Bed-leaving behavior detection and recognition based on time-series learning using Elman-Type Counter Propagation Networks

Abstract

This paper presents a bed-leaving detection method using Elman-type Counter Propagation Networks (ECPNs), a novel machine-learning-based method used for time-series signals. In our earlier study, we used CPNs, a form of supervised model of Self-Organizing Maps (SOMs), to produce category maps to learn relations among input and teaching signals. For this study, we inserted a feedback loop as the second Grossberg layer for learning time-series features. Moreover, we developed an original caster-stand sensor using piezoelectric films to measure weight changes of a subject on a bed to be loaded through bed legs. The features of our sensor are that it obviates a power supply for operations and that it can be installed on existing beds. We evaluated our sensor system by examining 10 people in an environment representing a clinical site. The mean recognition accuracy for seven behavior patterns is 71.1%. Furthermore, the recognition accuracy for three behavior patterns of sleeping, sitting, and leaving the bed is 83.6% Falsely recognized patterns remained inside of respective categories of sleeping and sitting. We infer that this system is applicable to an actual environment as a novel sensor system requiring no restraint of patients.