Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Movement intention detection using neural network for quadriplegic assistive machine

Biomedical signal lately have been a hot topic for researchers, as many journals and books related to it have been publish. In this paper, the control strategy to help quadriplegic patient using Brain Computer Interface (BCI) on basis of Electroencephalography (EEG) signal was used. BCI is a technology that obtain user's thought to control a machine or device. This technology has enabled people with quadriplegia or in other words a person who had lost the capability of his four limbs to move by himself again. Within the past years, many researchers have come out with a new method and investigation to develop a machine that can fulfill the objective for quadriplegic patient to move again. Besides that, due to the development of bio-medical and healthcare application, there are several ways that can be used to extract signal from the brain. One of them is by using EEG signal. This research is carried out in order to detect the brain signal to controlling the movement of the wheelchair by using a single channel EEG headset. A group of 5 healthy people was chosen in order to determine performance of the machine during dynamic focusing activity such as the intention to move a wheelchair and stopping it. A neural network classifier was then used to classify the signal based on major EEG signal ranges. As a conclusion, a good neural network configuration and a decent method of extracting EEG signal will lead to give a command to control robotic wheelchair

BRAINWAVE MANEUVERED WHEELCHAIR

In this world, there are millions of people who suffer from quadriplegic, paralysis, mobility disorder, neuromuscular disorder in which organ below the neck can’t be controlled by the patients. The system which has been developed in this project is using electroencephalogram based promising and important technology using Brain Computer Interface. It helps unblessed people to control the organ below neck using their own brain. Modern electroencephalogram-based Brain Computer Interface uses gel type electrodes and this type of technology is only limited to hospitals and laboratories and it requires 30 minutes to acquire a brain signal and this proposed system is very costly. But to overcome this cup type electrodes are used and overall cost is reduced to make it cost effective. It has been made portable, so that users can handle and carry it easily. It is possible to operate an electric wheelchair for individuals with disabilities using electroencephalogram signals of their eye movements, which is accomplished via the application of algorithms in MATLAB. Finally, the outcomes of this suggested system provide useful outputs for the user.Keywords: Algorithms; Brain Computer Interface (BCI); Electroencephalogram (EEG); Electric wheelchair and Eye movements

Design and development of eye movement data acquisition kit

There are several researches that have been done to improve the life among tetraplegia. It has become an attractive research field in the rehabilitation engineering because the eye movement have abilities as a communication tool for disabled people. However, the previous research has not much provided an appropriate design for the user among tetraplegia. Motivated from that, a new design of eye movement data acquisition kit has been developed. This paper aims to describe the design of eye movement data acquisition kit for the user among tetraplegia based on the proper electrode positions, the prototype as well as the signal conditioning circuits. Then, this EOG kit was used to acquire the eye signals for eye movement in the left and right direction. The eye movement data was obtained from the kit, which can be used as a significant communication tool among tetraplegia. The results show that the kit equipped with a proper signal conditioning is able to acquire the eye movement signal

Effectiveness of Morse Code as an Alternative Control Method for Powered Wheelchair Navigation

We applied Morse code as an alternative input method for powered wheelchair navigation to improve driving efficiency for individuals with physical disabilities. In lab trials performed by four testers, it demonstrated significant improvement in driving efficiency by reducing the driving time, compared to traditional single switch wheelchair navigation