Abstract
Among various tools invented to help improve peo-ple's oral health, water flossers can achieve better performance than traditional and electronic toothbrushes, and are less harmful than dental floss, especially for those with orthodontic teeth or tooth implant surgeries. However, the water flossers available in the market serve no monitoring or recording functions that can help consumers clean their teeth in a more efficient way. To capture users' motions, this study develops a novel smart water flosser, installing an Inertial Measurement Unit (IMU) sensor on the handle of the flosser. We determine the motion cycle using signal processing techniques and extract a set of statistical characteristics from the data set. We then train and compare different machine learning models as classifiers to recognize the motions of the handle. We find that the Random Forest model achieves the best detection accuracy at 97% and 85% of the whole feature set and optimized set, respectively. Finally we implement an Android App that connects the smart water flosser with a Bluetooth module to show the washing area in real-time and record relevant information for further guidance.
| Original language | English |
|---|---|
| Article number | 8647697 |
| Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
| DOIs | |
| State | Published - 2018 |
| Event | 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates Duration: 9 Dec 2018 → 13 Dec 2018 |
Keywords
- Android App
- IMU
- machine learning
- signal process
- water flosser
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