A Probabilistic Model of Human Activity Recognition with Loose Clothing.

Tianchen Shen, Irene Di Giulio, Matthew Howard

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

3 authors.

Tianchen ShenCentre for Robotics Research, Department of Engineeing, King's College London, London WC2R 2LS, UK.ORCID 0000-0001-7807-1966

Irene Di GiulioCentre for Human and Applied Physiological Sciences, King's College London, London SE1 1UL, UK.

Matthew HowardCentre for Robotics Research, Department of Engineeing, King's College London, London WC2R 2LS, UK.ORCID 0000-0003-2534-4865

Funding

Engineering and Physical Sciences Research Council EP/M507222/1King's College London and china scholarship council ‭202008320267‬

6 · The paper itself

Abstract

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve

Indexed as

ElectronicsModels, StatisticalClothingHuman ActivitiesHumansMotionelectronic textilehuman motion detection and trackingwearable sensing

Identifiers

PMID37430582

DOI10.3390/s23104669

PMCPMC10220539

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.