![]() Actually, one approach is to use bed-leaving sensors that signal when they leave from their beds. For protecting it, suitable provision is necessary after conducting assessments for respective elderly patients. Statistically, the tumbling and falling accident rates were 83.3% and 85.4%, respectively, when they had no support at an accidental moment. Numerous accidents occurred when elderly patients leave their bed and these accidents occurred in a place where nurses and caretakers are hard to keep their eye on. ![]() As related to this situation, Mitadera and Akazawa addressed that approximately half of falling or tumbling accidents are among elderly patients. For this situation, nurses and caretakers should monitor them inadequately, especially during sleep at night. Currently, few caretakers care for numerous elderly patients. Our prototype system is applicable and used for an actual environment as a novel sensor system without restraint for patients.Īlong with the longevity in our society, labor shortages will be severe, especially at hospitals, nursing homes, and nursing care facilities. However, falsely recognized patterns remained inside of respective behavior categories of sleeping and sitting. Particularly, the recognition accuracies for longitudinal sitting, terminal sitting, and left the bed were 83.3, 98.3, and 95.0%, respectively. The experimentally obtained result revealed that the mean recognition accuracy for seven behavior patterns was 75.5%. Our prototype system was evaluated by the examination with 10 subjects in an environment representing a clinical site. We developed a machine-learning-based method to recognize bed-leaving behavior patterns obtained from sensor signals. The noteworthy features of these sensors are their easy installation, low cost, high reliability, and toughness. A triaxial accelerometer is used for the pillow sensor, and piezoelectric elements are used for the pad sensors and the bolt sensor that were installed under a bed mat and a bed handrail, respectively. Our developed prototype system comprises three sensors: a pad sensor, a pillow sensor, and a bolt sensor. ![]() This chapter presents an unrestrained and predictive sensor system to analyze human behavior patterns, especially in a case that occurs when a patient leaves a bed.
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