@article{oai:muroran-it.repo.nii.ac.jp:00009701,
 author = {LV, Shaohe and LU, Yong and DONG,  Mianxiong and 董, 冕雄 and WANG, Xiaodong and DOU, Yong and ZHUANG, Weihua},
 issue = {6},
 journal = {IEEE Transactions on Human-Machine Systems},
 month = {May},
 note = {application/pdf, Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient-based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, whereas for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants.},
 pages = {789--800},
 title = {Qualitative Action Recognition by Wireless Radio Signals in Human–Machine Systems},
 volume = {47},
 year = {2017},
 yomi = {トウ, メンユウ}
}