Unrestrained multi-sensor systems for real-time prediction of bed-leaving behavior patterns

This paper presents an unrestrained sensor system that analyzes predictive behavior patterns that occur when a patient leaves a bed. Our system comprises three sensors: pad sensors installed under a bed mat, a pillow sensor to detect head movements, and a bolt sensor mounted to a bed handrail. We used a triaxial accelerometer for a pillow sensor and piezoelectric elements for the pad sensors and bolt sensor. The salient features of these sensors are their easy installation, low cost, high reliability, and toughness. Moreover, we developed a method of recognizing bed-leaving behavior patterns using machine-learning algorithms from signals obtained using the sensors. We evaluated our system by examining ten subjects in an environment representing a clinical site. The mean recognition accuracy for seven behavior patterns was 75.5%. Furthermore, the recognition accuracies for longitudinal sitting, terminal sitting, and left the bed were 83.3%, 98.3%, and 95.0%, respectively. In contrast, falsely recognized patterns remained inside of respective categories of sleeping and sitting. We believe that our system is applicable to an actual environment as a novel sensor system with no restraint of patients.