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. Our developed prototype system comprises three sensors: a pad sensor, a pillow sensor, and a bolt sensor. 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. The noteworthy features of these sensors are their easy installation, low cost, high reliability, and toughness. We developed a machine-learning-based method to recognize bed-leaving behavior patterns obtained from sensor signals. Our prototype system was evaluated by the examination with 10 subjects in an environment representing a clinical site. The experimentally obtained result revealed that the mean recognition accuracy for seven behavior patterns was 75.5%. Particularly, the recognition accuracies for longitudinal sitting, terminal sitting, and left the bed were 83.3, 98.3, and 95.0%, respectively. However, falsely recognized patterns remained inside of respective behavior categories of sleeping and sitting. Our prototype system is applicable and used for an actual environment as a novel sensor system without restraint for patients.
Part of the book: Piezoelectricity