Nazrul H. Adnan

Analysis of Finger Movement for Robotic Hand (MAPRoh-1) by Using Motion Capture and Flexible Bend Sensor

Since the beginning of twentieth century, human–computer interaction (HCI) and humanoid robot has been the trend of advance countries to show their achievement in the technology. Thus, it is rituals for others develop countries to tail their footstep. By means of self-construct robotic hand based on human hand behaviors, an experiment was conducted to investigate the characteristic of robotic finger movements. The purpose of this paper is to analyze the correlation between angle produced by motion capture system (MOCAP) and voltage produced by the flexible bend sensor attached to the robotic hand. At the end of the project, the relationship regarding both angle and voltage will be clarified by using regression method and the preliminary result indicates that voltage and angle variation is possibly linear to each other based on correlation of coefficient outcome.

Base fluid in improving heat transfer for EV car battery

This study examined the effects of base fluid (as coolants) channeling inside the heat exchanger in the process of the increase in thermal conductivity between EV car battery and the heat exchanger. The analysis showed that secondary cooling system by means of water has advantages in improving the heat transfer process and reducing the electric power loss on the form of thermal energy from batteries. This leads to the increase in the efficiency of the EV car battery, hence also positively reflecting the performance of the EV car. The present work, analysis is performed to assess the design and use of heat exchanger in increasing the performance efficiency of the EV car battery. This provides a preface to the use this design for nano-fluids which increase and improve from heat transfer.

Analysis of EMG-based muscles activity for stroke rehabilitation

This paper presents 18 fundamental movements for the rehabilitation of the stroke patient. The objective of this research is to develop the movement sequences which are suitable for the rehabilitation process and is focused on hemiparesis sufferers which are the most common among stroke patients. The muscle activities are analyzed using electromyography (EMG). 12 electrodes are attached to the right arm of the subject includes deltoid, bicep, tricep, flexor and extensor. The experimental results proof that it is likely to produce movement sequence for stroke rehabilitation based on each muscle activity.

Application nano-fluids for improvement heat transfer from small spaces: An overview

The real challenge in the processes of heat transfer by heat rejection from a constrained small space is a well known issue in the process of cooling small areas. Past research in this area covered numerous theoretical and experimental studies; however in-depth investigation of the nano-fluid flow is still insufficient. In recent years the tried researchers fill the gaps on this subject matter; the aim of this paper is to know the idea enhancing the performance of the heat transfer from literatures.

Glove-Based Virtual Interaction for the Rehabilitation of Hemiparesis Stroke Patient

This paper proposes the rehabilitation of a stroke patient by using dataglove called GloveMAP. GloveMAP is a low cost hand glove developed by using the flexible bending sensor and an accelerometer attached at the position of the fingers and wrist, respectively. In the studies, 22 fundamental movements of arm are evaluated and the results are employed to design the virtual environment. The combination of these fundamental movements focuses on Hemiparesis sufferers which are the most common in the stroke and have the inability to move one side of the body. The experimental results show the preliminary studies to investigate muscle activities. The research outcomes are used to design the rehabilitation based virtual environment (VR).

Human Classification Based on Gestural Motions by Using Components of PCA

Lately, a study of human capabilities with the aim to be integrated into machine is the famous topic to be discussed. Moreover, human are bless with special abilities that they can hear, see, sense, speak, think and understand each other. Giving such abilities to machine for improvement of human life is researchers aim for better quality of life in the future. This research was concentrating on human gesture, specifically arm motions for differencing the individuality which lead to the development of the hand gesture database. We try to differentiate the human physical characteristic based on hand gesture represented by arm trajectories. Subjects are selected from different type of the body sizes, and then acquired data undergo resampling process. The results discuss the classification of human based on arm trajectories by using Principle Component Analysis (PCA).