Personalized Physical Activity Monitoring Using Wearable Sensors

Gabriele Bleser, Daniel Steffen, Attila Reiss, Markus Weber, Gustaf Hendeby, Laetitia Fradet

In: Andreas Holzinger , Carsten Röcker , Martina Ziefle (Hrsg.). Smart Health - Open Problems and Future Challenges. Seiten 99-124 Lecture Notes in Computer Science (LNCS) 8700 ISBN 978-3-319-16225-6 Springer 2015.


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.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence