@article{oai:u-fukui.repo.nii.ac.jp:00024919,
 author = {北村, 和也 and 小高, 知宏 and 黒岩, 丈介 and 諏訪., いずみ and 白井, 治彦 and Kotamura, Kazuya and Odaka, Tomohiro and Kuroiwa, Jousuke and Suwa, Izumi and Shirai, Haruhiko},
 journal = {福井大学 大学院工学研究科 研究報告},
 month = {Mar},
 note = {In this paper, we proposed a method to identify human behavior using a 3-axis acceleration sensor of a smartphone. To realize context-aware services such as efficient energy-saving appliance controland elderly monitoring, high-accuracy in-home living activity recognition is essential. We tried to improve recognition accuracy by using deep learning for HAR(Human Activity Recognition). The proposed methods are CNN(Convolution Neural Network) and lstm(Long short-term memory)methods. An experiment was performed using the HASC dataset to verify the effectiveness ofthe method. The HASC data set is data of three-axis acceleration. As a result of the experiment, theresult using the CNN method was 95.4%, and the result using the LSTM method was 94.3%.},
 pages = {59--65},
 title = {加速度データからの機械学習による行動認識},
 volume = {68},
 year = {2020}
}