This article presents the design of movement sequences for arm rehabilitation of stroke patient. The objective of this research is to develop the best movement sequences suitable for arm rehabilitation of hemiparesis sufferers based on the features analyzed that represent muscle activity. 8 healthy subjects including both male and female performed four arm movement sequences task consist of arm lifting and reaching movement in real environment. Muscle activities are recorded using electromyography (EMG) involving deltoid anterior, deltoid lateral, biceps and triceps. Based on the previous research, Amount of movement (AOM) feature is calculated to observe the muscle activation for each movement sequence task. The experimental results show that it is likely to produce optimum arm movement sequences for arm rehabilitation and the sequences are suitable to deploy in virtual reality in future research.
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.
A wired glove system is developed by designing a low cost glove which has the similar function with the conventional dataglove and has been named as GloveMAP. The system involves the finger movements with some of grasping activities to investigate the force exerted on the fingertips during grasping a cylinder with different weight. Force sensing resistors (FSR) are attached to the thumb, index and middle fingers to obtain the voltage changes from the activities of fingers grasping. The output data from different weight of cylinder (bottle) during grasping are analyzed based on statistical approaches. The correlation between weight and force has been determined by comparing the gradient slope between both graphs.
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