Real-time Abnormal Behavior Recognition for Patient Monitoring in Hospitals

Hyewon Song, Jiwoo Kang, Taewan Kim

Abstract: Due to a shortage of medical staff, psychiatric nurses often find themselves responsible for as many as 16 or more patients, making it challenging to provide personalized attention to individuals requiring both physical and mental care. For this reason, we propose a real-time abnormal behavior recognition algorithm in hospitals. Our system utilizes real-time video analysis to detect and track the locations of mental patients, enabling the identification of their abnormal behaviors. Specifically, we have defined distinct abnormal behaviors commonly observed in closed wards, such as Self-Harm, Falldown, and Hit. To improve recognition performance, we applied the continual learning method, allowing the system to adapt and enhance its capabilities. In addition, the architecture can create our abnormal behavior dataset. The average abnormal behavior recognition accuracy of the system exceeds 90%. By decreasing the likelihood of encountering dangerous incidents, our proposed method not only improves the wellbeing of patients but also fosters a safer working environment for medical staff.