilesWearable Electronic DevicesElectric CapacitanceEquipment DesignHumans

Identifiers

PMID38982105
PMCPMC11233671

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.