Intelligent Wheelchair System Research Articles

Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

Electroencephalogram (EEG) signals classification plays a crucial role in brain computer interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it challenging to get them analyzed and modeled. In this paper, a novel method based on conditional empirical mode decomposition (CEMD) and one-dimensional multi-scale convolutional neural network (1DMSCNN) is proposed to recognize motor imagery (MI) EEG signals. In the CEMD algorithm, the correlation coefficient between the original EEG signal and each intrinsic modal component (IMF) is used as the first condition to select IMFs, and the relative energy occupancy rates between the IMFs are the second condition. The CEMD algorithm is applied to remove the noise of EEG signals. Then, an EEG signals combination method is proposed to encode event-related synchronization/de-synchronization (ERS/ERD) information between the channels. Finally, a model called 1DMSCNN is built to classify the processed EEG signals. The proposed method is applied to the dataset collected in our laboratory and BCI competition IV dataset 2b. The results indicate that the proposed method can achieve higher accuracy for EEG signals classification, compared with other state-of-the-art works. In addition, the proposed algorithm is applied to the online recognition of EEG signals, a BCI system that directly interacts with brain and wheelchair is designed and implemented. This system can directly command wheelchair to turn left and right through EEG signals. The online experimental results indicate that the designed intelligent wheelchair system is a feasible BCI application. It verifies the proposed algorithm can be used in expert and intelligent systems. Our method can provide a stimulus to the development of human-robot interaction.

Wheelchair Navigation System for Disabled and Elderly People.

An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy.

Intelligent wheelchair system based on sEMG and head gesture

Because the single channel surface electromyographic (sEMG) signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in this paper. A distributed parallelly decision fusion algorithm fused classification results of the two signals to form a final judgment. After sEMG was decomposed by wavelet packet, feature information of some subspace was weaken, because subspace dimension was very large. To solve the problem, the paper proposed an improved wavelet packet decomposition algorithm, which extracted sample entropy from four subspaces of improved wavelet packet decomposition and took it as the feature information. Experimental results show that the intelligent wheelchair system based on sEMG and head gesture has not only a simple operation and shorter operating time, but also a better stability and security.

A robotic intelligent wheelchair system based on obstacle avoidance and navigation functions

Powered wheelchairs offer a means of independent mobility for older adults and patients who are unable to walk and cannot propel a manual wheelchair. One concern is these adults and patients are unable to drive a powered wheelchair safely or properly. Intelligent wheelchairs offer an approach to address this problem by self-controlling the movement and direction of the wheelchair without patients and older adult's intervention. This paper describes the development and evaluation of a robotic intelligent wheelchair system (RIWS) based on obstacle avoidance and navigation functions that allow patients to navigate to scheduled outpatient appointments in a hospital safely and accurately. Experimental results show that all scheduled destinations were successfully reached by our RIWS.

An intelligent wheelchair based on automated navigation and BCI techniques.

In this paper, we propose an intelligent wheelchair system that relies on a brain computer interface (BCI) and automatic navigation. When in operation, candidate destinations and waypoints are automatically generated on the basis of the current environment. Then, the user selects a destination using a P300-based BCI. Finally, the navigation system plans a path and navigates the wheelchair to the determined destination. While the wheelchair is in motion, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be alleviated to a large degree. Furthermore, our system can adapt to changes in the environment. The experimental results demonstrated the effectiveness of our system.

An Intelligent Wheelchair Using Situation Awareness and Obstacle Detection

In this paper, we develop an Intelligent Wheelchair (IW) system for the people with various disabilities. The main function of our system is three-fold: 1) user intention recognition, 2) obstacle detection and avoidance, and 3) situation awareness. For this, it is composed of an electric powered wheelchair, two cameras, 8 ultrasonic sensors, laptop computer and DAQ board. Through analyzing the images obtained from a PC camera, it allows user for controlling the IW using their face movement and mouth shape. In addition to prevent the dangerous situation such as collision with people or traffic vehicles, the hybrid navigation system and situation awareness are simultaneously executed. Using a camera and 8 range sensors, our IW can detect various obstacles and find the safe path to avoid them. In particular, by analyzing the texture properties of the images, it can classify the current situation where a user is standing on as sidewalk, roadway and traffic intersection, which prevents the collisions of vehicle at the traffic intersection. Some participants tested our system and then the results show that it can provide a user friendly, convenient interface and safety wheelchair control.

Evaluation of an intelligent wheelchair system for older adults with cognitive impairments.

BackgroundOlder adults are the most prevalent wheelchair users in Canada. Yet, cognitive impairments may prevent an older adult from being allowed to use a powered wheelchair due to safety and usability concerns. To address this issue, an add-on Intelligent Wheelchair System (IWS) was developed to help older adults with cognitive impairments drive a powered wheelchair safely and effectively. When attached to a powered wheelchair, the IWS adds a vision-based anti-collision feature that prevents the wheelchair from hitting obstacles and a navigation assistance feature that plays audio prompts to help users manoeuvre around obstacles.MethodsA two stage evaluation was conducted to test the efficacy of the IWS. Stage One: Environment of Use – the IWS’s anti-collision and navigation features were evaluated against objects found in a long-term care facility. Six different collision scenarios (wall, walker, cane, no object, moving and stationary person) and three different navigation scenarios (object on left, object on right, and no object) were performed. Signal detection theory was used to categorize the response of the system in each scenario. Stage Two: User Trials – single-subject research design was used to evaluate the impact of the IWS on older adults with cognitive impairment. Participants were asked to drive a powered wheelchair through a structured obstacle course in two phases: 1) with the IWS and 2) without the IWS. Measurements of safety and usability were taken and compared between the two phases. Visual analysis and phase averages were used to analyze the single-subject data.ResultsStage One: The IWS performed correctly for all environmental anti-collision and navigation scenarios. Stage Two: Two participants completed the trials. The IWS was able to limit the number of collisions that occurred with a powered wheelchair and lower the perceived workload for driving a powered wheelchair. However, the objective performance (time to complete course) of users navigating their environment did not improve with the IWS.ConclusionsThis study shows the efficacy of the IWS in performing with a potential environment of use, and benefiting members of its desired user population to increase safety and lower perceived demands of powered wheelchair driving.

Intelligent Wheelchair System Based on Collaborative Kansei Engineering

Intelligent wheelchair system (IWS) is a potential selection for the elderly and disabled people to improve and enhance the quality of their daily lives and work. Based on the theory of collaborative Kansei engineering, a new intelligent wheelchair system has been developed. The system integrates the advanced Kansei characteristics of human such as affections, feelings, emotions, etc, to the wheelchair system. This new IWS is very comprehensive, creative and humanized. Modern wheelchair with the IWS helps to add recognition, understanding, and cooperative communications between the elderly and disabled people and the wheelchair system.

Towards an Intelligent Wheelchair System for Users With Cerebral Palsy

This paper describes and evaluates an intelligent wheelchair, adapted for users with cognitive disabilities and mobility impairment. The study focuses on patients with cerebral palsy, one of the most common disorders affecting muscle control and coordination, thereby impairing movement. The wheelchair concept is an assistive device that allows the user to select arbitrary local destinations through a tactile screen interface. The device incorporates an automatic navigation system that drives the vehicle, avoiding obstacles even in unknown and dynamic scenarios. It provides the user with a high degree of autonomy, independent from a particular environment, i.e., not restricted to predefined conditions. To evaluate the rehabilitation device, a study was carried out with four subjects with cognitive impairments, between 11 and 16 years of age. They were first trained so as to get acquainted with the tactile interface and then were recruited to drive the wheelchair. Based on the experience with the subjects, an extensive evaluation of the intelligent wheelchair was provided from two perspectives: 1) based on the technical performance of the entire system and its components and 2) based on the behavior of the user (execution analysis, activity analysis, and competence analysis). The results indicated that the intelligent wheelchair effectively provided mobility and autonomy to the target population.

Development of an evolutionary simulator and an overall control system for intelligent wheelchair

The goal of this research is to develop an intelligent wheelchair (IWC) system which aids an indoor safe mobility for elderly and disabled people with a new conceptual architecture which realizes autonomy, cooperativeness, and a collaboration behavior. In order to develop the IWC system in real environment, we need design-tools and flexible architecture. In particular, as more significant ones, this paper describes two key techniques which are an evolutionary simulation and an overall control mechanism. The evolutionary simulation technique corrects the error between the virtual environment in a simulator and real one in during the learning of an IWC agent, and coevolves with the agent. The overall control mechanism is implemented with subsumption architecture which is employed in an autonomous robot controller. By using these techniques in both simulations and experiments, we confirm that our IWC system acquires autonomy, cooperativeness, and a collaboration behavior efficiently.