Study on wearable pregnancy diagnosis device for rabbits based on photoelectric sensor

Abstract

Pregnancy diagnosis is essential for rabbits' reproductive management, which allows for re-insemination, and reduces the laboring interval in commercial operations. This study aimed to develop a wearable, non-invasive pregnancy identification device for rabbits. A sensing probe contained three LEDs (Light wavelength is 660 nm, 850 nm, and 930 nm respectively), two Si-based photodiodes, and a host with signal processing was developed. Room-temperature-vulcanized (RTV) silicone rubber, aluminum oxide powder, and dyes of different colors were used to make the abdominal tissue phantom of pregnant female rabbits (PRs) and non-pregnant female rabbits (NPRs) to test the sampling stability of the developed device. The results showed that the variation coefficient of the sampling data of the device was<0.013, and the stability met the use requirements. Sampling data from 419 female rabbits were used to build partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) models of different abdominal hair densities, including dense hair, sparse hair, hair removal, and hair mixing. The classification results of the two models showed that SVM had the best performance, the Ac, Re, Pre, and F-score are more than 80%, respectively. Then, the trained SVM model was then used as the identification model and downloaded to the developed device. Another 218 rabbits were used to verify the performance of the developed device, and the prediction performance was the best among the hair removal rabbits, with Ac, Re, Pre, and F-socre of 83.94%, 83.67%, 81.19%, and 82.41% respectively. The results show that the wearable device developed in this study has a good prediction performance for rabbits with abdominal hair removal, and provides a new idea for pregnancy diagnosis of rabbits.