Earthquake Disaster Awareness System

Earthquake is type of natural disaster which is usually very fast and unpredictable. When this kind of disaster happens, people will usually panic and exit or run away from the facilities. Without proper guide and safety instructions, this might result in unwanted consequences, such as accidents, injuries or leaving behind family members or officemates. Due to this problem, a disaster (earthquake) awareness system is being developed. The system will at first calculate the amount of vibration in the soil of the earth. This calculation will be approximated to the Richter scale and if it is significantly large, will send safety instructions in the form of messages displayed at LED signboards available at important locations in the vicinity of the building. With this awareness system, if an earthquake happens, people will know what to do and where to go even if they are not trained with the disaster drill exercises

Design and Construction of 4-DOF EMG-Based Robot Arm System

Electromyography (EMG) provides an alternative way of providing signal responses from the muscle. As such, the recent trend in developing myoelectric devices has spark the interest in this specific field of study. This is because the traditional controllers lack in certain parts which reduce the utilization of limbs to control devices mainly the robotic arm. However, noise such as crosstalk, motion artifact, ambient noise and inherent noise have become a major issue when handling EMG signals. The preparation of electromyography requires more attention in terms of muscle group selection, electrode placement and condition of the surrounding as it will affect the signal output. The aim of this project is to develop a 4 degree-offreedom (DOF) robotic arm that can be controlled using EMG signals. The correlation between the EMG signal and the robotic arm are required to be identified in order to analyze the performance of robotic arm. Review on the actuator, electromyography methods and microcontroller are done to evaluate the techniques used from past researches. The methods of this project include hardware development of robotics arm, development of forward kinematic, sensor calibration and electrode positioning and experiment on classification and validation of EMG signals based on hand gestures. The experiment showed that the sampling rate and arm position affect the EMG signal output. In addition, the controllability of the robotic arm was low because the motors are controlled independently. The objective of the project has been achieved as the EMG-controlled robotic arm has been successfully developed. The robotic arm is still available for improvement by adding multiple channel sensors and implementing a wireless system

Human Distance Estimation Using Quadcopter For Surveillance Purpose

Nowadays, quadcopters are commonly used. Quadcopters are unmanned aerial vehicles with four propellers to provide lift to fly and hover above ground. Quadcopter nowadays is a very common commercial item in everyday life. Some quadcopters are designed to do 3D or 2D mapping of a certain area or to take videos or just for entertainment purposes. Quadcopter is a very versatile item and is able to change into anything for example a quadcopter can also be used for security purposes to decrease the crime rate of our country. The objective of this study is to design and develop a quadcopter with image processing system to have the ability to measure the distance of a human from the drone itself. The quadcopter is designed to be small in size and have a mini computer like Raspberry Pi on top of it to compute the algorithm to calculate the distance of the human by using image processing technique through the camera which is setup on the drone. Human detecting algorithm YOLO and software Open CV is chosen to detect human and calculate the distance from the quadcopter. The results show that the system is quite limited by the capabilities of the hardware. The system shows an accuracy of more than 90 percent when the human is standing within a certain range. Both the accuracy of the distance sensing and human recognizing system is affected by the limitation of the hardware

An Analysis Of Sensor Placement For Vehicle's Blind Spot Detection And Warning System

Nowadays, the number of accidents involving motorized vehicles is increasing especially the side collision of the vehicles when the driver attempt to change from one lane to another either to left or right which is due to the carelessness of the driver and unsighted the blind spot. However, the cooperation of technology can overcome this problem. The key element is the ability to detect the incoming vehicle in the blind spot area. However, problems rises when the sensor used for the system only able to cover certain amount of area. The objectives of this study is to develop and implement a device that will warn the driver about the incoming vehicles in the blind spot area by blinking LED and to investigate the effectiveness of the system in terms of the position of the sensor used for the system. The developed system are equipped with an Arduino UNO microcontroller and SRF04 ultrasonic sensor. There are two experiments be conducted. The first experiment is carried out to make an analysis on the time response of the system with position of the sensor above the rear tire of the static vehicle. The second experiment is carried out to examine the time response of the system with same position of the sensor with the moving vehicle at certain constant velocity. The result shows that the sensor placement above the rear tire give a good performance in term of driver notification of the presence of vehicle at the blind spot region

A New Data Glove Approach For Malaysian Sign Language Detection

A normal human being sees, listens, and reacts to his/her surroundings. There are some individuals who do not have this important blessing. Such individuals, mainly deaf and dumb, depend on communication via sign language to interact with others. However, communication with ordinary individuals is a major concern for them since not everyone can comprehend their sign language. Furthermore, this will cause a problem for the deaf and dumb communities to interact with others, particularly when they attempt to involve with educational, social and work environments. In this research, the objectives are to develop a sign language translation system in order to assist the hearing or speech impaired people to communicate with normal people, and also to test the accuracy of the system in interpreting the sign language. As a first step, the best method in gesture recognition was chosen after reviewing previous researches. The configuration of the data glove includes 10 tilt sensors to capture the finger flexion, an accelerometer for recognizing the motion of the hand, a microcontroller and Bluetooth module to send the interpreted information to a mobile phone. Firstly the performance of the tilt sensor was tested. Then after assembling all connections, the accuracy of the data glove in translating some selected alphabets, numbers and words from Malaysian Sign Language is performed. The result for the first experiment shows that tilt sensor need to be tilted more than 85 degree to successfully change the digital state. For the accuracy of 4 individuals who tested this device, total average accuracy for translating alphabets is 95%, numbers is 93.33% and gestures is 78.33%. The average accuracy of data glove for translating all type of gestures is 89%. This fusion of tilt sensors and accelerometer could be improved in the future by adding more training and test data as well as underlying frameworks such as Hidden Markov Model

Analysis Of Spinal Electromyography Signal When Lifting An Object

Lifting and swinging are daily activities that human do using the spine.Furthermore,spine provides support during standing and walking.Therefore,it is very important in everyday activities and it will be inconvenient when it is injured.Technology has provided ways to machine and human integration in helping or supporting people in their daily tasks.To make this integration successful, machines or robots need to understand the human muscle activity.To do so,electromyography (EMG) a bio signal record the electricity generated by muscle was implemented.However,the signal often influenced by the unwanted noise.In this paper,the MVC normalization method is applied to determine the spinal EMG signal on lumbar multifidus muscle when lifting an object.In order to analyze the identity of spinal EMG signal,two statistical analyses are done;1) ANOVA analysis and 2)Boxplot analysis.The signal will go through 8th order Gaussian function or Exponential Weight Moving Average Filter before being analysed.Results show that Exponential Weight Moving Average Filter gives more consistent value compared to 8th order Gaussian function which is 0.0428V RMSE based on linear fitting done from the maximum amplitude gather from the boxplot analysis done