TY - GEN
T1 - Toothbrushing monitoring using wrist watch
AU - Huang, Hua
AU - Lin, Shan
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
PY - 2016/11/14
Y1 - 2016/11/14
N2 - Daily toothbrushing is essential for maintaining oral health. However, there is very limited technology to monitor the effectiveness of toothbrushing at home. In this paper, a system is built to monitor the brushing quality on all 16 tooth surfaces using a manual toothbrush and an off-Theshelf wrist watch. The toothbrush is modified by attaching small magnets to the handle, so that its orientation and motion can be captured by the magnetic sensor in the wrist watch. The toothbrushing gestures are recognized based on inertial sensing data from the wrist watch. As the acoustic signal collected from the watch is correlated with the motion of toothbrushing stroke, acoustic sensing algorithm is designed to assist in recognition. User-specific toothbrushing order is also utilized to improve the surface recognition. In extensive experiments with 12 users over 3 weeks, our system successfully recognized toothbrushing gestures with an average precision of 85.6%.
AB - Daily toothbrushing is essential for maintaining oral health. However, there is very limited technology to monitor the effectiveness of toothbrushing at home. In this paper, a system is built to monitor the brushing quality on all 16 tooth surfaces using a manual toothbrush and an off-Theshelf wrist watch. The toothbrush is modified by attaching small magnets to the handle, so that its orientation and motion can be captured by the magnetic sensor in the wrist watch. The toothbrushing gestures are recognized based on inertial sensing data from the wrist watch. As the acoustic signal collected from the watch is correlated with the motion of toothbrushing stroke, acoustic sensing algorithm is designed to assist in recognition. User-specific toothbrushing order is also utilized to improve the surface recognition. In extensive experiments with 12 users over 3 weeks, our system successfully recognized toothbrushing gestures with an average precision of 85.6%.
KW - Activity Recognition
KW - Toothbrushing Monitoring
KW - Wearables
UR - https://www.scopus.com/pages/publications/85007087068
U2 - 10.1145/2994551.2994563
DO - 10.1145/2994551.2994563
M3 - Conference contribution
AN - SCOPUS:85007087068
T3 - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
SP - 202
EP - 215
BT - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
PB - Association for Computing Machinery, Inc
T2 - 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Y2 - 14 November 2016 through 16 November 2016
ER -