Wheelchair Control Research Articles

Cognitive status is associated with performance of manual wheelchair skills in hospitalized older adults

Purpose To examine the relationship between manual wheelchair skills and cognitive function in hospitalized older adults. Methods The observational study included older adults who used a manual wheelchair following hip/knee surgery. Participants underwent a series of tests to evaluate manual wheelchair skills and cognitive performance. Four items appearing on the Wheelchair Skills Test: brake handling (locking/unlocking), a 10-metre forward roll, a 2-metre backward roll and rotating in place, were used to evaluate manual wheelchair skills. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), the Clock Drawing Test, and the Loewenstein Occupational Therapy Cognitive Assessment. The relationship between wheelchair skills and cognitive scores was assessed by a series of univariate linear regression analyses. Results Fifty older adults, aged 65–85, participated in the study. The MoCA-7 (orientation) explained 19.3% of the variance related to the turn in place wheelchair skill, 18.8% of the variance related to the two-metre backwards roll and 31.9% of the variance related to the 10-metre forward roll. The addition of gender (to the MoCA-7) increased the explained variance related to the 10-metre forward roll and turn in place skills to 38.5% and 28.5%, respectively. As for the brakes handling skill test, gender explained 18.3% of the variance. The addition of the CDT (to gender) increased the explained variance for the brakes handling skill to 31.4%. Conclusions Because cognitive impairments negatively affect the performance of wheelchair skills, rehabilitation therapists may need to adjust wheelchair mobility training methods for cognitively impaired older adults. Implication for rehabilitation Given the prevalence of older adults with cognitive impairments who use manual wheelchairs, it is critical to better understand the relationship between cognition and wheelchair skills. Poor results reported on the cognitive tests, specifically, visual attention and orientation, were found to be associated with poor performance of four manual wheelchair skills. Rehabilitation therapists should consider the cognitive status of older adults when teaching manual wheelchair skills, specifically in new users. Future studies should examine whether a customized preparation program, enhancing visuospatial orientation, can benefit manual wheelchair control in older adults.

Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern.

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.

A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.

In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.

Traded and combined cooperative control of a smart wheelchair

AbstractThis article deals with a human–machine cooperative system for the control of a smart wheelchair for people with motor disabilities. The choice of a traded control mode is first argued. The paper then pursues two objectives. The first is to describe the design of the cooperative system by focusing on the dialogue and the interaction between the pilot and the robot. The second objective is to introduce a new cooperative mode. In this one, three features are proposed: two semi-autonomous features, a wall following and a doorway crossing, during which the user can intervene punctually to rectify a trajectory or a path, and an assisted mode where, conversely, the machine intervenes in a manual control to avoid obstacles. This mode of intervention of an entity, human or machine, supervising a movement controlled by the other is referred as “combined control.” Examples of scenarios exploiting the cooperative capabilities of the system are presented and discussed.

CCA-Based Compressive Sensing for SSVEP-Based Brain-Computer Interfaces to Command a Robotic Wheelchair

People with severe physical disabilities are not able of using standard robotic wheelchairs, which generally demand some motor skills, and therefore total usage of associate muscles. Robotic wheelchairs commanded by Brain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have demonstrated to be an alternative for these end-users. In general, existing robotic wheelchairs commanded by BCIs require special platforms adapted to the EEG-BCI, and end-users need to attend a long training process for safely drive these devices. But many times these potential users do not have access to training sessions; due to mobility problems or technology access restrictions. This study proposes an EEG-based BCI with customizable configuration to be used in cloud architectures for remote control of robotic wheelchairs. This research explores two types of Steady State Visual Evoked Potential (SSVEP)-based BCI by applying Canonical Correlation Analysis (CCA) and compressive sensing (CS) as a novelty, adopting in one free calibration, and other including a calibration stage. The free-calibrated SSVEP recognition approach (CS-ncCCA) using compression ratio (CR) at 60% obtained accuracy (ACC) of 85% and information transfer rate (ITR) of 102 bits per minute (bpm), whereas the calibrated BCI (CS-wcCCA) applying also CR at 62% achieved ACC of 85% and ITR of 195 bpm. As a hilghlight, the proposed BCI allows significant reduction of the transmitted file size, and improves the communication latency that may be useful in remote and Cloud robotics applications, such as for users with severe motor disabilities, to train driving safely robotic wheelchairs via IoT.

Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

The wheelchair is one of the most common assistive technologies for people with motor impairment, due to its environmentally friendly features in terms of mobility and comfort. However, the operational method for conventional wheelchair is still inconvenience for people with finger problems. Therefore, in the project, a novel control method to operate an electric wheelchair, using hand gestures ( wrist rotation) is developed. Sixty-five (65) participants were involved in this study. Five hand gestures were considered and studied for forward, backward, right, left, and stop maneuvers while considering human ergonomics factor. In this study, the stop maneuver was determined based on the most comfortable hand position to mitigate a fatigue experience, with two gesture classifying methods further investigated. The first method based on threshold has a promising accuracy of 96%, and 91% precision. This method however requires a calibration every time a new user is introduced. The second method, a Naïve Bayes approach, was observed to solve the problem, as it has about 99% of both the accuracy and the precision. The evaluation of this method was then conducted with six participants that operated the wheelchair to follow the trajectories from start to end. The results showed that the participants comfortably controlled the developed wheelchair system to the goal without any collision. Results from experiments indicate that the proposed approach has high accuracy and the potential to solve the problem related to finger dependencies and hand fatigue.

Hybrid control slope assisted wheelchair for the person with lower limb disabilities for hilly region

This paper presents a hybrid control wheelchair with a slope assistive control, especially for the inclined plane and hilly region. This device uses multiple sensor networks to locate the surrounding environment and terrain to facilitate control movement. A BLDC hub motor is used to provide forward and backward motions. A high torque servo motor is used as the steering wheel. The central control unit can also be controlled by manually, automatically, and through the internet. The proposed wheelchair overcomes the limitations and drawbacks of uphill and downhill movements in the existing systems. The test was carried out using the prototype with an inclined 10-degree ramp, which proved that the slope assistance has a beneficial and significant impact in comparison with a manual wheelchair. It has been demonstrated that users can perform all required functions with a better control strategy, enhancing the effectiveness of traditional wheelchairs.

Eye-gaze control of a wheelchair mounted 6DOF assistive robot for activities of daily living

BackgroundBuilding control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human–robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living.MethodsIn this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living. The graphical user interface is designed and integrated with the developed control architecture to achieve the goal.ResultsWe evaluated the system by conducting a user study. Ten healthy participants performed five trials of three manipulation tasks using the graphical user interface and the developed control framework. The 100% success rate on task performance demonstrates the effectiveness of our system for individuals with motor impairments to control wheelchair and wheelchair-mounted assistive robotic manipulators.ConclusionsWe demonstrated the usability of using this eye-gaze system to control a robotic arm mounted on a wheelchair in activities of daily living for people with disabilities. We found high levels of acceptance with higher ratings in the evaluation of the system with healthy participants.

Implementation of wheelchair controller using mouth and tongue gesture

In this paper, a simple human-machine interface allowing people with severe disabilities to control a motorized wheelchair using mouth and tongue gesture is presented. The development of the proposed system consists of three principal phases: the first phase is mouth detection which performed by using haar cascade to detect the face area and template matching to detect mouth and tongue gestures from the lower face region. The second phase is command extraction; it is carried by determining the mouth and tongue gesture commands according to the detected gesture, the time taken to execute the gestures, and the previous command which is stored in each frame. Finally, the gesture commands are sent to the wheelchair as instruction using the Bluetooth serial port. The hardware used for this project were; laptop with universal serial bus (USB) webcam as a vision-based control unit, Bluetooth module to receive instructions comes from the vision control unit, standard joystick used in case of emergency, joystick emulator which delivers to the control board signals similar to the signals that are usually generated by the standard joystick, and ultrasonic sensors to provide safe navigation. The experimental results showed the success of the proposed control system based on mouth and tongue gestures.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

Fuzzy Control of Electric Wheelchair with Intuitive User Interface for Soil Cultivation

Fuzzy Control of Electric Wheelchair with Intuitive User Interface for Soil Cultivation

A Multimodal Direct Gaze Interface for Wheelchairs and Teleoperated Robots.

Gaze-based interfaces are especially useful for people with disabilities involving the upper limbs or hands. Typically, users select from a number of options (e.g. letters or commands) displayed on a screen by gazing at the desired option. However, in some applications, e.g. gaze-based driving, it may be dangerous to direct gaze away from the environment towards a separate display. In addition, a purely gaze based interface can present a high cognitive load to users, as gaze is not normally used for selection and/or control, but rather for other purposes, such as information gathering. To address these issues, this paper presents a cost-effective multi-modal system for gaze based driving which combines appearance-based gaze estimates derived from webcam images with push button inputs that trigger command execution. This system uses an intuitive "direct interface", where users determine the direction of motion by gazing in the corresponding direction in the environment. We have implemented the system for both wheelchair control and robotic teleoperation. The use of our system should provide substantial benefits for patients with severe motor disabilities, such as ALS, by providing them with a more natural and affordable method of wheelchair control. We compare the performance of our system to the more conventional and common "indirect" system where gaze is used to select commands from a separate display, showing that our system enables faster and more efficient navigation.

Wheelchair Control With Inductive Intra-Oral Tongue Interface for Individuals With Tetraplegia

High level spinal cord injury drastically reduces the quality of life of the injured person. Various systems attempted to interface the still intact or residual abilities following injury in various monomodal or multimodal designs to compensate for the highly affected mobility. An intra-oral inductive tongue computer interface (ITCI) has been designed to provide real-time discrete and proportional control for computers and assistive devices and to meet specific requirements for individuals with tetraplegia. Operation of the ITCI for wheelchair control was demonstrated with two participants with tetraplegia in a short-term training study. Additionally, two non-disabled individuals participated in the study. For each participant, the ability to drive a Permobil C500 with the ITCI was compared to that when driving the wheelchair with joystick (mouth-stick in one case) along two different lanes of 39 m, by reporting the speed along the lanes and the number of obstacles hit. The lanes consisted of 90°, 360°, and complex maneuver segments linked by linear segments. The ITCI featured a mouthpiece encapsulating two pads of inductive sensors, driving electronics, and battery. The mouthpiece was attached to the palate of the participant’s oral cavity with dental retainers. A piercing-like activation unit was attached to the tongue. Data were transmitted wirelessly to a central unit that controlled the wheelchair through wired interface. Among all participants, mean speeds along lane A or B reached maximal values between 0.42 and 0.74 m/s when driving with the ITCI, representing 41 to 71% of that obtained when driving with the joystick.

RANCANG BANGUN KURSI RODA ELEKTRIK BERBASIS INTERNET OF THINGS (IOT)

Wheelchairs are mobility devices that are often used by people who have difficulty walking on their feet, due to illness, injury, or disability. Most of the wheelchairs that exist today are still using the manual system. Ideally, a wheelchair is needed so that users feel free and comfortable in adjusting the wheelchair movement according to their wishes.
 This study aims to make an innovation in the form of designing an electric wheelchair based on the internet of things (IoT). In this tool, there are several integrated electronic components such as NodeMCU, voltage sensor that is connected to the android smartphone application. Delphi XE8 software functions to create control applications that can be controlled via a smartphone. The control system on a smartphone uses an internet connection to process the wheelchair control system. This tool is very dependent on the connection and stability of the internet network.
 Design of electric wheelchair based IoT, has the performance that a wheelchair can carry a maximum load of 30kg at a speed of 0.79m / second, plus an accumulator / battery load and a 6.4kg DC motor, so that the total load can be under of 36.4kg.

Automated Wheelchair Controller System using IoT

Abstract: The require of numerous paralyzed, disabled and dazzle people groups are fulfilled with manual wheelchair, but it finds troublesome to the impaired people groups to depend upon others continuously and indeed manual wheelchair makes troublesome to function through the joystick. There is a computerized based inquire about going through the wheelchair improvement but through of our framework we utilized sensors and insights-based algorithm which minimize the human exertion conjointly it makes the debilitated individual to function effortlessly without challenges. Our objective is to design and execute a wheelchair with different conceivable outcomes for the impaired people to function a wheelchair through voice acknowledgment, touch show conjointly mechanized GPS tracking moving wheelchair through line taking after. Where debilitated people can effectively connect with wheelchair through any one of these sources conjointly the wheelchair is adjusted without falling utilizing accelerometer sensor conjointly impediment recognizing sensor has been introduced for the security. Keywords: GPS Tracking, Sensors, Motors, Voice Recognize, Arduino

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

Brainwave assistive system for paralyzed individuals

The Brain-Computer Interface (BCI) is a system based on brainwaves that can be used to translate and comprehend the innumerable activities of the brain. Brainwave refers to the bioelectric impulses invariably produced in the human brain during neurotransmission, often measured as the action potential. Moreover, BCI essentially uses the widely studied Electroencephalography (EEG) technique to capture brainwave data. Paralysis generally occurs when there is a disturbance in the central nervous system prompted by a neurodegenerative or unforeseen event. To overcome the obstacles associated with paralysis, this paper on the brainwave-assistive system is based on the BCI incorporated with Internet-of-things. BCI can be implemented to achieve control over external devices and applications. For instance, the process of cursor control, motor control, neuroprosthetics and wheelchair control, etc. In this paper, the OpenBCI Cyton-biosensing board has been used for the collection of the EEG data. The accumulated EEG data is executed subsequently to obtain control over the respective systems in real-time. Hence, it can be concluded that the experiments of the paper support the idea of controlling an interfaced system through the real-time application of EEG data.

Brain Computer Interface Controlled Wheel Chair

Brain computer interface wheel chair specially designed for paralyzed and disabled person who are not capable to operate normal wheel chair. This wheel chair is based on (BCI) Brain computer interface, it can control the wheel chair from brain neurons by the help of BCI. Brain computer interface (BCI) is a computer-based system that obtain brain signal that can be controlled by Neurosky Sensor which is an electrophysiological process to archive the electrical activity of the brain. A BCI system recognize users to grant their determination by study their brain signals. This technology is very helpful for paralyzed and disabled person they can easily moves wheel chair in any direction.

Voice Controlled Wheel Chair for Persons with Disability

Many disabled people usually rely on others in their standard of living especially in getting from one place to a different. To use wheelchair, they have someone to assist them in moving the wheelchair. Having a wheelchair system will help handicapped persons become independent. The system could be a wireless wheelchair system with a voice recognition system for triggering and controlling all its movements. The wheelchair responds to the voice command by user to perform any movement. It integrates a microcontroller, android phone, control interface board to maneuver the wheelchair. By using the system, users are able to operate the wheelchair by simply talking to wheelchair microphone. the essential movement functions includes forward and reverse direction, left and right turns and stop. It utilizes Renesas Microcontroller to manage the system operations. It communicates with the voice recognition processor to detect word spoken and so determines the corresponding output command to drive the left and right motors. To accomplish this task, a programming language program is written and stored within the controller's memory. GSM module is employed to send an alert message to their care takers just in case of emergency situations.

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.