Electric Wheelchair Research Articles

Application of PID Control System in Mecanum Wheelchair

This research centers on the design and implementation of a control system for an electric wheelchair equipped with Mecanum wheels. The study details a comprehensive research methodology starting with the creation of a block diagram to guide system design, hardware selection, and overall implementation. The electric wheelchair system incorporates power resources, input devices, and energy output mechanisms, utilizing a 24 VDC battery and a joystick with a 10K ohm potentiometer connected to an Arduino Due microcontroller. The operational workflow of the system is defined, enabling the wheelchair to respond to joystick commands for forward, left turn, right turn, and other movements. A PID control system is employed to regulate motor movement, enhancing control precision. The Cohen-Coon tuning method is used to determine the PID controller's gain, ensuring efficient closed-loop control. Results from PID controller experiments under P control and PD control are presented, demonstrating the system's responses for different gain values. Optimal performance is observed with a Kp value of 80 and Kd value of 1.2, showcasing improved response speed, reduced rise time, enhanced setting time, and lower percent overshoot. In conclusion, the combined proportional and derivative control system, specifically with Kp = 80 and Kd = 1.2, proves to be effective in enhancing the Mecanum wheelchair's performance. This study provides valuable insights into precise parameter adjustments for optimal control in Mecanum wheelchair applications.

Developing elevator landing door safety retainers capable of withstanding 1,000 J using numerical simulation and experimental verification

ABSTRACT Current elevator landing door safety retainers are able to prevent fall accidents from an inadvertent bump against the door, as they are designed to endure a kinetic energy of 450 J. However, the recent increase in fall accidents among people in electric wheelchairs indicates that retainers are required to sustain stronger external shocks to prevent fall accidents against elevator landing doors. The purpose of this study is to develop elevator landing door safety retainers capable of enduring 1,000 J to prevent fall accidents of electric wheelchairs at the landing doors of elevators. In this study, crash analysis simulations were conducted after designing new scenarios that are not stated in the current regulations, considering the collision behavior of an electric wheelchair against an elevator landing door. The side hook was confirmed to play an important role in withstanding a collisional energy of 1,000 J. Crash analysis yielded different results according to the shape of the guide shoe. The analysis simulations were verified by performing experiments on various established scenarios. This study also confirmed the reliability of the developed safety retainers by implementing more realistic experiments on the crash of electric wheelchairs instead of the impactor proposed in the current regulations.

Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair.

Eye-Gaze Controlled Wheelchair Based on Deep Learning.

In this paper, we design a technologically intelligent wheelchair with eye-movement control for patients with ALS in a natural environment. The system consists of an electric wheelchair, a vision system, a two-dimensional robotic arm, and a main control system. The smart wheelchair obtains the eye image of the controller through a monocular camera and uses deep learning and an attention mechanism to calculate the eye-movement direction. In addition, starting from the relationship between the trajectory of the joystick and the wheelchair speed, we establish a motion acceleration model of the smart wheelchair, which reduces the sudden acceleration of the smart wheelchair during rapid motion and improves the smoothness of the motion of the smart wheelchair. The lightweight eye-movement recognition model is transplanted into an embedded AI controller. The test results show that the accuracy of eye-movement direction recognition is 98.49%, the wheelchair movement speed is up to 1 m/s, and the movement trajectory is smooth, without sudden changes.

Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

Self-mobility electric wheelchairs are very useful for people with disabilities, so they can move without help in indoor environments. To create one selfmobility electric wheelchair, modern methods for control such as computer vision and machine learning can be applied. In particular, this electric wheelchair can move from any position in the indoor environment to the desired destination. For accuracy, natural landmarks are used and the navigation of the wheelchair is determined using a Q-learning reinforcement learning algorithm. In particular, this algorithm is applied to find the best path for the wheelchair to reach the destination. The article proposes a method to build one 2D grid map for wheelchair movement based on natural landmarks in the indoor environment. The new point of this method is that the position of the wheelchair can be accurately determined from a certain landmark instead of many landmarks applied in traditional methods. Some practical experiments were performed to illustrate the effectiveness of the proposed method in the indoor environment. This proposed method can be developed in more complex environments with natural landmarks.

Design and Manufacturing of Electronic Detachable Wheelchair

Abstract: The electric-powered wheelchair is a wheelchair that is propelled by means of an electric motor rather than manual power. Motorized wheelchairs are useful for those who are not able to impel a manual wheelchair or who may need to employ a wheelchair for distances or over terrain which would be strenuous in a manual wheelchair. They may also be used not just by people with conventional mobility impairments, but also by people with cardiovascular and fatigue-based conditions. Electric wheelchairs have enhanced the quality of life for many people with physical disabilities through the mobility they afford. The selection of power chair will rely on many factors; including the kind of surface setting the chair will be driven over, the need to settle thresholds and curbs, and clearance widths in accustomed environment. The most fundamental job of the chair is to take input from the user, usually in the form of a small joystick, and decipher that motion into power to the wheels to move the person in the preferred direction. The last few years have seen abundant improvements and models that give the user unmatched control of the wheelchair in terms of both user effort and vehicle aptitude

Development of a Gaze-Driven Electric Wheelchair with 360° Camera and Novel Gaze Interface

A novel gaze-based user interface (UI) is proposed for the remote control of robots and electric wheelchairs. A task-based experiment showed that this UI is more suitable for remote control than a conventional interface. The UI was applied to the control of a commercially available low-cost electric wheelchair. By using a 360° camera and an eye tracker that can be used outdoors, the visibility of obstacles and the usability of the gaze-driven electric wheelchair were greatly improved. The gaze-driven electric wheelchair exhibited good performance in a task-based evaluation experiment.

Research on Intelligent Wheelchair Attitude-Based Adjustment Method Based on Action Intention Recognition.

At present, research on intelligent wheelchairs mostly focuses on motion control, while research on attitude-based adjustment is relatively insufficient. The existing methods for adjusting wheelchair posture generally lack collaborative control and good human-machine collaboration. This article proposes an intelligent wheelchair posture-adjustment method based on action intention recognition by studying the relationship between the force changes on the contact surface between the human body and the wheelchair and the action intention. This method is applied to a multi-part adjustable electric wheelchair, which is equipped with multiple force sensors to collect pressure information from various parts of the passenger's body. The upper level of the system converts the pressure data into the form of a pressure distribution map, extracts the shape features using the VIT deep learning model, identifies and classifies them, and ultimately identifies the action intentions of the passengers. Based on different action intentions, the electric actuator is controlled to adjust the wheelchair posture. After testing, this method can effectively collect the body pressure data of passengers, with an accuracy of over 95% for the three common intentions of lying down, sitting up, and standing up. The wheelchair can adjust its posture based on the recognition results. By adjusting the wheelchair posture through this method, users do not need to wear additional equipment and are less affected by the external environment. The target function can be achieved with simple learning, which has good human-machine collaboration and can solve the problem of some people having difficulty adjusting the wheelchair posture independently during wheelchair use.

Iranian translation and psychometric of the Quebec user evaluation of satisfaction with Assistive technology (QUEST2.0) on wheelchair users

Purpose QUEST2.0 is a practical tool specifically applied to assessing the satisfaction for awide array of assistive technology in a standard manner. Therefore, this study set to translate and evaluate the validity and reliability of the Iranian version of the QUEST2.0in Persian-speaking users of manual and electronic wheelchairs in Iran. Methods The present study recruited 130 users of manual and electric wheelchairs. Psychometric properties, including; content validity and construct validity and internal consistency and test–retest reliability, were testified. Results The content validity index of the questionnaire was 92%. The internal consistency was determined to be 0.89,0.88 and 0.74, respectively, for the whole questionnaire and dimensions of the device, and services. The test-retest reliability was 0.85, 0.80, and 0.94, respectively, for the whole questionnaire and dimensions of the device and services. Factor analysis confirmed the two-factor structure of the questionnaire. In the two-factor model, 57.75% of the total variance was explained by these two factors, of which 45.8% pertained to the first factor (device) and 11.95% to the second factor (service). Conclusions The results showed that QUEST2.0 had both valid and reliable for measuring satisfaction with assistive technology among wheelchair users. The assessment will also help facilitate the quality improvement processes in using assistive technology devices.

Promoting self-determination of persons with severe or profound intellectual disabilities: a systematic review and meta-analysis.

People with severe or profound intellectual disabilities (IDs) are believed to experience low levels of self-determination, which negatively affects their quality of life. This systematic review describes existing interventions aimed to support self-determination or components thereof and synthesises evidence on the interventions' effects. Eight databases were searched, turning in 76 articles for the final inclusion. The studies included 631 people with severe or profound IDs of whom 81% had multiple disabilities. The studies had quantitative (k=63), qualitative (k=7) and mixed study designs (k=6). Sample sizes ranged from 1 to 95 and a study quality index ranged from 40% to 100%. While many studies included several self-determination components and intervention elements, overall, 53 studies focused on the self-determination components choice making, independence and problem solving. Other studies included increased assistance (k=14); engagement in meaningful activities and relationships (k=10); community and societal participation (k=5); supporting the basic psychological needs autonomy, competence and relatedness (k=4); individuality and dignity (k=3); supportive decision-making (k=2); self-advocacy (k=2); and motivation (k=1). Intervention elements included technology (k=33); multiple-component training packages, goal setting, empowerment tactics and applied behaviour principles (k=17); training of caretakers (k=17); changes in policies and living arrangements (k=9); supporter responsiveness (k=1); drama therapy and storytelling (k=1); electrical wheelchair training (k=1); joint painting procedure (k=1); youth advocacy project (k=1); and multiliteracies training (k=1). Reflecting the heterogeneity of the field, only four studies tested a similar intervention for this population and were eligible for the meta-analysis, which combined showed a small effect size of 2.69. Further research is needed to explore relationships between individuals with severe or profound IDs and their relatives and health care professionals and create supportive environments that meet their basic psychological needs.

Design and Simulation of Removable Pavement Edge Climbing Electric Wheelchair for Elderly and Disabled Users

Design and Simulation of Removable Pavement Edge Climbing Electric Wheelchair for Elderly and Disabled Users

Intelligent Eye-Controlled Electric Wheelchair Based on Estimating Visual Intentions Using One-Dimensional Convolutional Neural Network and Long Short-Term Memory.

When an electric wheelchair is operated using gaze motion, eye movements such as checking the environment and observing objects are also incorrectly recognized as input operations. This phenomenon is called the "Midas touch problem", and classifying visual intentions is extremely important. In this paper, we develop a deep learning model that estimates the user's visual intention in real time and an electric wheelchair control system that combines intention estimation and the gaze dwell time method. The proposed model consists of a 1DCNN-LSTM that estimates visual intention from feature vectors of 10 variables, such as eye movement, head movement, and distance to the fixation point. The evaluation experiments classifying four types of visual intentions show that the proposed model has the highest accuracy compared to other models. In addition, the results of the driving experiments of the electric wheelchair implementing the proposed model show that the user's efforts to operate the wheelchair are reduced and that the operability of the wheelchair is improved compared to the traditional method. From these results, we concluded that visual intentions could be more accurately estimated by learning time series patterns from eye and head movement data.

Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities

Conventional wheelchairs are predominantly manual or joystick-operated electric wheelchairs. However, operating these wheelchairs can be difficult or impossible for individuals with severe physical disabilities. Due to losing control of their physical limbs, they depend on an attendant for assistance. As a remedy, bio-signals may be used as a control mechanism since they are readily available and can be acquired from any body part. This research proposes to use EOG signals to vail a control mechanism and test it in a virtual and actual electric wheelchair. The main contribution of the study is an investigation of the use of EOG to control an electric wheelchair in a virtual environment to determine safe control parameters for wheelchair use in complex environments. A customized data acquisition circuit was developed to acquire single-channel EOG signals using wet electrodes. The acquired signal was filtered and processed using feature extraction and classification techniques in MATLAB software. Two customized control environments were developed in Unity 3D, one with equally partitioned sections and the other with sections decreasing in size as the robot wheelchair approaches the target. Twenty-two test subjects (mean age 24.5, std 1.5) participated in the study, controlling the robot wheelchair in real-time with non or least instances of collision and oversteering. The system achieved an accuracy of 96.5% with a response time of 0.7s, translating to an ITR of 70.6 bits/min. Overall, the participants managed to navigate the virtual environment with a completion time of 101.94s ± 19.71 and 109.07s ± 13.25 for the male and female participants, respectively. In the scene with decreasing section sizes, 72% and 54% instances of collision and oversteering were reported, respectively, highlighting the need to consider the complexity of the control environment and the sufficiency of the participants' control skills to ensure safety in operations. The results confirm the usefulness of EOG as a control interface, with little or no need for recalibration. It provides a promising avenue for individuals with severe physical disabilities to operate wheelchairs independently in complex environments, enhancing their quality of life.

Design and fabrication of solar powered wheelchair

As per the census, 2011 in 2016 disabled population of India was around 2.66 Cr and currently, it is about 3 Cr, talking about the physically disabled the count is 54,36,826. Around 70–75% are above 15 years of age. Conventional manual wheelchairs need to be handled by the other person ultimately they have to rely on others for their movement and daily activities which may cause mental depression among them and sometimes they hesitate to ask for help so electric wheelchair is the best option in this situation. But due to the continuous decline of fossil fuels, there is a need to find alternative renewable sources; here solar energy is a good solution. In this paper, study shows conventional wheelchairs and their conversion into an electric wheelchair which is semi-automated using sensors and controllers. This paper studies power consumption and shows that by using renewable sources of energy we can save 60–70% of the energy and once the battery is charged it will give 3–4 h of runtime. From the following research study, it can be concluded that designed wheelchair is having good features and would be very convenient and friendly to the aged people and handicapped. The device will be very useful to people suffering from disabilities and physical problems that they can take advantage of, as well as an eco-friendly device that works on renewable sources of energy. The frequency of charging is acceptable, improving the present scenario in said area of research.

Shared Driving Assistance Design Considering Human Error Protection for Intelligent Electric Wheelchairs

To effectively provide the handicapped with mobility aids, studies on the shared autonomy of robotic systems have been widely cultivated. This study proposes an adaptive shared control strategy to realize reliable and safe driving assistance on an intelligent electric wheelchair with protection against human errors. The theoretical framework of the system is analyzed by the linearized reference wheelchair model and stable characteristics of obstacle avoidance behavior can be subsequently derived according to the Lyapunov analysis and Liénard-Chipart criterion. Based on the convex analysis, the relationships between human input and robot control are investigated to determine shared control weights. As such, safety and reliability can be guaranteed. To verify the performances of the proposed approach, human errors including skill-based errors, decision errors, and violations are considered in the experiments. The experimental results based on a comprehensive study show that the proposed method is capable of enhancing driving safety and reducing operation burden in terms of the designed criteria with fluency, smoothness, and time efficiency while protecting the user from human manual errors.

SIMULATION OF THE TRAJECTORY OF AN ELECTRIC WHEELCHAIR CONTROLLED THROUGH PROGRAMMABLE LOGIC CONTROLLER AND HMI

This paper presents a software module made by means of a programmable logic controller and an HMI (Human Machine Interface), designed to control the movement of an electric wheelchair. By developing this module, a major advantage is offered to people with disabilities, who have movement difficulties. By implementing this command on a wheelchair, people can move independently using this machine that can be controlled from a touch screen. In this direction, the user has the possibility to choose the following directions of movement of the car: forward-backward and left-right, as well as the possibility to impose the speed of movement. The software program was created using ladder diagram language, and the user interface is provided through an HMI.

Design and Construction of Electric Wheelchair with Mecanum Wheel

This research aimed to design and construct an electric wheelchair with mecanum wheels that can move in any desired direction and speed based on the joystick controller. This represents a significant improvement over traditional electric wheelchairs, which are limited to linear movement in a single direction. The research contribution of this study is the development of an electric wheelchair with mecanum wheels that allows for improved mobility and independence for wheelchair users. The design includes a joystick controller and the use of an average filter to improve the processing of the joystick. This represents a significant improvement over traditional electric wheelchairs, which are limited to linear movement in a single direction. The design and construction of the electric wheelchair followed the ISO 2570-2555 guidelines and utilized Arduino DUE as the main processor for controlling the rotation of the wheels. The gain of speed and angle of the analog joystick were determined using the technique of finding the resultant vector to control the direction and speed of the wheels. The resulting electric wheelchair had a standard structure and was able to move in the desired direction and speed based on the movement of the joystick controller, demonstrating the success of the design and construction in achieving its objective. In conclusion, the development of joystick control for electric wheelchairs is important and allows for the creation of significantly novel and improved designs such as the electric wheelchair with mecanum wheels presented in this research.

Discrete reference tracking control to swing up an electric wheelchair

AbstractMobility using wheelchair is a crucial feature for disabled people. Between a manual wheelchair and an electric wheelchair, there is a very interesting and promising solution consisting of an electrical kit that converts a manual wheelchair to an electric wheelchair. Autonomad‐Mobility is a company selling such kits, where the wheels are equipped with a brushless motor. It presents new possibilities for mobility, especially for crossing obstacles and running on different types of soil. Several methods have been tested, from linear to nonlinear ones including the linear parameter‐varying control framework. Nevertheless, the most dangerous and critical issue is to be able to produce a so‐called safe and robust swing‐up: the transition from standard mode (four wheels on the ground) to gyroscopic mode (the wheelchair balancing on the rear push‐wheels). The gyroscopic mode is a unique feature improving mobility by providing a way to overcome obstacles that would normally be unsurpassable due to the caster wheels. Whereas some results exist, the solution must be compatible with any wheelchair and any disabled person whatever is her/his height and weight. Robustness of this unstable feature is therefore the key point of the work knowing that “exact” modeling is unrealistic. With the model in descriptor form and using the quasi‐linear parameter‐varying framework new stabilization conditions formulated as linear matrix inequality constraints are developed for attaining the goal. Simulations and real‐time experiments demonstrate the interest of this approach.

Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.

Découvrir le fauteuil roulant électrique en établissement pour personnes adultes polyhandicapées : une opportunité de stimulation cognitive

Adults with severe cerebral palsy and restricted autonomy are able to engage in meaningful activities suitable for their locomotor abilities and appropriate to stimulate their cognitive skills. Experimental assistive mobility with an electric wheelchair is one example. Here, we present seven case reports of adults with multiple and severe disabilities, living in institutions. They were proposed training sessions, guided by a multidisciplinary team and dedicated to the discovery of using an electric wheelchair. We show that the learning milestones that are described in children with motor deficiency when beginning to use an electric wheelchair are also observed in our adults. For each individual, depending on the severity of his/her neurological damage, this experiment revealed the level (exploratory or operative) of cognitive ability that was reached after training. For all of them, the discovery of a self-initiated mobility was a source of intense emotional and attentional mobilisation.