Phone in the pocket: Pervasive self-tracking of physical activity levels

Jody Hausmann; Katarzyna Wac; Julien Bonjour

Phone in the pocket: Pervasive self-tracking of physical activity levels

Jody Hausmann^*, Katarzyna Wac, Julien Bonjour

^*Corresponding author af dette arbejde

2 Citationer (Scopus)

Abstract

Mobile (smart)phones prevail in our daily life activities, and in our research we aim for it to provide pervasive services for wellness. Therefore, we assess the phone's feasibility to unobtrusively, continuously and in real-time track its user's physical activity and the resulting energy expenditure (EE). Activity Level Estimator (ALE) is an Android OS application developed for that purpose. We have assessed the accuracy of ALE against the BodyMedia SenseWear (SW) device and the gold standard for EE estimation, i.e., an indirect calorimetry (IC) method. ALE has mean accuracy of 86% (vs. SW) to 93% (vs. IC) for walking, and in 24h it underestimates EE by 23% ALE is currently used for a long-term behavioral trends study with the University of Geneva students and faculty.

Originalsprog	Engelsk
Titel	Self-Tracking and Collective Intelligence for Personal Wellness - Papers from the AAAI Spring Symposium
Antal sider	2
Vol/bind	SS-12-05
Publikationsdato	20 aug. 2012
Sider	17-18
ISBN (Trykt)	9781577355540
Status	Udgivet - 20 aug. 2012
Begivenhed	2012 AAAI Spring Symposium - Stanford, CA, USA Varighed: 26 mar. 2012 → 28 mar. 2012

Konference

Konference	2012 AAAI Spring Symposium
Land/Område	USA
By	Stanford, CA
Periode	26/03/2012 → 28/03/2012

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Citationsformater

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title = "Phone in the pocket: Pervasive self-tracking of physical activity levels",

abstract = "Mobile (smart)phones prevail in our daily life activities, and in our research we aim for it to provide pervasive services for wellness. Therefore, we assess the phone's feasibility to unobtrusively, continuously and in real-time track its user's physical activity and the resulting energy expenditure (EE). Activity Level Estimator (ALE) is an Android OS application developed for that purpose. We have assessed the accuracy of ALE against the BodyMedia SenseWear (SW) device and the gold standard for EE estimation, i.e., an indirect calorimetry (IC) method. ALE has mean accuracy of 86% (vs. SW) to 93% (vs. IC) for walking, and in 24h it underestimates EE by 23% ALE is currently used for a long-term behavioral trends study with the University of Geneva students and faculty.",

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AU - Wac, Katarzyna

AU - Bonjour, Julien

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N2 - Mobile (smart)phones prevail in our daily life activities, and in our research we aim for it to provide pervasive services for wellness. Therefore, we assess the phone's feasibility to unobtrusively, continuously and in real-time track its user's physical activity and the resulting energy expenditure (EE). Activity Level Estimator (ALE) is an Android OS application developed for that purpose. We have assessed the accuracy of ALE against the BodyMedia SenseWear (SW) device and the gold standard for EE estimation, i.e., an indirect calorimetry (IC) method. ALE has mean accuracy of 86% (vs. SW) to 93% (vs. IC) for walking, and in 24h it underestimates EE by 23% ALE is currently used for a long-term behavioral trends study with the University of Geneva students and faculty.

AB - Mobile (smart)phones prevail in our daily life activities, and in our research we aim for it to provide pervasive services for wellness. Therefore, we assess the phone's feasibility to unobtrusively, continuously and in real-time track its user's physical activity and the resulting energy expenditure (EE). Activity Level Estimator (ALE) is an Android OS application developed for that purpose. We have assessed the accuracy of ALE against the BodyMedia SenseWear (SW) device and the gold standard for EE estimation, i.e., an indirect calorimetry (IC) method. ALE has mean accuracy of 86% (vs. SW) to 93% (vs. IC) for walking, and in 24h it underestimates EE by 23% ALE is currently used for a long-term behavioral trends study with the University of Geneva students and faculty.

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Phone in the pocket: Pervasive self-tracking of physical activity levels

Abstract

Konference

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