Detecting Deviation in Rodent Behaviour Through Home Cage Analysis

This project is concerned with applying machine learning to automate detection of behavioural changes in laboratory mice. Through video recording and RFID tracking it is possible to perform long-term standardised observation of subjects, allowing for high-level information to be extracted by researchers afterwards. The main aim of this project is to determine whether is is possible to determine the welfare state of the animal through the analysis of spontaneous behaviour gathered from home cages.

The first milestone for this project would be producing a classifier that, given a sufficient training set of mice in two distinct classes, can determine wth high accuracy whether a given mouse recording belongs to one class or the other. To start with the model will be trained to differentiate between two very distinct strains of mice, such as a strain with a normal circadian rhythm and a strain without any. At first the model will be trained using features that are more easily engineered. These would include: time spent drinking and feeding, activities such as climbing and burrowing, and sleeping patterns. If the model shows significant improvement with more elaborate features, then work would include engineering detection of more naunced behaviours such as grooming and social interactions.

The initial proposed method for characterising the time series data is to split the dataset into individual hours, and engineer features from the locomotor activity over that hour. Features that interpret periods and patterns of activity and sleep can then be fitted to more conventional machine learning techniques. Another avenue of exploration is to apply deeper learning strategies to attempt to recognise patterns in activity in a more undirected fashion.