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Journal articles on the topic 'Smart wheelchair navigation'
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1
Mathew, Anitha. "E Wheel: A Smart Wheelchair." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5497–500. http://dx.doi.org/10.22214/ijraset.2024.61238.
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Abstract: In this project, we propose the development of an innovative smart electric wheelchair tailored to address the needs of individuals with mobility impairments. The primary objective is to enhance their quality of life and promote independence through the integration of cutting-edge technologies. The wheelchair's standout feature lies in its incorporation of advanced control mechanisms, notably head gesture control. This intuitive navigation system allows users to maneuver the wheelchair effortlessly, while also offering joystick controls. Furthermore, the wheelchair integrates a pulse oximeter sensor, enabling realtime health monitoring. This functionality is crucial for individuals with mobility impairments who may require continuous monitoring of vital signs such as oxygen saturation and heart rate. The inclusion of Blink facilitates remote monitoring and caregiver alerts, providing peace of mind to both users and their caregivers. Safety is paramount in the design of this wheelchair. To ensure safe navigation in various environments, the wheelchair is equipped with obstacle detection and protection mechanisms.
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Ashwathnarayan, Satish Bhogannahalli, Deekshitha Arsa, Sharath Kumar Yerriyuru Narasimhaiah, Shreyas Anchan, and Giri Prasath. "Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs." International Journal of Reconfigurable and Embedded Systems (IJRES) 14, no. 1 (2025): 100. https://doi.org/10.11591/ijres.v14.i1.pp100-108.
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Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment.
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Satish, Bhogannahalli Ashwathnarayan, Deekshitha Arasa, Kumar Yerriyuru Narasimhaiah Sharath, Anchan Shreyas, and Giri Prasath. "Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs." International Journal of Reconfigurable and Embedded Systems (IJRES) 14, no. 1 (2025): 100–108. https://doi.org/10.11591/ijres.v14.i1.pp100-108.
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Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment.
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Akkineni, Kalpana, Thodeti Mounika, Malgari Pooja, et al. "Smart Glove or Sign Language and AI-Driven Wheelchair Navigation." MATEC Web of Conferences 392 (2024): 01061. http://dx.doi.org/10.1051/matecconf/202439201061.
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Gesture gloves are a promising solution for individuals who struggle with both mobility and communication. These gloves' sensor technology helps with nonverbal communication as well as mobility, which is especially beneficial for people who have trouble pushing manual wheelchairs[2]. The goal of gesture gloves, a notable development in assistive technology, is to empower people with disabilities by improving mobility and enabling effective communication through natural hand and finger movements[1]. The use of AI-controlled wheelchairs and smart gloves together raises the bar for assistive technology even higher. The smart glove uses flex sensors to read finger movements and translate them into messages for people who can't effectively use sign language. The AIcontrolled wheelchair simultaneously reacts to recognized gestures, removing the need for assistance from a person and providing an unprecedented degree of independence. For people with disabilities, this gesture-based communication and wheelchair control system offers a comprehensive and game-changing solution that seamlessly integrates into their everyday lives.
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J Phaneendra Balaji, Para Keerti, Adnan Bin Salam, Guguloth Sindhuja, and MD Adil Hussain. "Iot-Based Smart Wheelchair for Elderly Healthcare Monitoring." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2274–82. https://doi.org/10.47392/irjaeh.2025.0335.
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This paper presents the development and implementation of an Internet of Things (IoT)-enabled smart wheelchair designed to enhance mobility and independence for individuals with disabilities. By integrating advanced assistive technologies and internet connectivity, the system offers a comprehensive solution that addresses key challenges in wheelchair navigation and user autonomy. The smart wheelchair is equipped with a suite of sensors, including ultrasonic and infrared, to perceive its environment, detect obstacles, and provide real-time feedback to the user. These sensors enable the wheelchair to autonomously adjust its path, ensuring safe navigation in dynamic environments. Additionally, integrated GPS and mapping capabilities facilitate efficient route planning and precise location tracking, further enhancing user confidence and safety. A communication module allows for remote monitoring of the wheelchair's status, battery level, and location, providing caregivers with valuable information and ensuring prompt assistance when needed. This feature not only supports user safety but also promotes proactive care management. The IoT-enabled wheelchair aims to empower users with greater control over their mobility, improve their quality of life, and promote independent living. Looking ahead, future developments may include integration with smart home systems, personalized user profiles for optimized assistance, and the incorporation of advanced health monitoring features to further support users' well-being.
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Dore, Aruna. "Smart Mobility: Voice-Activated Wheelchair for Enhanced Accessibility." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40834.
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This project presents the development of a voice-activated wheelchair designed to enhance accessibility for individuals with limited mobility. Addressing the challenges posed by traditional manual and joystick-controlled wheelchairs, this innovative solution employs readily available and cost-effective components. The system utilizes an Arduino Uno microcontroller as the central processing unit, an L298N motor driver for controlling DC gear motors, and an HC-05 Bluetooth module for wireless communication. A custom-designed interface within a Bluetooth-enabled smartphone application facilitates voice command input, enabling hands-free navigation. This approach offers increased independence and ease of use for individuals who struggle with conventional control mechanisms. The project details the hardware and software implementation, including circuit design, Arduino coding, and app development. Performance testing evaluates the system's voice recognition accuracy, response time, and maneuverability. The results demonstrate the potential of this voice-controlled wheelchair to significantly improve mobility and quality of life for users with disabilities, offering a more intuitive and accessible means of independent movement. This project highlights the effectiveness of integrating voice control technology with assistive devices to create practical and affordable solutions. Key Words: Voice-activated wheelchair, Assistive device, Independent movement, Cost-effective, Accessibility, Hands-free navigation
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Cui, Jianwei, Yucheng Shang, Siji Yu, and Yuanbo Wang. "Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology." Actuators 13, no. 6 (2024): 230. http://dx.doi.org/10.3390/act13060230.
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The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.
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.P.Margale, Ketan, and S. D. .Mangate. "Smart and automatic wheelchair using Arduino Uno." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44224.
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This paper presents a novel approach to wheelchair control using hand gesture recognition, enabling enhanced mobility for individuals with disabilities. The system employs computer vision and sensor-based technologies to interpret predefined hand gestures, translating them into wheelchair movement commands. Using machine learning algorithms, the model ensures accurate gesture recognition with minimal latency. The proposed system enhances accessibility by reducing dependence on physical joysticks or voice commands, making it suitable for users with varying motor abilities. Experimental results demonstrate high accuracy in gesture detection and smooth wheelchair navigation. Future work includes optimizing real-time processing and integrating adaptive learning for personalized control. Keywords— Arduino Uno, Smart Wheelchair, Autonomous Navigation, Obstacle Avoidance, Controller, MPU-6050 sensor, DC Motors
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Bonci, Andrea, Sauro Longhi, Andrea MonteriÙ, and Massimo Vaccarini. "Navigation system for a smart wheelchair." Journal of Zhejiang University SCIENCE 6A, no. 2 (2005): 110–17. http://dx.doi.org/10.1631/jzus.2005.a0110.
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Andrea, Bonci, Longhi Sauro, Monteriù Andrea, and Vaccarini Massimo. "Navigation system for a smart wheelchair." Journal of Zhejiang University-SCIENCE A 6, no. 2 (2005): 110–17. http://dx.doi.org/10.1631/bf02847974.
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Nguyen, Thanh-Hai, Ba-Viet Ngo, and Thanh-Nghia Nguyen. "Vision-Based Hand Gesture Recognition Using a YOLOv8n Model for the Navigation of a Smart Wheelchair." Electronics 14, no. 4 (2025): 734. https://doi.org/10.3390/electronics14040734.
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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.
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Eng., Nadr Saleh Alenzi, and K. Prahlad Rao Dr. "Trajectory Path Tracking Evaluation of Smart Wheelchair By Image Processing Technique." Journal of Computers and Signas (JCS) 1, no. 1 (2020): 3–5. https://doi.org/10.5281/zenodo.4039816.
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Electrically powered wheelchair has basically two features, one is mechanical based movement system, and another is electrical drive to ease maneuvering rather than manually driven by assistant. In smart wheelchairs, third feature is added that would further simplify driving of powered wheelchair by conventional methods. In the recent years, <em>IoT</em> (Internet-of-Things) are dramatically changing the lifestyle of people which are being used not only for entertainment but also for actuating external devices as per their requirements. Smart phones are almost commonly being used by many people since they are affordable and are being added with multiple features like camera, video games, messaging, navigation facility and many more. We have previously developed a mobile phone application software to control the movement of an electrically powered wheelchair, thus making it as a smart wheelchair. In this paper, we reported the performance evaluation of the wheelchair control through the mobile app. A reference trajectory path was marked on the floor area and a route map was prepared on the Google Maps browser in the smart phone. The user of the wheelchair navigated its movement between source and destination points on the map to follow the reference track. The motion of wheelchair and the track was captured in a video clip. Then, by image processing technique, the wheelchair movement was tracked. It has been observed that during perpendicular turnings like corners inside the buildings or turnings on roads, there were slight deviations of the wheelchair with respect to the reference path. However, this can be minimized by proper training to the person using the smart wheelchair.
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Muhammed Olalekan Hassan, Segun Emmanuel Sunmonu, Raheem Olatunji, Jimoh, Olusegun William Adelere, Nurudeen Balogun Adeyemi, and Kabir Olayinka Hassan. "Development of a solar-powered smart wheelchair." GSC Advanced Research and Reviews 22, no. 3 (2025): 150–58. https://doi.org/10.30574/gscarr.2025.22.3.0069.
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A self-propelled wheelchair that will aid the physically challenged with mobility and the aged who are challenged with mobility was developed. The smart wheelchair aims to help them by giving them the freedom to engage with their environment and improve their standard of living. A device that was able to measure the temperature of the atmosphere was incorporated as well as a universal serial bus and a means of illumination incorporated into the wheelchair. It has a solar power generation system that serves as the power source for the electric motor which propels the wheelchair, and a control system that controls the movement and the navigation of the wheelchair.
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Sahoo, Sushil Kumar, and Bibhuti Bhusan Choudhury. "A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities." Journal of Decision Analytics and Intelligent Computing 3, no. 1 (2023): 221–42. http://dx.doi.org/10.31181/10001122023s.
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This research paper presents a comprehensive review of smart robotic wheelchairs and their impact on enhancing mobility and independence for individuals with disabilities. Traditional wheelchairs often impose limitations on users, resulting in reduced freedom of movement and limited accessibility. The emergence of smart robotic wheelchairs offers a promising solution to address these challenges. This paper provides an overview of wheelchair technology, identifies the specific challenges faced by individuals with disabilities, and explores the advantages and limitations of smart robotic wheelchairs through a review of previous research studies. The features and functionalities of smart robotic wheelchairs, including navigation and obstacle avoidance capabilities, autonomous and semi-autonomous modes, and customizable control options, are discussed. User experience and performance evaluation, along with the impact on mobility and independence, are examined. The paper concludes with future directions and recommendations to guide further research and development in this important field, aiming to empower individuals with disabilities and improve their quality of life.
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Huda, Md Rafiul, Md Liakot Ali, and Muhammad Sheikh Sadi. "Developing a real-time hand-gesture recognition technique for wheelchair control." PLOS One 20, no. 4 (2025): e0319996. https://doi.org/10.1371/journal.pone.0319996.
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At present, significant number of people in the world are having motor disabilities. They need to use wheelchair for performing regular movements and activities. However, there are a lot of issues and challenges in using the conventional wheelchairs. Safe navigation, independent mobility and low cost are the key issues for wheelchair users. This paper presents a new mathematical model for an efficient and real-time hand gesture recognition technique for a smart wheelchair control system, in line with contemporary technologies. The model is developed using the positions of the significant hand landmarks, distances among them and some critical thresholds which are determined by good number of samples of hands. The proposed method offers more users’ flexibility through less hand movement in operation of the wheelchair. The model is tested for hands of different sizes, irrespective of any background in indoor or outdoor under sunlight. The experimental study demonstrates that it outperforms the existing methods in terms of success rate with good performance metrics.
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Zhou, Huaming, Xiaoyu Yu, Qing Wu, Yuji Iwahori, Haibin Wu, and Aili Wang. "Design and Implementation on Multi-Function Smart Wheelchair." Embedded Selforganising Systems 10, no. 7 (2023): 18–22. http://dx.doi.org/10.14464/ess.v10i7.601.
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With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair.
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Tomari, Razali, Yoshnori Kobayashi, and Yoshinori Kuno. "SOCIALLY ACCEPTABLE SMART WHEELCHAIR NAVIGATION FROM HEAD ORIENTATION OBSERVATION." International Journal on Smart Sensing and Intelligent Systems 7, no. 2 (2014): 630–43. http://dx.doi.org/10.21307/ijssis-2017-673.
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Niitsuma, Mihoko, Terumichi Ochi, Masahiro Yamaguchi, and Koki Iwamot. "Design of Mutual Interaction Between a User and Smart Electric Wheelchair." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 2 (2012): 305–12. http://dx.doi.org/10.20965/jaciii.2012.p0305.
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This paper presents interaction between a user and a smart electric wheelchair. We propose a personal mobility tool (PMT) that integrates autonomous mobile robot navigation technology with intuitive and cognitive interaction between a user and a smart wheelchair. An intuitive and noncontinuous input method is proposed to enable a user to specify the direction in which the wheelchair is to go. Using an acceleration sensor and pressure sensors, the user gives a direction to the PMT, then the PMT determines the goal on an environmental map based on the direction. An output interface is used to help the user interpret robot behavior through informative communication between the user and the PMT. In this paper, a vibrotactile seat interface is presented.
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Zhang, Zhewen, Peng Xu, Chengjia Wu, and Hongliu Yu. "Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration." Biomimetics 9, no. 8 (2024): 492. http://dx.doi.org/10.3390/biomimetics9080492.
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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.
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Tiple, Mr Arpit, Mr Akshay Pakade, Miss Shilpa Madavi, et al. "Touch Controlled Wheelchair." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2052–57. http://dx.doi.org/10.22214/ijraset.2022.41700.
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Abstract: Smart robotic wheelchair has a great significance in life of a disabled person. With several merits, a wheelchair becomes a dilemma for a disabled person when comes to self-propulsion. This project describes an economical solution of robot control systems. The presented wheelchair control system can be used for different sophisticated robotic applications. The automatic robotic wheelchair comprises of the features like sensing hindrances and circuitry to avoid colliding to obstacle and emergency calling. Implementing embedded systems solution on self-propelled wheelchair enhances upgradability. This paper briefs a multifunctional wheelchair for disabled mob using, touch screen, ultrasonic sensor and Motor driver system interfaced through microcontroller which ultimately abolishes switching technology and thus optimizing hardware cost. Obstacle avoidance facility enables to drive safely in unknown as well as dynamic environments. Keywords: Easy navigation, Obstacle avoidance, Touch screen, Motors
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Tiple, Mr Arpit, Mr Akshay Pakade, Miss Shilpa Madavi, et al. "Touch Controlled Wheelchair." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2052–57. http://dx.doi.org/10.22214/ijraset.2022.41700.
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Abstract: Smart robotic wheelchair has a great significance in life of a disabled person. With several merits, a wheelchair becomes a dilemma for a disabled person when comes to self-propulsion. This project describes an economical solution of robot control systems. The presented wheelchair control system can be used for different sophisticated robotic applications. The automatic robotic wheelchair comprises of the features like sensing hindrances and circuitry to avoid colliding to obstacle and emergency calling. Implementing embedded systems solution on self-propelled wheelchair enhances upgradability. This paper briefs a multifunctional wheelchair for disabled mob using, touch screen, ultrasonic sensor and Motor driver system interfaced through microcontroller which ultimately abolishes switching technology and thus optimizing hardware cost. Obstacle avoidance facility enables to drive safely in unknown as well as dynamic environments. Keywords: Easy navigation, Obstacle avoidance, Touch screen, Motors
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Schwesinger, Dylan, Armon Shariati, Corey Montella, and John Spletzer. "A smart wheelchair ecosystem for autonomous navigation in urban environments." Autonomous Robots 41, no. 3 (2016): 519–38. http://dx.doi.org/10.1007/s10514-016-9549-1.
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B. Dattatreya, P. Srinuvasa rao, Dr.P. Srinivasulu, Dr.B. Madhukumar, and I. Praveena. "A Multi-Sensor Fusion Approach for Smart Wheelchair Navigation Using IoT and Artificial Intelligence." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 07 (2025): 3043–50. https://doi.org/10.47392/irjaeh.2025.0448.
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The development of intelligent assistive technologies has become increasingly essential in enhancing the mobility and independence of individuals with physical disabilities. This research presents a smart wheelchair system that integrates Internet of Things (IoT) and Artificial Intelligence (AI) with a multi-sensor fusion approach to enable efficient and autonomous navigation. Traditional wheelchairs often require manual operation, which can be challenging for users with severe mobility impairments. The proposed system addresses this limitation by incorporating an array of sensors—ultrasonic sensors, infrared (IR), gyroscope, accelerometer, and GPS—whose data are fused using a Kalman filter algorithm to ensure accurate real-time decision-making for obstacle detection, localization, and path planning. The AI module, built using lightweight deep learning models, enables intelligent environment interpretation and user interaction through voice and gesture recognition. The IoT framework allows for seamless data transmission to a cloud-based monitoring system, enabling caregivers and healthcare professionals to track location, system status, and user safety in real time. Additionally, a mobile application interface enhances user control and connectivity, further improving the overall user experience. Simulation and prototype testing demonstrate the system’s capability to navigate complex indoor and outdoor environments with over 92% obstacle avoidance accuracy and minimal response latency. The integration of sensor fusion and AI significantly improves navigation precision compared to single-sensor models. This project not only showcases the potential of emerging technologies in assistive devices but also offers a scalable solution adaptable to individual user needs. Future work will focus on optimizing energy consumption, improving AI adaptability through reinforcement learning, and conducting extended real-world trials. The proposed smart wheelchair system represents a meaningful step toward empowering individuals with mobility impairments, providing them with increased autonomy, safety, and a higher quality of life through intelligent technology integration.
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Skrzypczyk, Krzysztof, Adam Gałuszka, Witold Ilewicz, and Tomasz Antas. "Synthesis and evaluation of the smart electric powered wheelchair route stabilization concept – a simulation study." Archives of Control Sciences 25, no. 2 (2015): 263–73. http://dx.doi.org/10.1515/acsc-2015-0017.
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Abstract The paper addresses the problem of algorithm synthesis for controlling the motion of an electric powered wheelchair. The aim of the algorithm is to stabilize the wheelchair following a linear path and avoiding obstacles if occurred on its way. The main restriction imposed on the project is the application of simple low-cost sensors. That implies the system to cope with a number of inaccuracies and uncertainties related to the measurements. The goal of this work is to evaluate the possibility of the wheelchair project with a navigation system which aids a disable person to move in a complex and dynamic areas. Exemplary simulations are presented in order to discuss the results obtained.
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Bakouri, Mohsen, Mohammed Alsehaimi, Husham Farouk Ismail, et al. "Steering a Robotic Wheelchair Based on Voice Recognition System Using Convolutional Neural Networks." Electronics 11, no. 1 (2022): 168. http://dx.doi.org/10.3390/electronics11010168.
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Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.
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Favey, C., A. Zogaghi, R. Leroux, B. Vivan, and R. Farcy. "Autonomous Navigation of a Visually Impaired Person with Loss of Limb Sensitivity, via a Smart Wheelchair (Case Study)." Modelling, Measurement and Control C 81, no. 1-4 (2020): 30–34. http://dx.doi.org/10.18280/mmc_c.811-406.
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Presentation and analysis of the first experimentation of autonomous navigation, for a young man suffering from category 4 blindness, associated with disorders of the sensitivity of the limbs, moving thanks to the feelings of the accelerations and decelerations of an anti-collision and anti-fall electric wheelchair.
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Tomari, Razali, Yoshinori Kobayashi, and Yoshinori Kuno. "Analysis of Socially Acceptable Smart Wheelchair Navigation Based on Head Cue Information." Procedia Computer Science 42 (2014): 198–205. http://dx.doi.org/10.1016/j.procs.2014.11.052.
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Pradeep, Vishnu, Redouane Khemmar, Louis Lecrosnier, Yann Duchemin, Romain Rossi, and Benoit Decoux. "Self-Supervised Sidewalk Perception Using Fast Video Semantic Segmentation for Robotic Wheelchairs in Smart Mobility." Sensors 22, no. 14 (2022): 5241. http://dx.doi.org/10.3390/s22145241.
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The real-time segmentation of sidewalk environments is critical to achieving autonomous navigation for robotic wheelchairs in urban territories. A robust and real-time video semantic segmentation offers an apt solution for advanced visual perception in such complex domains. The key to this proposition is to have a method with lightweight flow estimations and reliable feature extractions. We address this by selecting an approach based on recent trends in video segmentation. Although these approaches demonstrate efficient and cost-effective segmentation performance in cross-domain implementations, they require additional procedures to put their striking characteristics into practical use. We use our method for developing a visual perception technique to perform in urban sidewalk environments for the robotic wheelchair. We generate a collection of synthetic scenes in a blending target distribution to train and validate our approach. Experimental results show that our method improves prediction accuracy on our benchmark with tolerable loss of speed and without additional overhead. Overall, our technique serves as a reference to transfer and develop perception algorithms for any cross-domain visual perception applications with less downtime.
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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.
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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair
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Sharma, Vinod, Richard Simpson, Edmund LoPresti, and Mark Schmeler. "Driving Backwards Using a Semi-Autonomous Smart Wheelchair System (DSS): A Clinical Evaluation." Applied Bionics and Biomechanics 9, no. 4 (2012): 347–65. http://dx.doi.org/10.1155/2012/125725.
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Some wheelchair users have difficulty looking backward when backing up in confined spaces due to limited neck range of motion or low vision, which can lead to collisions which may result in personal injury or property damage. The Drive Safe System (DSS) was evaluated in a controlled laboratory setting with blindfolded able-bodied individuals on various backward driving tasks. Performance with the DSS was compared with a standard white cane used for navigation assistance by people with visual impairment. Results indicate that the DSS significantly reduced the number of collisions compared to using a cane (p= 0.0001) alone. There was no difference in task completion time when participants were using the cane or the DSS (p= 0.915). Users rated the DSS favourably as they experience less total workload (p= 0.026), less physical demand (p= 0.006), felt less frustrated (p= 0.002) and put less effort (p= 0.007) to achieve better performance when using the DSS, compared to using a cane. These findings suggest that the DSS can be a viable powered mobility solution for wheelchair users with visual impairments.
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Moon, M., Y. Bang, K. Yu, and J. Kim. "A WALKING DISTURBANCE INDEX SUGGESTIONS FOR OPTIMIZED PATH SEARCH FOR THE PEOPLE WITH REDUCED MOBILITY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W4 (October 19, 2015): 63–65. http://dx.doi.org/10.5194/isprsarchives-xl-2-w4-63-2015.
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Recently, due to the increased penetration of smart devices and the development of geographic information system (GIS) technology, various route guidance services for pedestrians have been developed. However, until now, pedestrian navigation services for the people with reduced mobility (people who experience discomfort in transportation) including wheelchair users, the elderly, and pregnant women have not been provided. In this study, we present a walking disturbance index methodology for searching an optimized path for the people with reduced mobility by defining the factors that affect the walking of the people with reduced mobility and deriving the weights of these factors. In future research, we expect to be able to provide a navigation system that gives an optimized path for the people with reduced mobility using this method.
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S Sastry, Dr Anitha. "Gesture Based Wheel Chair Control." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48216.
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Abstract— This paper presents the design and development of a gesture-controlled smart wheelchair system intended for individuals with severe physical impairments. The proposed system utilizes head movements and eye blink detection as primary inputs to enable hands-free navigation. An accelerometer sensor interprets directional head gestures, while an infrared sensor detects intentional eye blinks for auxiliary control functions. To enhance safety, ultrasonic sensors are integrated to detect obstacles and edges, with real-time alerts delivered through a buzzer, vibration motor, and a 16x2 LCD display. The entire system is managed by an ATmega328P microcontroller, which coordinates sensor data processing and motor control via an L298N motor driver. Experimental results demonstrate accurate gesture recognition, reliable obstacle detection, and responsive feedback, making the system a cost-effective and user-friendly solution for assistive mobility. Keywords—Wheelchair direction control, ATMEGA328P, Edge detection, Eye-blink sensor, Obstacle detection, Ultrasonic sensor, Head movement control.
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Hou, Jiwei, Patrick Hübner, and Dorota Iwaszczuk. "Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds." Applied Sciences 15, no. 3 (2025): 1151. https://doi.org/10.3390/app15031151.
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Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response.
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Lee, Lae-Kyoung, and Se-Young Oh. "Development of Smart Wheelchair System and Navigation Technology For Stable Driving Performance In Indoor-Outdoor Environments." Journal of the Institute of Electronics and Information Engineers 52, no. 7 (2015): 153–61. http://dx.doi.org/10.5573/ieie.2015.52.7.153.
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Meddeber, Wassila, Benaoumeur Aour, and Karim Labadi. "Smart Wheelchair Localization and Navigation Based on Multi-Sensor Data Fusion Using Hybrid-Filter Method (HF)." Revue d'Intelligence Artificielle 38, no. 2 (2024): 387–96. http://dx.doi.org/10.18280/ria.380202.
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Krieg-Brückner, Bernd, Thomas Röfer, Hui Shi, and Bernd Gersdorf. "Mobility Assistance in the Bremen Ambient Assisted Living Lab." GeroPsych 23, no. 2 (2010): 121–30. http://dx.doi.org/10.1024/1662-9647/a000009.
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Various mobility assistants have been developed to enable the Rolland wheelchair and iWalker walker to behave intelligently in order to compensate for diminishing physical and cognitive faculties: A safety assistant ensures that the brakes are applied in time, a driving assistant avoids any obstacles and assists the user when going through doors, and the navigation assistant guides the unit along a route or can drive the user around in an autonomous manner. At the Bremen Ambient Assisted Living Lab, users can interact with these mobility assistants and the smart environment installed there. The goal is to evaluate new ambient assisted living technologies regarding their everyday usability. Various interaction modes are investigated, such as a head joystick, a touch screen, and natural language dialog.
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Nakahara, Masataka, Motoya Koga, and Satoshi Fujimoto. "Development of an AR Navigation System “Smart Information & Mobility Town (SiMT)” in Commercial Facilities." SPACE International Journal of Conference Proceedings 1, no. 2 (2021): 1–6. http://dx.doi.org/10.51596/sijocp.v1i2.21.
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In recent years, with the development of IT technology, various information services in cities have become convenient and easily obtainable for everyone. However, information support provisions for users in cities using these IT technologies are still not sufficient. Many of these technologies are aimed at the public, and not all have been optimised for persons with disabilities, such as wheelchair users, the elderly, pregnant women, those with strollers, and so on. There is also insufficient support to provide information needed to move around in public transportation facilities such as stations, airports, and extensive commercial facilities, which have become increasingly common in recent years in Japan. Therefore, in this study, we used AR technology to develop an application, “Smart Info. & Mobility Town” (SiMT), aimed at supporting movement in SAKURA MACHI Kumamoto, a commercial complex built in Kumamoto City, Kumamoto Prefecture, Japan in 2019. The research method is as follows. 1; Summarised the outline of the target facility, SAKURA MACHI Kumamoto. 2; Used AR technology to develop an application, “Smart Info. & Mobility Town” (SiMT), aimed at supporting movement in the commercial complex. 3; Conducted demonstration experiments, proposed future developments and possibilities of SiMT, and summarised this study. SiMT, which was developed on a trial basis, uses a 3D map created from 3D scan data of SAKURA MACHI Kumamoto and utilises the camera function of a smartphone to synthesise CG images of routes from the current location to the destination to support users’ movements. We also developed this technology and conceived future technology development, such as support in the event of a disaster and support for the mobility of vulnerable road users.
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Nandini Jain, Archit Patre, Avleen Kaur, Pongiannan R K, and Sujatha R. "AI-Embedded Wheelchairs for Assistive Mobility and Health Monitoring: A Comparative Review and Integrated Framework." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2487–95. https://doi.org/10.47392/irjaeh.2025.0370.
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The amalgamation of artificial intelligence (AI), the Internet of Things (IoT), and embedded systems has catalyzed substantial progress in assistive mobility; nonetheless, notable deficiencies persist in terms of cost, adaptability, and comprehensive usefulness. This paper synthesizes various seminal works to examine contemporary smart wheelchair architectures, highlighting significant limitations: disjointed sensor-actuator systems, excessive costs in low-resource environments, and insufficient contextual responsiveness. We present a theoretical framework that utilizes ESP32’s dual-core edge processing to integrate navigation control, medical-grade health monitoring, and adaptive power management. A comparative analysis indicates that this integration may resolve India's infrastructure difficulties by focusing on: Latency below 200ms, 95% accuracy in vital signs and, Continuous operation for less than 5% of commercial expenses. Whereas previous research concentrates on singular attributes, our approach underscores compassionate autonomy—a design philosophy that prioritizes fail-safes and multi-modal adaptation. Hardware validation is pending, although this evaluation underscores the feasibility of low-cost, integrated assistive environments for worldwide applications.
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Selvanesan, Elwin Nesan, Poh Kiat Ng, Kia Wai Liew, et al. "Design and Development of a Multifunctional Stepladder: Usability, Sustainability, and Cost-Effectiveness." Eng 6, no. 4 (2025): 79. https://doi.org/10.3390/eng6040079.
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This study presents the design, development, and evaluation of a multifunctional stepladder that integrates four functionalities: a stepladder, Pilates chair, wheelchair, and walking aid. Unlike existing research that focuses on single-function assistive devices, this study uniquely integrates a stepladder, wheelchair, walking aid, and Pilates chair into one multifunctional device, offering a compact, space-saving solution that addresses multiple daily needs in a single design. Building upon previous research, which conceptualized a multifunctional stepladder by synthesizing ideas, features, and functions from patent literature, existing products, and scientific articles, this study focuses on the design and testing phases to refine and validate the concept. Using sustainable materials like mild steel and aluminium, the design was optimized through structural simulations, ensuring durability under loads of up to 100 kg. Usability tests revealed that the invention significantly reduced task completion times, saved five times the space compared to single-function products, and provided enhanced versatility. Cost analysis highlighted its affordability, with a retail price of MYR 1392—approximately 35% lower than the combined cost of its single-function counterparts. Participant feedback noted strengths such as eco-friendliness, practicality, and ergonomic design, alongside areas for improvement, including portability, armrests, and storage. Future work includes enhanced portability for stair navigation, outdoor usability tests, and integration of smart technologies. This multifunctional stepladder significantly contributes to caregivers by reducing the physical burden of managing multiple assistive devices, enhancing efficiency in daily caregiving tasks, and providing a safer, more convenient tool that supports both mobility and exercise for elderly users. This multifunctional stepladder also offers a sustainable, cost-effective, and user-centric solution, addressing usability gaps while supporting global sustainability and accessibility initiatives.
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TAKIMOTO, Atsuro, and Mihoko NIITSUMA. "1A1-M06 Smart Electric Wheelchair Navigation Based on Direction Input from Human : Method of a Suitable Destination Selection Using an Environment Map(Integrating Ambient Intelligence)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _1A1—M06_1—_1A1—M06_4. http://dx.doi.org/10.1299/jsmermd.2013._1a1-m06_1.
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Busaeed, Sahar, Iyad Katib, Aiiad Albeshri, Juan M. Corchado, Tan Yigitcanlar, and Rashid Mehmood. "LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired." Sensors 22, no. 19 (2022): 7435. http://dx.doi.org/10.3390/s22197435.
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Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.
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Sharmila, A., Ankur Saini, Shubham Choudhary, T. Yuvaraja, and S. G. Rahul. "Solar Powered Multi-Controlled Smart Wheelchair for Disabled: Development and Features." Journal of Computational and Theoretical Nanoscience 16, no. 11 (2019): 4889–900. http://dx.doi.org/10.1166/jctn.2019.8401.
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As per the report presented by the World Health Organization, it is well aware that 15% of the total world’s population is physically challenged. Accessibility of health care services is limited to people with physical disabilities. The utilization of battery powered wheelchairs with excellent navigational capabilities is one of the extraordinary strides towards the incorporation of severely physically and mentally challenged people. Motion, movement and localization are significant issues for the blind, paraplegic and handicapped people who are accompanied by eminent tiresome work. There exist different systems to override the problems described, allowing the end-user to perform safe movements and complete certain daily life tasks. Considering the said issues as a motivation, this work presents the design and development of Solar Powered Multi-Controller Smart Wheelchair. The developed smart wheelchair uses eye blink sensor to steer the wheelchair for quadriplegia patient along with Joystick and Keypad module for several kinds of disabilities. In addition, more liberty is provided to the disabled person by using additional sensors such as heartbeat sensor and a temperature sensor which continuously monitors the health condition of the patient. Additionally, a urine level indicator is also used to avoid inconvenience to the patient. If the patient falls down along with a wheelchair, a fall detection system in the wheelchair detects the same. All the detail can be shared with hospital staff and the patient’s guardian during a contingency condition, so that the staffs and guardians can take immediate actions. The safety of the patient and the wheelchair with respect to the incorporation of solar power is highly given priority during this system design.
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Yadgire, Shrikrishna. "E-Wheel Connect: IOT-Integrated Wheelchair." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45480.
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Many people suffer from temporary or permanent physical disabilities due to illness or accidents. For those daily activities are difficult and require a wheelchair for locomotion and is very essential. Manual or electrical wheelchair are satisfying most of the low and middle level disability person where they can use the wheelchair independently. However, in severe cases, it is difficult or impossible to use wheelchair independently. IoT-based smart wheelchair enhances mobility and independence for individuals with mobility impairments. The project integrates sensors, actuators, and communication modules with mobile app integration to enable obstacle detection and avoidance. Extensive testing validates the system's effectiveness in navigating complex environments. The project showcases the potential of IoT technology in creating intelligent wheelchair systems, empowering users with increased mobility, safety, and independence. It also provides an opportunity for visually or physically impaired persons with obstacle detection system, which minimize the chance of collision which using it. We tried to provide all the features required in a smart wheelchair at lowest price for the people to afford it without any difficulty and to use utmost independent on others. Keywords —Smart Wheelchair, IoT-Technology, Automatic Lighting, Obstacle detection, Ultrasonic Sensor, Blynk, Rain shade Activation, Remote operation
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Sahoo, Sushil Kumar, and Bibhuti Bhusan Choudhury. "Autonomous navigation and obstacle avoidance in smart robotic wheelchairs." Journal of Decision Analytics and Intelligent Computing 4, no. 1 (2024): 47–66. http://dx.doi.org/10.31181/jdaic10019022024s.
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This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.
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Chow, H. N., and Y. Xu. "Learning Human Navigational Skill for Smart Wheelchair in a Static Cluttered Route." IEEE Transactions on Industrial Electronics 53, no. 4 (2006): 1350–61. http://dx.doi.org/10.1109/tie.2006.878296.
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Gowtham, Priya L., S. Pavithra, S. Prema, and L. Sanmathi. "AI enhanced eye detection wheelchair with smart monitoring using deep learning." i-manager’s Journal on Future Engineering and Technology 19, no. 4 (2024): 1. http://dx.doi.org/10.26634/jfet.19.4.20592.
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People with high-level cervical spinal cord injuries can experience significant impairments in their ability to control their environment, including challenges in operating a smartphone or navigating a power wheelchair. The use of eye- tracking technology has been crucial in improving communication and control for individuals with tetraplegia. However, traditional eye-tracking systems often have limitations in terms of accuracy, calibration time, and practicality. To overcome these limitations, researchers have explored the use of Convolutional Neural Networks (CNNs) in AI-enhanced eye-tracking technology. CNNs are a type of deep learning algorithm that can learn complex patterns in image data, allowing for more accurate and reliable eye tracking. AI-enhanced eye tracking that utilizes triple blinking is a novel approach showing great potential for improving the accuracy and efficiency of eye tracking technology. By employing advanced machine learning algorithms, this method can detect and track eye movements based on the number of blinks, providing a more reliable and efficient way to interact with digital devices. This technology has the potential to revolutionize the way people engage with digital devices, making them more accessible and user-friendly for individuals with disabilities or impairments. The findings related to AI-enhanced eye tracking using triple blinking suggest that it can be a viable alternative to traditional eye tracking technology, which can be costly, time-consuming, and difficult to use. Furthermore, this approach is highly customizable and can be adapted to meet the specific needs and preferences of individual users. As such, it has the potential to significantly enhance the quality of life for individuals with motor impairments, visual impairments, or other disabilities that affect their ability to use traditional eye tracking technology. AI- enhanced eye tracking using triple blinking is a promising innovation that could contribute to a more inclusive and accessible digital world. With continued research and development, even more innovative solutions and applications for this technology are expected in the future.
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Ton, C., Z. Kan, and S. S. Mehta. "Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields." Robotica 36, no. 4 (2017): 463–83. http://dx.doi.org/10.1017/s0263574717000510.
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SUMMARYThis paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.
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Marianingsih, Susi, Widodo Widodo, Marla Sheilamita S. Pieter, Evanita Veronica Manullang, and Hendry Y. Nanlohy. "Machine Vision for the Various Road Surface Type Classification Based on Texture Feature." Journal of Mechanical Engineering Science and Technology (JMEST) 6, no. 1 (2022): 40. http://dx.doi.org/10.17977/um016v6i12022p040.
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The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.
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Tomari, Razali, Yoshnori Kobayashi, and Yoshinori Kuno. "SOCIALLY ACCEPTABLE SMART WHEELCHAIR NAVIGATION FROM HEAD ORIENTATION OBSERVATION." International Journal on Smart Sensing and Intelligent Systems 7, no. 2 (2017). https://doi.org/10.21307/ijssis-2017-673.
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Robotic wheelchairs should move among humans without bringing about uncomfortable situation to them. This paper tackles this issue to propose a method of navigation in indoor environments with presence of humans based on the observation of head information obtained from color and range images. Initially head regions in the color image are tracked and their orientations are estimated using AdaBoost based particle filter trained to classify multiple-pose faces. Then the head orientation data are integrated with the head position data in the range image to determine the wheelchair motion so that it can smoothly move among humans. Experimental results demonstrate the feasibility of the proposed approach
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Bakouri, Mohsen, Sultan Alanazi, Abdullah Alqarni, et al. "Evaluation of Conventional and Smart Wheelchair Technologies in the Kingdom of Saudi Arabia." Journal of Disability Research 3, no. 3 (2024). http://dx.doi.org/10.57197/jdr-2024-0041.
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A significant number of people depend on the help of others to facilitate their mobility in wheelchairs. Physical disabilities hinder the mobility of persons with disabilities, making them unable to move around using conventional (manually operated) wheelchairs. Furthermore, people lack the ability to regain an upright posture if the chair falls. This study aims to evaluate conventional and smart wheelchair technologies in the Kingdom of Saudi Arabia. This study was conducted through a quantitative and cross-sectional research methodology to evaluate the effectiveness of wheelchair technologies in the Kingdom of Saudi Arabia. This is done by collecting data from wheelchair users, experts, and specialists in this field, such as medical engineers and others familiar with wheelchairs. The evaluation included the opinions of users and experts on the use of traditional and smart chairs in terms of the technologies used, ease of use, advantages, disadvantages, etc. More than 590 users and experts from all regions of the Kingdom participated in the study, and their answers were characterized by objectivity and realism. About 39% of participants indicated that smart wheelchairs are very effective, and 37.6% confirmed that these chairs can be used on many surfaces, while 23.4% expressed their appreciation that smart wheelchairs are useful for various purposes and terrains. On the other hand, the results showed that smart wheelchairs had high purchase and maintenance costs of 37.4%. This was followed by concerns about more maintenance requirements (23.5%), the need for constant charging (20.4%), and the need for users to be aware of energy use (18.7%). The results show some advantages of using traditional wheelchairs, such as low maintenance, lightweight, and often affordability. This type of chair faces many difficulties, such as crossing slopes and long distances, requiring some strength for use, and users needing assistance to move around. The study revealed significant progress in wheelchair technology, especially in smartphones and navigation. Evaluating existing wheelchair technology in Saudi Arabia has shown significant advances, especially in urban areas and healthcare institutions. Motorized electric wheelchairs are becoming increasingly popular due to their ability to enhance users’ mobility. However, the availability of high-end wheelchair models and their accompanying accessories remains limited.