Selecting a Best Compromise Direction for a Powered Wheelchair Using PROMETHEE

Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration

As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications and prospects of smart nursing wheelchairs, with particular emphasis on their assistive nursing functions, multiple-sensor fusion technology, and human-machine interaction interfaces. First, we describe the assistive functions of nursing wheelchairs, including position changing, transferring, bathing, and toileting, which significantly reduce the workload of nursing staff and improve the quality of care. Second, we summarized the existing multiple-sensor fusion technology for smart nursing wheelchairs, including LiDAR, RGB-D, ultrasonic sensors, etc. These technologies give wheelchairs autonomy and safety, better meeting patients' needs. We also discussed the human-machine interaction interfaces of intelligent care wheelchairs, such as voice recognition, touch screens, and remote controls. These interfaces allow users to operate and control the wheelchair more easily, improving usability and maneuverability. Finally, we emphasized the importance of multifunctional-integrated care wheelchairs that integrate assistive care, navigation, and human-machine interaction functions into a comprehensive care solution for users. We are looking forward to the future and assume that smart nursing wheelchairs will play an increasingly important role in medicine and geriatric care. By integrating advanced technologies such as enhanced artificial intelligence, intelligent sensors, and remote monitoring, we expect to further improve patients' quality of care and quality of life.

Mini-review: Robotic wheelchair taxonomy and readiness

Robotic wheelchair research and development is a growing sector. This article introduces a robotic wheelchair taxonomy, and a readiness model supported by a mini-review. The taxonomy is constructed by power wheelchair and, mobile robot standards, the ICF and, PHAATE models. The mini-review of 2797 articles spanning 7 databases produced 205 articles and 4 review articles that matched inclusion/exclusion criteria. The review and analysis illuminate how innovations in robotic wheelchair research progressed and have been slow to translate into the marketplace.

Deep Learning Architecture to Assist With Steering a Powered Wheelchair

This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. A disabled driver uses a joystick to provide desired speed and direction, and the Neural Network provides a safe direction for the wheelchair. Results from the Neural Network are mixed with desired speed and direction to avoid obstacles. Inputs originate from a joystick and from three ultrasonic transducers attached to the chair. The resultant course is a blend of desired directions and directions that steer the chair to avoid collision. A rule-based approach is used to create a training and test set for the Neural Network system and applies deep learning to predict a safe route for a wheelchair. The user can over-ride the new system if necessary.

A New Coefficient of Rankings Similarity in Decision-Making Problems

Multi-criteria decision-making methods are tools that facilitate and help to make better and more responsible decisions. Their main objective is usually to establish a ranking of alternatives, where the best solution is in the first place and the worst in the last place. However, using different techniques to solve the same decisional problem may result in rankings that are not the same. How can we test their similarity? For this purpose, scientists most often use different correlation measures, which unfortunately do not fully meet their objective.

In this paper, we identify the shortcomings of currently used coefficients to measure the similarity of two rankings in decision-making problems. Afterward, we present a new coefficient that is much better suited to compare the reference ranking and the tested rankings. In our proposal, positions at the top of the ranking have a more significant impact on the similarity than those further away, which is right in the decision-making domain. Finally, we show a set of numerical examples, where this new coefficient is presented as an efficient tool to compare rankings in the decision-making field.