/library/oar/handle/123456789/12483 Sat, 08 Nov 2025 15:23:45 GMT 2025-11-08T15:23:45Z /library/oar/handle/123456789/12939 Title: Proportional and simultaneous myoelectric control of a robotic arm Abstract: In the past years, machine and robot technologies have progressed rapidly, improving human life and making tasks much easier to execute. In a world that caters to the able-bodied, someone who has lost his/her upper limb often becomes dependent on other people in order to perform the simplest of tasks. Human-machine interfaces (HMI) are used to help these people perform speci c limb movements. HMIs provide control of a device using only signals produced by the body which are called biosignals. This project's aim is to develop an HMI for the control of a robotic arm manipulator through the use of non-invasive surface electromyography (EMG) signals. These signals can provide information on the limb movement being performed, such as its position in 3D space. Current research is focused on developing a reliable prosthetic device which incorporates simultaneous control of di erent joints. In this project, a model which converts EMG signals from multiple muscles to elbow and shoulder angles for simultaneous and proportional control is developed and tested in real-time. Furthermore an application to this project is demonstrated where a robotic arm replicates the user's wrist position in real-time as the user moves in the horizontal plane, stops and points towards an object. This system's performance was assessed using the correlation coe cient (CC) measure which was found to be 0.908 0.043 and 0.951 0.015 for the x and y directions respectively. This project focuses on developing a system which caters for di erent speeds and exible variety of movements. In this project seven di erent sequential and simultaneous movements in di erent planes were successfully modelled. The average root mean square error (RMSE) for sequential movements varied from 5.43° to 12.34° while the best performing simultaneous joint model resulted with an average cross-validated RMSE of 9.03° 0.33° for the elbow angle estimation and 7.30° 0.85° for the shoulder angle estimation. Incorporating these models into a single model would be the nal step in creating a fully functional prosthetic device which could replace the functions of the elbow and shoulder. Description: B.ENG.(HONS) Fri, 01 Jan 2016 00:00:00 GMT /library/oar/handle/123456789/12939 2016-01-01T00:00:00Z /library/oar/handle/123456789/12938 Title: System integration and control of a mini robotic manipulator Abstract: This dissertation is a continuous work on a series of projects in which it presents and summarises the work carried out on an anthropomorphic arm. The results are generalizable to other types of manipulators. The main aim of this project is to design and test various control algorithms for a mini robotic manipulator. Such applications pose specific requirements that are in general different from those of traditional robot design. Control algorithms were developed both for the gripper and for the arm. A controller for path trajectories is proposed. The control architecture was analysed, and experiments were carried out in the five degrees of freedom . The experimental results, which consist of control algorithms such as a PID controller, confirm the performance of the developed hardware and control strategies. Description: B.ENG.(HONS) Fri, 01 Jan 2016 00:00:00 GMT /library/oar/handle/123456789/12938 2016-01-01T00:00:00Z /library/oar/handle/123456789/12937 Title: Thermographic analysis of the abdominal region of pregnant women Abstract: Thermography’s use in the biomedical field is becoming increasingly popular and is also providing promising results in numerous medical fields such as breast screening and early detection of diabetes. Although the ultimate aim of this project would be an investigation on foetal thermography, one has to first run a preliminary study to understand the characteristics of the abdomen for both pregnant and non-pregnant subjects. The objectives of this project were to try and find a suitable acclimatisation period for the abdomen and to try and minimise the amount of time which the subject spends in front of the camera. In order to examine the temperature variations, a series of thermal images were acquired and two unforeseen circumstances were discovered. The first one is the challenge in thermal tracking which is difficult partly due to the subject’s movement and partly due to homogenous patterns across the abdomen. The best tracking performance was obtained using the summation of squared differences method. The second unanticipated situation was the fact that most studies assume an acclimatisation period of 15 to 20 minutes, even though a decrease in temperature and other variations were still present after that period. There are no reliable studies on the indication of the suitability of the acclimatisation period which consider more than 20 minutes of acclimatisation. To carry out the second objective, different mathematical models and algorithms were explored to provide proper curve fitting that satisfies both the dynamic characteristics of the data across time and the final temperature estimation. The RMSE for the dynamic characteristics is 0.1 or less whilst the RMSE for the final temperature estimation is less than 0.05, when considering between 70 and 100 minutes of data. The temperature difference between different quadrants across the abdomen shows statistical significant difference in all cases except one. This shows that most of the temperature differences are not just by chance and hence the underlying structures of the abdomen clearly affect the abdomen’s temperature. This kind of investigation on temperature changes has never been undertaken and although there is room for improvement, this dissertation shows that thermography can be a useful tool for future studies about foetal thermography. Description: B.ENG.(HONS) Fri, 01 Jan 2016 00:00:00 GMT /library/oar/handle/123456789/12937 2016-01-01T00:00:00Z /library/oar/handle/123456789/12936 Title: A brain-computer interface for rapid image searching Abstract: Even though there have been great advances in computer vision systems, no system has come close to replicating the complexity of the human vision system for object detection. Humans can recognize objects of interest at a glance, even when the objects are shown under different lighting and different angles. The recognition of a target object evokes an identifiable brain activity pattern in an individual, which can be recorded using electroencephalography (EEG). This pattern can be used to increase the efficiency of object detection by using the human vision system for object recognition, and computer processing power to analyse the EEG data and determine whether an object of interest was shown. The aim of this project is to implement a brain-computer interface (BCI) to decode EEG data and determine objects of interest from a series of images shown at a high rate by using rapid serial visual presentation (RSVP). An overview of the system would compose of a stimulus consisting of a series of images containing both target and non-target images. A participant would be subjected to a stimulus and the EEG data would be recorded and used to classify the images shown as target or non-target images by using features extracted from the EEG data to train a classifier. In this project, a stimulus was implemented and data synchronised with the stimulus was recorded from eight subjects. The stimulus consisted of images shown at a rate of five images per second using RSVP. The recorded data was then processed and different feature extraction methods were used to classify the data into target or non-target images. The different feature extraction methods analysed are the decimation method, the all points from t-test result (APT) method, consecutive points from t-test result (CPT) method and the mean of consecutive points from t-test result (MCPT) method. A Fisher linear discriminant analysis (LDA) classifier was used and provided positive results, where the best performing feature extraction method proved to be the decimation method. This method provided a target detection rate of 75 per cent and non-target detection rate of 86 per cent. Description: B.ENG.(HONS) Fri, 01 Jan 2016 00:00:00 GMT /library/oar/handle/123456789/12936 2016-01-01T00:00:00Z