Multisensory Data Fusion for Human Activities Classification and Fall Detection

Li, H., Shrestha, A., Fioranelli, F. , Le Kernec, J. , Heidari, H. , Pepa, M., Cippitelli, E., Gambi, E. and Spinsante, S. (2017) Multisensory Data Fusion for Human Activities Classification and Fall Detection. In: IEEE Sensors 2017, Glasgow, UK, 30 Oct - 01 Nov 2017, (Accepted for Publication)

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Abstract

Significant research exists on the use of wearable sensors in the context of assisted living for activities recognition and fall detection, where the radar sensors have been studied only recently in this domain. This paper approaches the performance limitation of using individual sensors, especially for classification of similar activities by implementing information fusion of features extracted from experimental data collected by different sensors, namely a tri-axial accelerometer, a micro-Doppler radar, and a depth camera. Preliminary results confirm that combining information from heterogeneous sensors improves the overall performance of the system. The classification accuracy attained by means of this fusion approach improves by 11.2% compared to radar-only use, and by 16.9% compared to the accelerometer. Furthermore, adding features extracted from a RGB-D Kinect sensor, the overall classification accuracy increases up to 91.3%.

Item Type:Conference Proceedings
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pepa, Mr Matteo and Li, Mr Haobo and Fioranelli, Dr Francesco and Shrestha, Mr Aman and Heidari, Dr Hadi and Le Kernec, Dr Julien
Authors: Li, H., Shrestha, A., Fioranelli, F., Le Kernec, J., Heidari, H., Pepa, M., Cippitelli, E., Gambi, E., and Spinsante, S.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
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