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Real-time Activity and Posture Recognition with Combined Acceleration Sensor Data from Smartphone and Wearable Device  

Lee, Hosung (경희대학교 컴퓨터공학과)
Lee, Sungyoung (경희대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.41, no.8, 2014 , pp. 586-597 More about this Journal
Abstract
The next generation mobile computing technology is recently attracting attention that smartphone and wearable device imbedded with various sensors are being deployed in the world. Existing activity and posture recognition research can be divided into two different ways considering feature of one's movement. While activity recognition focuses on catching distinct pattern according to continuous movement, posture recognition focuses on sudden change of posture and body orientation. There is a lack of research constructing a system mixing two separate patterns which could be applied in real world. In this paper, we propose a method to use both smartphone and wearable device to recognize activity and posture in the same time. To use smartphone and wearable sensor data together, we designed a pre-processing method and constructed recognition model mixing signal vector magnitude and orientation pattern features of vertical and horizontal. We considered cycling, fast/slow walking and running activities, and postures such as standing, sitting, and laying down. We confirmed the performance and validity by experiment, and proved the feasibility in real world.
Keywords
activity recognition; posture recognition; smartphone; wearable sensor;
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