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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...
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...
This paper presents a sensor system that predicts behavior patterns that occur when a patient leaves a bed. We originally developed plate-shaped sensors using piezoelectric elements. Existing sensors such as clip sensors and mat sensors require that patients be restrained. The features of our sensors are that they require no power supply or patient restraint for privacy problems. Moreover, we developed...
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