Wheelchair Control Research Articles

Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors

This paper presents a hand gesture based control of an omnidirectional wheelchair using inertial measurement unit (IMU) and myoelectric units as wearable sensors. Seven common gestures are recognized and classified using shape based feature extraction and Dendogram Support Vector Machine (DSVM) classifier. The dynamic gestures are mapped to the omnidirectional motion commands to navigate the wheelchair. A single IMU is used to measure the wrist tilt angle and acceleration in three axis. EMG signals are extracted from two forearm muscles namely Extensor Carpi Radialis and Flexor Carpi Radialis and processed to provide Root Mean Square (RMS) signal. Initiation and termination of dynamic activities are based on autonomous identification of static to dynamic or dynamic to static transition by setting static thresholds on processed IMU and myoelectric sensor data. Classification involves recognizing the activity pattern based on periodic shape of trajectories of the triaxial wrist tilt angle and EMG-RMS from the two selected muscles. Second order Polynomial coefficients extracted from the sensor trajectory templates during specific dynamic activity cycles are used as features to classify dynamic activities. Classification algorithm and real time navigation of the wheelchair using the proposed algorithm has been tested by five healthy subjects. Classification accuracy of 94% was achieved by DSVM classifier on ‘k’ fold cross validation data of 5 users. Classification accuracy while operating the wheelchair was 90.5%.

Move Your Wheelchair with Your Eyes

Abstract: In the proposed study, our goal is to move paralyzed people with their eyes. Otherwise, use this document as an instruction set. Paper titles should be written in uppercase and lowercase letters, not all uppercase. For this purpose, we use their Electrooculogram (EOG) signals obtained from EOG goggles completely designed by the authors. Through designed EOG goggles, vertical-horizontal eye movements and voluntary blink detection are verified by using 5 Ag-AgCl electrodes located around the eyes. EOG signals utilized to control wheelchair motion by applying signal processing techniques. The main steps of signal processing phase are pre-processing, maximum-minimum value detection and classification, respectively. At first, pre-processing step is used to amplify and smooth EOG signals. In maximum-minimum value detection we obtain maximum and minimum voltage levels of the eye movements. Furthermore, we determine the peak time of blink to distinguish voluntary blinks from involuntary blinks. Finally, at classification step k-Nearest Neighbouring (k-NN) technique is applied to separate eye movement signals from each other. Several computer simulations are performed to show the effectiveness of the proposed EOG based wheelchair control system. According to the results, proposed system can communicate paralyzed people with their wheelchair and by this way they will be able to move by their selves.

Differences in outcomes between the JoyBar control and standard wheelchair joystick control on two maneuverability tasks: a pilot study

Purpose: To determine if older adult, novice wheelchair users who drive a power wheelchair with a JoyBar control complete maneuverability tasks in less time and with less error than those who drive a power wheelchair with a standard joystick control.Materials and methods: A parallel randomized controlled trial design conducted at a medical rehabilitation and research centre with ambulatory older adults aged 60 and above (n = 27). The intervention was the JoyBar alternative wheelchair control. The primary outcome measure was total time to complete each of the two maneuverability tasks. The secondary outcome measure was total number of errors during each of the maneuverability tasks.Results: An independent, two sampled t-test was conducted and revealed that the JoyBar group took a greater amount of time to complete both maneuverability tasks than the control group (p < .05). No significant differences (p < .05) were found in rates of error on either task between the JoyBar and joystick groups.Conclusions: Maneuverability of a powered wheelchair by novice wheelchair users was not improved through the use of the JoyBar when compared to a standard wheelchair joystick, as measured by rates of error and time to complete maneuverability tasks.Implications for rehabilitationClients who are new to powered wheelchair use may perform maneuverability tasks faster, with equivalent accuracy, using a standard joystick versus the JoyBar.Clients who use a JoyBar may require adjustments to the programming of their wheelchair to ensure optimal performance.Additional training may be required to achieve proficiency in maneuverability tasks with a JoyBar versus a standard joystick.

An EOG-Based Human-Machine Interface for Wheelchair Control.

Nonmanual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately, and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG) based HMI is proposed for wheelchair control. A total of 13 flashing buttons, each of which corresponds to a command, are presented in the graphical user interface. These buttons flash on a one-by-one manner in a predefined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. For healthy subjects/patients with spinal cord injuries, the proposed HMI achieved an average accuracy of 96.7%/91.7% and a response time of 3.53 s/3.67 s with 0 false positive rates (FPRs). Using one channel of vertical EOG signals associated with eye blinks, the proposed HMI can accurately provide sufficient commands with a satisfactory response time. The proposed HMI provides a novel nonmanual approach for severely paralyzed individuals to control a wheelchair. Compared with a newly established EOG-based HMI, the proposed HMI can generate more commands with higher accuracy, lower FPR, and fewer electrodes.

Retrospective Analysis of Predictors of Proficient Power Mobility in Young Children With Severe Motor Impairments

ObjectivesTo determine if child characteristics, maternal education, intervention parameters, type of wheelchair control mechanism, or a combination of these variables were associated with proficient power mobility skills in children with severe motor impairments aged 14 to 30 months; and to determine if performance on the Wheelchair Skills Checklist (WSC) was associated with performance on the Powered Mobility Program (PMP). DesignSecondary data analyses on data collected from 2 previously completed randomized controlled trials (RCTs). SettingIntervention and outcomes measurements took place in natural environments. ParticipantsParticipants included children who were assigned to the intervention groups in 2 RCTs (N=31). InterventionChildren practiced maneuvering individually customized power wheelchairs for 12 months in natural environments. Main Outcome MeasuresProficiency was assessed using the WSC and the PMP. The Battelle Developmental Inventory and Merrill-Palmer-Revised were used to assess baseline cognition and motor skills. Baseline mobility was assessed using the Pediatric Evaluation of Disability Inventory. ResultsCognition, fine motor skills, and wheelchair control mechanism were associated with proficiency. Cognition, type of wheelchair control, and diagnosis all predicted proficiency while controlling for other covariates using multiple regression analysis. Agreement between the WSC and PMP was 94.7%. ConclusionsCognition, type of wheelchair control, and diagnosis might predict power mobility proficiency in young children with severe motor impairments. These factors however should not be used to determine whether a child has the opportunity to participate in a training program. Agreement between the WSC and PMP could help researchers and clinicians compare results across studies that use only one of these outcome measures.

Model Predictive Control of an autonomous wheelchair

This paper presents the development of a MPC architecture for an autonomous wheelchair, that aims at reaching a desired goal while considering physical actuator limitations, obstacle avoidance and comfort constraints. The controller has been designed in such a way that it can be easily applied to an existing commercial wheelchair, without the need for a redesign of the on-board motor controllers. Moreover, thanks to the introduction of an inner feedback linearizing loop, a linear MPC has been considered, allowing for an efficient real-time implementation of the control system. The experimental results show the effectiveness of the proposal, and demonstrate that MPC can be considered a promising control approach to develop autonomous vehicles.

Prediction of user preference over shared-control paradigms for a robotic wheelchair.

The design of intelligent powered wheelchairs has traditionally focused heavily on providing effective and efficient navigation assistance. Significantly less attention has been given to the end-user's preference between different assistance paradigms. It is possible to include these subjective evaluations in the design process, for example by soliciting feedback in post-experiment questionnaires. However, constantly querying the user for feedback during real-world operation is not practical. In this paper, we present a model that correlates objective performance metrics and subjective evaluations of autonomous wheelchair control paradigms. Using off-the-shelf machine learning techniques, we show that it is possible to build a model that can predict the most preferred shared-control method from task execution metrics such as effort, safety, performance and utilization. We further characterize the relative contributions of each of these metrics to the individual choice of most preferred assistance paradigm. Our evaluation includes Spinal Cord Injured (SCI) and uninjured subject groups. The results show that our proposed correlation model enables the continuous tracking of user preference and offers the possibility of autonomy that is customized to each user.

A Brain Machine Interface for command based control of a wheelchair using conditioning of oscillatory brain activity.

In this research a new method of wheelchair control using a Brain Computer Interface (BCI) is proposed, in an attempt to bridge the gap between in-lab and real life applications, we believe it would provide a high level control over the BCI instead of the normal low level commands. It is anticipated to emphasis on mu rhythm to provide the control signals. The wheelchair is equipped with a mapping system, which scans the area and provides a map containing information about the user's current location and next possible destinations, then provides an optimized list of possible trajectories to reach the destination. The paradigm allows users to control the interface using motor imagery and issue commands to switch between possible trajectories and then confirm the choice. Commands trigger the motion of the wheelchair to the intended destination using a user selected path with speed up to 0.5 m/s. The interface also allows the user to interact with different robots through a common robotic system. Evaluation results indicate that this paradigm is indeed usable and could lead to promising outcomes.

Voice controlled Smart Electric-Powered wheelchair based on Artificial Neural Network

Electric Powered Wheelchair (EPW) used by people with disabilities, who cannot move or with a limited ability to move. The traditional technique that used to control the powered wheelchair can be a challenging and stressful operation. Therefore, there is a demand to find alternative ways to perform this process. In that, the voice recognition is a natural form of pointing that can use to replace the joystick whilst still allowing for similar control. Neural network one of the effective ways to recognize the speech commands, because of its ability to grant a trainable powered wheelchair controller with a convenient way, ease of use and attractiveness. In this situation, the neural networks are providing a trainable system for each person irrespectively of his disability. Smart Electric-Powered Wheelchair (SEPW) is an electric-powered wheelchair based on Artificial Neural Networks (ANN) Technique can recognize the voice commands and replace the traditional control of powered wheelchairs with intelligent technology. So, a neural network for the feed-forward Multi-layer perceptron has been trained to recognize isolated spoken words (commands), such as ‘right, ‘left,' ‘stop' and so on. Initially, the design of the system's network started with one hidden layer using five neurons (according to [1] formula). During the training process, the number of hidden neurons has been increased to keep the mean square error (MSE) of the system minimum as possible. Furthermore, the system structure is designed based on a Multi Layers Perceptron (MLP) neural system for five output neurons, which represent the powered wheelchair's commands

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Abstract Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

Obstacle Avoidance Method for Electric Wheelchairs Based on a Multi-Layered Non-Contact Impedance Model

This paper proposes an obstacle avoidance method based on a multi-layered non-contact impedance model for control of the biosignal-based electric wheelchair. The proposed system can calculate a virtual repulsive force before the collision by multi-layered impedance fields covered around it. This system therefore regulates desired path to avoid obstacles in a variety of situations. In the experiments, the mobile robot passed through obstacles smoothly, and could stop emergently to avoid the obstacle in front of the robot owing to virtual forces calculated by the proposed model.

Enhancement of steady‐state visual evoked potential‐based brain–computer interface systems via a steady‐state motion visual stimulus modality

Steady‐state visual evoked potential (SSVEP)‐based brain–computer interface (BCI) systems are among the most accurate assistive devices for patients with severe disabilities. However, existing visual stimulation patterns lead to eye fatigue, which affects the system performance. Therefore, in this study, we propose two improvements to SSVEP‐based BCI systems. First, we propose a novel visual stimulator that incorporates a visual motion stimulus for the steady‐state visual stimulus to reduce eye fatigue while maintaining the advantages associated with SSVEPs. We also propose two corresponding feature extraction algorithms, i.e. SSVEP detection and visual attention detection, to capture the phenomena of steady‐state motion visual stimulus responses. The accuracy of the test was ∼83.6%. Second, we propose a novel hybrid BCI‐SSVEP system and a motion visual stimulus hybrid BCI system to enhance the SSVEP‐based BCI system during a state of eye fatigue. Participants used the SSVEP system until reaching a fatigued state and then began using a hybrid motion visual stimulus. The accuracy of the proposed system was ∼85.6%. The proposed improvements can be incorporated into practical BCI systems for wheelchair control. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Low-cost Hybrid Wheelchair Controller for Quadriplegias and Paralysis Patients

Low-cost Hybrid Wheelchair Controller for Quadriplegias and Paralysis Patients

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

BackgroundCertain diseases affect brain areas that control the movements of the patients’ body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair.MethodA proposal to control a wheelchair through a Brain–Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs.ResultsSeventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session.ConclusionThe results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.

Probabilistic vs linear blending approaches to shared control for wheelchair driving.

Some people with severe mobility impairments are unable to operate powered wheelchairs reliably and effectively, using commercially available interfaces. This has sparked a body of research into "smart wheelchairs", which assist users to drive safely and create opportunities for them to use alternative interfaces. Various "shared control" techniques have been proposed to provide an appropriate level of assistance that is satisfactory and acceptable to the user. Most shared control techniques employ a traditional strategy called linear blending (LB), where the user's commands and wheelchair's autonomous commands are combined in some proportion. In this paper, however, we implement a more generalised form of shared control called probabilistic shared control (PSC). This probabilistic formulation improves the accuracy of modelling the interaction between the user and the wheelchair by taking into account uncertainty in the interaction. In this paper, we demonstrate the practical success of PSC over LB in terms of safety, particularly for novice users.

The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: An experimental assessment

The exact manner in which control is shared between a human and an autonomous system is a crucial factor for assistive robots that provide physical support to people with severe motor impairments. There has however been little comparative study between different control-sharing paradigms within the field of assistive robotics. We present a control architecture for a robotic “smart” wheelchair that allows for the seamless interchange and evaluation of any number of control-sharing paradigms, and so facilitates comparative study between them. We present an implementation of four control sharing paradigms, and results from a study that compares all four to each other and teleoperation, and moreover using multiple control interfaces and across multiple sessions. Experimental results suggest that (i) task performance metrics differ with each control interface, (ii) performance increases with increasing autonomy assistance however it is not statistically significantly different between higher levels of autonomy, (iii) metrics related to user effort show a decrease with increasing autonomy, which is more emphasized with more limited control interface and (iv) how much the autonomy is utilized differs greatly between control paradigms, but not control interfaces. Moreover, (v) for almost all performance metrics, there is a consistent performance increase in Session 2 compared to Session 1, and for both control interfaces. Lastly, subjective questionnaires (control paradigm preference and perceived utility) reveal both similarities and differences between SCI and uninjured subjects. No single control paradigm is the clear winner in performance or preference, suggesting that it will be important to offer end-users multiple control options to accommodate their individual needs and preferences.

WEARABLE REAL TIME HEALTH TRACKERS AND ASSISTED-MOBILITY FOR NON-AMBULANT PATIENTS

This paper presents an approach to facilitate mobility for the non-ambulant patients using eye movements and wearable health tracking. The patient’s wheelchair movement is controlled using eye ball movements and also some biomedical assistance functionalities are considered to match the current day needs. The proposed work first detects face from input video, then eye portion will be localized, and finally eye ball (pupil) is detected and tracked using computer vision techniques. The direction of movement is assessed and a command is disseminated to the wheelchair control system. The wearable sensors and\or alarms mounted on patients will update current health status indications on to the monitoring panels. The wearable devices have evolved very smartly which are capable enough to take care of the patient health in real time even during assisted mobility.

Modeling and Simulation of Two Wheelchair Accessories for Pushing Doors

ABSTRACTIndependent mobility is vital to individuals of all ages, and wheelchairs have proven to be great personal mobility devices. The tasks of opening and navigating through a door are trivial for healthy people, while the same tasks could be difficult for some wheelchair users. A wide range of intelligent wheelchair controllers and systems, robotic arms, or manipulator attachments integrated with wheelchairs have been developed for various applications, including manipulating door knobs. Unfortunately, the intelligent wheelchairs and robotic attachments are not widely available as commercial products. Therefore, the current manuscript presents the modeling and simulation of a novel but simple technology in the form of a passive wheelchair accessory (straight, arm-like with a single wheel, and arc-shaped with multiple wheels) for pushing doors open from a wheelchair. From the simulations using different wheel shapes and sizes, it was found that the arc-shaped accessory could push open the doors faster and with almost half the required force as compared to the arm-like accessory. Also, smaller spherical wheels were found to be best in terms of reaction forces on the wheels. Prototypes based on the arc-shaped accessory design will be manufactured and evaluated for pushing doors open and dodging or gliding other obstacles.

A low cost genetic algorithm based control scheme for wheelchair control in hospital environment

In this paper, a control strategy is proposed for assisting patients, with wheelchairs, to move around within a hospital. The strategy is tested using a mobile robot that is navigated in a hospital while using obstacle avoidance scheme. The mobile robot is operated using both genetic algorithm and A* algorithm for a thorough comparison of both schemes. The comparison of the results confirmed that genetic algorithm give a far better solution for motion planning of a wheelchair in a hospital environment.

A Human-Centered Design of Electric Wheelchair Controller With Dual Control Access for Both Drivers of Disabled People and Caregiver

This paper presents a design process of a novel electric wheelchair controller to enable a dual control access for both the users in the wheelchair and their caregivers. This design applied human-centered design frameworks, processes, and tools: Kumar's seven modes of the design innovation process and IDEO, Palo Alto, CA, design method cards. After such design process, a design of dual control access controller of an electric wheelchair is established. A real-size prototype has been built to prove the design concept; and further, the method of control switching between people in and behind the wheelchair has been optimized with the established prototype.