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In this study, we designed a system that cooperates with users to always provide the most up-to-date map and the most suitable route and to cooperate with disability units to ensure that unexpected obstacles on the route are removed. It is believed that this system will contribute to (1) eliminating obstacles more quickly and preventing other people with disabilities from being aggrieved until the obstacles are removed; (2) increasing awareness of the responsibility towards people with disabilities in society; (3) greater integration of people with disabilities; and (4) increasing the economic and social welfare of society. The design process includes analysis of the current situation, data acquisition, web mapping, mobile application development, and experimental testing phases. The system successfully passed the tests. However, the optimum level of expected benefits of the system depends on users’ consistent use of the mobile application complaint tool and authorities’ consistent evaluation of the complaints received. As the number of mobile application users increases, the number of complaints will gradually increase, yet after a while, as the awareness and sensitivity of society on this issue gradually increase, the number of complaints will gradually decrease, and the expected benefits will be achieved.

Self-mobility electric wheelchairs are very useful for people with disabilities, so they can move without help in indoor environments. To create one selfmobility electric wheelchair, modern methods for control such as computer vision and machine learning can be applied. In particular, this electric wheelchair can move from any position in the indoor environment to the desired destination. For accuracy, natural landmarks are used and the navigation of the wheelchair is determined using a Q-learning reinforcement learning algorithm. In particular, this algorithm is applied to find the best path for the wheelchair to reach the destination. The article proposes a method to build one 2D grid map for wheelchair movement based on natural landmarks in the indoor environment. The new point of this method is that the position of the wheelchair can be accurately determined from a certain landmark instead of many landmarks applied in traditional methods. Some practical experiments were performed to illustrate the effectiveness of the proposed method in the indoor environment. This proposed method can be developed in more complex environments with natural landmarks. [ABSTRACT FROM AUTHOR]

This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization. [ABSTRACT FROM AUTHOR]

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built. [ABSTRACT FROM AUTHOR]

This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization.

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.

Multimodal tongue drive system (mTDS) is an assistive technology, which uses speech, tongue, and head movements of people with tetraplegia to help them control devices such as wheelchairs, PCs, or smartphones. In order to process tongue gestures, it is required to solve the magnetic dipole equation, which is computationally expensive to execute in a wearable embedded hardware. In this paper, a support vector machine with linear kernel-based algorithm is proposed, which uses an additional magnetometer that has 96.1% accuracy with a 2.2% sensitivity and 1.8% specificity among 15 participants, is computationally efficient for mTDS, generates high-performance classification results despite small changes of magnet positions (−5 to 5 mm) and orientations (−10° to 10°) from a four-dimensional robot emulated tongue movement data compared to five other algorithms. This algorithm generates more accurate results to control devices independently despite the Midas-touch problem between speech and tongue movements. [ABSTRACT FROM AUTHOR]

People with tetraplegia often use multiple different assistive devices for access and mobility which requires constant caregiver assistance to help them replace these devices. In this paper, a new standalone tongue drive system (TDS) is designed which captures tongue gesture and generates commands to access both a computer and drive a wheelchair, using the same platform. Fifteen able-bodied participants completed a PC access and four power wheelchair (PWC) navigation tasks in comparison to keyboard arrow keys and joystick, respectively. An overall mean game score of TDS is 83.4% of arrow keys and improved 46.41% from first to last trials. Latched mode PWC driving was found to be the fastest (60.7% of joystick) and unlatched mode was the most accurate ($2.3\times $ joystick error) among different TDS driving options. The average task completion time for wheelchair driving was 8.2% less compared to the previous studies on the same track. [ABSTRACT FROM AUTHOR]

Navigating a powered wheelchair and avoiding collisions is often a daunting task for new wheelchair users. It takes time and practice to gain the coordination needed to become a competent driver and this can be even more of a challenge for someone with a disability. We present a cost-effective virtual reality (VR) application that takes advantage of consumer level VR hardware. The system can be easily deployed in an assessment centre or for home use, and does not depend on a specialized high-end virtual environment such as a Powerwall or CAVE. This paper reviews previous work that has used virtual environments technology for training tasks, particularly wheelchair simulation. We then describe the implementation of our own system and the first validation study carried out using thirty three able bodied volunteers. The study results indicate that at a significance level of 5 percent then there is an improvement in driving skills from the use of our VR system. We thus have the potential to develop the competency of a wheelchair user whilst avoiding the risks inherent to training in the real world. However, the occurrence of cybersickness is a particular problem in this application that will need to be addressed. [ABSTRACT FROM PUBLISHER]

This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography (sEMG) technique. The raw sEMG signals are collected from the upper limb muscles which are then processed, characterized, and classified to extract necessary features for the generation of control signals to be used for the automated movement of the wheelchair. An artificial neural network-based classifier is constructed to classify the patterns and features extracted from the raw sEMG signals. The classification accuracy of the extracted parameters from the sEMG signals is found to be relatively high in comparison with the existing methods. The extracted parameters used to generate control signals that are then fed into a microcomputer-based control system (MiCS). A solar-powered wheelchair prototype is developed, and the above MiCS is introduced to control its maneuver using the sEMG signals. The prototype is then thoroughly tested with sEMG signals from patients of different age groups. Also, the life cycle cost analysis of the proposed wheelchair revealed that it is financially feasible and cost-effective. [ABSTRACT FROM AUTHOR]

In case of motor impairments, steering a wheelchair can become a hazardous task. Joystick jerks induced by uncontrolled motions may lead to wall collisions when a user steers a wheelchair along a corridor. This work introduces a low-cost assistive and guidance system for indoor corridor navigation in a wheelchair, which uses purely visual information, and which is capable of providing automatic trajectory correction and haptic guidance in order to avoid wall collisions. A visual servoing approach to autonomous corridor following serves as the backbone to this system. The algorithm employs natural image features which can be robustly extracted in real time. This algorithm is then fused with manual joystick input from the user so that progressive assistance and trajectory correction can be activated as soon as the user is in danger of collision. A force feedback in conjunction with the assistance is provided on the joystick in order to guide the user out of his dangerous trajectory. This ensures intuitive guidance and minimal interference from the trajectory correction system. In addition to being a low-cost approach, it can be seen that the proposed solution does not require an a-priori environment model. Experiments on a robotised wheelchair equipped with a monocular camera prove the capability of the system to adaptively guide and assist a user navigating in a corridor. [ABSTRACT FROM AUTHOR]

The goal of this study is to investigate the use of emotion as a braking system for wheelchair navigation. In the first part emotion is detected based on ElectroEncephalography (EEG) technology and emotion induction experiments. Using different techniques for features extraction (Welch and Wavelets), selection (Principal Component Analysis (PCA) and Genetic Algorithm (GA)) and classification (Support Vector Machine (SVM), Multi Layer Perceptron (MLP) and Linear Discriminate Analysis (LDA)), the best combination was assigned to Wavelets-GA-MLP. In the second part, in order to validate the impact of emotion as velocity modulator, a comparison between emotion-based and non emotion-based wheelchair navigation scenarios in a simulated environment was conducted. The assessment was based on four parameters: obstacles hit, navigation path, execution time and outbound points of gaze (POG). While the first two emotion introductions showed better results, this was not the case for the third. These findings can be utilized in order to prescribe a suitable wheelchair according to the subject pathology. [ABSTRACT FROM AUTHOR]

Navigating within an unknown indoor environment using an electric wheelchair is a challenging task, especially if the user suffers from severe disabilities. In order to reduce fatigability and increase autonomy, control architectures have to be designed that would assist users in wheelchair navigation. We present a framework for vision-based autonomous indoor navigation in an electric wheelchair capable of following corridors, and passing through open doorways using a single doorpost. Visual features extracted from cameras on board the wheelchair are used as inputs for image based controllers built-in the wheelchair. It has to be noted that no a-priori information is utilized except for the assumption that the wheelchair moves in a typical indoor environment while the system is coarsely calibrated. The designed control schemes have been implemented onto a robotized wheelchair and experimental results show the robust behaviour of the designed system. [ABSTRACT FROM AUTHOR]

In this study, an asynchronous wheelchair navigation system using a hybrid of EEG signal and EOG artifacts embedded in EEG signals is demonstrated. The EEG signals are recorded at three different locations on the scalp in the occipital and motor cortex regions. First, an EEG signal related to eyelid position is analyzed and used to determine whether the eyes are closed or open. If the eyes are closed, no wheelchair movement is allowed. If the eyes are open, EOG traces (artifacts) from two other EEG signals are examined to infer the gaze direction of the eyes. A sliding window is utilized to position important cues in the trace signals at the center of the window for effective classification. The variance of the EEG signal is used to determine the eyelid position by thresholding. Then, features extracted from the EOG traces are used as inputs to a pair of minimum distance classifiers whose outputs reveal the gaze shift performed by the eyes. The wheelchair navigation system is designed to move forward and backward in a total of six different directions. However, the number of distinct gaze direction that can be used as commands to move the wheelchair is only three. Therefore, we model the system as a finite state machine with three modes, each containing three states to overcome this deficiency. The system is equipped with proximity sensor to avoid collision with obstacles. A stop command is also available for safety measures. In a real-time experiment involving 20 participants, the system performed well as it registered a high accuracy of 97.88% with an average of computational time less than 1 s. The system was also tested by five participants in a navigation experiment where each participant successfully completed all tasks without collision while showing improvement in maneuvering ability over attempts. [ABSTRACT FROM AUTHOR]

A novel wireless dry EEG brain‐controlled wheelchair with five steering behaviours is devised, which is based on two‐stage control strategies combining sustained and brief motor imagery brain–computer interfaces (SB‐MI‐BCIs). Compared with the existing single modal brain–computer interface (S‐BCI) controlled wheelchair, this new wheelchair has achieved more control commands and higher information transfer rate. Compared with their previous hybrid BCIs controlled wheelchair, subjects can control the wheelchair spontaneously and efficiently just by imagining the left hand, the right hand or both hands without any other visual attentions (e.g. P300 or SSVEP). The experimental results demonstrate that the SB‐MI‐BCIs can efficiently provide multiple independent control signals for wheelchair navigation. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an Assistive Navigation System (ANS) for a Robotic Wheelchair (RW) relying on a Brain–Computer Interface (BCI), as the Human–Machine Interface (HMI). A two-layer collaborative control approach is proposed to steer the RW, taking into account both user and machine commands. The first layer, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on context. More specifically, user commands are enabled for a set of situations requiring user decision, namely, bifurcations, multiple-directions caused by new obstacles in the environment, and deadlocks. The second layer is a user-intent matching responsible for determining the suitable steering command that better fits the user selection, taking into account the user competence to steer the wheelchair, and situation awareness of potential directions at a given location. A P300-based BCI allows the selection of commands to steer the RW. Experimental results using RobChair (Pires and Nunes (2002) [7], Lopes et al. (2007) [42]) are presented, showing the effectiveness of the proposed methodologies. The ANS was validated with ten able-bodied participants, and one participant with cerebral palsy, in two different scenarios: a structured known environment, and a structured unknown environment with moving objects. The overall result was that all participants were able to successfully operate the device, showing a high level of robustness of both, the BCI system, and the navigation system. [Copyright &y& Elsevier]

Global Positioning System (GPS) plays a central role in solving the location-based needs of wheelchair users and efficient route guidance. An essential process in wheelchair navigation is matching the position obtained from GPS or other sensors on a sidewalk network. This process of map matching in turn assists in making decisions under uncertainty. However, GPS-based wheelchair navigation systems have difficulties in tracking wheelchairs in urban areas due to poor satellite availability. To overcome this, a fuzzy logic-based algorithm is applied to effectively perform matching wheelchair movements on sidewalks. Fuzzy logic, based on fuzzy reasoning concepts, in many circumstances can take noisy and imprecise input to yield numerically accurate output. Experiments on a university campus validate it practicable. [ABSTRACT FROM AUTHOR]

To operate a wheelchair, people with severe physical disabilities may require assistance, which can be provided by robotization. However, medical experts report that an excess of assistance may lead to loss of residual skills, so that it is important to provide just the right amount of assistance. This work proposes a collaborative control system based on weighting the robot's and the user's commands by their respective efficiency to reactively obtain an emergent controller. Thus, the better the person operates, the more control he/she gains. Tests with volunteers have proven, though, that some users may require extra assistance when they become stressed. Hence, we propose a controller that can change the amount of support taking into account supplementary biometric data. In this work, we use an off-the-shelf wearable pulse oximeter. Experiments have demonstrated that volunteers could use our wheelchair in a more efficient way due to the proposed biometric modulated collaborative control. [ABSTRACT FROM PUBLISHER]

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time. [ABSTRACT FROM AUTHOR]

The NavChair Assistive Wheelchair Navigation System is being developed to reduce the cognitive and physical requirements of operating a power wheelchair for people with wide ranging impairments that limit their access to powered mobility. The NavChair is based on a commercial wheelchair system with the addition of a DOS-based computer system, ultrasonic sensors, and an interface module interposed between the joystick and power module of the wheelchair. The obstacle avoidance routines used by the NavChair in conjunction with the ultrasonic sensors are modifications of methods originally used in mobile robotics research. The NavChair currently employs three operating modes: general obstacle avoidance, door passage, and automatic wall following. Results from performance testing of these three operating modes demonstrate their functionality. In additional to advancing the technology of smart wheelchairs, the NavChair has application to the development and testing of “shared control” systems where a human and machine share control of a system and the machine can automatically adapt to human behaviors [ABSTRACT FROM PUBLISHER]

The NavChair Assistive Wheelchair Navigation System is an adaptive shared control system being developed to provide mobility to those individuals who would otherwise find it difficult or impossible to use a power wheelchair due to cognitive, perceptual, or motor impairments. The NavChair provides task-specific navigation assistance to the wheelchair operator in the form of several distinct operating modes, each of which distributes control differently between the wheelchair and the operator. This paper describes the NavChair's mechanism for automatically selecting the most appropriate operating mode based on a combination of the wheelchair's immediate situation and its global location. Results from two experimental evaluations of the adaptation method are presented [ABSTRACT FROM PUBLISHER]