Authors: Yuki Oguri; Shogo Matsuno; Minoru Ohyama
Addresses: Graduate School of Information Environment, Tokyo Denki University, Chiba, Japan ' Department of Computer Science and Engineering, Toyohashi University of Technology, Aichi, Japan ' Graduate School of Information Environment, Tokyo Denki University, Chiba, Japan
Abstract: We present a high-accuracy recognition method for various activities using smartphone sensors based on device positions. Many researchers have attempted to estimate various activities, particularly using sensors such as the built-in accelerometer of a smartphone. Considerable research has been conducted under conditions such as placing a smartphone in a trouser pocket; however, few have focused on the changing context and influence of the smartphone position. Herein, we present a method for recognising seven types of activities considering three smartphone positions, and conducted two experiments to estimate each activity and identify the actual state under continuous movement at a university campus. The results indicate that the seven states can be classified with an average accuracy of 98.53% for three different smartphone positions. We also correctly identified these activities with 91.66% accuracy. Using our method, we can create practical services such as healthcare applications with a high degree of accuracy.
Keywords: smartphone; activity recognition; device position; accelerometer; barometer; support vector machine; SVM.
International Journal of Space-Based and Situated Computing, 2018 Vol.8 No.2, pp.88 - 95
Available online: 22 Aug 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article