Monitoring vital signs and activities in pig pens during the farrowing period is crucial for reducing pre-weaning piglet mortality and enhancing farm productivity. Traditional methods focus on either vital signs or activities separately, falling short in the dynamically changing farm environment. This paper introduces a multiscale vibration sensing method which dynamically adjusts sensor amplifier gain to detect both vital signs (e.g. heartbeats and respirations) and larger-scale activities (e.g. walking, eating, nursing, etc.). Preliminary trials demonstrate the system's potential to adapt to rapidly changing conditions by switching between high sensitivity for vital signs and reduced sensitivity for activity sensing depending on the detected vibration signal.

This paper addresses challenges in monitoring human behavior in crowds through floor vibration sensing, overcoming limitations like subjective manual observation, visual occlusions, and audio interference. Our approach involves tackling limited-data vibration signal tasks by conducting pre-training across modalities, leveraging publicly available audio datasets. By leveraging self-supervised representation learning to pre-train on publicly available audio datasets, our approach reduces data requirements, improves robustness, and minimizes the need for human labeling efforts. Evaluation using in-game stadium vibration data with YouTube audio dataset demonstrates up to 5.8 × error reduction for crowd behavior.

Heart rate is a critical metric for human cardiovascular health. Most common methods for measuring human heart rate involve wearable devices (e.g., electrocardiography, smart watches). However, such devices can cause discomfort to some patients, especially the elderly or young children. This paper presents FreePulse, a heart rate monitoring system for seated subjects using ambient vibrations. FreePulse builds on our past work using vibrations in the building structures around us to measure human activities and health. As people’s hearts beat, they push on the surfaces the body is touching, creating vibrations in those structures. We combine structure response characterization with human pulse modelling to identify these pulse-induced vibrations from ambient vibration. In testing, FreePulse has shown up to 96% pulse rate accuracy on average, competitive with consumer-grade wearable devices.