SmartCalve: Design and Implementation of an IoT Device for Early Calving Detection in Cattle

Abstract

Bovine calving is a critical event in livestock farming that requires timely human intervention to prevent calf and cow mortality. Existing detection systems suffer from invasiveness, high cost, or limited real-time precision, restricting their adoption in conventional farming environments. This work presents CalvingSafe, a non-invasive IoT-based multi-node kinematic monitoring system designed for realtime detection of bovine parturition. The system integrates a distributed array of MPU6050 inertial measurement units mounted along the bovine tail, managed by a TCA9548A I2C multiplexer and controlled by an ESP8266 microcontroller. Raw accelerometer data is converted into spherical coordinates (R, φ, θ) and transmitted wirelessly to a processing backend. A proprietary dataset of 3,060 kinematic frames was extracted from 60 video sequences using an OpenCV-based multi-segmental 3D tracking pipeline, covering three behavioral states: normal behavior, waste elimination, and pre-calving agitation. The hardware prototype was assembled and validated through serial monitor testing, confirming the integrity of the full data acquisition chain across all three sensor nodes.