PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration

TL;DR

PigV$^2$ is a novel ground vibration-based system for continuous monitoring of pig heart and respiratory rates, addressing limitations of existing methods and enabling better animal health management.

Contribution

This paper introduces PigV$^2$, the first system to monitor pig vital signs via ground vibrations, overcoming challenges of signal overlap and sensor variability.

Findings

Achieved 3.4% error in heart rate estimation.

Achieved 8.3% error in respiratory rate estimation.

Validated effectiveness through real-world deployment with 30 pigs.

Abstract

Pig vital sign monitoring (e.g., estimating the heart rate (HR) and respiratory rate (RR)) is essential to understand the stress level of the sow and detect the onset of parturition. It helps to maximize peri-natal survival and improve animal well-being in swine production. The existing approach mainly relies on manual measurement, which is labor-intensive and only provides a few points of information. Other sensing modalities such as wearables and cameras are developed to enable more continuous measurement, but are still limited due to animal discomfort, data transfer, and storage challenges. In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations. Our approach leverages the insight that both heartbeat and respiration generate ground vibrations when the sow is lying on the floor. We infer vital information by sensing and analyzing these vibrations. The main challenge in developing PigV$^2$ is the overlap of vital- and non-vital-related information in the vibration signals, including pig movements, pig postures, pig-to-sensor distances, and so on. To address this issue, we first characterize their effects, extract their current status, and then reduce their impact by adaptively interpolating vital rates over multiple sensors. PigV$^2$ is evaluated through a real-world deployment with 30 pigs. It has 3.4% and 8.3% average errors in monitoring the HR and RR of the sows, respectively.