Abstract: The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements.
Abstract: The wheelchair is the major means of transport for
physically disabled people. However, it cannot overcome architectural
barriers such as curbs and stairs. In this paper, the authors proposed
a method to avoid falling down of a wheeled inverted pendulum type
robotic wheelchair for climbing stairs. The problem of this system
is that the feedback gain of the wheels cannot be set high due to
modeling errors and gear backlash, which results in the movement
of wheels. Therefore, the wheels slide down the stairs or collide with
the side of the stairs, and finally the wheelchair falls down. To avoid
falling down, the authors proposed a slider control strategy based on
skyhook model in order to decrease the movement of wheels, and a
rotary link control strategy based on the staircase dimensions in order
to avoid collision or slide down. The effectiveness of the proposed
fall avoidance control strategy was validated by ODE simulations and
the prototype wheelchair.
Abstract: This paper presents the ‘Eye Ball Motion Controlled
Wheelchair using IR Sensors’ for the elderly and differently abled
people. In this eye tracking based technology, three Proximity
Infrared (IR) sensor modules are mounted on an eye frame to trace
the movement of the iris. Since, IR sensors detect only white objects;
a unique sequence of digital bits is generated corresponding to each
eye movement. These signals are then processed via a micro
controller IC (PIC18F452) to control the motors of the wheelchair.
The potential and efficiency of previously developed rehabilitation
systems that use head motion, chin control, sip-n-puff control, voice
recognition, and EEG signals variedly have also been explored in
detail. They were found to be inconvenient as they served either
limited usability or non-affordability. After multiple regression
analyses, the proposed design was developed as a cost-effective,
flexible and stream-lined alternative for people who have trouble
adopting conventional assistive technologies.
Abstract: Assistive robotics are playing a vital role in advancing the quality of life for disable people. There exist wide range of systems that can control and guide autonomous mobile robots. The objective of the control system is to guide an autonomous mobile robot using the movement of eyes by means of EOG signal. The EOG signal is acquired using Ag/AgCl electrodes and this signal is processed by a microcontroller unit to calculate the eye gaze direction. Then according to the guidance control strategy, the control commands of the wheelchair are sent. The classification of different eye movements allows us to generate simple code for controlling the wheelchair. This work was aimed towards developing a usable and low-cost assistive robotic wheel chair system for disabled people. To live more independent life, the system can be used by the handicapped people especially those with only eye-motor coordination.
Abstract: A new cost effective, eye controlled method was introduced to guide and control a wheel chair for disable people, based on Electrooculography (EOG). The guidance and control is effected by eye ball movements within the socket. The system consists of a standard electric wheelchair with an on-board microcontroller system attached. EOG is a new technology to sense the eye signals for eye movements and these signals are captured using electrodes, signal processed such as amplification, noise filtering, and then given to microcontroller which drives the motors attached with wheel chair for propulsion. This technique could be very useful in applications such as mobility for handicapped and paralyzed persons.
Abstract: In this paper motion analysis on a winding
stair-climbing is investigated using our proposed rotational arm type
of robotic wheelchair. For now, the robotic wheelchair is operated in
an open mode to climb winding stairs by a dynamic turning, therefore,
the dynamics model is required to ensure a passenger-s safety.
Equations of motion based on the skid-steering analysis are developed
for the trajectory planning and motion analysis on climbing winding
stairs. Since the robotic wheelchair must climb a winding staircase
stably, the winding trajectory becomes a constraint equation to be
followed, and the Baumgarte-s method is used to solve for the
constrained dynamics equations. Experimental results validate the
behavior of the prototype as it climbs a winding stair.