ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare.

Low-latency Imaging and Inference from LoRa-enabled CubeSats

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, primarily for research, education, and Earth observation. The vast majority of these CubeSats experience significant latency (several hours) from the time an image is captured to the time it is available on the ground. This is primarily due to the limited availability of dedicated satellite ground stations that tend to be bulky to deploy and expensive to rent. This paper explores using LoRa radios in the ISM band for low-latency downlink communication from CubeSats, primarily due to the availability of extensive ground LoRa infrastructure and minimal interference to terrestrial communication. However, the limited bandwidth of LoRa precludes rich satellite Earth images to be sent - instead, the CubeSats can at best send short messages (a few hundred bytes). This paper details our experience in communicating with a LoRa-enabled CubeSat launched by our team. We present Vista, a communication system that makes software modifications to LoRa encoding onboard a CubeSat and decoding on commercial LoRa ground stations to allow for satellite imagery to be communicated, as well as wide-ranging machine learning inference on these images. This is achieved through a LoRa-channel-aware image encoding that is informed by the structure of satellite images, the tasks performed on it, as well as the Doppler variation of satellite signals. A detailed evaluation of Vista through trace-driven emulation with traces from the LoRa-CubeSat launch (in 2021) shows 4.56 dB improvement in LoRa image PSNR and 1.38x improvement in land-use classification over those images.