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Keywords = smart wheelchair

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28 pages, 9665 KiB  
Article
Long-Range RFID Indoor Positioning System for an Autonomous Wheelchair
by João S. Pereira
Sensors 2025, 25(8), 2542; https://doi.org/10.3390/s25082542 - 17 Apr 2025
Viewed by 689
Abstract
A new Radio-Frequency Identification (RFID) indoor positioning system (IPS) has been developed to operate in environments where the Global Positioning System (GPS) is unavailable. Traditional RFID tracking systems, such as anti-theft systems in clothing stores, typically work within close proximity to exit doors. [...] Read more.
A new Radio-Frequency Identification (RFID) indoor positioning system (IPS) has been developed to operate in environments where the Global Positioning System (GPS) is unavailable. Traditional RFID tracking systems, such as anti-theft systems in clothing stores, typically work within close proximity to exit doors. This paper presents a novel RFID IPS capable of locating and tracking passive RFID tags over a larger area with greater precision. These tags, costing approximately EUR 0.10 each, are in the form of small stickers that can be attached to any item requiring tracking. The proposed system is designed for an autonomous wheelchair, built from scratch, which will be identified and monitored using passive RFID tags. Our new RFID IPS, with a 12 m range, is implemented in this “smart” wheelchair. Full article
(This article belongs to the Special Issue Advances in RFID-Based Indoor Positioning Systems)
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42 pages, 55621 KiB  
Article
Design and Development of a Multifunctional Stepladder: Usability, Sustainability, and Cost-Effectiveness
by Elwin Nesan Selvanesan, Poh Kiat Ng, Kia Wai Liew, Kah Wei Gan, Peng Lean Chong, Jian Ai Yeow and Yu Jin Ng
Eng 2025, 6(4), 79; https://doi.org/10.3390/eng6040079 - 17 Apr 2025
Viewed by 859
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Interdisciplinary Insights in Engineering Research)
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26 pages, 3876 KiB  
Review
Integrating AI and Assistive Technologies in Healthcare: Insights from a Narrative Review of Reviews
by Daniele Giansanti and Antonia Pirrera
Healthcare 2025, 13(5), 556; https://doi.org/10.3390/healthcare13050556 - 4 Mar 2025
Cited by 4 | Viewed by 3950
Abstract
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, [...] Read more.
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
(This article belongs to the Section TeleHealth and Digital Healthcare)
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24 pages, 7979 KiB  
Article
Vision-Based Hand Gesture Recognition Using a YOLOv8n Model for the Navigation of a Smart Wheelchair
by Thanh-Hai Nguyen, Ba-Viet Ngo and Thanh-Nghia Nguyen
Electronics 2025, 14(4), 734; https://doi.org/10.3390/electronics14040734 - 13 Feb 2025
Cited by 2 | Viewed by 2483
Abstract
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. [...] Read more.
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
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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22 pages, 11693 KiB  
Article
Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
by Jiwei Hou, Patrick Hübner and Dorota Iwaszczuk
Appl. Sci. 2025, 15(3), 1151; https://doi.org/10.3390/app15031151 - 23 Jan 2025
Cited by 1 | Viewed by 1263
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Current Research in Indoor Positioning and Localization)
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30 pages, 1461 KiB  
Article
Adoption and Use of Customized Wheelchairs Manufactured for Persons Living with Disability: Modified UTUAT-2 Perspective
by Thywill Cephas Dzogbewu, Timothy Whitehead, Deon Johan de Beer and George Torrens
Designs 2025, 9(1), 3; https://doi.org/10.3390/designs9010003 - 30 Dec 2024
Cited by 2 | Viewed by 1722
Abstract
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 [...] Read more.
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
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10 pages, 4558 KiB  
Proceeding Paper
An IoT-Based Smart Wheelchair with EEG Control and Vital Sign Monitoring
by Rowida Meligy, Anton Royanto Ahmad and Samir Mekid
Eng. Proc. 2024, 82(1), 46; https://doi.org/10.3390/ecsa-11-20489 - 26 Nov 2024
Cited by 2 | Viewed by 3372
Abstract
This study introduces an innovative smart wheelchair designed to improve mobility and health monitoring for individuals with disabilities. Overcoming the limitations of traditional wheelchairs, this smart wheelchair integrates a tri-wheel mechanism, enabling smooth navigation across various terrains, including stairs, thus providing greater autonomy [...] Read more.
This study introduces an innovative smart wheelchair designed to improve mobility and health monitoring for individuals with disabilities. Overcoming the limitations of traditional wheelchairs, this smart wheelchair integrates a tri-wheel mechanism, enabling smooth navigation across various terrains, including stairs, thus providing greater autonomy and flexibility. The wheelchair is equipped with two smart Internet of Things (IoT)-based subsystems for control and vital sign monitoring. Besides a joystick, the wheelchair features an electroencephalography (EEG)-based brain–computer interface (BCI) for hands-free control. Utilizing support vector machine (SVM) algorithms has proven effective in classifying EEG signals. This feature is especially beneficial for users with severe physical disabilities, allowing them to navigate more independently. In addition, the smart wheelchair has comprehensive health monitoring capabilities, continuously tracking vital signs such as heart rate, blood oxygen levels (SpO2), and electrocardiogram (ECG) data. The system implements an SVM algorithm to recognize premature ventricular contractions (PVC) from ECG data. These metrics are transmitted to healthcare providers through a secure IoT platform, allowing for real-time monitoring and timely interventions. In the event of an emergency, the system is programmed to automatically send alerts, including the patient’s location, to caregivers and authorized relatives. This innovation is a step forward in developing assistive technologies that support independent living and proactive health management in smart cities. Full article
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22 pages, 1932 KiB  
Review
Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration
by Zhewen Zhang, Peng Xu, Chengjia Wu and Hongliu Yu
Biomimetics 2024, 9(8), 492; https://doi.org/10.3390/biomimetics9080492 - 14 Aug 2024
Cited by 9 | Viewed by 3421
Abstract
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 [...] Read more.
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. Full article
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18 pages, 17571 KiB  
Article
Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
by Xiaoxi Zhang, Yang Chen, Modar Hassan and Kenji Suzuki
Sensors 2024, 24(15), 4828; https://doi.org/10.3390/s24154828 - 25 Jul 2024
Cited by 1 | Viewed by 1603
Abstract
We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The [...] Read more.
We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller’s occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans’ preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user’s physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment. Full article
(This article belongs to the Section Electronic Sensors)
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11 pages, 5434 KiB  
Article
An Innovative Device Based on Human-Machine Interface (HMI) for Powered Wheelchair Control for Neurodegenerative Disease: A Proof-of-Concept
by Arrigo Palumbo, Nicola Ielpo, Barbara Calabrese, Remo Garropoli, Vera Gramigna, Antonio Ammendolia and Nicola Marotta
Sensors 2024, 24(15), 4774; https://doi.org/10.3390/s24154774 - 23 Jul 2024
Cited by 2 | Viewed by 1965
Abstract
In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control [...] Read more.
In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control a robotic wheelchair for people with limited mobility. The test group comprised 11 healthy subjects (six male, five female, mean age 35.2 ± 11.7 years). A novel platform that integrates a smart wheelchair and an eye-tracking-enabled head-mounted display was proposed to reduce the cognitive requirements needed for wheelchair movement and control. The approach’s effectiveness was demonstrated by evaluating our system in realistic scenarios. The demonstration of the proposed AR head-mounted display user interface for controlling a smart wheelchair and the results provided in this paper could highlight the potential of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as remote control, cognitive rehabilitation, the implementation of patient autonomy with severe disabilities, and telemedicine. Full article
(This article belongs to the Special Issue Computational Intelligence Based-Brain-Body Machine Interface)
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21 pages, 4887 KiB  
Article
Driving Assistance System with Obstacle Avoidance for Electric Wheelchairs
by Esranur Erturk, Soonkyum Kim and Dongyoung Lee
Sensors 2024, 24(14), 4644; https://doi.org/10.3390/s24144644 - 17 Jul 2024
Cited by 3 | Viewed by 2154
Abstract
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 [...] Read more.
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
(This article belongs to the Section Sensors Development)
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17 pages, 5502 KiB  
Article
Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology
by Jianwei Cui, Yucheng Shang, Siji Yu and Yuanbo Wang
Actuators 2024, 13(6), 230; https://doi.org/10.3390/act13060230 - 20 Jun 2024
Viewed by 3154
Abstract
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. [...] Read more.
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. Full article
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16 pages, 4908 KiB  
Article
Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG
by Lassaad Zaway, Nader Ben Amor, Jalel Ktari, Mohamed Jallouli, Larbi Chrifi Alaoui and Laurent Delahoche
Future Internet 2024, 16(5), 158; https://doi.org/10.3390/fi16050158 - 3 May 2024
Cited by 3 | Viewed by 2255
Abstract
Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution [...] Read more.
Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology. Full article
(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction)
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16 pages, 1685 KiB  
Article
Novel Extension Control Instrument for Power Wheelchair Based on Kalman Filter Head Motion Detection
by Yixin Zhang, Zhuohang Ying, Xinyu Tian, Siyuan Jin, Junjie Huang and Yinan Miao
Actuators 2024, 13(4), 141; https://doi.org/10.3390/act13040141 - 11 Apr 2024
Cited by 3 | Viewed by 2162
Abstract
People with upper limb disabilities or high quadriplegia have extremely high requirements for the maneuverability and functionality of power wheelchairs. Normal wheelchairs cannot meet travel tasks, while smart customized wheelchairs are expensive and cannot be popularized. Therefore, a novel extension control instrument for [...] Read more.
People with upper limb disabilities or high quadriplegia have extremely high requirements for the maneuverability and functionality of power wheelchairs. Normal wheelchairs cannot meet travel tasks, while smart customized wheelchairs are expensive and cannot be popularized. Therefore, a novel extension control instrument for power wheelchairs with low cost, strong scalability, and convenient usage is proposed, which can realize the control of the wheelchair by sensing a change of head posture. The device is divided into a head motion sensing unit (HMSU) and a wheelchair assistance control unit (WACU). The mapping relationship between the head attitude and the subject’s motion intention is established. The inertial measurement module in the HMSU collects the head attitude data and uses the Kalman filtering method to obtain the accurate Euler angle. The WACU is fixed on the original controller of the wheelchair. The joystick is inserted into the extended control mechanism and controlled, instead of the hand, through a 2-degree-of-freedom servo system combined with the pinion and rack push rod structure, thus controlling the movement of the wheelchair. In proceeding, the system can also detect the distance of objects in the environment in real time through the three-direction (front, left, right) ultrasonic ranging sensors installed on the WACU, with a certain obstacle avoidance function. The prototype experiments prove that the extension control instrument developed in this paper based on the Kalman filter can quickly and accurately identify head motion and accurately control the movement of the wheelchair. It is easy to operate and has strong universality, which presents a new low-cost solution for the travel of patients with disabilities. Full article
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15 pages, 4166 KiB  
Article
Applying Image Analysis to Build a Lightweight System for Blind Obstacles Detecting of Intelligent Wheelchairs
by Jiachen Du, Shenghui Zhao, Cuijuan Shang and Yinong Chen
Electronics 2023, 12(21), 4472; https://doi.org/10.3390/electronics12214472 - 31 Oct 2023
Cited by 1 | Viewed by 1805
Abstract
Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle [...] Read more.
Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle detection model, named GC-YOLO, and built an obstacle dataset that consists of incomplete target images captured in the blind spot view of the smart wheelchair. The feature extraction operations are simplified in the backbone and neck sections of GC-YOLO. The backbone network uses GhostConv in the GhostNet network to replace the ordinary convolution in the original feature extraction network, reducing the model size. Meanwhile, the CoordAttention is applied, aiming to reduce the loss of location information caused by GhostConv. Further, the neck stem section uses a combination module of the lighter SE Attention module and the GhostConv module to enhance the feature extraction capability. The experimental results show that the proposed GC-YOLO outperforms the YOLO5 in terms of model parameters, GFLOPS and F1. Compared with the YOLO5, the number of model parameters and GFLOPS are reduced by 38% and 49.7%, respectively. Additionally, the F1 of the proposed GC-YOLO is improved by 10% on the PASCAL VOC dataset. Moreover, the proposed GC-YOLO achieved mAP of 90% on the custom dataset. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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