Evidence map›Paper›PMID 37430744›Full record

ArticleSensors (Basel, Switzerland)2023

IoT System for Real-Time Posture Asymmetry Detection.

Monica La Mura, Marco De Gregorio, Patrizia Lamberti, Vincenzo Tucci

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. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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.

Monica La MuraDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy.ORCID 0000-0001-8410-2511
Marco De GregorioDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy.
Patrizia LambertiDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy.ORCID 0000-0002-5037-8074
Vincenzo TucciDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy.ORCID 0000-0002-3952-5256

Funding

University of Salerno 300638FRB19TUCCIUniversity of Salerno 300638FRB20LAMBE
6 · The paper itself

Abstract

The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common musculoskeletal disorders related to the persistence of incorrect sitting postures during work hours. This work proposes a low-cost IoT measurement system for monitoring the sitting posture symmetry and generating a visual alert to warn the worker when an asymmetric position is detected. The system employs four force sensing resistors (FSR) embedded in a cushion and a microcontroller-based read-out circuit for monitoring the pressure exerted on the chair seat. Java-based software performs the real-time monitoring of the sensors' measurements and implements an uncertainty-driven asymmetry detection algorithm. The shifts from a symmetric to an asymmetric posture and vice versa generate and close a pop-up warning message, respectively. In this way, the user is promptly notified when an asymmetric posture is detected and invited to adjust the sitting position. Every position shift is recorded in a web database for further analysis of the sitting behavior.

Indexed as

PostureSitting PositionAlgorithmsDatabases, FactualHumansInternetdigital healthforce sensing resistors (FSR)IoTmonitoringposturereal-time

Identifiers

PMID37430744
PMCPMC10222481

What OpenQuestion holds

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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.