Recognition of movements during sleeping is the foundation of sleep quality assessment. This paper proposes an unobtrusive system for activity detection in bed, which collects data by using force sensors, develops user’s individualized model according to the theory of statistical pattern recognition, and identifies the user’s activity through algorithm of maximum similarity at last. This system does not interfere with the normal life and is easy to deploy. The prototype was implemented and the results showed that the recognition accuracy of sleeping behaviors is above 80%, which illustrates the feasibility and practical value of the system.