Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns

This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear charac...

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Published inSensors (Basel, Switzerland) Vol. 20; no. 5; p. 1415
Main Authors Madokoro, Hirokazu, Nakasho, Kazuhisa, Shimoi, Nobuhiro, Woo, Hanwool, Sato, Kazuhito
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 05.03.2020
MDPI
Subjects
Accuracy
Aged
Aging
Algorithms
ambient sensors
Beds
Cameras
Caregivers
Datasets
home agent
Humans
Japan
life monitoring
Load
Load tests
Machine Learning
Monitoring, Physiologic - instrumentation
Nursing
Older people
Pattern recognition
Polyethylene terephthalate
quality of life
random forest
Security Measures
Sensors
Japan
life monitoring
ambient sensors
random forest
machine learning
quality of life
home agent
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Summary:This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear characteristic between loads and output was obtained from a load test to evaluate sensor output characteristics. Moreover, the output values change linearly concomitantly with speed to attain the sensor with the equivalent load. We obtained benchmark datasets of continuous and discontinuous behavior patterns from ten subjects. Recognition targets using our sensor prototype and their monitoring system comprise five behavior patterns: sleeping, longitudinal sitting, lateral sitting, terminal sitting, and leaving the bed. We compared machine learning algorithms of five types to recognize five behavior patterns. The experimentally obtained results revealed that the proposed sensor system improved recognition accuracy for both datasets. Moreover, we achieved improved recognition accuracy after integration of learning datasets as a general discriminator.
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These authors contributed equally to this work.
This paper is an extended version of our paper published in Madokoro, H.; Nakasho, K.; Shimoi, N.; Woo, H.; Sato, K. Invisible Sensors for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns Proceedings of the 5th International Conference on Sensors Engineering and Electronics Instrumentation Advances, Tenerife, Spain, 25–27 September 2019.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20051415