Evidence map›Paper›PMID 41779855›Full record

ArticleScience advances2026

Textile suit for anywhere full-body motion capture.

Huanbo Sun, Yao Feng, Pei-Chun Kao, Michael J Black, Rebecca Kramer-Bottiglio

Abstract read
In one paragraph

Article in Science advances, 2026. 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

5 authors.

Huanbo SunYale University, New Haven, CT, USA.ORCID 0000-0002-2363-5776
Yao FengMax Planck Institute for Intelligent Systems, Tübingen, Germany.
Pei-Chun KaoUniversity of Massachusetts Lowell, Lowell, MA, USA.ORCID 0000-0001-8603-3898
Michael J BlackMax Planck Institute for Intelligent Systems, Tübingen, Germany.ORCID 0000-0001-6077-4540
Rebecca Kramer-BottiglioYale University, New Haven, CT, USA.ORCID 0000-0003-2324-8124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable technology has shown notable promise for tracking human motion, offering valuable insights for fields ranging from biomechanics to healthcare. Traditional motion capture systems, however, are often bulky and disruptive, making them impractical for daily use. Advances in textile-based sensing offer a promising alternative, enabling seamless integration of air- and sweat-permeable sensors into everyday clothing. Here, a sensorized textile suit designed for unobtrusive full-body motion capture is presented. The suit is capable of accurately tracking complex movements without interfering with routine activities. This wearable, using an individual-customized network of fabric-based sensors, autonomously identifies and monitors movement angles and patterns, providing insights into physical range, activity frequency, and exertion levels. Language models are shown to interpret motion data into descriptive language, enhancing its potential for real-world applications. This sensorized textile suit and corresponding algorithms represent a step forward in accessible, continuous movement monitoring in the form of everyday clothing, opening avenues for studying human behavior and health in natural environments.

Indexed as

ClothingMotion CaptureTextilesWearable Electronic DevicesAlgorithmsBiomechanical PhenomenaHumansMovement

Identifiers

PMID41779855
PMCPMC12959391

What OpenQuestion holds

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LicenceCC BY
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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.