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

Content Provider	IEEE Xplore Digital Library
Author	Madokoro, H. Kakuta, K. Fujisawa, R. Shimoi, N. Sato, K. Li Xu
Copyright Year	2014
Description	Author affiliation: Dept. of Machine Intell. & Syst. Eng., Akita Prefectural Univ., Akita, Japan (Madokoro, H.; Kakuta, K.; Fujisawa, R.; Shimoi, N.; Sato, K.; Li Xu)
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.
Starting Page	540
Ending Page	545
File Size	1384348
Page Count	6
File Format	PDF
ISSN	20937121
e-ISBN	9788993215076
DOI	10.1109/ICCAS.2014.6987838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Control, Robotics and Systems (ICROS)
Subject Keyword	Piezoelectric films Visualization Bed-leaving detection Accuracy Films Quality of Life Capacitance Elman-type Counter Propagation Networks Sensors Caster-stand sensors
Content Type	Text
Resource Type	Article

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