Comparison of three different physiological wristband sensor systems and their applicability for resilience- and work load monitoring

Binsch, O.; Wabeke, T.R.; Valk, P.J.L.

doi:10.1109/bsn.2016.7516272

Cited by 12 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the Fit-Bit Surge, our results showed a significant underestimation of steps counted for walking when compared to the manual count but a very small underestimation when jogging. Discrepancies in treadmill walking and jogging for the FitBit Surge were also observed by Binsch, Wabeke, & Valk (2016) and a significant underestimation of steps in free motion walking was recorded by Modave et al (2017). The Polar A360, significantly underestimated steps when both walking and jogging.…”

Section: Heart Ratementioning

confidence: 85%

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Montes

Tandy

Young

et al. 2019

IJKSS

View full text Add to dashboard Cite

Introduction: Wearable Technology Devices are used to promote physical activity. It is unknown whether different devices measure heart rate and step count consistently during walking or jogging in a free motion setting and on a treadmill. Purpose: To compare heart rate and step count values for the Samsung Gear 2, FitBit Surge, Polar A360, Garmin Vivosmart HR+, Scosche Rhythm+ and the Leaf Health Tracker in walking and jogging activities. Methods: Forty volunteers participated. Devices were worn simultaneously in randomized configurations. 5-minute intervals of walking and jogging were completed in free motion and treadmill settings with matching paces. Heart rates at minutes 3, 4, and 5 were averaged for the devices along with the criterion measure, the Polar T31 monitor. Step count criterion measure was the mean of two manual counters. A 2x6 (environment vs device) repeated measures ANOVA with Bonferroni post-hoc was performed with significance set at p<0.05. Results: There was no significant interaction or main effects for walking heart rate. Jogging heart rate saw significant environment and device main effects. Walking step count had a significant interaction between the devices and the environment. Jogging step count had a significant device main effect. Conclusions: There may be some conditions such as heart rate measurements taken while walking or step count measurements taken while jogging/running that may only require treadmill-based validity testing.

show abstract

Section: Heart Ratementioning

confidence: 85%

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Montes

Tandy

Young

et al. 2019

IJKSS

View full text Add to dashboard Cite

show abstract

“…On the one hand, the market for smartbands is growing (Wade, 2017 ); thus, smartbands connected to vehicles could be an option for assessing psychophysiological parameters inside cars. On the other hand, the MS Band 2 has already been used in research for assessing mental workload in different environments (Binsch et al, 2016 ; Cropley et al, 2017 ; Reinerman-Jones et al, 2017 ; Schmalfuß et al, 2018 ), activity recognition in a home setting (Filippoupolitis et al, 2016 ), and for predicting and regulating personal thermal comfort in buildings (Laftchiev and Nikovski, 2016 ; Li et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Beggiato

Hartwich

Krems

2018

Front. Hum. Neurosci.

View full text Add to dashboard Cite

As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies.

show abstract

“…They concluded that wearable PPG wristbands used in their research could be considered as reliable tools for measuring the heart rate while the subjects under study are at resting conditions. On the other hand, when there are body movements on tasks that require more physical activity, sensor readings become more inaccurate and less reliable [58]. Ge et al compared the accuracy of heart rate measurements of two off the shelf devices, i.e.…”

Section: ) Quantitative Analysismentioning

confidence: 99%

HRV and Stress: A Mixed-Methods Approach for Comparison of Wearable Heart Rate Sensors for Biofeedback

et al. 2021

View full text Add to dashboard Cite

Stress is one of the most significant health problems in today's world. Existing work has used heart rate variability (HRV) to detect stress and provide biofeedback in order to regulate it. There has been a growing interest in using wearable biosensors to measure HRV. Each of these sensors acquires heart rate data using different technologies for various bodily locations, therefore posing a challenge for researchers to decide upon a particular device in a research experiment. Previous work has only compared different sensing devices against a gold standard in terms of data quality, thus overlooking qualitative analysis for the usability and acceptability of such devices. This paper introduces a mixed-methods approach to compare the data quality and user acceptance of the six most common wearable heart rate monitoring biosensors. We conducted a 70-minute data collection procedure to obtain HRV data from 32 participants followed by a 10-minute semi-structured interview on sensors' wearability and comfort, long-term use, aesthetics, and social acceptance. We performed quantitative analysis consisting of correlation and agreement analysis on the HRV data and thematic analysis on qualitative data obtained from interviews. Our results show that the electrocardiography (ECG) chest strap achieved the highest correlation and agreement levels in all sessions and had the lowest amount of artifacts, followed by the photoplethysmography (PPG) wristband, ECG sensor board kit and PPG smartwatch. In all three sessions, wrist-worn devices showed a lower amount of agreement and correlation with the reference device. Qualitative findings from interviews highlight that participants prefer wrist and arm-worn devices in terms of aesthetics, wearability, and comfort, followed by chest-worn devices. Moreover, participants mentioned that the latter are more likely to invite social judgment from others, and they would not want to wear it in public. Participants preferred the chest strap for short-term use and the wrist and arm-worn sensors over long-time.

show abstract

Comparison of three different physiological wristband sensor systems and their applicability for resilience- and work load monitoring

Cited by 12 publications

References 1 publication

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

HRV and Stress: A Mixed-Methods Approach for Comparison of Wearable Heart Rate Sensors for Biofeedback

Contact Info

Product

Resources

About