Evidence map›Paper›PMID 31003456›Full record

ArticleSensors (Basel, Switzerland)2019

Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study.

Yekta Said Can, Niaz Chalabianloo, Deniz Ekiz, Cem Ersoy

Abstract read
In one paragraph

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 86 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 7 pooled it
–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

86 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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26 more citing papers are in PubMed but not listed here.

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.

Yekta Said CanDepartment of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey. yekta.can@boun.edu.tr.
Niaz ChalabianlooDepartment of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey. niaz.chalabianloo@boun.edu.tr.
Deniz EkizDepartment of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey. deniz.ekiz@boun.edu.tr.ORCID 0000-0002-8130-3841
Cem ErsoyDepartment of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey. ersoy@boun.edu.tr.ORCID 0000-0001-7632-7067

Funding

Horizon 2020 Framework Programme Marie Skłodowska-Curie grant agreement No 722022.Turkish Ministry of Development TAM Project number DPT2007K120610.
6 · The paper itself

Abstract

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

Indexed as

Monitoring, PhysiologicWearable Electronic DevicesAdultFemaleGalvanic Skin ResponseHeart RateHumansMachine LearningMalePhotoplethysmographySkinSmartphoneStress, Psychologicaldaily life psychophysiological dataelectrodermal activityheart rate variabilitymachine learningphotoplethysmographysmartwatchstress recognitionwearable sensors

Identifiers

PMID31003456
PMCPMC6515276

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.