Wheelchair Control Research Articles

A New Control Approach for a Multi-Controlled Wheelchair Utilizing Deep Learning Framework and Raspberry Pi

This article describes the construction and control mechanisms of a multiple control wheelchair (MCW) aimed to aid individuals with amyotrophic lateral sclerosis (ALS) and lower limb disabilities. The MCW has been tested in the laboratory on a total of 10 patients, among them 3 with lower limb disabilities, 6 elderly patients, and 1 patient with ALS. The proposed wheelchair can navigate in any direction using object detection techniques powered by OpenCV. Employing a deep learning algorithm, it performs real-time object detection and tracks its current position in various environmental conditions. The MCW can be controlled using a joystick, internet of things (IoT), or a combination of both. A novel aspect of this work is the implementation of a deep learning framework on a Raspberry Pi processor, allowing the wheelchair to navigate in any direction based on the user’s needs. The Raspberry Pi interfaces with all sensors, camera, and additional peripherals through serial communication, enabling monitoring and control via an android mobile application. This setup effectively reduces the physical distance between the user and the caregiver. During the test, a range of functional measurements were taken, including assessments of the object detection process and the performance of hardware components such as sensors and motors connected to the system. The current system is unable to precisely determine an object’s position during detection, but ongoing research is focused on enhancing the system’s capability to accurately map objects in 3D space for improved wheelchair navigation.

Comparison of Manual, Automatic, and Voice Control in Wheelchair Navigation Simulation in Virtual Environments: Performance Evaluation of User and Motion Sickness.

Mobility is essential for individuals with physical disabilities, and wheelchairs significantly enhance their quality of life. Recent advancements focus on developing sophisticated control systems for effective and efficient interaction. This study evaluates the usability and performance of three wheelchair control modes manual, automatic, and voice controlled using a virtual reality (VR) simulation tool. VR provides a controlled and repeatable environment to assess navigation performance and motion sickness across three scenarios: supermarket, museum, and city. Twenty participants completed nine tests each, resulting in 180 trials. Findings revealed significant differences in navigation efficiency, distance, and collision rates across control modes and scenarios. Automatic control consistently achieved faster navigation times and fewer collisions, particularly in the supermarket. Manual control offered precision but required greater user effort. Voice control, while intuitive, resulted in longer distances traveled and higher collision rates in complex scenarios like the city. Motion sickness levels varied across scenarios, with higher discomfort reported in the city during voice and automatic control. Participant feedback, gathered via a Likert scale questionnaire, highlighted the potential of VR simulation for evaluating user comfort and performance. This research underscores the advantages of VR-based testing for rapid prototyping and user-centered design, offering valuable insights into improving wheelchair control systems. Future work will explore adaptive algorithms to enhance usability and accessibility in real world applications.

Multimodal Control for Powered Wheelchair

Aim: Over the years, joystick-controlled wheelchairs have helped in the mobility of non-ambulant people. However, existing wheelchairs may not be completely helpful for people suffering from additional medical conditions such as, dysarthria, cerebral palsy, and diplegic users. The current work aims to develop a speech and gesture multimodal controlled wheelchair system for people with multiple disabilities. The motivation behind this work is to cater to their needs by customizing their wheelchairs for ease of movement and to instill a feeling of independence when they commute. Methods: The speech recognition module of the proposed wheelchair is developed to cater the needs of both normal and dysarthric speakers. The current work makes use of convolution and residual convolution based neural architectures to model and recognise the speech commands. Further, a range-based gesture recognition system is developed in the current work using MPU 6050. Results: The proposed speech recognition module of the wheelchair has an accuracy up to 98% and 92% for normal and dysarthric speakers respectively. Further the gesture recognition module has an accuracy of 100% for all users as it is completely threshold based. Conclusion: The proposed speech module is ported on to a Raspberry pi 4B+ module and the gesture module is ported using an Arduino Uno. These modules are integrated with the control system (i.e., joystick) of the wheelchair to ensure movement. The current work ensures a small footprint and easy mobility of the wheelchair.

A Multimodal Asynchronous Human-Machine Interaction Method Based on Electrooculography and Speech Recognition for Wheelchair Control

A Multimodal Asynchronous Human-Machine Interaction Method Based on Electrooculography and Speech Recognition for Wheelchair Control

Wheelchair Control Using Finger Gestures IoT-Based Arduino

This paper presents the development of a wheelchair control system using Arduino and wireless finger gestures. The system incorporates various components such as Arduino Uno and Arduino Nano boards, ultrasonic and IR sensors, an LN298 motor driver module, motors, an IR flame/fire sensor, a buzzer, batteries, and HC12 wireless modules. The flex sensor is utilized to detect finger movements and gestures, while the ultrasonic and IR sensors provide obstacle detection capabilities. The Arduino boards process the sensor data and interpret the finger gestures, mapping them to specific commands for wheelchair control. The LN298 motor driver module interfaces with the motors to enable the execution of the desired motions based on the recognized gestures. IR flame/fire sensor monitors fire, and the buzzer provides audible feedback. The HC12 wireless modules facilitate wireless communication between the Arduino boards. The paper aims to create an intuitive and efficient control system for a wheelchair, allowing users to navigate their surroundings using wireless finger gestures while incorporating obstacle detection and air quality monitoring features.

A comprehensive study on Mecanum wheel-based mobility and suspension solutions for intelligent nursing wheelchairs

Intelligent nursing wheelchairs significantly enhance mobility and independence for elderly individuals with disabilities. However, traditional designs often suffer from large turning radii that restrict their functionality in confined spaces. Addressing this critical challenge, this study introduces an innovative design utilizing a Mecanum wheel chassis that allows for omnidirectional movement, significantly improving maneuverability and stability. Our design incorporates independently controlled Mecanum wheels, overcoming traditional constraints and enhancing user autonomy. To address issues such as wheel spacing variations and hub center tilting, which can lead to slipping and inaccurate motion control, we developed a novel suspension system that stabilizes the chassis, minimizes slipping risks, and boosts motion control accuracy. Experimental validations, including shock absorption and positioning tests, demonstrate that our suspension system markedly enhances the wheelchair's control performance and stability, thereby providing users with enhanced precision and potentially improving their quality of life.

VocalVision: Smart Wheelchair Maintenance with Pressure Sensors and Machine Learning

The Vocal Vision system introduces an innovative approach to enhancing electric wheelchair maintenance and control. It utilizes a network of sensors embedded within the wheelchair's wheels to gather real-time data on tire pressure, temperature, tread wear, and alignment. This data is wirelessly transmitted to a central control unit. Advanced algorithms, incorporating machine learning and predictive analytics, analyze the data to detect irregularities and predict maintenance needs. Users can control direction, speed, and perform complex maneuvers with precision using voice commands and eye gestures. The wheelchair integrates OpenCV for eye gesture recognition and Google Speech Recognition API for voice commands, enabling intuitive control methods. This proposed method introduces a new assistive technology for individuals with disabilities, leveraging cutting-edge technologies.

Electric Wheelchair Manufacturing Analysis For Physical Disabilities Reviewed from the Aspect of Ergonomics

The high rate of physical disability is a trigger and spur in the development of research/research for people with disabilities to help with their daily activities, one of which is with a wheelchair. There is a sense of concern to see manual wheelchairs used daily by people with physical disabilities, especially with their abnormal hands and feet, so it becomes a problem in itself because they are quite tricky and tired of pedaling a manual wheelchair. This condition impacts their work productivity, thereby reducing their income/economy. Therefore, the purpose of this research is to manufacture electric wheelchairs belonging to two people with physical disabilities (i.e., belonging to Bagus and Huda). The ergonomics tests are carried out, including (a) Compatibility of the anthropometry of the user's body and the dimensions of the wheelchair, (b) Results of the user's postural analysis with RULA (Rapid Upper Limb Assessment) (c) Conformity of the dimensions of the wheelchair with the standard, and (d) Evaluation of the usability of the wheelchair based on observation. The results obtained for the Bagus electric wheelchair are from the anthropometric side. The popliteal height is longer than the distance between the seat and the footrest in the wheelchair. For Bagus to be more comfortable in using his wheelchair and for his legs not to be crooked (which will potentially cause more excellent musculoskeletal disorders), it is recommended that the distance between the seat and the footrest be extended. As for Huda electric wheelchairs, in general, they have been modified to adjust to the needs and conditions of Huda's body and make the design of Huda wheelchairs more ergonomic. However, the postural analysis showed that the forearm position was raised, and the hand was twisted when Huda operated the electric wheelchair controller. It is, therefore, recommended to lower the position of the controller in line with the wheelchair mount.

An Innovative Device Based on Human-Machine Interface (HMI) for Powered Wheelchair Control for Neurodegenerative Disease: A Proof-of-Concept.

In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control a robotic wheelchair for people with limited mobility. The test group comprised 11 healthy subjects (six male, five female, mean age 35.2 ± 11.7 years). A novel platform that integrates a smart wheelchair and an eye-tracking-enabled head-mounted display was proposed to reduce the cognitive requirements needed for wheelchair movement and control. The approach's effectiveness was demonstrated by evaluating our system in realistic scenarios. The demonstration of the proposed AR head-mounted display user interface for controlling a smart wheelchair and the results provided in this paper could highlight the potential of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as remote control, cognitive rehabilitation, the implementation of patient autonomy with severe disabilities, and telemedicine.

Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology

The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

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.

Helping Hands for Handicaps

Abstract: We have more advanced wheelchairs in today's generation. it includes joystick wheelchair, head motion control wheelchair, gesture control wheelchair etc. But with the advance technology it is expensive too, which is not affordable for most of the middle-class families. In order to provide an advanced wheelchair in an affordable price, we have to use less components to reduce the cost so that a person can buy it with a price less than the today’s basic bikes price. To achieve this, we used some basic electronic components such as Arduino MINI and NANO, gyro sensor, Node MCU, Motors etc. To get extra security we integrated some calling and GPS location features too. The person sitting in the wheelchair can control the wheelchair through hand gestures. Additionally, whenever the person sitting in the wheelchair faces any problem, such as if the wheelchair falls, the caretaker will receive a notification and through the GPS location, the caretaker will be able to reach their location.

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology.

Design of Hand Gesture Control Wheel Chair

This study designated a wheelchair which can be meticulous using the user hand gesture. The purpose of this research was to come up with a hand gesture powered wheelchair system intended to provide assistance with the use of hand directions, which would allow users to travel efficiently and with greater ease. The system is made up of various electronics components such as the microcontroller, accelerometer, Bluetooth model, ultrasonic sensor, motor driver, among others. The microcontroller is programmed. The transmitter circuit board is made up of accelerometer sensor like ADSL335 which is able to sense three parameters. The microcontroller forms the main part or the brain of the system, and performs most of the functions. The microcontroller sends the readings sensed by the hand to the Bluetooth transmitter. The ultrasonic sensor detected obstacles, also sent the distance measured to the microcontroller. The microcontroller processed the information to the motor driver. The motor driver then powered the motors to either move forward or backward. It was observed that when the system was activated, the movement of the Wheelchair immediately start to follow the orientation of the user’s hand. The wheelchair was able to be perform five (5) different gestures. The gesture follows the range that had been set. However, this range depends on the position of the accelerometer.

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.

Development of Field Tests for Cardiovascular Fitness Assessment in Wheelchair.

It is essential to consider both physique and physical fitness factors to minimize the risk of injuries and optimize athletic performance among elite athletes. Athletes with disabilities face limitations in fitness assessments compared to their healthy counterparts. The aim of this study was to revalidate established cardiovascular fitness assessment methods and develop field tests for wheelchair athletes. As representatives registered at the Korea Paralympic Committee's Athletes Training Center in Icheon, athletes with physical disabilities participating in para ice hockey (n = 14), who were capable of wheelchair control, were volunteered. Prior to cardiovascular fitness assessments using an ergometer and a shuttle run, demographic characteristics were surveyed, and physical measurements and muscle strength (grip strength) were recorded. All the participants performed one ergometer test based on cardiovascular fitness criteria, and for shuttle run validation, two trials were conducted using existing audio cues (National Physical Fitness 100, 20 m shuttle run). For the development of the shuttle run, considering wheelchair turning, signal-to-sound intervals were increased by 1 s and 1.5 s, respectively, in two trials. An analysis of the correlation with the maximal oxygen consumption (VO2max) in comparison to the reference criterion, an ergometer, demonstrated high correlations in the first trial (r = 0.738) and the second trial (r = 0.780). Similarly, significant correlations were observed with the maximum heart rate (HRmax) in the first trial (r = 0.689) and the second trial (r = 0.896). Thus, the 15 m shuttle run is validated as a field test for assessing cardiovascular fitness in athletes with disabilities. Correlation analysis with maximal oxygen uptake (VO2max) compared to the reference criterion, an ergometer, revealed a correlation of 0.815 with a 1 s interval audio cue and 0.355 with a 1.5 s interval audio cue. A high correlation was observed with the 1 s interval audio cue. Regarding the maximum heart rate (HRmax), the correlations were 0.665 with a 1 s interval audio cue. Once again, a high correlation was noted with the 1 s interval audio cue. The field test selected for measuring cardiovascular fitness in wheelchair athletes involved performing a 15 m shuttle run while in the wheelchair. The test utilized an audio cue with a 1 s increased interval between the signal sounds.

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.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Nonlinear Model Predictive Control for a Self-Balancing Wheelchair

Nonlinear Model Predictive Control for a Self-Balancing Wheelchair

Control of Sports Type Cycling Wheelchair for Inclusive Sports

Control of Sports Type Cycling Wheelchair for Inclusive Sports