Daily activity recognition based on acoustic signals and acceleration signals estimated with Gaussian process
Masafumi Nishida, Norihide Kitaoka, Kazuya Takeda · 2015
We have created corpus of daily activities using wearable sensors. The corpus consists of sound and image data from a camera and motion signals from a smartphone for both indoor and outdoor activities over 72 continuous hours. We propose a method that can interpolate acceleration signals to any sample points with a Gaussian process in order to recognize daily activities. We conducted recognition experiments of daily activities using our corpus. Experimental results showed that the proposed method can improve recognition accuracy compared to a conventional method. This demonstrates the effectiveness of estimating acceleration signals with a Gaussian process to recognize daily activities.