This paper presents a method for automatically detecting and recognising unusual events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find and analyse the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We apply adaptive background subtraction to segment the person using the stairs, followed by affine flow computation over the segmented region. A hidden Markov model (HMM) is then used to analyse the temporal progression of the affine features. A single HMM is trained on sequences of normal stair use, and a threshold is used to detect unusual events in new data. We also introduce a temporal segmentation method using a conditional random field (CRF). We demonstrate our system on a data set with three persons.