@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00202380,
 author = {Thilina, Dissanayake and Takuya, Maekawa and Takahiro, Hara and Thilina, Dissanayake and Takuya, Maekawa and Takahiro, Hara},
 book = {マルチメディア,分散協調とモバイルシンポジウム2019論文集},
 month = {Jun},
 note = {This paper presents a novel method to estimate a location label of a user using sensor data from his/her smart devices (smartwatch and smartphone) without using labeled training data collected in his/her environments. Specifically， we attempt to predict the user's location semantics， i.e.， location classes such as restroom and meeting room. We propose a novel matrix operation method that automatically finds acceleration data motifs specific to a certain location class， e.g.， brushing teeth action observed at bathroom， based on the idea of Gini coefficient. We evaluated the proposed method in 4 real environments., This paper presents a novel method to estimate a location label of a user using sensor data from his/her smart devices (smartwatch and smartphone) without using labeled training data collected in his/her environments. Specifically， we attempt to predict the user's location semantics， i.e.， location classes such as restroom and meeting room. We propose a novel matrix operation method that automatically finds acceleration data motifs specific to a certain location class， e.g.， brushing teeth action observed at bathroom， based on the idea of Gini coefficient. We evaluated the proposed method in 4 real environments.},
 pages = {770--776},
 publisher = {情報処理学会},
 title = {Preliminary Investigation on Recognizing Environment-dependent Human Actions using a Fusion of Wi-Fi Based Place Clustering and Motif Detection from Accelerometer Data},
 volume = {2019},
 year = {2019}
}