@inproceedings{HayakawaAlFaloujiSchickhuberetal., author = {Hayakawa, Shuhei and Al-Falouji, Ghassan and Schickhuber, Gerald and Mandl, Roland and Yoshida, Takahiro and Hangai, Seiichiro}, title = {A Method of Toothbrush Position Measurement using AR Markers}, series = {2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech): 10-12 March 2020, Kyoto, Japan}, booktitle = {2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech): 10-12 March 2020, Kyoto, Japan}, publisher = {IEEE}, doi = {10.1109/LifeTech48969.2020.1570619103}, pages = {91 -- 93}, abstract = {Appropriate daily brushing of the teeth is important for preventing oral diseases. Therefore, a personal assistant for assessing and coaching appropriate toothbrushing is needed. Herein, a three-dimensional toothbrush position measurement method using augmented reality (AR) markers is proposed. The AR markers are detected via a brushing video captured using a smartphone camera. The AR markers are installed on each surface of a dodecahedron attached at the rear end of the toothbrush. This report describes the proposed method, the resulting toothbrush position measurement accuracy, and the optimal number of markers needed for an accurate measurement of the position.}, language = {en} } @inproceedings{HiyamaSehrYoshidaetal., author = {Hiyama, Toru and Sehr, Armin and Yoshida, Takahiro and Hangai, Seiichiro}, title = {Text/Language-Independent Unknown Speaker Rejection Method Using LSP Codes}, series = {IEEE 9th Global Conference on Consumer Electronics (GCCE 2020): 13-16 Oct. 2020, Kobe, Japan}, booktitle = {IEEE 9th Global Conference on Consumer Electronics (GCCE 2020): 13-16 Oct. 2020, Kobe, Japan}, doi = {10.1109/GCCE50665.2020.9291835}, abstract = {Scam phone calls pretending relatives have become a major social problem. However, current countermeasure against such scam phone calls stays only enlightenment activities although there is a limited number of technical measures in telephones. In this study, we have proposed a text-independent unknown speaker rejection method which recognizes whether a known speaker or an unknown speaker on receiving a call. In this method, we used a line spectral pair (LSP) spectrogram as the feature and a convolutional neural network (CNN) as the identification engine. From the evaluated results, the proposed method recognized known/unknown speakers with the false acceptance rate (FAR) of 13.1\% in Japanese speech and 25.2\% in German speech, respectively.}, language = {en} }