Search Results (49)

Electric wheelchairs significantly enhance mobility for individuals with disabilities, but navigating confined or crowded spaces remains a challenge. This paper presents a mechatronic anticollision system based on Time-of-Flight (ToF) sensors designed to improve wheelchair navigation in such environments. The system performs eight-plane 3D environmental scans in 214–358 ms, with a vertical field of view of 12.4° and a detection range of up to 4 m—sufficient for effective obstacle avoidance. Unlike existing solutions like the YDLIDAR T-mini Plus, which has a narrow vertical field of view and a longer detection range that may be excessive for indoor spaces, or the xLIDAR, which struggles with shorter detection ranges, our system balances an optimal detection range and vertical scanning area, making it especially suitable for wheelchair users. Preliminary tests confirm that our system achieves high accuracy, with a standard deviation as low as 0.003 m and a maximum deviation below 0.05 m at a 3-m range on high-reflectivity surfaces (e.g., white and light brown). Our solution offers low power consumption (140 mA) and USB communication, making it an energy-efficient and easy-to-integrate solution for electric wheelchairs. Future work will focus on enhancing angular precision and robustness for dynamic, real-world environments. Full article

Independent living is a central goal for people with disabilities, and the accessibility of the home environment plays a key role in achieving it. In particular, circulation areas within the household are essential to ensure autonomous and safe mobility. Although regulations guide the design of accessible housing, they do not always account for the specific needs of users. This study proposes a method for evaluating the design of universally accessible housing (UAH) through virtual reality simulations, with an emphasis on circulation areas. The Design Science Research Methodology (DSRM) was used to structure the study, guiding the development of an immersive virtual environment that integrates a housing model designed according to physical accessibility standards established by Chilean regulations. The simulation recreated everyday situations related to independent living, assessing indicators such as collisions with environmental elements, the time required to perform specific tasks, and the difficulty of maneuvering a wheelchair. The results show that the use of virtual reality enables the early identification of accessibility barriers from the end-user perspective, allowing design adjustments before construction and contributing to more inclusive and user-centered planning. Full article

This study evaluates the safety aspects of a prototype electric vehicle designed to enable wheelchair users to independently perform simple farm tasks in rural settings, like sample collection and crop monitoring. The vehicle, built at CREA, features four in-wheel electric motors, a pneumatic suspension system, and a secure wheelchair anchoring system. Tests at the CREA experimental farm assessed the vehicle’s whole-body vibrations on different surfaces (asphalt, headland, dirt road) using two tyre models and multiple speeds. A triaxial accelerometer on the wheelchair seat measured vibrations, which were analysed in accordance with ISO standards. Frequency analysis revealed significant vibrations in the 2–40 Hz range, with the Z-axis consistently showing the highest accelerations, which increased with the speed. Tyre A generally induced higher vibrations than Tyre B, likely due to the tread design. At high speeds, the effective accelerations exceeded safety thresholds on asphalt and headland. Statistical analysis confirmed speed as the dominant factor, with the surface type also playing a key role—headland generated the highest vibrations, followed by dirt road and asphalt. The results of these first tests highlighted the high potential of the vehicle to improve the agricultural mobility of disabled people, granting safety conditions and low vibration levels on all terrains at speeds up to 10 km h⁻¹. At higher speeds, however, the vibration levels may exceed the exposure limits, depending on the irregularities of the terrain and the tyre model. Overcoming these limitations is achievable through the optimization of the suspensions and tyres and will be the subject of the next step of this study. This technology could also support wheelchair users in construction, natural parks, and urban mobility. Full article

A novel electromyography (EMG)-based wheelchair interface was developed that uses contractions from the temporalis muscle to control a wheelchair. To aid in the training process for users of this interface, a serious training game, Limbitless Journey, was developed to support patients. Amyotrophic Lateral Sclerosis (ALS) is a condition that causes progressive motor function loss, and while many people with ALS use wheelchairs as mobility devices, a traditional joystick-based wheelchair interface may become inaccessible as the condition progresses. Limbitless Journey simulates the wheelchair interface by utilizing the same temporalis muscle contractions for control of in-game movements, but in a low-stress learning environment. A usability study was conducted to evaluate the serious-game-based training platform. A major outcome of this study was qualitative data gathered through a concurrent think-aloud methodology. Three cohorts of five participants participated in the study. Audio recordings of participants using Limbitless Journey were transcribed, and a sentiment analysis was performed to evaluate user perspectives. The goal of the study was twofold: first, to perform a think-aloud usability study on the game; second, to determine whether accessible controls could be as effective as manual controls. The user comments were coded into the following categories: game environment, user interface interactions, and controller usability. The game environment category had the most positive comments, while the most negative comments were primarily related to usability challenges with the flexion-based controller. Interactions with the user interface were the main topic of feedback for improvement in future game versions. This game will be utilized in subsequent trials conducted at the facility to test its efficacy as a novel training system for the ALS population. The feedback collected will be implemented in future versions of the game to improve the training process. Full article

The integration of artificial intelligence (AI) into assistive technologies is an emerging field with transformative potential, aimed at enhancing autonomy and quality of life for individuals with disabilities and aging populations. This overview of reviews, utilizing a standardized checklist and quality control procedures, examines recent advancements and future implications in this domain. The search for articles for the review was finalized by 15 December 2024. Nineteen review studies were selected through a systematic process identifying prevailing themes, opportunities, challenges, and recommendations regarding the integration of AI in assistive technologies. First, AI is increasingly central to improving mobility, healthcare diagnostics, and cognitive support, enabling personalized and adaptive solutions for users. The integration of AI into traditional assistive technologies, such as smart wheelchairs and exoskeletons, enhances their performance, creating more intuitive and responsive devices. Additionally, AI is improving the inclusion of children with autism spectrum disorders, promoting social interaction and cognitive development through innovative devices. The review also identifies significant opportunities and challenges. AI-powered assistive technologies offer enormous potential to increase independence, reduce reliance on external support, and improve communication for individuals with cognitive disorders. However, challenges such as personalization, digital literacy among the elderly, and privacy concerns in healthcare contexts need to be addressed. Notably, AI itself is expanding the concept of assistive technology, shifting from traditional tools to intelligent systems capable of learning and adapting to individual needs. This evolution represents a fundamental change in assistive technology, emphasizing dynamic, adaptive systems over static solutions. Finally, the study emphasizes the growing economic investment in this sector, forecasting significant market growth, with AI-driven assistive devices poised to transform the landscape. Despite challenges such as high development costs and regulatory hurdles, opportunities for innovation and affordability remain. This review underscores the importance of addressing challenges related to standardization, accessibility, and ethical considerations to ensure the successful integration of AI into assistive technologies, fostering greater inclusivity and improved quality of life for users globally. Full article

Electric wheelchairs are the primary means of transportation that enable individuals with disabilities to move independently to their desired locations. This paper introduces a novel, low-cost smart wheelchair system designed to enhance the mobility of individuals with severe disabilities through hand gesture recognition. Additionally, the system aims to support low-income individuals who previously lacked access to smart wheelchairs. Unlike existing methods that rely on expensive hardware or complex systems, the proposed system utilizes an affordable webcam and an Nvidia Jetson Nano embedded computer to process and recognize six distinct hand gestures—“Forward 1”, “Forward 2”, “Backward”, “Left”, “Right”, and “Stop”—to assist with wheelchair navigation. The system employs the “You Only Look Once version 8n” (YOLOv8n) model, which is well suited for low-spec embedded computers, trained on a self-collected hand gesture dataset containing 12,000 images. The pre-processing phase utilizes the MediaPipe library to generate landmark hand images, remove the background, and then extract the region of interest (ROI) of the hand gestures, significantly improving gesture recognition accuracy compared to previous methods that relied solely on hand images. Experimental results demonstrate impressive performance, achieving 99.3% gesture recognition accuracy and 93.8% overall movement accuracy in diverse indoor and outdoor environments. Furthermore, this paper presents a control circuit system that can be easily installed on any existing electric wheelchair. This approach offers a cost-effective, real-time solution that enhances the autonomy of individuals with severe disabilities in daily activities, laying the foundation for the development of affordable smart wheelchairs. Full article

Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing a driving assistance system to prevent accidents and enhance user safety. The system incorporates ultrasonic sensors and a front-facing camera to detect obstacles and provide real-time warnings. The proposed system operates independently of stable server communication and employs embedded hardware for fast object detection and environmental recognition, ensuring immediate guidance in various scenarios. In this research, we utilized the existing yolov8 model as is. But we attempted to improve performance by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. Thus, the YOLO model was accelerated during inference on the specialized hardware in our experiments. Performance was evaluated in diverse environments to assess its usability. Results demonstrated high accuracy in detecting obstacles and providing timely warnings. Leveraging hardware acceleration for YOLOv8 delivers faster, scalable, and robust object detection, making it a great platform for enhancing driving safety on edge and embedded devices. These findings provide a strong foundation for future advancements in safety assistance systems for electric wheelchairs and other mobility devices. Future research will focus on enhancing system performance and integrating additional features to create a safer environment for electric wheelchair users. Full article

Mobility is essential for individuals with physical disabilities, and wheelchairs significantly enhance their quality of life. Recent advancements focus on developing sophisticated control systems for effective and efficient interaction. This study evaluates the usability and performance of three wheelchair control modes manual, automatic, and voice controlled using a virtual reality (VR) simulation tool. VR provides a controlled and repeatable environment to assess navigation performance and motion sickness across three scenarios: supermarket, museum, and city. Twenty participants completed nine tests each, resulting in 180 trials. Findings revealed significant differences in navigation efficiency, distance, and collision rates across control modes and scenarios. Automatic control consistently achieved faster navigation times and fewer collisions, particularly in the supermarket. Manual control offered precision but required greater user effort. Voice control, while intuitive, resulted in longer distances traveled and higher collision rates in complex scenarios like the city. Motion sickness levels varied across scenarios, with higher discomfort reported in the city during voice and automatic control. Participant feedback, gathered via a Likert scale questionnaire, highlighted the potential of VR simulation for evaluating user comfort and performance. This research underscores the advantages of VR-based testing for rapid prototyping and user-centered design, offering valuable insights into improving wheelchair control systems. Future work will explore adaptive algorithms to enhance usability and accessibility in real world applications. Full article

The mobility and independence of people with disabilities could be significantly improved by wheelchairs. Wheelchair adoption is a complex process that is influenced by various factors, including personal demands, social dynamics, and technological advancements. To effectively promote wheelchair adoption and enhance the quality of life for people with mobility issues, it is crucial to understand the adoption of wheelchairs from a holistic perspective. A model comprising six hypotheses was developed based on the UTUAT-2 (Unified Theory of Acceptance and Use of Technology) framework with modifications. The data was analyzed from 330 individuals living with a disability using SPSS and Smart PLS. The study revealed that performance expectancy, effort expectancy, habit, social influence, and perceived infrastructure individually influence the intention to use wheelchairs. The results further revealed that price value and facilitating conditions were not significant predictors of intention to use a wheelchair. The results also showed that aesthetic design moderates the effect of effort expectancy, habit, social influence, price value, and perceived infrastructure on behavioral intention. Through a multidimensional lens, the paper offers practical recommendations to improve the adoption of wheelchairs for people with mobility impairments. Full article

The convenience of commuting for individuals using mobility aids (IMAs) depends on various features of urban road infrastructure. The present research selected different pathways based on the relevance and convenience of IMAs in three regions of Saudi Arabia, including Riyadh, Qassim, and Hail. A survey questionnaire was developed to evaluate the satisfaction of IMAs with four critical criteria of road infrastructure, including travel distance, slope, availability of footpaths, and number of junctions, using a 5-point Likert scale from very low to very high. A sufficient sample size of this exceptional proportion of the population from different genders, age groups, education levels, employment status, number of disability years, and types of mobility aid participated in the survey. The main reasons for dissatisfaction of more than 50% of the participants were inadequate infrastructure design of entrances to public facilities, pedestrian crossings, and junctions. Social stigma and inadequate assistive technology were also highlighted by around 20% of the participants. The fuzzy synthetic evaluation identified length, slope, and footpaths along the pathway as the most critical features based on the subjective opinion of the participants, of which around 65% have been using artificial limbs or manual wheelchairs. PROMETHEE II aggregated the importance of weights estimated by the participants’ opinion and performance scores of infrastructure features to effectively rank ten pathways in three major cities of the selected regions, using partial and complete outranking. The framework developed in the present study helps concerned organizations to comply with the Vision 2030 goal of a vibrant society in Saudi Arabia by identifying critical pathways and improving infrastructure design to ensure safety, convenience, and satisfaction for IMAs. Full article

Background: Appropriate wheelchair and seating systems are key to allow for the participation of people with Duchenne Muscular Dystrophy. There is little research that focuses on their long-term seating issues and what topics they want studied. Methods: This mixed-method study with adults with Duchenne used an Internet-based survey about wheelchair mobility and discomfort, followed by in-depth interviews. Unanticipated remarks in the interviews led to a second survey regarding the effects of cold weather on wheelchair driving. Descriptive and qualitative analyses were performed. Results: Thirty-seven individuals completed the original survey: 78% used a powered wheelchair, 58% were uncomfortable in their wheelchair, and 94% felt the need to change their seating position. In-depth interviews (N = 9) revealed three themes: seating and pain management, caregiver cooperation, and temperature sensitivity. Almost all (8/9) interviewees explained that cold was a barrier to their participation. In the second survey (N = 13), 11 reported that cold affected their driving, with 10 remarking that it prevented them from participating in daily life activities and 11 stating that the effects of cold on wheelchair driving should be studied. Conclusions: It is essential to ask end-users to identify key issues to ensure the relevance of research to people with disabilities. Full article

Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain–computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model’s effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI. Full article

Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classification algorithms to segment and identify pavement types and surface materials in this scenario. Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. The methodology involves two experiments: the first uses free prompting with random street-view images, employing Grounding Dino and SAM algorithms to assess performance across categories. The second experiment evaluates standardized pavement classification using the Deep Pavements dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. The study presents open resources, such as the Deep Pavements dataset and a fine-tuned CLIP-based model, demonstrating a significant improvement in the true positive rate (TPR) from 56.04% to 93.5%. Our findings highlight both the potential and limitations of current open-vocabulary algorithms and emphasize the importance of diverse training datasets. This study advances urban feature mapping by offering a more intuitive and accurate approach to geospatial data extraction, enhancing urban accessibility and mobility mapping. Full article

Given the unique challenges faced by manual wheelchair users, improving methods to accurately measure and enhance their participation in community life is critical. This study explores a comprehensive method to evaluate the real-world community mobility and participation of manual wheelchair users by combining GPS mobility tracking, heart rate, and activity journals. Collecting qualitative and quantitative measures such as the life space assessment, wheelchair user confidence scale, and physical performance tests alongside GPS mobility tracking from ten manual wheelchair users provided insight into the complex relationship between physical, psychological, and social factors that can impact their daily community mobility and participation. This study found significant, strong correlations between the recorded journal time outside of the home and the GPS mean daily heart rate (r = −0.750, p = 0.032) as well as between the upper limb strength assessments with cardiovascular assessments, physiological confidence, and GPS participation indicators (0.732 < r < 0.884, 0.002 < p < 0.039). This method of manual wheelchair user assessment reveals the complex relationships between different aspects of mobility and participation. It provides a means of enhancing the ability of rehabilitation specialists to focus rehabilitation programs toward the areas that will help manual wheelchair users improve their quality of life. Full article

A system has been developed to convert manual wheelchairs into electric wheelchairs, providing assistance to users through the implemented algorithm, which ensures safe driving and obstacle avoidance. While manual wheelchairs are typically controlled indoors based on user preferences, they do not guarantee safe driving in areas outside the user’s field of vision. The proposed model utilizes the dynamic window approach specifically designed for wheelchair use, allowing for obstacle avoidance. This method evaluates potential movements within a defined velocity space to calculate the optimal path, providing seamless and safe driving assistance in real time. This innovative approach enhances user assistance and safety by integrating state-of-the-art algorithms developed using the dynamic window approach alongside advanced sensor technology. With the assistance of LiDAR sensors, the system perceives the wheelchair’s surroundings, generating real-time speed values within the algorithm framework to ensure secure driving. The model’s ability to adapt to indoor environments and its robust performance in real-world scenarios underscore its potential for widespread application. This study has undergone various tests, conclusively proving that the system aids users in avoidance obstacles and ensures safe driving. These tests demonstrate significant improvements in maneuverability and user safety, highlighting a noteworthy advancement in assistive technology for individuals with limited mobility. Full article