https://www.um.edu.mt/library/oar/handle/123456789/66076 Wed, 08 Apr 2026 11:38:06 GMT 2026-04-08T11:38:06Z https://www.um.edu.mt/library/oar/handle/123456789/66971 Title: A portable in-shoe measurement system to acquire dense continuous foot temperature maps Abstract: People suffering from diabetes are at risk of developing ulcerations, which, if left untreated, could also lead to amputation. Monitoring of the foot temperature can help identify ulcerations sites before there are any visible signs on the skin. Various studies have shown that elevated temperatures in the foot may be indicative of ulceration. Over the years there have been numerous devices that were designed for foot temperature monitoring, both for clinical and home use. The technologies used vary from infrared (IR) thermometry, liquid crystal thermography (LCT), IR thermography and a vast range of analogue and digital temperature sensors that were incorporated in different measurement platforms. The aim of this thesis is to design an in-shoe portable system that utilises a high-density sensing array to record temperature data from the foot. Software was designed to visualise the recorded temperature data, representing it in format suitable to both clinical and non-clinical users. Various testing was done to validate the system performance, and eventually carried out trial walks with healthy subjects for the analysis of temperature data. The system was able to record temperatures continuously, with the analysed results in line with findings from previous studies. With the systems currently available for in-shoe temperature monitoring, the device designed in this project enable more in-depth analysis of the temperature variations of the foot within the shoe. Description: M.SC.BIOMED.CYB. Wed, 01 Jan 2020 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/66971 2020-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/66970 Title: An EEG-based biometric system Abstract: Biometric systems have gained increased popularity in modern society since they provide an extra sense of security. A biometric system refers to a system that is capable of identifying an individual from a number of individuals by using specific biometric features. Standard biometric features used in common biometric systems include fingerprints, voice, and facial features. However, in recent years, studies considering electroencephalography (EEG) as a biometric feature have become more popular. The main advantage of using EEG in biometrics when compared to common biometric features is that it is not prone to spoofing due to the difficulty in replicating the signal, making the system much more secure. From previous studies, it was noted that very few considered the use of steady state visually evoked potentials (SSVEP) as biometric features, and no reasearch up to date considered the phase information from SSVEP. glsssvep are oscillatory responses in the EEG elicited when an individual is subject to a visual stimulus. On this basis, a study was conducted to increase the performance in biometric systems using magnitude information as biometric trait. Moreover, an initial investigation on the phase information in SSVEP signals was conducted to check viability of using phase information as biometric features. Data was collected from ten subjects, using three different stimulus frequencies. To investigate the effect of ageing in the EEG, data from each individual was recorded in three different sessions. From the initial study it was concluded that phase information carries distinctive properties, and remains significantly unchanged across time, which are very important characteristics in a biometric trait. Data recorded from across the three different sessions was used to test the biometric systems. The best overall classification accuracy for the biometric system using magnitude information was 53.8%. The best classification accuracy for one subject for the biometric system using phase information was 57%, when using only one feature for discrimination. The results do not show high accuracy results in identification of individuals. However these results can be improved further with better classification algorithms and larger feature vectors. Description: M.SC.BIOMED.CYB. Wed, 01 Jan 2020 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/66970 2020-01-01T00:00:00Z