Evidence map›Paper›PMID 40218620›Full record

ArticleSensors (Basel, Switzerland)2025

Innovative Applications of Laser Technology for Bedridden People Posture Monitoring.

David Sánchez Canzobre, Pablo Pardiñas Torrado, Javier Lamas Vigo, Alberto Ramil Rego

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

4 authors.

David Sánchez CanzobreCentro de Investigación en Tecnologías Navales e Industriales, Universidade da Coruña, Campus Industrial de Ferrol, 15403 Ferrol, Spain.ORCID 0009-0002-2022-897X
Pablo Pardiñas TorradoCentro de Investigación en Tecnologías Navales e Industriales, Universidade da Coruña, Campus Industrial de Ferrol, 15403 Ferrol, Spain.
Javier Lamas VigoCentro de Investigación en Tecnologías Navales e Industriales, Universidade da Coruña, Campus Industrial de Ferrol, 15403 Ferrol, Spain.ORCID 0000-0002-3497-9363
Alberto Ramil RegoCentro de Investigación en Tecnologías Navales e Industriales, Universidade da Coruña, Campus Industrial de Ferrol, 15403 Ferrol, Spain.ORCID 0000-0002-5333-9425

Funding

MCIN/AEI/10.13039/501100011033 and ERDF "A way of making Europe", by the European Union. Grant PID2021-123948OB-100
6 · The paper itself

Abstract

This article presents an innovative system for the automated classification of postures of bedridden people using laser technology. The proposed method leverages low-cost structured light hardware and a custom algorithm to determine the side on which a person is lying. The solution prioritizes patient privacy by avoiding the capture of visual images while maintaining high accuracy in classifying postures. The system includes an enhanced hardware design with structured infrared light that is tailored for affordability and simplicity, and it incorporates an improved algorithm capable of robust posture detection. The experimental results obtained under real-world conditions validate the system's performance and highlight its potential for deployment in clinical and home care environments. This work expands upon a previous study presented at the 6th International Conference on Application of Optics and Photonics, offering additional insights, refined methodologies, and a broader scope of validation.

Indexed as

LasersPostureAlgorithmsHumansMonitoring, Physiologicdiffractive optical elementdisabled people carelaserstructured light

Identifiers

PMID40218620
PMCPMC11991273

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.