Wearables data integration: Data-driven modeling to adjust for differences in Jawbone and Fitbit estimations of steps, calories, and resting heart-rate

Shah, Yash; Dunn, Jocelyn; Huebner, Erich; Landry, Steven J.

doi:10.1016/j.compind.2017.01.003

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…Bland-Altman plots show the relationship between inter-device recording differences and are a common method of measurement comparison for continuous variables [ 21 , 22 ]. As shown in a past study comparing Jawbone and Fitbit fitness tracking devices, the Bland-Altman plots typically show the differences between devices over mean values [ 23 ]. Simple linear regression models were run in R, regressing the seat sensor resting rates on the reference Neulog resting rates, to calculate RMSE and R 2 values.…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Detection of Respiration and Heart Rate with a Vehicle Seat Sensor

Wusk

Gabler

2018

Sensors

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This study demonstrates the feasibility of using a seat sensor designed for occupant classification from a production passenger vehicle to measure an occupant’s respiration rate (RR) and heart rate (HR) in a laboratory setting. Relaying occupant vital signs after a crash could improve emergency response by adding a direct measure of the occupant state to an Advanced Automatic Collision Notification (AACN) system. Data was collected from eleven participants with body weights ranging from 42 to 91 kg using a Ford Mustang passenger seat and seat sensor. Using a ballistocardiography (BCG) approach, the data was processed by time domain filtering and frequency domain analysis using the fast Fourier transform to yield RR and HR in a 1-min sliding window. Resting rates over the 30-min data collection and continuous RR and HR signals were compared to laboratory physiological instruments using the Bland-Altman approach. Differences between the seat sensor and reference sensor were within 5 breaths per minute for resting RR and within 15 beats per minute for resting HR. The time series comparisons for RR and HR were promising with the frequency analysis technique outperforming the peak detection technique. However, future work is necessary for more accurate and reliable real-time monitoring of RR and HR outside the laboratory setting.

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Section: Methodsmentioning

confidence: 99%

Non-Invasive Detection of Respiration and Heart Rate with a Vehicle Seat Sensor

Wusk

Gabler

2018

Sensors

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“…Flexible and wearable devices are concerned about the potential of personal health monitoring in a non-invasive, user-friendly, and cost-effective way [90][91][92]. Early efforts in the wearable device field were studied on physical activities such as steps, burned calories, or heart rhythms [93][94][95]. In recent years, the appearance of wearable devices has changed dramatically, and more and more researchers have shifted from tracking vital signs to focusing on healthcare applications, such as diabetes management or remote monitoring of elderly people [96][97][98].…”

Section: Flexible and Wearable Devicesmentioning

confidence: 99%

Electrochemical Biosensors Based on Micro‐fabricated Devices for Point‐of‐Care Testing: A Review

Zhang

Zhou

2021

Electroanalysis

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Point-of-care testing (POCT) is becoming a hot research topic that allows rapid, on-site, and non-professional measurements outside the central laboratory. The micro-fabricated devices prepared by various micromachining technologies have shown the advantages of low reagent consumption, high-throughput samples, and wearability. This review presents the recent progress of electrochemical biosensors based on various micro-fabri-cated devices for POCT and the corresponding electrochemical techniques. Signal amplification strategies based on enzyme and nanotechnology are also illustrated for the more sensitive POCT applications of these micro-fabricated devices. Consequently, the trends and challenges of electrochemical biosensors based on micro-fabricated devices in POCT diagnosis are discussed.

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“…More specifically, worldwide wearable device vendors distributed a total of 125.5 million devices, which is an increase from the 104.3 million units distributed in 2016, making a 20.4% growth (Jia et al, 2018). The majority of research on wearable technology has focused on the accuracy and reliability of these devices (Nazari et al, 2017;Shah et al, 2017;Hernando et al, 2018) as well as promoting PA (Chiauzzi et al, 2015;Jo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Effects of Heart Rate Monitoring on Ratings of Perceived Exertion and Attention Allocation in Individuals of Varying Fitness Levels

Braun-Trocchio

Williams

Harrison

et al. 2022

Front. Sports Act. Living

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There has been a rapid increase in the use of wearable technology-based physical activity trackers. Most of these physical activity trackers include tracking and displaying the individual's heart rate (HR). There is little known about how HR monitoring influences the perception of exertion and attention allocation. Shifting attentional focus toward the body (association), such as monitoring HR, instead of environmental stimuli (dissociation) may increase one's perceived level of exertion. The purpose of the study was to examine the effects of HR monitoring on ratings of perceived exertion (RPE) and attention allocation during an exertive stepping task in individuals of varying fitness levels. The YMCA stepping task normative values determined fitness levels. For the experimental condition, participants were randomly assigned to one of two conditions (i.e., HR monitoring or control) and completed a stepping task with a weighted vest at 20% of their bodyweight. HR, RPE, and attention allocation were collected at 30-s intervals. Performing the stepping task resulted in a gradual increase of HR and RPE along with a shift from dissociative to associative attention across all conditions. Monitoring one's HR during the task resulted in more dissociative attention allocation, however, no RPE differences were reported between the two conditions. Unfit individuals reported lower levels of RPE during the first time point compared to fit individuals despite having higher HR throughout the task. The results of this study have relevance for applied practitioners implementing physical activity interventions with individuals who monitor their HR.

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Wearables data integration: Data-driven modeling to adjust for differences in Jawbone and Fitbit estimations of steps, calories, and resting heart-rate

Cited by 14 publications

References 4 publications

Non-Invasive Detection of Respiration and Heart Rate with a Vehicle Seat Sensor

Non-Invasive Detection of Respiration and Heart Rate with a Vehicle Seat Sensor

Electrochemical Biosensors Based on Micro‐fabricated Devices for Point‐of‐Care Testing: A Review

The Effects of Heart Rate Monitoring on Ratings of Perceived Exertion and Attention Allocation in Individuals of Varying Fitness Levels

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