Validation of the Heart:Rate Signal Provided by the Zephyr BioHarness 3.0

Nepi, Daniele; Sbrollini, Agnese; Agostinelli, Angela; Maranesi, Elvira; Morettini, Micaela; Nardo, Francesco Di; Fioretti, Sandro; Pierleoni, Paola; Pernini, Luca; Valenti, Salvatore; Burattini, Laura

doi:10.22489/cinc.2016.106-358

Cited by 22 publications

(12 citation statements)

References 25 publications

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“…The Zephyr Bioharness sensor used in our experiments provides an internal algorithm that calculates HRV based on ECG data. However, in their validation research, Nepi et al concluded that the HRV data provided by this sensor is not reliable [ 36 ]. This, and the fact that the calculations take place only after the initial five minutes of measured HR, were the reasons that we decided not to use HRV data as an input for our models.…”

Section: Psychophysiological Measuresmentioning

confidence: 99%

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Čertický

Sinčák

et al. 2019

Sensors

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Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

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Section: Psychophysiological Measuresmentioning

confidence: 99%

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Čertický

Sinčák

et al. 2019

Sensors

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“…In recent years wearable devices have attracted significant attention in the scientific community for the evaluation of heart rhythm over extended periods of time in out-of-laboratory settings due to their characteristics of non-invasiveness, flexibility, and compatibility with the skin 8 , 9 . Innovations in wearable technologies have made it possible to record both the electric impulses 10 , 11 (i.e., electrocardiography) and the precordial movements of the heart. Among various techniques, ballistocardiography-BCG (i.e., a measurement of the ballistic forces generated by the ejection of blood into the vessels) and seismocardiography-SCG 12 (i.e., a recording of the chest wall vibrations in response to the atrioventricular contractions) are gaining broad interest for a non-invasive detection of the heart activity in everyday settings 13 , where traditional methods show their main limitations.…”

Section: Introductionmentioning

confidence: 99%

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology

Presti

Santucci

Massaroni

et al. 2021

Sci Rep

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Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

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“…Demographic data were collected by survey. Cardiorespiratory signals were recorded through the chest strap BioHarness 3.0 by Zephyr (www.zephyranywhere.com), a reliable wearable device [[1], [2], [3], [4], [5], [6]] that directly records the ECG (mV; raw data) and automatically computes the HR series (bpm; processed data) and the BR series (cpm; processed data).…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Sport Database: Cardiorespiratory data acquired through wearable sensors while practicing sports

et al. 2019

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Sport Database is a collection of 126 cardiorespiratory data, acquired through wearable sensors from 81 subjects while practicing 10 different sports. Each cardiorespiratory dataset consists of demographic info (gender, age, weight, height, smoking habit, alcohol consumption and weekly training rate), cardiorespiratory signals (electrocardiogram, heart-rate series, RR-interval series and breathing-rate series) and training notes. Demographic info was collected by survey. Cardiorespiratory signals were acquired through the chest strap BioHarness 3.0 by Zephyr. Eventually, training notes including the sport-dependent training protocol, were manually annotated. Sport Database may be useful to support: 1) the investigation of cardiorespiratory system adaptations to different types of physical exercise; 2) the development of automatic algorithms finalized to real-time health monitoring of athletes and preventive identification of subjects at increased risk of sport-related sudden cardiac death; and, 3) clinical testing of the BioHarness 3.0 by Zephyr. Further acquisitions could involve other sports, other cardiovascular signals and/or parameters, data from different biological systems, and other acquisition devices.

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Validation of the Heart:Rate Signal Provided by the Zephyr BioHarness 3.0

Abstract: Abstract

Cited by 22 publications

References 25 publications

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology

Sport Database: Cardiorespiratory data acquired through wearable sensors while practicing sports

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