Evidence map›Paper›PMID 35891018›Full record

ArticleSensors (Basel, Switzerland)2022

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion.

Katia Bourahmoune, Karlos Ishac, Toshiyuki Amagasa

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
11.8field-weighted citation impact, top 1% of its field
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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Katia BourahmouneGraduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan.ORCID 0000-0001-5572-7811
Karlos IshacFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.ORCID 0000-0001-6215-3297
Toshiyuki AmagasaCenter for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Japan.ORCID 0000-0003-0595-2230
University of Tsukuba · JPUniversity of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system's performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method's performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.

Indexed as

PostureSitting PositionHumansMachine LearningRecognition, PsychologySensationapplied machine learninghuman well-beingIoTposture recognitionpressure sensing

Identifiers

PMID35891018
PMCPMC9320787
OpenAlexW4285803720

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.