scholarly journals Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4448 ◽  
Author(s):  
Günther Sagl ◽  
Bernd Resch ◽  
Andreas Petutschnig ◽  
Kalliopi Kyriakou ◽  
Michael Liedlgruber ◽  
...  
Keyword(s):  
Heart Rate ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Quantified Self ◽  
High Quality ◽  
Laboratory Equipment ◽  
Skin Response ◽  
Physiological Sensors

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.

scholarly journals Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Kalliopi Kyriakou ◽  
Bernd Resch
Keyword(s):  
Spatial Analysis ◽  
Urban Areas ◽  
Hot Spot ◽  
Low Cost ◽  
Field Studies ◽  
Physiological Signals ◽  
Sensor Data ◽  
Skin Response ◽  
Density Analysis ◽  
Physiological Sensors

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

scholarly journals IDENTIFYING THE MENTAL STATUS OF BEDRIDDEN ELDERLY BY THE BODY TEMPERATURE, HEART RATE, AND SKIN CONDUCTIVITY USING AN ARDUINO SENSOR MODULE

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
W.B.P.N. Herath ◽  
R.A.K.I. Ranasinghe ◽  
M.P.C. Sandaru ◽  
I.A.S. Lakmali ◽  
A.G.N.K. Aluthgama ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Galvanic Skin Response ◽  
Emotional State ◽  
The Body ◽  
Blood Capillary ◽  
Sensor Module ◽  
Skin Response ◽  
Skin Conductivity ◽  
The Relationship

Addressing the emotional and mental health of the bedridden elderly is necessary as they are more likely to be depressed being isolated and dependent on a caregiver for a prolonged time. Several studies have been carried out to identify the mental stress of patients through their skin conductivity. The variations in the sympathetic nervous system reflect the emotional state of a person. This is demonstrated by the Galvanic Skin Response and thus can be used as a denotation of psychological or physiological arousal. Such arousal causes the blood capillary dilation, increment of sweat gland activities making the skin further conductive to electricity. In this study we develop a sensor module composed of a Galvanic Skin Response sensor for the bed ridden elderly and identify the relationship between body temperature, heart rate and GSR of them. The experiment is conducted upon 10 bed ridden elderly aged from 60 – 80 years of the Mihinthale region. The observations demonstrate a correlation between the heart rate, body temperature, skin conductivity and the human physiological states.

scholarly journals Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1406
Author(s):  
Rok Novak ◽  
David Kocman ◽  
Johanna Amalia Robinson ◽  
Tjaša Kanduč ◽  
Dimosthenis Sarigiannis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Particulate Matter ◽  
Low Cost ◽  
Minute Ventilation ◽  
Dose Assessment ◽  
Sensor Data ◽  
Activity Data ◽  
Time Activity ◽  
Activity Monitors ◽  
Coarse Information

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.

scholarly journals Automated P-Wave Quality Assessment for Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Diogo Tecelão ◽  
Peter Charlton
Keyword(s):  
At Risk ◽  
Quality Assessment ◽  
Wearable Sensors ◽  
P Wave ◽  
Sensor Data ◽  
Wearable Sensor ◽  
High Quality ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Morphology

Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), an arrhythmia which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. However, ECG signals acquired by wearable sensors are susceptible to artefact, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high-quality P-waves, for AF prediction. We designed a two-stage algorithm which uses P-wave template-matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high-quality P-waves with high sensitivity (93%) and good specificity (82%), indicating that it may have utility for identifying high-quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high-quality P-waves could be used to predict AF, improving patient outcomes, and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation.

Are Loot Boxes Addictive? Analyzing Participant’s Physiological Arousal While Opening a Loot Box

2019 ◽  
pp. 155541201989535 ◽  
Author(s):  
Andrew Brady ◽  
Garry Prentice
Keyword(s):  
Heart Rate ◽  
Video Games ◽  
Adult Male ◽  
Negative Correlation ◽  
Galvanic Skin Response ◽  
Physiological Arousal ◽  
Design Experiment ◽  
Gaming Addiction ◽  
Levels Of Use ◽  
Skin Response

Loot boxes in video games have blurred the lines between gaming and gambling. Research suggests the thrill from gambling comes from associated increases in physiological arousal not possible monetary gains. Gamers performing microtransactions to purchase loot boxes can lead to similar increases in physiological arousal. However, problematic gamblers no longer receive increases in arousal and become hyposensitive to reward. This hyposensitivity may also be present in problematic gamers. Twenty-five adult male participants took part in the within-participant design experiment measuring heart rate (HR) and galvanic skin response (GSR) while they were gaming and performing a microtransaction. GSR increased from baseline. HR decreased while performing a microtransaction. There was a negative correlation between gaming addiction scores and participants’ physiological arousal. The increases in arousal suggest microtransactions in gaming could potentially lead to problematic levels of use, while hyposensitivity could explain the higher gaming addiction relating to lower arousal during gameplay.

Assessment of Autonomic Responses to Different Driving and Road Conditions

2000 ◽  
Vol 44 (37) ◽  
pp. 679-679
Author(s):  
Byung-Chan Min ◽  
Soon-Cheol Chung ◽  
Sang-Gyun Kim ◽  
Byung-Woon Min ◽  
Chul-Jung Kim ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Skin Temperature ◽  
Galvanic Skin Response ◽  
Physiological Responses ◽  
Mean Value ◽  
Autonomic Responses ◽  
Skin Response ◽  
Curved Road

The purpose of this study was to compare changes in autonomic responses due to different driving and road conditions. We measured physiological responses of the 10 health subjects such as Heart Rate Variability (HRV), Galvanic Skin Response (GSR) and skin temperature in rest and stimulation conditions. The ratio of LF/HF significantly increased and averaged R-R interval decreased on the abrupt stopping and starting and abruptly curved road conditions, compared to other conditions. Mean value of GSR increased and mean value of skin temperature decreased in the abrupt stopping and starting and abruptly curved road conditions, compared to other conditions.

scholarly journals Multimodal-Multisensory Experiments

2020 ◽  
Author(s):  
Moein Razavi ◽  
Takashi Yamauchi ◽  
Vahid Janfaza ◽  
Anton Leontyev ◽  
Shanle Longmire-Monford ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Response Time ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Body Movement ◽  
Human Mind ◽  
Body Motion ◽  
Mouse Cursor ◽  
Skin Response ◽  
Electroencephalogram Eeg

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

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