Wheelchair Navigation Research Articles

Fuzzy-EKF Controller for Intelligent Wheelchair Navigation

AbstractThe electric wheelchair gives more autonomy and facilitates movement for handicapped persons in the home or in a hospital. Among the problems faced by these persons are collision with obstacles, the doorway, the navigation in a hallway, and reaching the desired place. These problems are due to the difficult manipulation of an electric wheelchair, especially for persons with severe disabilities. Hence, we tried to add more functionality to the standard wheelchair in order to increase movement range, security, environment access, and comfort. In this context, we have developed an automatic control method for indoor navigation. The proposed control system is mounted on the electric wheelchair for the handicapped, developed in the research laboratory CEMLab (Control and Energy Management Laboratory-Tunisia). The proposed method is based on two fuzzy controllers that ensure target achievement and obstacle avoidance. Furthermore, an extended Kalman filter was used to provide precise measurements and more effective data fusion localization. In this paper, we present the simulation and experimental results of the wheelchair navigation system.

Combination of the DVZ Method, Particle Filter, and Fuzzy Controller for Electric Wheelchair Navigation

Electric wheelchair is one of the many engines used for the movement of aged and disabled people. This paper introduces an obstacle avoidance using deformable virtual zone (DVZ), particle filter to improve localization and fuzzy controller to join desired target. This controller is developed to increase the independence of disabled and aged people, specifically those who suffer not only disability in the lower limbs but also visual disturbances. To overcome these problems, different perceptive abilities or sensors were introduced. In this context, we developed a control system composed by fuzzy controller to join a target, DVZ method for obstacle avoidance, and particle filter for localization. Also, we present the simulation results of the wheelchair navigation system.

Using finite state machine and a hybrid of EEG signal and EOG artifacts for an asynchronous wheelchair navigation

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 1s. 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.

A visual servoing approach for autonomous corridor following and doorway passing in a wheelchair

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.

HMM based automated wheelchair navigation using EOG traces in EEG

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi–autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

Emotion detection for wheelchair navigation enhancement

SUMMARYThe 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.

Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation.

The aim of this paper is to investigate the influence of mental fatigue on Positive 300 (P300) and Steady State Visual Evoked Potentials (SSVEP) during virtual wheelchair navigation. For this purpose, experimental protocols were setup in order to induce mental fatigue, P300 and SSVEP. Next, the correlation between mental fatigue and P300/SSVEP parameters were investigated. At the end, the best correlated features from both modalities were used as inputs for three classification techniques. Depending on the subject samples (healthy vs palsy), The best overall classification rate reached 80% for P300 modality. The results of this investigation constitute the first steps towards an anticipatory system that can assist the wheelchair driver during navigation, depending on his mental fatigue level.

EVALUATION AND APPLICATION OF A HYBRID BRAIN COMPUTER INTERFACE FOR REAL WHEELCHAIR PARALLEL CONTROL WITH MULTI-DEGREE OF FREEDOM

There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi-degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real-world.

Understanding route choices for wheelchair navigation

Purpose: To validate a personalized routing technique with wheelchair users, understand their route choices and acquire their feedback on the necessity of wheelchair navigation and the importance of personalized routes. Method: A routing technique using a weighting method, called Absolute Restriction Method (ARM), was employed to compute personalized routes based on users’ routing preferences. The evaluation involves five manual wheelchair users. The study protocol consists of three sessions: pre-activity, activity and post-activity sessions. The evaluation included a comparison between personalized routes and shortest feasible routes, in terms of route characteristics and users’ ratings of important parameters. Results: Subjects travelled a 14.64% longer distance along the personalized routes than the shortest feasible routes. However, all personalized routes had better path quality (slope and surface condition) than the shortest feasible routes. Four out of five subjects rated the parameters they deemed most important higher for the personalized route than for the shortest feasible route. Conclusions: The study confirmed that the shortest route criterion is not always suitable for individuals with mobility impairments. Personalized routes that take into account individual characteristics, route preferences and environmental characteristics are a promising solution to lessen the difficulties that manual wheelchair users face when navigating unfamiliar environments.Implications for RehabilitationWheelchair users indicate the importance of personalized routes for individuals with mobility impairments.In regard to evaluation results, although subjects travelled 14.64% more distances in average along the personalized routes than the shortest feasible routes, they rated the personalized routes better path quality and less effort to travel.

Design of a Multiple Bloom Filter for Distributed Navigation Routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes.

Analysis of Socially Acceptable Smart Wheelchair Navigation Based on Head Cue Information

Smart wheelchair can be defined as a standard power electrical wheelchair that equipped with a mobile robotic technology to assist the user in a number of situations. Most of the smart wheelchair work focusing on safety issue and less work considers a socially acceptable issue. Since wheelchairs are normally used in human-shared environment, it is important to ensure the assistive motion generated from the wheelchair is safe and comfortable to the human in the surrounding. Here the framework for catering such an issue is proposed. The system initially infers human's state from head cue information. Next, the information is interpreted for modeling human's comfort zone (CZ) based on rules rooted from Proxemics concept. Finally, the wheelchair's motion is generated by avoiding both, the CZ and the in place obstacle. Experimental results demonstrate the feasibility of the proposed framework

Performance Evaluation of Spatial Correlation-based Feature Detection and Matching for Automated Wheelchair Navigation System

A wheelchair navigation system have emerged in response to high demand for mobile location-aware applications. Nevertheless, single localization technology have several limitations and vulnerabilities; to provide a universal localization solution for various environment. In this paper, we present our new new feature detection and matching algorithm approach for automated wheelchair navigation system. This kind approach is using multi localization solution, which are; Wireless LAN and camera. Thus, these location information are combined by model fitting in order to find the absolute of user target position. As a finding, our experimental results indicate positioning accuracy of 0–6 m with a 23 % trial by given five (5) different locations.

A Sensorized Garment Controlled Virtual Robotic Wheelchair

This paper presents the design and performance of a body-machine-interface (BoMI) system, where a user controls a robotic 3D virtual wheelchair with the signals derived from his/her shoulder and elbow movements. BoMI promotes the perspective that system users should no longer be operators of the engineering design but should be an embedded part of the functional design. This BoMI system has real-time controllability of robotic devices based on user-specific dynamic body response signatures in high-density 52-channel sensor shirt. The BoMI system not only gives access to the user's body signals, but also translates these signals from user's body to the virtual reality device-control space. We have explored the efficiency of this BoMI system in a semi-cylinderic 3D virtual reality system. Experimental studies are conducted to demonstrate, how this transformation of human body signals of multiple degrees of freedom, controls a robotic wheelchair navigation task in a 3D virtual reality environment. We have also presented how machine learning can enhance the interface to adapt towards the degree of freedoms of human body by correcting the errors performed by the user.

On the Construction of a Skill-Based Wheelchair Navigation Profile

Assisted wheelchair navigation is of key importance for persons with severe disabilities. The problem has been solved in different ways, usually based on the shared control paradigm. This paradigm consists of giving the user more or less control on a need basis. Naturally, these approaches require personalization: each wheelchair user has different skills and needs and it is hard to know a priori from diagnosis how much assistance must be provided. Furthermore, since there is no such thing as an average user, sometimes it is difficult to quantify the benefits of these systems. This paper proposes a new method to extract a prototype user profile using real traces based on more than 70 volunteers presenting different physical and cognitive skills. These traces are clustered to determine the average behavior that can be expected from a wheelchair user in order to cope with significant situations. Processed traces provide a prototype user model for comparison purposes, plus a simple method to obtain without supervision a skill-based navigation profile for any user while he/she is driving. This profile is useful for benchmarking but also to determine the situations in which a given user might require more assistance after evaluating how well he/she compares to the benchmark. Profile-based shared control has been successfully tested by 18 volunteers affected by left or right brain stroke at Fondazione Santa Lucia, in Rome, Italy.

Case-based reasoning emulation of persons for wheelchair navigation

Testing is a key stage in system development, particularly in systems such as a wheelchair, in which the final user is typically a disabled person. These systems have stringent safety requirements, requiring major testing with many different individuals. The best would be to have the wheelchair tested by many different end users, as each disability affects driving skills in a different way. Unfortunately, from a practical point of view it is difficult to engage end users as beta testers. Hence, testing often relies on simulations. Naturally, these simulations need to be as realistic as possible to make the system robust and safe before real tests can be accomplished. This work presents a tool to automatically test wheelchairs through realistic emulation of different wheelchair users. Our approach is based on extracting meaningful data from real users driving a power wheelchair autonomously. This data is then used to train a case-based reasoning (CBR) system that captures the specifics of the driver via learning. The resulting case-base is then used to emulate the driving behavior of that specific person in more complex situations or when a new assistive algorithm needs to be tested. CBR returns user's motion commands appropriate for each specific situation to add the human component to shared control systems. The proposed system has been used to emulate several power wheelchair users presenting different disabilities. Data to create this emulation was obtained from previous wheelchair navigation experiments with 35 volunteer in-patients presenting different degrees of disability. CBR was trained with a limited number of scenarios for each volunteer. Results proved that: (i) emulated and real users returned similar paths in the same scenario (maximum and mean path deviations are equal to 23 and 10cm, respectively) and similar efficiency; (ii) we established the generality of our approach taking a new path not present in the training traces; (iii) the emulated user is more realistic - path and efficiency are less homogeneous and smooth - than potential field approaches; and (iv) the system adequately emulates in-patients - maximum and mean path deviations are equal to 19 and 8.3cm approximately and efficiencies are similar - with specific disabilities (apraxia and dementia) obtaining different behaviors during emulation for each of the in-patients, as expected. The proposed system adequately emulates the driving behavior of people with different disabilities in indoor scenarios. This approach is suitable to emulate real users' driving behaviors for early testing stages of assistive navigation systems.

Electric wheelchair navigation using virtual reality: From intention to action

Electric wheelchair navigation using virtual reality: From intention to action

Evaluation of a Smartphone Platform as a Wireless Interface Between Tongue Drive System and Electric-Powered Wheelchairs

Tongue drive system (TDS) is a new wireless assistive technology (AT) for the mobility impaired population. It provides users with the ability to drive powered wheelchairs (PWC) and access computers using their unconstrained tongue motion. Migration of the TDS processing unit and user interface platform from a bulky personal computer to a smartphone (iPhone) has significantly facilitated its usage by turning it into a true wireless and wearable AT. After implementation of the necessary interfacing hardware and software to allow the smartphone to act as a bridge between the TDS and PWC, the wheelchair navigation performance and associated learning was evaluated in nine able-bodied subjects in five sessions over a 5-week period. Subjects wore magnetic tongue studs over the duration of the study and drove the PWC in an obstacle course with their tongue using three different navigation strategies; namely unlatched, latched, and semiproportional. Qualitative aspects of using the TDS-iPhone-PWC interface were also evaluated via a five-point Likert scale questionnaire. Subjects showed more than 20% improvement in the overall completion time between the first and second sessions, and maintained a modest improvement of ∼9% per session over the following three sessions.

Intelligent single switch wheelchair navigation

We have developed an intelligent single switch scanning interface and wheelchair navigation assistance system, called intelligent single switch wheelchair navigation (ISSWN), to improve driving safety, comfort and efficiency for individuals who rely on single switch scanning as a control method. ISSWN combines a standard powered wheelchair with a laser rangefinder, a single switch scanning interface and a computer. It provides the user with context sensitive and task specific scanning options that reduce driving effort based on an interpretation of sensor data together with user input. Trials performed by 9 able-bodied participants showed that the system significantly improved driving safety and efficiency in a navigation task by significantly reducing the number of switch presses to 43.5% of traditional single switch wheelchair navigation (p < 0.001). All participants made a significant improvement (39.1%; p < 0.001) in completion time after only two trials.Implications for RehabilitationIntelligent navigation algorithm helps investigators develop smart wheelchair technology that may ultimately provide independent mobility to a segment of the population that currently finds it difficult or impossible to operate a conventional wheelchair.intelligent single switch wheelchair navigation improves the driving safety, comfort, and efficiency for individuals who rely on single switch scanning as a wheelchair control method by significantly reducing the number of switch presses and completion time.

Robotic wheelchair controlled through a vision-based interface

SUMMARYIn this work, a vision-based control interface for commanding a robotic wheelchair is presented. The interface estimates the orientation angles of the user's head and it translates these parameters in command of maneuvers for different devices. The performance of the proposed interface is evaluated both in static experiments as well as when it is applied in commanding the robotic wheelchair. The interface calculates the orientation angles and it translates the parameters as the reference inputs to the robotic wheelchair. Control architecture based on the dynamic model of the wheelchair is implemented in order to achieve safety navigation. Experimental results of the interface performance and the wheelchair navigation are presented.

A fuzzy logic map matching for wheelchair navigation

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.