Comparison of feature-level and kernel-level data fusion methods in multi-sensory fall detection

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 false alarm rate constraint, the detection rate improves when using signals from multiple sensors, compared to the baseline where no fusion was employed.