Computer Engineering Commons^™

Leveraging Smartphone Sensor Data For Human Activity Recognition, Xizhe Yin

Electronic Thesis and Dissertation Repository

Using smartphones for human activity recognition (HAR) has a wide range of applications including healthcare, daily fitness recording, and anomalous situations alerting. This study focuses on human activity recognition based on smartphone embedded sensors. The proposed human activity recognition system recognizes activities including walking, running, sitting, going upstairs, and going downstairs. Embedded sensors (a tri-axial accelerometer and a gyroscope sensor) are employed for motion data collection. Both time-domain and frequency-domain features are extracted and analyzed. Our experiment results show that time-domain features are good enough to recognize basic human activities. The system is implemented in an Android smartphone platform.

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