Smart robotic wheelchairs have emerged as promising assistive devices to enhance mobility and independence for individuals with mobility impairments. The successful integration of advanced sensor systems plays a critical role in improving the functionality and safety of these wheelchairs. This paper presents a comprehensive review of the design and implementation of advanced sensor systems for smart robotic wheelchairs. Through an extensive literature review, the limitations of existing sensor technologies are identified, and the potential of advanced sensors is explored. Vision-based sensors, range and proximity sensors, force and pressure sensors, inertial sensors, and environmental sensors are discussed in detail. Furthermore, this review highlights the design considerations, hardware components, software development, and calibration procedures involved in implementing advanced sensor systems. Evaluation and performance analysis metrics are discussed to assess the effectiveness of the sensor systems. The research findings indicate that advanced sensor systems have the potential to significantly enhance the functionality and safety of smart robotic wheelchairs. However, challenges such as sensor integration, data fusion, and user feedback must be addressed. This review paper concludes by discussing the implications of advanced sensor systems in improving wheelchair functionality and user experience, and proposes future directions for research in this domain.

The Low-Cost Voice Controlled Wheelchair with Raspberry Pi is an innovative assistive technology designed to improve the mobility and independence of people with disabilities. This research aims to develop a wheelchair system that can be operated using voice commands at an affordable price, making it accessible to a wider range of individuals with limited mobility. The device is built on the Raspberry Pi, a reasonably priced, credit-card-sized computer, and uses an easy-to-use yet efficient voice recognition technique to let users control the wheelchair with their vocal commands. A Raspberry Pi, a microphone, and motor controllers are some of the system's hardware components. The software uses Python programming language and open-source voice recognition technology to recognize voice commands, making it easy for users to navigate their environment independently. The system has been tested on a prototype and has shown promising results in terms of accuracy and reliability. The Low-Cost Voice Controlled Wheelchair with Raspberry Pi can give disabled persons new levels of mobility and independence, enhancing their quality of life and enhancing their capacity to carry out daily tasks.

The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

A wide range of obstacles faced by people with disabilities, such as visual impairment, motor disability, and communication difficulties, have shown significant promise for being addressed by computer vision. The state-of-the-art in computer vision-based assistive technology is examined in this report along with major future research topics and obstacles. In particular, this study explores how computer vision can be used for object recognition, navigation, facial recognition, sign language interpretation, and gesture-based control interfaces. It also discusses the benefits and drawbacks of various methodologies and technologies and offers examples of how computer vision can be incorporated into current assistive technologies to boost their efficacy. The ethical and privacy issues surrounding the use of computer vision in assistive technologies are covered in this study effort. The study also highlights the need for protocol standardization, better user-centered design, and the assessment of real-world effectiveness as future research objectives for improving the use of computer vision in assistive technology. Overall, this paper sheds light on how computer vision might completely alter the world of assistive technologies for people with impairments.