ArticleSensors (Basel, Switzerland)2019
A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Exploration and analysis of the application of the internet of things in nursing education in the 5G era: innovation of nursing education methods.BMC medical education · 2026Review
- Intelligent systems for sitting posture monitoring and anomaly detection: an overview.Journal of neuroengineering and rehabilitation · 2024Review
- A Multimodal Approach for Real Time Recognition of Engagement towards Adaptive Serious Games for Health.Sensors (Basel, Switzerland) · 2022Article
- Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair.Sensors (Basel, Switzerland) · 2022Article
- A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders.Sensors (Basel, Switzerland) · 2021Article
- Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution.Sensors (Basel, Switzerland) · 2021Article
- Detection of sitting posture using hierarchical image composition and deep learning.PeerJ. Computer science · 2021Article
- A Novel Helmet Fitness Evaluation Device Based on the Flexible Pressure Sensor Matrix.Sensors (Basel, Switzerland) · 2019Article
- Implementation of Distracted Estimation System based on Sensor Fusion through Correlation Analysis with Concentration.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the chair. The position of the sensors was selected for maximizing the detection of variations of user's posture. The effectiveness of the designed system was evaluated through an experiment where increasing stress levels were obtained by administering a Stroop test. The collected results had been analyzed by considering three different time intervals based on the difficulty level of the test (low, medium, and high). A transition analysis conducted on postures assumed during the test showed that participants reached a different posture at the end of the test, when the cognitive engagement increased, with respect to the beginning. This evidence highlighted the presence of movement presumably due to the increased cognitive engagement. Overall, the performed analysis showed the proposed monitoring system could be used to identify body posture variations related to different levels of engagement of a seated user while performing cognitive tasks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.