Stool Image Analysis for Precision Health Monitoring by Smart Toilets
Precision health monitoring is facilitated by long-term data collection that establishes a health baseline and enables the detection of deviations from it. With the advent of the Internet of Things, monitoring of daily excreta from a toilet is emerging as a promising tool to achieve the long-term collection of physiological data. This paper describes a stool image analysis approach that accurately and efficiently tracks stool form and visible blood content using a Smart Toilet. The Smart Toilet, can discreetly image stools in toilet plumbing outside the purview of the user. We constructed a stool image dataset with 3,275 images, spanning all seven types of the Bristol Stool Form Scale, a widely used metric for stool classification. We used ground-truth data obtained through the labeling of our dataset by two gastroenterologists. We addressed three limitations associated with the application of computer-vision techniques to a smart toilet system: (i) uneven separability between different stool form categories; (i) class imbalance in the dataset; (ii) limited computational resources in the microcontroller integrated with the Smart Toilet. We present results on the use of class-balanced loss, and hierarchical and compact convolutional neural network (CNN) architectures for training a stool-form classifier. We also present results obtained using perceptual color quantization coupled with mutual information to optimize the color-feature space for the detection of stool images with gross (visible) blood content. For the classification of stool-form, we achieve a balanced accuracy of 81.66% using a hierarchical CNN based on MobileNetV2. For gross blood detection, the decision tree (DT) classifier provides 74.64% balanced accuracy.
