@techreport{oai:ipsj.ixsq.nii.ac.jp:00219886,
 author = {山岸, 信夫 and 鏑木, 崇史 and Shinobu, Yamagishi and Takashi, Kaburagi},
 issue = {1},
 month = {Aug},
 note = {Human Activity Recognition is a field concerned with the identification of human activities using digital sensors such as cameras and motion sensors. The field is becoming increasingly relevant to the daily lives of people in areas with an abundance of technology in the form of “smart” devices. The smartphone is an popular example of a device that can be used to detect activities. However, the current methods for identifying activities tend to have a high computational cost that prevent them from occuring on devices with less computation capabilities. Thus, in this paper we suggest the Clockwork Recurrent Neural Network (CWRNN) model developed by Koutnik in 2014 [1] as a way to decrease computational cost. The results of the implementation of CWRNN against Long Short-Term Memory (LSTM) suggest that LSTMs are superior for classification accuracy, though the CWRNN model used has potential to be further optimized, Human Activity Recognition is a field concerned with the identification of human activities using digital sensors such as cameras and motion sensors. The field is becoming increasingly relevant to the daily lives of people in areas with an abundance of technology in the form of “smart” devices. The smartphone is an popular example of a device that can be used to detect activities. However, the current methods for identifying activities tend to have a high computational cost that prevent them from occuring on devices with less computation capabilities. Thus, in this paper we suggest the Clockwork Recurrent Neural Network (CWRNN) model developed by Koutnik in 2014 [1] as a way to decrease computational cost. The results of the implementation of CWRNN against Long Short-Term Memory (LSTM) suggest that LSTMs are superior for classification accuracy, though the CWRNN model used has potential to be further optimized},
 title = {Computational Cost Reduction for Human Activity Recognition Using Clockwork Recurrent Neural Networks},
 year = {2022}
}