Daily activity recognition based on acoustic signals and acceleration signals estimated with Gaussian process
- Resource Type
- Conference
- Authors
- Nishida, Masafumi; Kitaoka, Norihide; Takeda, Kazuya
- Source
- 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015 Asia-Pacific. :279-282 Dec, 2015
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Acceleration
Acoustics
Gaussian processes
Hidden Markov models
Kernel
Feature extraction
Bicycles
- Language
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.