Evidence map›Paper›PMID 31615054›Full record

ArticleSensors (Basel, Switzerland)2019

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors.

Günther Sagl, Bernd Resch, Andreas Petutschnig, Kalliopi Kyriakou, Michael Liedlgruber, Frank H Wilhelm

Open access · goldAbstract 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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

11 citing papers in PubMed, 24 citations in OpenAlex.

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  10. Urban Emotion Sensing Beyond 'Affective Capture': Advancing Critical Interdisciplinary Methods.International journal of environmental research and public health · 2020
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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 at 2 institutions in 2 countries.

Günther SaglDepartment of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, Austria. guenther.sagl@gmail.com.
Bernd ReschDepartment of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, Austria. bernd.resch@sbg.ac.at.ORCID 0000-0002-2233-6926
Andreas PetutschnigDepartment of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, Austria. Andreas.Petutschnig@sbg.ac.at.ORCID 0000-0001-5029-2425
Kalliopi KyriakouDepartment of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, Austria. Kalliopi.Kyriakou@sbg.ac.at.ORCID 0000-0002-9387-852X
Michael LiedlgruberDepartment of Psychology, University of Salzburg, 5020 Salzburg, Austria. Michael.Liedlgruber@sbg.ac.at.
Frank H WilhelmDepartment of Psychology, University of Salzburg, 5020 Salzburg, Austria. Frank.Wilhelm@sbg.ac.at.
University of Salzburg · ATHarvard University · US

Funding

Austrian Science Fund FWF I-3022Österreichische Forschungsförderungsgesellschaft FFG 865208
6 · The paper itself

Abstract

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study's results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

Indexed as

BenchmarkingWearable Electronic DevicesAdultAlgorithmsFemaleHumansLinear ModelsMaleYoung Adultbenchmarkingcorrelation statisticspsychophysiologyquantitative analysissensor data analysissignal analysissimilarity measurestime series analysiswearable sensors

Identifiers

PMID31615054
PMCPMC6832271
OpenAlexW2979902129

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.