Personalized Physical Activity Monitoring Using Wearable Sensors

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Bleser, GabrieleGerman Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany (author)

Personalized Physical Activity Monitoring Using Wearable Sensors

Article/chapterEnglish2015

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2015-02-25
Cham :Springer International Publishing,2015
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LIBRIS-ID:oai:DiVA.org:liu-115399
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115399URI
https://doi.org/10.1007/978-3-319-16226-3_5DOI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:ref swepub-contenttype
Subject category:kap swepub-publicationtype

Notes

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Annan medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Other Medical Engineering hsv//eng
Physical activity monitoring; ADL; Strength exercises; Personalization; Wearable sensors; Inertial sensors; HCI; Ambient assisted living

Added entries (persons, corporate bodies, meetings, titles ...)

Steffen, DanielGerman Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany (author)
Reiss, AttilaACTLab, University of Passau, 94032, Passau, Germany (author)
Weber, MarkusGerman Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany (author)
Hendeby, Gustaf,1978-Linköpings universitet,Reglerteknik,Tekniska högskolan(Swepub:liu)gushe66 (author)
Fradet, LaetitiaUniversité de Poitiers, 86000, Poitiers, France (author)
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, GermanyACTLab, University of Passau, 94032, Passau, Germany (creator_code:org_t)

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In:Smart HealthCham : Springer International Publishing, s. 99-12497833191622569783319162263

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