Stool and urine can say a lot about a person’s health. Our project, Loggo, is a toilet-mounted Raspberry Pi with a camera, microphone, and pressure-sensitive mat. The device periodically photographs the contents of the toilet bowl and collects audio samples. We use a neural network based on ResNet50 to classify each image according to the type of stool in the bowl, if any. We also use another model to categorize the audio as urine, a flush, or silence. When the image classifier detects toilet paper or the audio classifier detects a flush, we cease data collection and prepare to send the results to the user's phone via WiFi. The user can view a history of bathroom activity and observe trends over time on our custom application to monitor their gastrointestinal health.