Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil

Widespread Pb (lead) contamination of urban soil significantly impacts food safety and public health and hinders city greening efforts. However, most existing technologies for measuring Pb are labor-intensive and costly. In this study, we propose SoilScanner, a radio frequency-based wireless system that can detect Pb in soils. This is based on our discovery that the propagation of different frequency band radio signals is affected differently by different salts such as NaCl and Pb(NO3)2 in the soil. In a controlled experiment, manually adding NaCl and Pb(NO3)2 in clean soil, we demonstrated that different salts reflected signals at different frequencies in distinct patterns. In addition, we confirmed the finding using uncontrolled field samples with a machine learning model. Our experiment results show that SoilScanner can classify soil samples into low-Pb and high-Pb categories (threshold at 200 ppm) with an accuracy of 72%, with no sample with > 500 ppm of Pb being misclassified. The results of this study show that it is feasible to build portable and affordable Pb detection and screening devices based on wireless technology.

Beyond-Voice: Towards Continuous 3D Hand Pose Tracking on Commercial Home Assistant Devices

Increasingly popular home assistants are widely utilized as the central controller for smart home devices. However, current designs heavily rely on voice interfaces with accessibility and usability issues; some latest ones are equipped with additional cameras and displays, which are costly and raise privacy concerns. These concerns jointly motivate Beyond-Voice, a novel deep-learning-driven acoustic sensing system that allows commodity home assistant devices to track and reconstruct hand poses continuously. It transforms the home assistant into an active sonar system using its existing onboard microphones and speakers. We feed a high-resolution range profile to the deep learning model that can analyze the motions of multiple body parts and predict the 3D positions of 21 finger joints, bringing the granularity for acoustic hand tracking to the next level. It operates across different environments and users without the need for personalized training data. A user study with 11 participants in 3 different environments shows that Beyond-Voice can track joints with an average mean absolute error of 16.47mm without any training data provided by the testing subject.