Control Systems and Electronic Instrumentation Applied to Autonomy in Wheelchair Mobility: The State of the Art

EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W)

The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users' needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals

This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort

The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.

The Symmetry of the Muscle Tension Signal in the Upper Limbs When Propelling a Wheelchair and Innovative Control Systems for Propulsion System Gear Ratio or Propulsion Torque: A Pilot Study

Innovative wheelchair designs require new means of controlling the drive units or the propulsion transmission systems. The article proposes a signal to control the gear ratio or the amount of additional propulsion torque coming from an electric motor. The innovative control signal in this application is the signal generated by the maximum voluntary contraction (MVC) of the muscles of the upper limbs, transformed by the central processing unit (CPU) into muscle activity (MA) when using a wheelchair. The paper includes research on eight muscles of the upper limbs that are active when propelling a wheelchair. Asymmetry in the value for MVC was found between the left and right limbs, while the belly of the long radial extensor muscle of the wrist was determined to be the muscle with the least asymmetry for the users under study. This pilot research demonstrates that the difference in mean MVCmax values between the left and the right limbs can range from 20% to 49%, depending on the muscle being tested. The finding that some muscle groups demonstrate less difference in MVC values suggests that it is possible to design systems for regulating the gear ratio or additional propelling force based on the MVC signal from the muscle of one limb, as described in the patent application from 2022, no. P.440187.

Wheelchair prototype controlled by position, speed and orientation using head movement

A prototype that simulates a wheelchair was built using electronic commercial devices and software implementation with the aim to operate the prototype using head movement and analyzing the system response. The controllers were simulated using MATLAB® toolbox and Python™ libraries. The mean time response of the system with manual control was 37,8 s. The mean orientation control response with constant speed was 36,5 s and the mean orientation control response with variable speed was 44,2 s in a specific route. The variable speed response is slower than constant speed due to head motion error. The system was rated such as" very good" by 10 participants using a System Usability Scale (SUS).

Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

People with severe disabilities require assistance to perform their routine activities; a Human-Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user's learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.

Design and Implementation of a Position, Speed and Orientation Fuzzy Controller Using a Motion Capture System to Operate a Wheelchair Prototype

The design and implementation of an electronic system that involves head movements to operate a prototype that can simulate future movements of a wheelchair was developed here. The controller design collects head-movements data through a MEMS sensor-based motion capture system. The research was divided into four stages: First, the instrumentation of the system using hardware and software; second, the mathematical modeling using the theory of dynamic systems; third, the automatic control of position, speed, and orientation with constant and variable speed; finally, system verification using both an electronic controller test protocol and user experience. The system involved a graphical interface for the user to interact with it by executing all the controllers in real time. Through the System Usability Scale (SUS), a score of 78 out of 100 points was obtained from the qualification of 10 users who validated the system, giving a connotation of “very good”. Users accepted the system with the recommendation to improve safety by using laser sensors instead of ultrasonic range modules to enhance obstacle detection.