A Mechanical Bed for Elderly Care to Assist while Standing, Sitting and Lying In this article, a new design of bed for patient care is suggested, allowing hospital patients or people requiring home assistance to be able to get up on their own. This system is fully mechanical, which firstly reduces the risk of failure and the durability of the system, and secondly ensures autonomous operation without the need for a power supply. This system has three positions, the lying, sitting and standing position. A fourbar linkage controls the position of the bed, which is divided into three parts directly connected to the linkage, simplifying the kinematics of the bed. In addition to its manual operability, this innovative bed concept includes a safety system that locks the position at all times, preventing any sudden moves to keep the patient comfortable, while still allowing for a rapid position change.

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