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Human motion capturing and activity recognition using wearable sensor networks

Gabriele Bleser-Taetz; Bertram Taetz; Paul Lukowicz
In: Athanasios Karafillidis; Robert Weidner (Hrsg.). Developing Support Technologies. Pages 191-206, Springer, 2018.


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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