Evidence map›Paper›PMID 40529117›Full record

ArticleWearable technologies2025

Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model.

Supachai Vorapojpisut, Suphawit Sansuk, Phoomtai Yindee, Darawadee Panich, Vinitha Puengtanom, Sairag Saadprai

Abstract read
In one paragraph

Article in Wearable technologies, 2025. 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

6 authors.

Supachai VorapojpisutFaculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.ORCID https://orcid.org/0000-0002-6320-9130
Suphawit SansukFaculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
Phoomtai YindeeFaculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
Darawadee PanichFaculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
Vinitha PuengtanomFaculty of Allied Health Sciences, Thammasat University, Pathumthani, Thailand.
Sairag SaadpraiFaculty of Allied Health Sciences, Thammasat University, Pathumthani, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Posture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess their posture. The Posture Lab focuses on two key aspects: Normal Head Posture (NHP) versus Forward Head Posture (FHP) measurement and thoracic spine kyphosis. Craniovertebral (CA) and Shoulder Angles (SA) quantify NHP and FHP. The Kyphosis Angle (KA) is for measuring normal thoracic spine and kyphosis. To measure these angles, the system uses computer vision technology with ArUco markers detection via a webcam to analyze head positions. Additionally, wearable accelerometer sensors measure kyphosis by checking the angles of inclination. The framework includes a web-based user interface for registration and specialized desktop applications for different measurement protocols. A RESTful API enables system communication and centralized data storage for reporting. The Posture Lab serves as an effective tool for organizations to evaluate employee postures and supports early intervention strategies, allowing timely referrals to healthcare providers if any potential musculoskeletal issues are identified. The Posture Lab has also shown medium to very high correlations with standard 2D motion analysis methods - Kinovea - for CA, SA, and KA in FHP with kyphosis measurements (

Indexed as

ArUco markerscomputer visionmusculoskeletal disordersprimary health carewearable sensors

Identifiers

PMID40529117
PMCPMC12170950

What OpenQuestion holds

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Registered trials

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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.