Robotic Wheelchair Research Articles

Real-Time Home Automation System Using BCI Technology.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Robotic dog for navigation of a rehabilitation wheelchair robot in a highly constrained environment

Robotic dog for navigation of a rehabilitation wheelchair robot in a highly constrained environment

Perceptions and assessment of a novel robotic wheelchair transfer system

Objectives Wheelchair transfers risk injury to users and caregivers. Conventional transfer devices are injury-prone and time inefficient. The Powered Personal Transfer System (PPTS), utilizing a modified Electric Powered Wheelchair (EPW) and a hospital bed, provides a no-lift solution for bed-to-wheelchair transfers. Objective 1: Assess PPTS workload compared to existing methods. Objective 2: Evaluate PPTS EPW in daily mobility tasks. Objective 3: Perform Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) wheelchair standards testing for PPTS EPW stability and performance. Methods Fifteen professional and family caregivers, experienced in assisting EPW users, performed transfers between the bed and EPW using the PPTS. Subsequently, participants drove the PPTS EPW providing ratings on the ease of performing mobility tasks. Wheelchair testing was conducted following RESNA standards. Setting : Simulated bedroom in a laboratory setting. Results Participants reported low workload demands for employing the PPTS and indicated a preference for the PPTS over existing transfer devices/methods. Ease of performing everyday mobility tasks was not significantly different between the modified PPTS and the commercially available original manufacturer equipment EPW (p > 0.05). RESNA wheelchair standards testing confirmed that the PPTS EPW preserves functionality, stability and performance when compared to similar commercially available EPWs. Conclusion The PPTS demonstrated promise in offering a practical, low demanding, and safe solution for transfers. It has the potential to enhance user and caregiver safety by reducing the incidence of caregiver injuries associated with assisting in transfer tasks. In addition to its efficiency and ease of use, it is an advancement in assistive technology for wheelchair transfers.

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.

A complex approach to increasing the quality of the biocontrol of robotic wheelchairs

A complex approach to increasing the quality of the biocontrol of robotic wheelchairs

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.

Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.

Design and implementation of advanced sensor systems for smart robotic wheelchairs: A review

Smart robotic wheelchairs have emerged as promising assistive devices to enhance mobility and independence for individuals with mobility impairments. The successful integration of advanced sensor systems plays a critical role in improving the functionality and safety of these wheelchairs. This paper presents a comprehensive review of the design and implementation of advanced sensor systems for smart robotic wheelchairs. Through an extensive literature review, the limitations of existing sensor technologies are identified, and the potential of advanced sensors is explored. Vision-based sensors, range and proximity sensors, force and pressure sensors, inertial sensors, and environmental sensors are discussed in detail. Furthermore, this review highlights the design considerations, hardware components, software development, and calibration procedures involved in implementing advanced sensor systems. Evaluation and performance analysis metrics are discussed to assess the effectiveness of the sensor systems. The research findings indicate that advanced sensor systems have the potential to significantly enhance the functionality and safety of smart robotic wheelchairs. However, challenges such as sensor integration, data fusion, and user feedback must be addressed. This review paper concludes by discussing the implications of advanced sensor systems in improving wheelchair functionality and user experience, and proposes future directions for research in this domain.

Fundamental Development of a Navigation Control System of a Wheelchair Robot with an Autonomous Mobile Robot

Fundamental Development of a Navigation Control System of a Wheelchair Robot with an Autonomous Mobile Robot

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

Evaluating Material Alternatives for low cost Robotic Wheelchair Chassis: A Combined CRITIC, EDAS, and COPRAS Framework

Evaluating Material Alternatives for low cost Robotic Wheelchair Chassis: A Combined CRITIC, EDAS, and COPRAS Framework

A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities

This research paper presents a comprehensive review of smart robotic wheelchairs and their impact on enhancing mobility and independence for individuals with disabilities. Traditional wheelchairs often impose limitations on users, resulting in reduced freedom of movement and limited accessibility. The emergence of smart robotic wheelchairs offers a promising solution to address these challenges. This paper provides an overview of wheelchair technology, identifies the specific challenges faced by individuals with disabilities, and explores the advantages and limitations of smart robotic wheelchairs through a review of previous research studies. The features and functionalities of smart robotic wheelchairs, including navigation and obstacle avoidance capabilities, autonomous and semi-autonomous modes, and customizable control options, are discussed. User experience and performance evaluation, along with the impact on mobility and independence, are examined. The paper concludes with future directions and recommendations to guide further research and development in this important field, aiming to empower individuals with disabilities and improve their quality of life.

Dual-Quaternion-Based SLERP MPC Local Controller for Safe Self-Driving of Robotic Wheelchairs

In this work, the motion control of a robotic wheelchair to achieve safe and intelligent movement in an unknown scenario is proposed. The primary objective is to develop a comprehensive framework for a robotic wheelchair that combines a global path planner and a model predictive control (MPC) local controller. The A* algorithm is employed to generate a global path. To ensure safe and directional motion for the wheelchair user, an MPC local controller is implemented taking into account the via points generated by an approach combined with dual quaternions and spherical linear interpolation (SLERP). Dual quaternions are utilized for their simultaneous handling of rotation and translation, while SLERP enables smooth and continuous rotation interpolation by generating intermediate orientations between two specified orientations. The integration of these two methods optimizes navigation performance. The system is built on the Robot Operating System (ROS), with an electric wheelchair equipped with 3D-LiDAR serving as the hardware foundation. The experimental results reveal the effectiveness of the proposed method and demonstrate the ability of the robotic wheelchair to move safely from the initial position to the destination. This work contributes to the development of effective motion control for robotic wheelchairs, focusing on safety and improving the user experience when navigating in unknown environments.

Monitoring Methods for Biocontrol of Robotic Wheelchairs

Monitoring Methods for Biocontrol of Robotic Wheelchairs

Compressive sensing applied to SSVEP-based brain–computer interface in the cloud for online control of a virtual wheelchair

People with severe motor disabilities have great challenges in achieving independence, particularly driving or controlling manually robotic wheelchairs. Existing assistive technologies for robotic wheelchair control often fail to address user needs adequately, becoming necessary an appropriate evaluation of usability and user’s satisfaction for these systems. In fact, user experience and satisfaction are crucial in determining effectiveness, usability and acceptance of these technologies. The aim is to enhance the quality of life of individuals with severe motor disabilities, increasing their independence to perform some daily activities, such as locomotion. This study proposes a cloud Steady-State Visual Evoked Potential (SSVEP)-based Brain–Computer Interface (BCI) system to evaluate its performance through the user experience in commanding a virtual electric-powered wheelchair in a training simulator. Moreover, this study compares traditional wheelchair command modes, such as input interfaces using JoyStick (JS) and Eye Tracker (ET), through standardized questionnaires to measure motivation, ease of use, workload, discomfort, sense of presence and other relevant parameters. The analysis of user experience reveals high levels of motivation, enjoyment, and usability when using the cloud SSVEP-based BCI proposed here. Although our BCI demands higher mental work and discomforts than both ET and JS, its potential for improving the user’s performance is demonstrated here. The findings of this research provide valuable insights for researchers and practitioners in the field of assistive technologies, enabling the development of more personalized systems based on the user’s experience. The main contributions of this study lie in a cloud BCI approach and a comprehensive evaluation methodology to evaluate the user’s experience.

Optimized Deep Learning Model Using Modified Whale’s Optimization Algorithm for EEG Signal Classification

Brain-Computer Interface (BCI) is a technology in which Electroencephalogram (EEG) signals are utilized to create a link between a person’s mental state and a computer-based signal processing system that decodes the signals without needing muscle movement. The mental process of picturing the movement of a body component without actually moving that body part is known as Motor Imagery (MI). MI BCI is a Motor Imagery-based Brain-Computer Interface that allows patients with motor impairments to interact with their environment by operating robotic prostheses, wheelchairs, and other equipment. Feature extraction and classification are essential parts of the EEG signal processing for MI BCI. In this work, Whales Optimization Algorithm with an Improved Mutualism Phase is proposed to find the optimal Convolutional Neural Network architecture for the classification of motor imagery tasks with high accuracy and less computational complexity. The Neurosky and BCI IV 2a datasets were used to evaluate the proposed methodology. Experiments demonstrate that the suggested technique outperforms other competing methods regarding classification accuracy values at 94.1% and 87.7% for the Neurosky and BCI datasets, respectively.

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs.

A Review on Human-Robot Interaction and User Experience in Smart Robotic Wheelchairs

This review research paper provides an analysis of the current state of Human-Robot Interaction (HRI) and User Experience (UX) in the context of smart robotic wheelchairs. It explores the advancements in HRI techniques, including multimodal interfaces, gesture recognition, voice commands, and brain-computer interfaces, and evaluates their impact on user experience factors such as usability, learnability, efficiency, and satisfaction. The paper discusses the role of artificial intelligence and machine learning in enhancing HRI capabilities and personalization of wheelchair behavior. The review highlights gaps in current research and identifies future directions to improve the immersive experience of smart wheelchair users. Overall, this comprehensive review contributes to a deeper understanding of the factors influencing user acceptance, satisfaction, and system performance, guiding the development of more intuitive and user-centered smart robotic wheelchairs for individuals with mobility impairments.

Evaluation of Power Wheelchair Dynamic Suspensions for Tip Prevention in Non-ADA Compliant Surfaces

ObjectiveTo evaluate the driving performance and usability of a mobility enhancement robot (MEBot) wheelchair with 2 innovative dynamic suspensions compared with commercial electric powered wheelchair (EPW) suspensions on non-American with Disabilities Act (ADA) compliant surfaces. The 2 dynamic suspensions used pneumatic actuators (PA) and electro-hydraulic with springs in series electrohydraulic and spring in series (EHAS). DesignWithin-subjects cross-sectional study. Driving performance and usability were evaluated using quantitative measures and standardized tools, respectively. SettingLaboratory settings that simulated common EPW outdoor driving tasks. Participants10 EPW users (5 women, 5 men) with an average age of 53.9±11.5 years and 21.2±16.3 years of EPW driving experience (N=10). InterventionNot applicable. Main Outcome Measure(s)Seat angle peaks (stability), number of completed trials (effectiveness), Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), and systemic usability scale (SUS). ResultsMEBot with dynamic suspensions demonstrated significantly better stability (all P<.001) than EPW passive suspensions on non-ADA-compliant surfaces by reducing seat angle changes (safety). Also, MEBot with EHAS suspension significantly completed more trials over potholes compared with MEBot with PA suspension (P<.001) and EPW suspensions (P<.001). MEBot with EHAS had significantly better scores in terms of ease of adjustment (P=.016), durability (P=.031), and usability (P=.032) compared with MEBot with PA suspension on all surfaces. Physical assistance was required to navigate over potholes using MEBot with PA suspension and EPW suspensions. Also, participants reported similar responses regarding ease of use and satisfaction toward MEBot with EHAS suspension and EPW suspensions. ConclusionsMEBot with dynamic suspensions improve safety and stability when navigating non-ADA-compliant surfaces compared with commercial EPW passive suspensions. Findings indicate MEBot readiness for further evaluation in real-world environments.

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.