Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

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

doi:10.3390/s19204448

Cited by 20 publications

(26 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, this allows for apples-to-apples comparison. In addition, Schmidt et al’s work is comprehensive in terms of the machine learning algorithms examined, and their results are comparable to what has been reported recently on using machine learning approaches for stress detection [ 16 ].…”

Section: Resultssupporting

confidence: 68%

See 1 more Smart Citation

Stress detection using deep neural networks

Liu

2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing. Developing robust methods for the rapid and accurate detection of human stress is of paramount importance. Methods Prior research has shown that analyzing physiological signals is a reliable predictor of stress. Such signals are collected from sensors that are attached to the human body. Researchers have attempted to detect stress by using traditional machine learning methods to analyze physiological signals. Results, ranging between 50 and 90% accuracy, have been mixed. A limitation of traditional machine learning algorithms is the requirement for hand-crafted features. Accuracy decreases if features are misidentified. To address this deficiency, we developed two deep neural networks: a 1-dimensional (1D) convolutional neural network and a multilayer perceptron neural network. Deep neural networks do not require hand-crafted features but instead extract features from raw data through the layers of the neural networks. The deep neural networks analyzed physiological data collected from chest-worn and wrist-worn sensors to perform two tasks. We tailored each neural network to analyze data from either the chest-worn (1D convolutional neural network) or wrist-worn (multilayer perceptron neural network) sensors. The first task was binary classification for stress detection, in which the networks differentiated between stressed and non-stressed states. The second task was 3-class classification for emotion classification, in which the networks differentiated between baseline, stressed, and amused states. The networks were trained and tested on publicly available data collected in previous studies. Results The deep convolutional neural network achieved 99.80% and 99.55% accuracy rates for binary and 3-class classification, respectively. The deep multilayer perceptron neural network achieved 99.65% and 98.38% accuracy rates for binary and 3-class classification, respectively. The networks’ performance exhibited a significant improvement over past methods that analyzed physiological signals for both binary stress detection and 3-class emotion classification. Conclusions We demonstrated the potential of deep neural networks for developing robust, continuous, and noninvasive methods for stress detection and emotion classification, with the end goal of improving the quality of life.

show abstract

Section: Resultssupporting

confidence: 68%

“…Much research has been conducted on using physiological signals to detect stress [ 13 − 16 ]. Almost all past approaches analyzed a combination of physiological signals, including signals collected from the electrocardiogram [ 17 ], electrodermal activity [ 18 ], and electromyography [ 19 ] sensors.…”

Section: Introductionmentioning

confidence: 99%

Stress detection using deep neural networks

Liu

2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…They confirmed that the chosen physiological parameters are sensible from a psychophysiological and emotion psychological perspective, according with a review of Autonomic Nervous System activity in emotions [57]. The measurements were collected in a laboratory setting with the Empatica E4 and BioHarness (Zephyr, Boulder, CO, USA, a chest belt measuring a variety of cardiological parameters) selected after technology evaluation and laboratory tests [58].…”

Section: Biosensing Data-developing An Algorithm To Identify 'Momentsmentioning

confidence: 71%

Urban Emotion Sensing Beyond ‘Affective Capture’: Advancing Critical Interdisciplinary Methods

Pykett

Chrisinger

Kyriakou

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

The use of mobile sensor methodologies in urban analytics to study ‘urban emotions’ is currently outpacing the science required to rigorously interpret the data generated. Interdisciplinary research on ‘urban stress’ could help inform urban wellbeing policies relating to healthier commuting and alleviation of work stress. The purpose of this paper is to address—through methodological experimentation—ethical, political and conceptual issues identified by critical social scientists with regards to emotion tracking, wearables and data analytics. We aim to encourage more dialogue between the critical approach and applied environmental health research. The definition of stress is not unambiguous or neutral and is mediated by the very technologies we use for research. We outline an integrative methodology in which we combine pilot field research using biosensing technologies, a novel method for identifying ‘moments of stress’ in a laboratory setting, psychometric surveys and narrative interviews on workplace and commuter stress in urban environments.

show abstract

“…We assess the physiological signals resulting from perceptions and emotions of pedestrians using wearable biosensors. Based on technology evaluation and laboratory tests [ 52 ], we recommend the Empatica E4 (a wristband measuring GSR and ST) and the Zephyr Bioharness (a chest strap measuring a variety of cardiological parameters like ECG, HRV, and others). Our sensor fusion method extracts emotion information (moments of stress) from the measured data using a customized signal analysis procedure [ 53 ].…”

Section: Methodsology: a Mixed-methods Approach For Analyzing Urbanmentioning

confidence: 99%

An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability

Resch

Puetz

Bluemke

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

Human-centered approaches are of particular importance when analyzing urban spaces in technology-driven fields, because understanding how people perceive and react to their environments depends on several dynamic and static factors, such as traffic volume, noise, safety, urban configuration, and greenness. Analyzing and interpreting emotions against the background of environmental information can provide insights into the spatial and temporal properties of urban spaces and their influence on citizens, such as urban walkability and bikeability. In this study, we present a comprehensive mixed-methods approach to geospatial analysis that utilizes wearable sensor technology for emotion detection and combines information from sources that correct or complement each other. This includes objective data from wearable physiological sensors combined with an eDiary app, first-person perspective videos from a chest-mounted camera, and georeferenced interviews, and post-hoc surveys. Across two studies, we identified and geolocated pedestrians’ and cyclists’ moments of stress and relaxation in the city centers of Salzburg and Cologne. Despite open methodological questions, we conclude that mapping wearable sensor data, complemented with other sources of information—all of which are indispensable for evidence-based urban planning—offering tremendous potential for gaining useful insights into urban spaces and their impact on citizens.

show abstract

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Cited by 20 publications

References 50 publications

Stress detection using deep neural networks

Stress detection using deep neural networks

Urban Emotion Sensing Beyond ‘Affective Capture’: Advancing Critical Interdisciplinary Methods

An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability

Contact Info

Product

Resources

About