Wheelchair Control System Research Articles

Designing of a Trajectory Control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

Designing of a Trajectory Control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

Development of a Motion Controller for the Electric Wheelchair of Quadriplegic Patients Using Head Movements Recognition

The robotic wheelchair is designed to allow independent movement for patients with severe musculoskeletal and neuromuscular disorders that interfere with normal wheelchair propulsion by hands. Control of the movement of the wheelchair necessities residual motion of the upper extremity which was not applicable for patients with upper extremity disability as in Quadriplegic patients. Several research efforts have been proposed to help quadriplegic patients independently use the robotic wheelchair. However, the proposed solutions had many problems with accuracy, simplicity, and cost. The current work aims to build an accurate, simple, and low-cost control system for robotic wheelchairs using head movement. The proposed system was based on the use of the MEMs Inertial measurement unit (IMU) to sense the head movement gestures and transfer the gestures into signals to control the wheelchair movements. A new lightweight algorithm for the detection of head gestures was proposed to improve the system’s accuracy. The proposed system has been applied and tested on volunteers. The results revealed that the accuracy of the proposed method in recognizing head movement was 97% on average.

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.

Coordination between Visualization and Execution of Movements

Individuals living with disabilities often encounter difficulties in executing coordinated movements, particularly those with limited mobility due to conditions such as paralysis, arthritis, or spinal cord injuries. Traditional electric wheelchairs, primarily controlled through upper body strength, may be inaccessible to those with severe upper limb impairments. This study aimed to develop an innovative solution using Electromyography (EMG) technology to enable individuals with limited upper body mobility to control wheelchairs effectively. The objective was to create a user-friendly EMG-based wheelchair control system that interprets hand gestures and converts them into multi-directional commands. The research involved the development of an EMG-based wheelchair control system that recorded EMG signals from the users' hands and analyzed them for controlling forward, left, and right movements. Extensive testing was conducted with three subjects to evaluate the system's accuracy. The results were quantified regarding error percentages and compared among the subjects. The study yielded promising outcomes. Subject A exhibited a 1% error rate for right-hand movement and a 1.85% error rate for left-hand movement. Subject B achieved a 2.84% error rate for right-hand movement and a 1% error rate for left-hand movement. Subject C demonstrated a 3.84% error rate for right-hand movement and a 0% error rate for left-hand movement. The developed EMG-based wheelchair control system offers a viable solution to enhance mobility and independence for individuals with limited upper body mobility. The microprocessor-integrated design significantly improves the reliability and user-friendliness of the system. The research underscores the potential of EMG technology in bridging the gap between visualization and execution of movements for those facing mobility challenges.

GPS based smart wheelchair monitoring and control system

GPS based smart wheelchair monitoring and control system

Smart Glove or Sign Language and AI-Driven Wheelchair Navigation

Gesture gloves are a promising solution for individuals who struggle with both mobility and communication. These gloves' sensor technology helps with nonverbal communication as well as mobility, which is especially beneficial for people who have trouble pushing manual wheelchairs[2]. The goal of gesture gloves, a notable development in assistive technology, is to empower people with disabilities by improving mobility and enabling effective communication through natural hand and finger movements[1]. The use of AI-controlled wheelchairs and smart gloves together raises the bar for assistive technology even higher. The smart glove uses flex sensors to read finger movements and translate them into messages for people who can't effectively use sign language. The AIcontrolled wheelchair simultaneously reacts to recognized gestures, removing the need for assistance from a person and providing an unprecedented degree of independence. For people with disabilities, this gesture-based communication and wheelchair control system offers a comprehensive and game-changing solution that seamlessly integrates into their everyday lives.

Combining Mindwave, MPU6050, Ultrasonic, and Internet of Things for a reliable, safe, and monitored wheelchair control system

This paper proposes the development of a wheelchair control system by combining a Mindwave EEG (electroencephalogram) sensor, an MPU6050 inertial sensor, an ultrasonic sensor, and IoT technology. Stroke survivors often face challenges in controlling their wheelchairs due to cognitive and physical impairments, requiring specific solutions to ensure their safety and comfort. The integration of the Mindwave EEG sensor captures the user's cognitive state, such as level of attention and focus, while the MPU6050's inertial sensor monitors physical movement and changes in head orientation. Additionally, the ultrasonic sensor is incorporated to detect obstacles and provide proximity information for safe navigation. This wheelchair control system is also linked with IoT technology to enable monitoring of the user's cognitive, physical, and environmental condition and remote assistance. In this paper, to implement this wheelchair control system, a T-BEAM with an ESP32 controller is used, which is equipped with GPS to provide wheelchair location data and LORA radio communication for remote data transmission, and a TTGO LORA32 to forward the data to Arduino IoT Cloud service over Wi-Fi. The results of the development show that the wheelchair control system is safer and more monitored, and users are easier and more flexible in operating wheelchairs.

Design and Evaluation of the Extended FBS Model Based Gaze-Control Power Wheelchair for Individuals Facing Manual Control Challenges.

This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function-Behavior-Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design requirements from the extended FBS model and prioritized using the MosCow method. This innovative system relies on the user's natural gaze and comprises three levels: perception, decision making, and execution. The perception layer senses and acquires information from the environment, including user eye movements and driving context. The decision-making layer processes this information to determine the user's intended direction, while the execution layer controls the wheelchair's movement accordingly. The system's effectiveness was validated through indoor field testing, with an average driving drift of less than 20 cm for participates. Additionally, the user experience scale revealed overall positive user experiences and perceptions of the system's usability, ease of use, and satisfaction.

Wheelchair control system with hand movement using accelerometer sensor

Disability is a condition where there is an imbalance of interaction between biological conditions with the surrounding environment, which includes disruption, limited activities, and restrictions on participation. With the increasing population of people with disabilities in walking, either due to congenital physical disabilities, accidents, or motor functions due to aging, the need for wheelchairs increases. In this research, a wheelchair control system with hand movements using an accelerometer sensor was made. The system consists of a transmitter on the back of the hand and a receiver on the wheelchair. In the transmitter's part, MPU-6050 sensor detects hand movements, whereby hand movements can be upward, downward, straight, left tilted, and right tilted. The data from MPU-6050 sensor was processed by Arduino Nano then sent using the nRF24L01 module. In the receiver's part, the data were processed by Arduino Uno which activates the BTS7960 motor drive module and drives the DC motor. The test results show that hand movements with an angle of 45° can move the wheelchair forward, backward, turn right, turn left, and stop at a speed of 2 km/h with a maximum test load of 80 kg.

Experimental evaluation of a novel stability control system for two-wheeled robotic wheelchairs

A conventional robotic wheelchair containing four wheels (two active driving wheels and two passive casters) is statically stable with poor manoeuvrability. In comparison, a two-wheeled robotic wheelchair (TWRW) without the support of casters offers much better manoeuvrability but is inherently unstable and requires a stability control. Most stability controllers rely on the driving torques of the wheels which are high in magnitude and result in large energy consumption. Various disturbances in the system also affect the performance of the controller.To address these problems, this paper presents a novel control approach where the stability control is achieved through the motion of a pendulum-like movable mechanism added to the TWRW. A scaled-down TWRW is designed to evaluate the performances of the controllers based on PID control and second order sliding mode control (SOSMC). Experimental results show that under the proposed controller approach, the stability of the TWRW is achieved with much less torque, power, and energy consumption than the conventional control systems.

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.

A Survey on Visual based Wheelchair Control System for ALS Impaired Patients

A Survey on Visual based Wheelchair Control System for ALS Impaired Patients

A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control

An efficient recognition model is highly recommended while trying to analyze brain signal pattern for Motor Imagery (MI) signal. Therefore, this study aims to develop an optimized model based on a deep learning approach using Multi-Layer Perceptron (MLP) in order to help a large community of disability people by allowing them to control the wheelchair using their MI Brain signal. In this paper, dataset is used which is belong to BCI Competition dataset IV/2b and consists of two parts, each of them contains on 160 trails for a single subject. To preprocess the brain signal, Butterworth band pass filter used to remove unwanted signal (Alpha and Beta) and remain on the brain signal, then followed by feature extraction technique using Discrete Wavelet Transform (DWT). After that, Multi-Layer Perceptron (MLP) classifier based training parameters utilized to optimize the performance of the proposed system through using grid search optimization to improve performance of distinguishing between the two directional wheelchair commands. Cross-validations with ten groups were adopted to boost the modeling accuracy with dataset of all subjects (1440 trials) and the single subjects (160 trails). The results of this study showed that the efficiency of the optimized MLP model increased by 3% over the large dataset compared to the non-optimized model. It can be concluded that the optimized model can be deployed in a MI based BCI wheelchair control system to help the disability people in their daily activities.

Fuzzy Inference-Based Driving Control System for Pushrim-Activated Power-Assisted Wheelchairs Considering User Characteristics

Pushrim-activated power-assisted wheelchairs that assist the driving power by electric motors are effective in the daily lives of physically disabled people in terms of expanding the range of daily activities and comfortable driving even on rough and uphill roads. Meanwhile, users with unbalanced pushing power of the right and left arms due to some disability have difficulty controlling the wheelchair as he or she requests. For example, users with a lower-right arm strength have difficulty simultaneously driving forward and to the left. Therefore, a power-assist control system that considers the wheelchair operation characteristics of the individual user should be designed. The present article proposes a driving control system of pushrim-activated power-assisted wheelchairs with the target position estimation based on a learning model of individual operation characteristics. An individual estimation model based on fuzzy inference is constructed based on the right- and left-pushing torque information, which is properly revised to improve the position estimation accuracy. In addition, an operation training system with visual presentations to improve the proficiency of the user is effectively used. Experimental results using trial subjects, including a physically disabled person with arthrogryposis multiplex congenita, show that assisted driving intended by users can be realized, which verifies the effectiveness of the proposed driving control system and training system.

Wheelchair control system for the disabled based on EMOTIV sensor gyroscope

In this study, a wheelchair control system (WCS) was designed and constructed that utilizes an EMOTIV gyroscope sensor for comfortable wheelchair movement. The proposed WCS involves an electroencephalograph (EEG), DC motors, an electronic wheelchair, a microcontroller, and a laptop. The control system algorithm is bulit in Arduino microcontroller using C++ language. The WCS comprises 15 states and is dependent on head motions. There are 12 motor speed settings and eight wheel rotary radius cases. The WCS was tested by 10 women and men aged between 20 and 70 years. The system was implemented and tested in both indoor and outdoor environments to represent smooth and rough surfaces. It was also evaluated at various directions and speeds as well as at rotational radii (interior and exterior areas), and it prevented collisions with barriers using an ultrasonic sensor and reduced the wheelchair movement response time. The control system algorithm achieves an average response time of approximately 1–2 s to move the wheelchair in the specified direction. The performance metrics were also investigated and included sensitivity, specificity, and accuracy with encouraging results: The average WCS accuracy is 99.16%, sensitivity is 99.2%, and specificity is 99.15%. The overall performance is approximately 98%. The accuracy and response times of this WCS outperforms previous research findings.

Arduino UNO Based Wheelchair Control using Bluetooth and Android Mobile

The main goal of this project is to design and construct a low-cost wheelchair control system that uses an Android phone. Patients with diverse physical limitations have relied on wheelchairs to assist them with movement and to meet their day-to-day prerequisites. However, in other circumstances, the mobility of a wheelchair is dependent on another person. It includes people who are unable to lift their arms or push the wheels forward, such as paraplegics, quadriplegics, stroke patients, the elderly, and others. Joystick-oriented wheelchairs, which are supposed to be a solution for these people, might cause a variety of issues since they demand fundamental shoulder mobility. For the categories described above of patients, this is not always practicable. Furthermore, because it is wireless and can be worn on either hand, this solution does not have the positioning restrictions that a joystick wheelchair could have, allowing the user to sit in their preferred posture for the least amount of pain. The goal of this initiative is to enable autonomous mobility for people with disabilities. Thus, this research study provides a Bluetooth-based wheelchair prototype that maybe operated by an Android smartphone application. A transmitter and a receiver that can communicate wirelessly makeup the framework. The 433 Mhz RF Transmitter and Receiver Unit was utilized for wireless transmission since it delivers data through an antenna at speeds ranging from 1Kbps to 10Kbps and has a range of up to 100 metres, The wheelchair's receiver unit receives signals from the transmitter unit through an Arduino Uno micro-controller, which causes the wheels to turn in the desired direction. Motor drivers that convert electricity as needed by the wheels makeupmakeup the reception unit. This technology offers an alternative to commercial wheelchairs since it is more effective, less expensive, and simpler to control.

Touch Controlled Wheelchair

Abstract: Smart robotic wheelchair has a great significance in life of a disabled person. With several merits, a wheelchair becomes a dilemma for a disabled person when comes to self-propulsion. This project describes an economical solution of robot control systems. The presented wheelchair control system can be used for different sophisticated robotic applications. The automatic robotic wheelchair comprises of the features like sensing hindrances and circuitry to avoid colliding to obstacle and emergency calling. Implementing embedded systems solution on self-propelled wheelchair enhances upgradability. This paper briefs a multifunctional wheelchair for disabled mob using, touch screen, ultrasonic sensor and Motor driver system interfaced through microcontroller which ultimately abolishes switching technology and thus optimizing hardware cost. Obstacle avoidance facility enables to drive safely in unknown as well as dynamic environments. Keywords: Easy navigation, Obstacle avoidance, Touch screen, Motors

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.

PCCR Based Wheelchair Control System [Society News

Quadriplegia is an extremely serious disease. Due to the impaired function of patients' limbs, it is difficult for them to move freely by a wheelchair. This dilemma has become a huge obstacle to the rehabilitation of the disabled, and meanwhile added extra burden to society. To improve the quality of their lives, our team designs a vision-controlled automatic wheelchair based on the pupil center and cornea reflection (PCCR) technique. By constructing a multi-sensor real-time intelligent control system, the wheelchair realizes eye-controlled movements and other auxiliary functions such as emergency communication and rollover alarm. Through continuous optimization of multiple versions, a more intelligent, highly reliable, and low-cost wheelchair is manufactured.

Wheelchair Control System based Eye Gaze

The inability to control the limbs is the main reason that affects the daily activities of the disabled which causes social restrictions and isolation. More studies were performed to help disabilities for easy communication with the outside world and others. Various techniques are designed to help the disabled in carrying out daily activities easily. Among these technologies is the Smart Wheelchair. This research aims to develop a smart eye-controlled wheelchair whose movement depends on eye movement tracking. The proposed Wheelchair is simple in design and easy to use with low cost compared with previous Wheelchairs. The eye movement was detected through a camera fixed on the chair. The user's gaze direction is obtained from the captured image after some processing and analysis. The order is sent to the Arduino Uno board which controls the wheelchair movement. The Wheelchair performance was checked using different volunteers and its accuracy reached 94.4% with a very short response time compared with the other existing chairs.