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2018 | OriginalPaper | Buchkapitel

Human Motion Capturing and Activity Recognition Using Wearable Sensor Networks

verfasst von : Gabriele Bleser, Bertram Taetz, Paul Lukowicz

Erschienen in: Developing Support Technologies

Verlag: Springer International Publishing

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Abstract

Wearable sensor networks enable human motion capture and activity recognition in-field. This technology found widespread use in many areas, where location independent information gathering is useful, e.g., in healthcare and sports, workflow analysis, human-computer-interaction, robotics, and entertainment. Two major approaches for deriving information from wearable sensor networks are in focus here: the model-based estimation of 3D joint kinematics based on networks of inertial measurement units (IMUs) and the activity recognition based on multimodal body sensor networks using machine learning algorithms. The characteristics, working principles, challenges, potentials, and target applications of these two approaches are described individually and in synergy.

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Vorheriges Kapitel Comprehensive Heuristic for Research on Assistance Systems in Organizational Contexts
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Metadaten
Titel
Human Motion Capturing and Activity Recognition Using Wearable Sensor Networks
verfasst von
Gabriele Bleser
Bertram Taetz
Paul Lukowicz
Copyright-Jahr
2018
Buch
Developing Support Technologies

Print ISBN: 978-3-030-01835-1

Electronic ISBN: 978-3-030-01836-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01836-8_19

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