This PhD project will use data modelling techniques to explore the behavioural transmission of mental health conditions from mother to infant. This will involve an extensive analysis of coded video data of mother-infant interactions captured using wearable headcams in CoCo90s and comparing to other data in partner cohorts. Initial data analysis will involve computing the frequencies, durations, and rates per minute of behaviours for each subject. Following this, statistically significant inferences between modes will be extracted using graphical modelling, Bayesian inference and pattern recognition methodologies. Additionally, behavioural comparisons will be drawn between mothers with and without mental health conditions. It is hoped that findings from this research will be used to inform interventions to improve mental health outcomes for mother and infant.