Abstract

In this work, we studied the problem of fall detection using signals from tri-axial wearable sensors. In particular, we focused on the comparison of methods to combine signals from multiple tri-axial accelerometers which were attached to different body parts in order to recognize human activities. To improve the detection rate while maintaining a low false alarm rate, previous studies developed detection algorithms by cascading base algorithms and experimented on each sensory data separately. Rather than combining base algorithms, we explored the combination of multiple data sources. Based on the hypothesis that these sensor signals should provide complementary information to the characterization of human's physical activities, we benchmarked a feature level and a kernel-level fusions to learn the kernel that incorporates multiple sensors in the support vector classifier. The results show that given the same …

Date

2016

Authors

Che-Wei Huang, Shrikanth Narayanan

Conference

2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)

Pages

1-6

Publisher

IEEE