| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 102 === Older individuals living alone at home or wards of a hospital are usually at great risk of delayed assistance following a fall; therefore, the research of fall detection systems has been greatly growing in these years. This thesis aims to develop a Kinect-based fall detection and activity monitoring system to provide more safety for older individuals living alone. The raw data from the Kinect depth images are processed directly rather than the skeletal tracking information. The system starts from the detection of the ground plane and then a simple dynamic background subtraction algorithm is used to identify foreground pixels from the changes between the currently detected ground plane and the original background ground plane. A foreground object with at least a minimum size and height is considered to be a candidate of the older individual and then this moving object will be tracked and analyzed. Decision trees are adopted to divide the daily activities of the older individual into five major types: standing, walking, sitting, lying, and squatting. The system will issue a warning signal to caregivers whenever a fall event is detected. In addition, the system will automatically generate a fall events record (e.g., when, where and how the fall happened, etc) which provides much valuable information for the health care providers. In addition to the detection of falls, the proposed system can also provide information about the daily activities (e.g., the time period of lying in a bed, time period of entering a lavatory, time period of walking, etc.) for the analysis purpose.
The performance of the proposed system was verified by three experimental scenarios. The first experimental scenario is designed to test whether false alarms would happen under normal daily movements. The second experimental scenario is designed to test whether falls could be correctly detected under no change of environments. The third experimental scenario is designed to test whether falls could be correctly detected if there are some changes in environments. Among 90 fall events, the precision ratio and the recall ratio were 94% and 96%, respectively.
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