Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

Waranrach Viriyavit,Virach Sornlertlamvanich

doi:10.1155/2020/5689860

Waranrach Viriyavit, Virach Sornlertlamvanich

Open Access

https://doi.org/10.1155/2020/5689860

Copy DOI

Abstract

Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.

Highlights

Due to the significant growth of the elderly population in today’s demography, the needs of geriatric care have increased
The National Statistical Office of Thailand reported that 11.6% of elderly people have experienced a fall, and 46.3% of them were treated and 7.8% of them were hospitalized as an inpatient [1]
We propose a bed position classification based on a neural network combined with a Bayesian network, with signals from only four sensors

Summary

Introduction

Due to the significant growth of the elderly population in today’s demography, the needs of geriatric care have increased. Continuous monitoring is inevitable for the elderly to prevent falls and bedsores This requires a large number of caregivers with respect to the growth of the elderly population. Some previous studies used commercial pressure mat systems to detect the bed position [13,14,15,16,17,18,19,20,21] Their proposed pressure mat systems need a large number of sensors which are not practical and are costly in actual practice. The studies have shown promising results in bed position classification, their approaches still require quite a large number of sensors. The caregiver to turn the elderly patient’s body when staying in the same position for almost the allowed time period (normally two hours) to prevent bedsores

Materials

Position Detection

Combination of the Neural Network and Bayesian

Experiment and Result

Findings

Conclusion