  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.
