Literature DB >> 30387751

Wearable Motion-Based Heart Rate at Rest: A Workplace Evaluation.

Javier Hernandez, Daniel McDuff, Karen Quigley, Pattie Maes, Rosalind W Picard.   

Abstract

This paper studies the feasibility of using low-cost motion sensors to provide opportunistic heart rate assessments from ballistocardiographic signals during restful periods of daily life. Three wearable devices were used to capture peripheral motions at specific body locations (head, wrist, and trouser pocket) of 15 participants during five regular workdays each. Three methods were implemented to extract heart rate from motion data and their performance was compared to those obtained with an FDA-cleared device. With a total of 1358 h of naturalistic sensor data, our results show that providing accurate heart rate estimations from peripheral motion signals is possible during relatively "still" moments. In our real-life workplace study, the head-mounted device yielded the most frequent assessments (22.98% of the time under 5 beats per minute of error) followed by the smartphone in the pocket (5.02%) and the wrist-worn device (3.48%). Most importantly, accurate assessments were automatically detected by using a custom threshold based on the device jerk. Due to the pervasiveness and low cost of wearable motion sensors, this paper demonstrates the feasibility of providing opportunistic large-scale low-cost samples of resting heart rate.

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Year:  2018        PMID: 30387751     DOI: 10.1109/JBHI.2018.2877484

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  3 in total

1.  Feasibility of Heart Rate and Respiratory Rate Estimation by Inertial Sensors Embedded in a Virtual Reality Headset.

Authors:  Claudia Floris; Sarah Solbiati; Federica Landreani; Gianfranco Damato; Bruno Lenzi; Valentino Megale; Enrico Gianluca Caiani
Journal:  Sensors (Basel)       Date:  2020-12-14       Impact factor: 3.576

Review 2.  Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications.

Authors:  Szymon Sieciński; Paweł S Kostka; Ewaryst J Tkacz
Journal:  Sensors (Basel)       Date:  2020-11-22       Impact factor: 3.576

3.  Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network.

Authors:  Baoying Wang
Journal:  J Healthc Eng       Date:  2022-01-25       Impact factor: 2.682

  3 in total

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