@inproceedings{913822857ef34cb28ac140103825907b,
title = "Action Recognition Through Device Sensors",
abstract = "We have proposed a new form of action recognition using motion sensors built in mobile and wearable devices. Due to the miniaturization of hardware sensors, action classification through mobile sensors has become a much more attainable task. Using Android and Tizens integrated development environment, we have devised applications for each of these devices to document raw sensor data for analysis. Utilizing dynamic time warping, we attempt to recognize and classify actions based on differences in euclidean distances to build a strong database for further development.",
keywords = "action classification, action recognition, dynamic time warping, time series, Wearable device",
author = "Jevons Wang and Jiacheng Shang and Jie Wu",
note = "Funding Information: We would like to thank Daniel Bautista from University of California, Davis and Nicholas Boyd from Saint. Joseph{\textquoteright}s University for help testing the application. We would also like to thank Dr. Jie Wu for providing us with the necessary technology to conduct this research and the National Science Foundation for giving us this opportunity. Publisher Copyright: {\textcopyright} 2017 IEEE.; null ; Conference date: 22-10-2017 Through 25-10-2017",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/MASS.2017.84",
language = "English",
series = "Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "492--496",
booktitle = "Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017",
}