Wheelchair Control Research Articles

Automatic Controlled Wheel Chair Using Multi-Intelligence Technique

Tremendous magnification in medical technology is incrementing day by day with excellent remedying methodologies and making persons to be salubrious, but paralysis disease is unable to remedy perpetually whereas partial remedying is possible. Peoples are affected with plenarily and/or quarterly paralyzed persons were unable to survive themselves, even though with felicitous medication is available. Paralyzed persons need an opportune conveyance to move themselves independently for their personal needs. Now a day’s advancement of automation in electric conveyance designs is predicated on the desideratum of application. In this paper, a new wheelchair design methodology was presented with multi controlled schemes for the fully or partially paralyzed persons to move independently without help of any caretaker. Three controlling schemes were developed and integrated to make the robust of the wheelchair design. A prototype model is developed and presents in the paper, it was tested rigorously to analyze the performance of the proposed methodology. Results show the efficacy of the method.

The Design Of Wheelchair Systems With Raspberry Pi 3-Based Joystick Analog And Voice Control

In this study, an electric wheelchair that combines two controls: joystick analog and voice control is designed. IC MCP3008 is used to navigate wheelchairs by using Josytick, where joystick analog data will be converted into digital data. The movements resulted from the joystick analog on the xAxis axis (horizontally) are the right turn and left turn, and on the yAxis axis (vertically) are forward and backward. The movements on the yAxis and xAxis axes set by the user affects the speed of the wheelchair. Meanwhile, the AMR-Voice application on Android is used to navigate wheelchairs by using sound. There are five commands in this voice control: “Forward”, “backward”, “left”, “right”, “stop”. The order will be sent to Raspberry Pi 3 via the HC-06 module to then be recognized for the command. If the voice commands are received accordingly, Raspberry Pi 3 will provide an activation signal to the motor driver to move the wheelchair in the direction corresponding to the command given by the user. Voice control testing on wheelchairs is tested in quiet rooms and noisy rooms. The results of the wheelchair control testing with sound indicate that the accuracy and speed of the wheelchair response rely heavily on Internet connection and room conditions. The average response when the condition of the room is quiet is 0.16 s and when the condition of the room is noisy is 5.18 s. Wheelchairs with joystick control and the voice made can be used for the disabled, whether for those who can move their fingers or not, at a low cost so that they can be an alternative in developing countries.

Eye Controlled Wheelchair Using Raspberry Pi

The purpose of this eye-controlled wheelchair is to eliminate the need for support for people with disabilities. The proposed wheelchair control technique depends on eye movements. The camera is mounted on the user’s head to take eye photos and track eye pupil movements using face landmarks recognition technology. Based on eye pupil movements, the wheelchair motor moves left, right and forward. For security, an ultrasonic sensor is introduced at the front of the wheelchair to identify obstruction and consequently stop the wheelchair. The complete system is controlled by Raspberry Pi.

Mobile arm supports in Duchenne muscular dystrophy: a pilot study of user experience and outcomes

Purpose This pilot study examined whether two different types of non-powered mobile arm supports (MAS) enhanced upper limb function and independence with activities of daily living (ADLs) in people with Duchenne muscular dystrophy (DMD). Method A mixed methods cross-sectional design was used. Participants were four males with DMD, aged 16 to 20 years (M = 18.25 years). Two participants were current MAS users, and two had previously used MAS. To explore experiences of MAS use, semi-structured interviews were undertaken, then transcribed verbatim and analysed thematically. To measure the impact of MAS on arm function and ADLs, participants using MAS completed the Performance of the Upper Limb (PUL) and the DMD Upper Limb Patient Reported Outcome Measure with and without MAS. Participants no longer using MAS only completed the PUL without MAS. Results MAS enhanced upper limb function and independence with ADLs in three of four participants. Eating and drinking was most often positively impacted by MAS. Access to informal support for MAS set-up, and ongoing input from a clinician or assistive technology (AT) supplier with MAS expertise, were important enablers to successful MAS use. Barriers to use included inadequate upper limb strength, interference of MAS with wheelchair controls, and AT funding delays. Conclusion MAS should be considered by individuals with DMD and clinicians working with them as the disease progresses and strength declines. Potential barriers to MAS use need to be addressed to ensure maximum utility. AT funding delays must also be minimized to avoid impact on outcomes of people with DMD. Implications for Rehabilitation Individuals with DMD, and clinicians working with them, may consider MAS to aid achievement of functional goals as the disease progresses and upper limb function declines. Both objective and subjective outcome measures should be used when evaluating the effectiveness of MAS. Consideration should be given to the potential interference of MAS with wheelchair controls and the availability of informal or paid supports (e.g., family, support workers, teachers) to aid MAS use. Efficient and timely funding of MAS is required, as delays may negatively impact outcomes for people with progressive neurological conditions, such as DMD.

Voice Controlled Wheelchair Based on ATmega

The dependency of people depends on others especially when they get to travel from one place to another. Those who are physically ill require a wheelchair for their daily needs, and who can help them to make the wheelchair move. .By having a controlled wheel-chair system will empower weakened individuals to wind up independent. The structure is a remote wheelchair control system that uses a voice acknowledgment application for actuating and controlling all of its improvements. The wheelchair responds to the voice request from its customers to play out any advancements limits. It joins a microcontroller, Bluetooth module, control interface engine, transfer engine, which uses a board through which a wheelchair can move. By using the structure, the customers can work in the wheel-chair by basically address the mobile phone antenna. The fundamental improvement limits fuse advance and alter heading, left and right turns and stop It utilizes an Atmega328 to control the system assignments. It talks with the Bluetooth module to recognize word communicated and after that chooses the contrasting yield request with drive, the right and left engines. To accomplish this endeavor, in the controller's memory a low-level registering program is set.

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Flex Sensor Based Glove to Control Wheel Chair and Sign Language Translator for Speech Impaired People

The idea of this paper is proposed for rehabilitation of people who have mobility issues particularly for the paralytic Stroke Patients and mute person with difficulty to communicate in a society. To overcome these complications, this system is developed to control the wheelchair based on finger movement and it also provides the voice output. It consists of flex sensors to control the wheelchair and in another mode it uses flex sensor and voice module to provide audio output. Person can move the wheelchair in any direction by gesture. Gesture is a good medium to give command because it can be used in a noisy condition and so he/she can communicate in public places. The patients can control the wheelchair through hand gesture, so they can survey themselves for their basic needs and need not to depend on others to guide those patients. Thus, it is multipurpose hand gloves to control motion of wheelchair and also to convert hand gestures to speech.

Wheelchair Control System based on Gyroscope of Wearable Tool for the Disabled

A wheelchair control system based on Gyroscope of wearable tool can serve the disabled, especially in helping them move freely. The recent evolution of new technology means that unassisted, free movement has become possible. For this purpose, human–machine interface hands-free command of an electric-powered wheelchair can be achieved. In this paper, an electroencephalogram instrument, namely the EMOTIV Insight, was implemented in a human–computer interface to acquire the user’s head motion signals. The system can be operated based on the user’s head motions to carry out motion orders and control the motor of the wheelchair. The proposed system consists of an EMOTIV Insight brain-based gyroscope to sense head tilt, a DC motor driver to control wheelchair speed and directions, an eclectic-powered wheelchair, microcontroller, and laptop. We implemented the system in practice and tested it on smooth and rough surfaces in indoor/outdoor settings. The experimental results were greatly encouraging: disabled users were able to drive the wheelchair without any limitations. We obtained a significant average response time of 2 seconds. In addition, the system had accuracy, sensitivity, and specificity of 99%, 99.16%, and 98.83%, respectively.

Human Brain Imbued Higher Brain Centre Activated Robot

Brain Computer Interface (BCI) Systems give venue for human brain to communicate with an external device via a non-physiological path. It replaces the use of nerves and muscles of the human brain with a combination of hardware and software, which translates electrophysiological signals into physical actions. It differs from neuro-modulation in that it allows for bidirectional information flow. These are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory motor function. From initial demonstrations of electroencephalography-based spelling and single-neuron based device control, researchers have gone on to use electroencephalographic, intra-cortical, electro-corticographic and other brain signals for increasingly complex control of cursors, robotic arms, prosthetics, wheelchairs, and other devices related to rehabilitation engineering. Automated devices obviously can acquire intelligence and may also work similarly or differently to tackle wide range of tasks. But there is no automated robotic device available which is having enough intelligence to replicate itself. Generally the brain signal is received by electrodes placed on the scalp and is converted into programmed codes and then translated into commands that can control an output device, such as a spelling program, a motorized wheelchair, or a prosthetic limb. Feedback from the device can enable the user to modify their brain signals in order to maintain the performance of the automated device in a much more efficient manner. If those decoded codes (signals) coming from the subjects’ brain could be applied as inbuilt program of an artificial intelligence robot, then that robot might behave in a way as the subject with respect to his/her brain function. This paper has the objective to design and develop a higher brain centre activated robot that is still not available.

Clinician awareness of brain computer interfaces: a Canadian national survey

BackgroundIndividuals with severe neurological disabilities but preserved cognition, including children, are often precluded from connecting with their environments. Brain computer interfaces (BCI) are a potential solution where advancing technologies create new clinical opportunities. We evaluated clinician awareness as a modifiable barrier to progress and identified eligible populations.MethodsWe executed a national, population-based, cross-sectional survey of physician specialists caring for persons with severe disability. An evidence- and experience-based survey had three themes: clinician BCI knowledge, eligible populations, and potential impact. A BCI knowledge index was created and scored. Canadian adult and pediatric neurologists, physiatrists and a subset of developmental pediatricians were contacted. Secure, web-based software administered the survey via email with online data collection.ResultsOf 922 valid emails (664 neurologists, 253 physiatrists), 137 (15%) responded. One third estimated that ≥10% of their patients had severe neurological disability with cognitive capacity. BCI knowledge scores were low with > 40% identifying as less than “vaguely aware” and only 15% as “somewhat familiar” or better. Knowledge did not differ across specialties. Only 6 physicians (4%) had patients using BCI. Communication and wheelchair control rated highest for potentially improving quality of life. Most (81%) felt BCI had high potential to improve quality of life. Estimates suggested that > 13,000 Canadians (36 M population) might benefit from BCI technologies.ConclusionsDespite high potential and thousands of patients who might benefit, BCI awareness among clinicians caring for disabled persons is poor. Further, functional priorities for BCI applications may differ between medical professionals and potential BCI users, perhaps reflecting that clinicians possess a less accurate understanding of the desires and needs of potential end-users. Improving knowledge and engaging both clinicians and patients could facilitate BCI program development to improve patient outcomes.

Stability and Direction Control of a Two-Wheeled Robotic Wheelchair Through a Movable Mechanism

A two-wheeled robotic wheelchair (TWRW) has a better manoeuvrability than a conventional four-wheeled wheelchair. However, it is not statically stable near the upright posture or a posture desired by the rider, and an active stability controller is required. Stability control becomes more challenging when a TWRW is also required to move in a desired direction. To rely on wheels' motions to achieve both stability and direction control tend to impose a large burden on the wheels' driving motors or other types of actuators in terms of their driving torque and power consumption. Various disturbances in the system also affect the performance of the controller. To solve these problems, this paper presents a stability and direction controller based on the motion of a pendulum-like movable mechanism added to assist the wheels to produce control actions. The dynamic model of the TWRW is established through the Euler-Lagrange formulation in which the disturbances caused by model uncertainties and rider's motion are considered. A robust second-order sliding mode control is then developed for the stability and the direction control of a TWRW. Simulation results are presented to validate the effectiveness of the proposed method.

Human Assist Control of Electric Wheelchair by Using Control Barrier Function

Human Assist Control of Electric Wheelchair by Using Control Barrier Function

Implementation of a Biometric Interface in Voice Controlled Wheelchairs

In order to assist physically handicapped persons in their movements, we developed an embedded isolated word speech recognition system (ASR) applied to voice control of smart wheelchairs. However, in spite of the existence in the industrial market of several kinds of electric wheelchairs, the problem remains the need to manually control this device by hand via joystick; which limits their use especially by people with severe disabilities. Thus, a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty. The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks. The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors, a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card. The ASR architecture consists of two main modules. The first one is the speech parameterization module (features extraction) and the second module is the classifier which identifies the speech and generates the control word to motors power unit. The training and recognition phases are based on Hidden Markov Models (HMM), K-means, Baum-Welch and Viterbi algorithms. The database consists of 39 isolated speaker words (13 words pronounced 3 times under different environments and conditions). The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit. The results and experiments obtained gave promising recognition ratio and accuracy around 99% in clean environment. However, the system accuracy decreases considerably in noisy environments, especially for SNR values below 5 dB (in street: 78%, in factory: 52%).

Steering Assist Control for Cycling Wheelchair Considering Sense of Agency Based on Environmental and Operation Force Information

Steering Assist Control for Cycling Wheelchair Considering Sense of Agency Based on Environmental and Operation Force Information

Voice Controlled Wheelchair for Physically Disabled People

Most of the disabled people usually depend on others in their daily life especially in moving from one place to another. For the wheelchair users, continuously they need someone to help them in getting the wheelchair moving. By using a wheelchair control system they become more independent. The moto of this research project is to design and fabricate a voice controlled wheelchair for physically disabled people. The wheelchair control system which deploys a voice recognition system for triggering and controlling all its movements. It integrates a microcontroller, voice recognition through google assistant, motor control interface board to move the wheelchair. By using the system, the users are able to operate the wheelchair by simply speaking and commanding through Google assistant. The basic functioning process includes forward and reverse direction, left and right turns and stop. It uses a PIC controller manufactured by Microchip Technology to control the system operations. It communicates with the voice recognition through google assistant and works with the commands which is saved as the number system directed from the Ada-fruit cloud. The speech is given and then determines the corresponding output command to drive the left and right motors. To finish this task, an assembly language program is written and stored in the controller's memory.

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy.

The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.

Automated Gesture Based Wireless Wheelchair Control by Means of Accelerometer

According to a research there are about 6 million populations in the world who are paralysed and needs a wheelchair for their mobility. Earlier the wheel chairs had to be moved and be externally supported by any person. To help overcome this “joystick-controlled wheelchairs” are developed. But in regular use, these joystick-controlled wheelchairs became difficult to use. Especially in the case of paralysed people, the use of joystick became more difficult due to the hard buttons and unidirectional use of the joysticks. To overcome these problems, we’ve tried to develop a “gesture-controlled wheelchair” which can be moved with a slight tilt of the hand. This can be used in both hands and can be controlled to come to the user from a distance. The current work is implemented with Arduino based devices such as Arsuino NANO and UNO processors and programmed through Arduino IDE.

Mapping study of computer vision tools applied to aid in wheelchair control

Mapping study of computer vision tools applied to aid in wheelchair control

Utilising P300 via Non-Invasive Brain Computer Interface to Control Wheelchair

Physically challenged people have certain difficulties that prevents a public from living a natural life. The main advantage or need for design smart wheel chair is mobility. There are products catering to disabled person to satisfy also assist them to overcome the issues they face in move from one place to another, in various ways. The proposed system allows a person in need to move a wheelchair using his/her brainwave signals. That will be achieved by using a NeuroSky TGAM1 module and Arduino Mega 2560 over a Bluetooth communication establishment. The ultimate outcome is to comfort and cater to a larger population of the physically challenged with a simpler, cheaper yet effective alternative.

Recognizing Facial Expression in Video Processing

Theoretical, the Micro-expressions convey explicit nonverbal data, for instance the facial development caused by agony. In any case, as a result of their neighborhood and short nature, it is hard to distinguish MEs. This paper displays a novel recognition technique by perceiving a nearby and worldly example of facial development. In our framework, with the reason of improving the discovery precision, fleeting neighborhood highlights are created from the video in a sliding window of 300ms (mean span of a ME). These highlights are separated from a projection in PCA space and structure a particular example amid ME which is the equivalent for all MEs. Utilizing a traditional characterization calculation (SVM), MEs are then recognized from other facial developments. At long last, a worldwide combination examination is connected on the entire face to dispose of false positives. Trials are performed on MATLAB. The discovery results demonstrate that the proposed technique beats the most prevalent discovery technique by the investigation of different measurements.