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Open AccessArticle

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

1
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
2
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
3
Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(20), 4448; https://doi.org/10.3390/s19204448
Received: 22 August 2019 / Revised: 8 October 2019 / Accepted: 10 October 2019 / Published: 14 October 2019
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. View Full-Text
Keywords: wearable sensors; psychophysiology; sensor data analysis; time series analysis; signal analysis; similarity measures; correlation statistics; quantitative analysis; benchmarking wearable sensors; psychophysiology; sensor data analysis; time series analysis; signal analysis; similarity measures; correlation statistics; quantitative analysis; benchmarking
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MDPI and ACS Style

Sagl, G.; Resch, B.; Petutschnig, A.; Kyriakou, K.; Liedlgruber, M.; Wilhelm, F.H. Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors. Sensors 2019, 19, 4448. https://doi.org/10.3390/s19204448

AMA Style

Sagl G, Resch B, Petutschnig A, Kyriakou K, Liedlgruber M, Wilhelm FH. Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors. Sensors. 2019; 19(20):4448. https://doi.org/10.3390/s19204448

Chicago/Turabian Style

Sagl, Günther; Resch, Bernd; Petutschnig, Andreas; Kyriakou, Kalliopi; Liedlgruber, Michael; Wilhelm, Frank H. 2019. "Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors" Sensors 19, no. 20: 4448. https://doi.org/10.3390/s19204448

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