A Literature Review on the Smart Wheelchair Systems

This study offers an in-depth analysis of smart wheelchair (SW) systems, charting their progression from early developments to future innovations. It delves into various Brain-Computer Interface (BCI) systems, including mu rhythm, event-related potential, and steady-state visual evoked potential. The paper addresses challenges in signal categorization, proposing the sparse Bayesian extreme learning machine as an innovative solution. Additionally, it explores the integration of emotional states in BCI systems, the application of alternative control methods such as EMG-based systems, and the deployment of intelligent adaptive interfaces utilizing recurrent quantum neural networks. The study also covers advancements in autonomous navigation, assistance, and mapping, emphasizing their importance in SW systems. The human aspect of SW interaction receives considerable attention, specifically in terms of privacy, physiological factors, and the refinement of control mechanisms. The paper acknowledges the commercial challenges faced, like the limitations of indoor usage and the necessity for user training. For future applications, the research explores the potential of autonomous systems adept at adapting to changing environments and user needs. This exploration includes reinforcement learning and various control methods, such as eye and voice control, to improve adaptability and interaction. The potential integration with smart home technologies, including advanced features such as robotic arms, is also considered, aiming to further enhance user accessibility and independence. Ultimately, this study seeks to provide a thorough overview of SW systems, presenting extensive research to detail their historical evolution, current state, and future prospects.