Analysis and Prediction of Patient Falls from Beds Using an Infrared Depth Sensor

Falling down is a common symptom of geriatric syndromes, and fractures and intracranial hemorrhages triggered by falling down lead to serious problems and impair life functioning. Moreover, it sometimes leads to a higher risk of death. In Japan in recent years, the number of fatalities from traffic accidents has been declining, whereas the number of fatalities from falls has been leveling off. In 2020, 8851 people died from falls, whereas the number of fatalities from traffic accidents was 2199. The number of fatalities among the elderly due to falls is approximately four times the number of fatalities from traffic accidents. Therefore, in this paper, we propose a system that analyzes the body by using Kinect, an infrared depth sensor for tracking a skeletal model of a user. In this study, the goal is for the predicted fall values from Kinect-measured data and the predicted fall values from motion-capture-measured data to be close to the predicted values, so that this technology can eventually be used in clinical practice. On the basis of information from the skeletal model, the system analyzes element indices such as the center of gravity and body tilt of people in need of nursing care when falling down. Then, it predicts the risk factor for falling down. This information is used for detecting warning signs for falling down in the early stages. Finally, this study will contribute to decreasing number of falls from the bed.