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"ML-applied novel capacitive sensing technology"

Capacitive sensing technology is one of the primary sensing modalities to perceive the physical world, like pressure, distance, proximity, humidity, acceleration. The present work focused on advanced novel capacitive-based sensing technique that has been seldomly explored before and the machine learning solutions in different application scenarios. Two types of capacitive-based sensing systems have been build, including the hardware, firmware, and applications. The first one is an oscillating magnetic field system composed of the traditional ceramic capacitance and a customized coil-based inductance. The system shows robust, accurate navigation functionality indoors/outdoors/underwater with an ML solution. After minimizing the system, we developed a wearable magnetic field system to efficiently monitor the social distancing, aiming to decrease the risk of virus infection. We focused on a ubiquitous concept for the second advanced capacitive sensing system, the human body capacitance(HBC). Previous work on the HBC mainly focused on electrostatic discharge protection, especially in healthcare institutions. Our work designed different wearable, low cost, low power consumption prototypes that can measure the value of the HBC continuously in real-time and utilize this concept for individual/cooperative activity recognition. The activity classification based on HBC shows significant improvement regarding the IMU-only solutions. Some unique motion-sensing abilities were founded based on HBC, which is beyond the ability of traditional motion sensors like accelerometer and gyroscope.

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