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

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.