Vision-based detection of unusual patient activity

Automated patient monitoring in hospital environments has gained increased attention in the last decade. An important problem is that of behaviour analysis of psychiatric patients, where adequate monitoring can minimise the risk of harm to hospital staff, property and to the patients themselves. For this task, we perform a preliminary investigation on visual-based patient monitoring using surveillance cameras. The proposed method uses statistics of optical flow vectors extracted from the patient movements to identify dangerous behaviour. In addition, the method also performs foreground segmentation followed by blob tracking in order to extract shape and temporal characteristics of blobs. Dangerous behaviour includes attempting to break out of safe-rooms, self-harm and fighting. The features considered include a temporal and multi-resolution analysis of blob coarseness, blob area, movement speed and position in the room. This information can also be used to normalise the other features according to estimated position of the patient in the room. In this preliminary study, experiments in a real hospital scenario illustrate the potential applicability of the method