Control Of Electric Wheelchair Research Articles

Control of Electric Wheelchair by Brain‐Computer Interface Using Mixed Reality and Virtual Sound Source

AbstractA brain‐computer interface for operating an electric wheelchair (wheelchair BCI) has been studied to support independent living for physically disabled patients. The current wheelchair BCI systems are facing the problem that the wheelchair cannot be moved or rotated to an arbitrary place or direction in a single measurement. To solve this problem, we developed a BCI that uses audiovisual stimuli based on mixed‐reality (MR) and virtual sound sources. During the online analysis, six out of seven participants were able to move or rotate the wheelchair to the target location or direction at a rate higher than the chance level. Thus, our results indicate that audiovisual stimulation with MR and virtual sound sources may be useful for intuitive wheelchair operation. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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.

Study of the Electric Wheelchair Control with a Gait Sensing System

Study of the Electric Wheelchair Control with a Gait Sensing System

Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair.

The research on the electroencephalography (EEG)-based brain-computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.

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.

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.

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

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

Assisting Control of Electric Wheelchair for Collision Avoidance By Using a Depth Sensor

Assisting Control of Electric Wheelchair for Collision Avoidance By Using a Depth Sensor

Electric Wheelchair Control Using Head Velocity-Based Gesture Recognition Specializing in Turning Motion

Electric Wheelchair Control Using Head Velocity-Based Gesture Recognition Specializing in Turning Motion

Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

<p class="0abstract">Quadriplegia is a paralysis condition in both arms and legs so that the patient is only able to move his neck and head. Manual or electric wheelchairs with joystick or switch control as a tool for people with paralysis certainly cannot be controlled independently by quadriplegia sufferers. This study aimed to help quadriplegia sufferers not to depend on others in carrying out daily activities by developing electric wheelchairs that can be controlled independently. The bioelectric signal which has only been used for diagnostic purposes can be utilized as an electric wheelchair control system for quadriplegia sufferers. In this study, electric wheelchairs were controlled by electromyography (EMG) signals from muscle contractions that can be driven by quadriplegia sufferers, namely the neck and face muscles. The increase in EMG signal amplitude during the muscle contraction is used as a trigger for the electric motor in a wheelchair to move forward, backward, turn right, and turn left. An electronic circuit for signal conditioning was used to amplify the EMG signal leads and filter frequencies that are not needed by the system before being processed by the microcontroller circuit. The use of wireless systems was developed to reduce the use of cables connecting electrodes to patients with electronic devices that will provide comfort to the user. Based on the results of the data collection on the wheelchair system, the detectability and selectivity values were for the 100% and 94% forward commands, 94.33% and 100% reverse commands, 92.31%, and 96% right turn commands and 97.96% and 94.12% left turn commands. The electric wheelchair system with EMG signal control is expected to help the mobility of quadriplegia sufferers.</p>

Smart Wheelchair Controls System Using Intelligent Algorithm and Biosensor Signal

Introduction Quadriplegia caused by spinal cord injury can occur in people of any age range and can be caused by any etiology (traumatic or no traumatic) and directly affects both motor and sensory neurological functions. The aim of this research project is developing a control device for an electric wheelchair for mobility of people with spinal cord injury) in closed or controlled environments, with quadriplegia (very limited mobility or complete immobility of upper and lower limbs. Specifically, the system takes the head position data (head tracking) of the user supplied by a webcam and through an application developed on a Raspberry PI3 single board computer finally, the system generates the movement of the wheelchair depending on the position and movement of the user's head through an electromechanical system with servomotors using intelligent algorithm. The system was tested on 10 immobilized people with upper and lower limbs obtaining a learning response between 1 and 5 minutes, the device is fully portable and adaptable to any electric wheelchair with joystick control. The developed device worked successfully and was compared with other head control devices for electric wheelchairs in the current market, it is, economical and versatile non-invasive system as a solution for mobility autonomy for people with quadriplegia. Keywords: Quadriplegia, Spinal Cord Injurie, Head tracking, Raspberry PI3, Joystick, Servomotors, intelligent algorithm Method During the research process, the importance of the assistive devices has been evidenced as a fundamental component of the rehabilitation, autonomy and improvement of the quality of life. More than 90% of people with spinal cord injuries require some type of wheelchair. These chairs should be appropriate for the needs and limitations of patients and to navigate properly in their environment. Other possible needs for assistive technology are home modifications for home control and communication systems for people with quadriplegia.For the development of the prototype, a Raspberry PI3 with 7-inch LCD touch screen was used. The necessary peripherals for the correct operation are connect at development card for the operation of the electric wheelchair control, using algorithms that ensure the correct operation of the device in adverse conditions for the user. In the final phase of development, a user interface was programmed in Python. The software is user friendly and intuitive for quadriplegic users. The system sends the control signal to the motors according to the instructions indicated by the user. The algorithm process the instruction and signal to guarantees the integrity of the user in advance condition. Results and Conclusions Control system for translation devices using cephalic tracking used for mobility of people in tetraplegia or quadriplegia condition is an option for self-mobility and given to the user autonomy and better quality of life in closed or controlled spaces.The tests carried out during the current research resulted in a low rate crash on the way obstacles and low deviation from the original trajectory, less than 7% for a user learning time between 1 and 6 minutes.The proposed system differ from the other cephalic tracking developments reviewed in the state of the art, with effective autonomy by the use of control algorithms References Wolters Kluwer "ISCOS - Textbook on comprehensive management of spinal cord injuries ". Harvinder Singh Chhabra. New Delhi, India. ISBN: 9351294404. 2005.[2] D. J. Kupetz ; S. A. Wentzell ; B. F. BuSha "Head motion controlled power wheelchair". Proceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference (NEBEC). 2016. ISSN: 00002013. doi: 10.1109/NEBC.2010.5458224.[3] J Aleksandar Pajkanović , Branko Dokić. "Wheelchair Control by Head Motion". SERBIAN JOURNALOF ELECTRICAL ENGINEERING. Vol. 10 N° 1, pp: 135-151. February 2015. ISSN: 14514869. doi:10.2298/SJEE1301135P[4] Vignesh S.N, Vivek Kumar. A, Bharathi Kannan.K. "Head Motion Controlled Robotic Wheel Chair". International Journal of Emerging Technology and Innovative Engineering. Vol. 1, Ussue 3, pp: 176-179. March 2015. ISSN: 2394 - 6598. Figure 1

Human Assist Control of Electric Wheelchair by Using Control Barrier Function

Human Assist Control of Electric Wheelchair by Using Control Barrier Function

Design and Low-Cost Implementation of an Electric Wheelchair Control

This paper discusses the design and low-cost implementation of direction and speed controller for an electric wheelchair actuated using a permanent magnet direct current (PMDC) motor. Most of the works are performed either with the simulation of PMDC motor control with various techniques or studied the performance of a single type of controller in real-time with the wheelchair. In this work, the authors simulated the control of PMDC motor with three different controllers and tuned the parameters for real-time implementation of electric wheelchair control using PMDC motor. The authors attempted the simple solution using push button-based interface as well as a graphical user interface for the direction and speed control of electric wheelchair. The signal processing path of the controller, decode the user command input for the direction and desired speed. The controller generates necessary pulse width modulated signals to the H-bridge driver for directions forward, backward, left, and right direction as well as speed control of wheelchair. The user speed control commands low, slightly medium, medium, high, and very high speed are sensed to limit the speed of wheelchair. The performance capability of the intelligent neural network and fuzzy logic controller is studied and compared with proportional-integral-derivative (PID) controller with variations in speed. The PID, neural network and fuzzy logic controller are designed using Matlab-Simulink for PMDC motor and controller parameters are tuned for driving the wheelchair in the real-time implementation using the ATmega 328P microcontroller. The fuzzy logic controller in the electric wheelchair demonstrated the improved performance with less peak overshoot and faster settling with less oscillation compared to PID and neural network controller.

BCI‐based control of electric wheelchair using fractal characteristics of EEG

Brain–computer interfaces (BCIs) have been attracting attention as interfaces that connect a brain to an external device. The implementation of a BCI requires establishing a technique that accurately recognizes the brain state, and a host of challenges remains for the implementation. This paper uses the emotion fractal analysis method (EFAM), which quantifies emotions based on data obtained from an electroencephalogram (EEG), to propose a BCI system that accurately discriminates and recognizes emotions such as delight, anger, sorrow, and pleasure, and uses that information to manipulate an electric wheelchair. The EFAM's emotion recognition rates for the four emotions, namely delight, anger, sorrow, and pleasure, were 81.11, 79.25, 73.16, and 85.42%, respectively, and its emotion isolation rates were 97.88, 98.08, 98.07, and 97.86%, respectively. Based on emotion data obtained in real time by the EFAM, we developed a novel BCI circuit that manipulates an electric wheelchair. Using this BCI circuit allows us to adjust the speed of an electric wheelchair in proportion to the intensity of the emotion. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Route control of electric wheelchair by dynamic window approach

Route control of electric wheelchair by dynamic window approach

筋電位・眼電位・脳波による電動車いすの操縦

筋電位・眼電位・脳波による電動車いすの操縦

A multi levels data fusion approach for an electric wheelchair control

Purpose Assistive technology has allowed providing valuable assistance to people with special needs. Several studies have targeted “smart” wheelchairs. However, these prototypes are not yet commercialized, mainly for security reasons. The objective of this study is to ameliorate the control success rate and fulfill maximally the security constraint.

生体信号デバイスを用いた電動車いすの操縦に関する研究

生体信号デバイスを用いた電動車いすの操縦に関する研究

2A1-H09 眼電位(EOG)を用いた電動車いすの制御

2A1-H09 眼電位(EOG)を用いた電動車いすの制御

Electric wheelchair control system using brain-computer interface based on alpha-wave blocking

A brain-computer interface (BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram (EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject’s triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.