[PAST EVENT] An Electric Toothbrushing Monitoring System Based on Magneto-Inductive Sensing
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An Electric Toothbrushing Monitoring System Based on Magneto-Inductive Sensing
Speaker: Hua Huang from Stony Brook
Electric toothbrushes are widely used for home oral care, but many users do not achieve desired hygiene results due to insufficient brushing coverage or incorrect brushing techniques. Existing electric toothbrushing monitoring systems that are based on cameras or motion sensors have poor performance due to visibility obstructions or vibration noises. This talk will cover the technical challenges in achieving fine-grained brushing coverage monitoring and incorrect brushing technique detection based on magneto-inductive sensing. This design is inspired by my observation that the motor inside an electric toothbrush generates a unique magnetic field, which can serve as a reliable signal for position and orientation tracking. I will start by showing how to approximately model the spatial distribution of the motor magnetic field and achieve position tracking. Then I will discuss how to estimate the motor's orientation by exploiting the time domain features of the magnetic field using deep learning. Finally, I will discuss our toothbrushing monitoring algorithm based on Maximum-Likelihood Estimation that requires zero user training and maintains robustness against user movements.
Hua Huang is a PhD candidate in the Department of Electrical and Computer Engineering at Stony Brook University. Mr Huang's research interests include cyber physical systems, mobile and ubiquitous computing, and Internet of Things. He is particularly interested in developing new sensing technologies to achieve accurate and convenient motion tracking. Recently his work is focused on activity recognition systems for toothbrushing and driving.