WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches

Sungkyu Park; Marios Constantinides; Luca Maria Aiello; Daniele Quercia; Paul Van Gent

doi:10.1109/MIC.2020.3017867

WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches

Sungkyu Park, Marios Constantinides, Luca Maria Aiello, Daniele Quercia, Paul Van Gent

Informatics

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

Human physiology is a window to our physical, mental, and emotional states; our well-being. Today, a new wave of objective data derived from consumer grade body sensors-like those equipped by smartwatches-paves the way toward a new approach in how well-being is being measured, continuously and unobtrusively. Here, we developed a framework for collecting and analyzing physiological data using smartwatches in-the-wild, and demonstrated its robustness in data obtained away from controlled laboratory settings. We found that changes in people's heart rate and heart rate variability are predictive not of momentary well-being (a scientific idea that continues to live on in the absence of in-the-wild evidence, aka, zombie theory) but of daily well-being.

Original language	English
Article number	9171435
Pages (from-to)	10-17
Number of pages	8
Journal	IEEE INTERNET COMPUTING
Volume	24
Issue number	5
DOIs	https://doi.org/10.1109/MIC.2020.3017867
Publication status	Published - 1 Sept 2020

Keywords

O.1 Affect sensing and analysis
O.2 Modeling human emotion
O.6.1 Methods of data collection

Access to Document

10.1109/MIC.2020.3017867

Cite this

@article{d1bab2210c0d45c6beacb2bebe850b95,

title = "WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches",

abstract = "Human physiology is a window to our physical, mental, and emotional states; our well-being. Today, a new wave of objective data derived from consumer grade body sensors-like those equipped by smartwatches-paves the way toward a new approach in how well-being is being measured, continuously and unobtrusively. Here, we developed a framework for collecting and analyzing physiological data using smartwatches in-the-wild, and demonstrated its robustness in data obtained away from controlled laboratory settings. We found that changes in people's heart rate and heart rate variability are predictive not of momentary well-being (a scientific idea that continues to live on in the absence of in-the-wild evidence, aka, zombie theory) but of daily well-being. ",

keywords = "O.1 Affect sensing and analysis, O.2 Modeling human emotion, O.6.1 Methods of data collection",

author = "Sungkyu Park and Marios Constantinides and Aiello, {Luca Maria} and Daniele Quercia and {Van Gent}, Paul",

year = "2020",

month = sep,

day = "1",

doi = "10.1109/MIC.2020.3017867",

language = "English",

volume = "24",

pages = "10--17",

journal = "IEEE INTERNET COMPUTING",

issn = "1089-7801",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "5",

}

TY - JOUR

T1 - WellBeat

T2 - A Framework for Tracking Daily Well-Being Using Smartwatches

AU - Park, Sungkyu

AU - Constantinides, Marios

AU - Aiello, Luca Maria

AU - Quercia, Daniele

AU - Van Gent, Paul

PY - 2020/9/1

Y1 - 2020/9/1

N2 - Human physiology is a window to our physical, mental, and emotional states; our well-being. Today, a new wave of objective data derived from consumer grade body sensors-like those equipped by smartwatches-paves the way toward a new approach in how well-being is being measured, continuously and unobtrusively. Here, we developed a framework for collecting and analyzing physiological data using smartwatches in-the-wild, and demonstrated its robustness in data obtained away from controlled laboratory settings. We found that changes in people's heart rate and heart rate variability are predictive not of momentary well-being (a scientific idea that continues to live on in the absence of in-the-wild evidence, aka, zombie theory) but of daily well-being.

AB - Human physiology is a window to our physical, mental, and emotional states; our well-being. Today, a new wave of objective data derived from consumer grade body sensors-like those equipped by smartwatches-paves the way toward a new approach in how well-being is being measured, continuously and unobtrusively. Here, we developed a framework for collecting and analyzing physiological data using smartwatches in-the-wild, and demonstrated its robustness in data obtained away from controlled laboratory settings. We found that changes in people's heart rate and heart rate variability are predictive not of momentary well-being (a scientific idea that continues to live on in the absence of in-the-wild evidence, aka, zombie theory) but of daily well-being.

KW - O.1 Affect sensing and analysis

KW - O.2 Modeling human emotion

KW - O.6.1 Methods of data collection

UR - http://www.scopus.com/inward/record.url?scp=85091294913&partnerID=8YFLogxK

U2 - 10.1109/MIC.2020.3017867

DO - 10.1109/MIC.2020.3017867

M3 - Article

AN - SCOPUS:85091294913

SN - 1089-7801

VL - 24

SP - 10

EP - 17

JO - IEEE INTERNET COMPUTING

JF - IEEE INTERNET COMPUTING

IS - 5

M1 - 9171435

ER -

WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this