Πλοήγηση ανά Συγγραφέας "Pavlidou, Elisavet"

Τώρα δείχνει 1 - 3 of 3
  • Μικρογραφία εικόνας
    Τεκμήριο
    Implementation of a mobile app for psycho-emotional assessment (Android and iOS).
    (ΕΛ.ΜΕ.ΠΑ., ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ (ΣΜΗΧ), Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, 2020-09-21) Pavlidou, Elisavet; Παυλίδου, Ελισάβετ
    Cancer is one of the most common chronic diseases, and the second leading cause of death in the developed countries. During treatment patients face several side effects, which are painful both physically and emotionally. This results to the need of seeking psychological support and constant monitoring of their psychological and physical condition. Diagnosis, follow-up and treatment, consist a great interest to the research field. The rapid development of the technology has significantly improved the treatment of cancer as its goal is early diagnosis and finding the right treatment. That process depends mainly on the type and the stage of the cancer and on the continuous monitoring of the patient throughout recovery. As technology and medicine evolve at a rapid pace, the need for better quality of life is growing. In this context the “mobile health” field is being added. This term refers to the variety of applications, which have been implemented for smartphones or wearables that are powered by user data. Such systems are either used exclusively by the patient as a personal health record or as an interface between the patients and the medical staff. This process aims in a model that focuses on the patient and the way they handle their health status. In this thesis we will deal with the implementation of a questionnaire of the mobile application MyPal, with which the user will rate his/her emotional state and then this will be visualized. Finally, special emphasis has been placed on the interface of the application, as well as in the usability, as it targets at the elderly and mostly patients are unfamiliar with mobile technology.
  • Μικρογραφία εικόνας
    Τεκμήριο
    Multimodal pain intensity assessment based on physiological biosignals.
    (ΕΛΜΕΠΑ, Σχολή Μηχανικών (ΣΜΗΧ), ΠΜΣ Μηχανικών Πληροφορικής, 2024-07-15) Pavlidou, Elisavet; Παυλίδου, Ελισάβετ; Tsiknakis, Emmanouil; Τσικνάκης, Εμμανουήλ
    Pain is a multifaceted and subjective experience that can affect individuals both physically and emotionally. According to the most widely accepted definition, it is characterized as the “unpleas ant sensory and emotional experience associated with, or resembling that associated with actual or potential tissue damage”. Visual analog scales, numeric rating scales, and various questionnaires, all relying on patient-reported outcome measurements, are prevalent methods within healthcare and research domains for assessing the presence, incidence, and severity of pain. Nevertheless, self-report evaluation tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. Pain can be classified as either acute or chronic. Acute pain is sudden, sharp, and usually, temporary. Conversely, persistent pain can result in prolonged suffering. Chronic pain is defined as enduring discomfort lasting beyond six months, often representing a disease state. Diagnosing and managing chronic pain can be challenging due to its sustained nature. In the case of individ uals hospitalized with chronic conditions, precise pain assessment is imperative for the effective application of pain management strategies. Currently, pain detection and accurate classification continue to pose a significant challenge in both the medical field and research endeavors. The objective of this master thesis is to automate pain recognition and assessment through physiological-data-driven predictive models. This study emphasizes the substantial importance of integrating multimodal physiological signals and explor ing their most efficient combination. Furthermore, we explored the influence of using multiple types of signal (multimodal approach) on any improvements in performance compared to using just one type of signal (unimodal approach). Additionally, we aimed to scrutinize the influence of demographic factors such as gender and age on pain perception and how they may affect the out comes of our experiments. We used the BioVid Part A dataset as input in our pipeline, incorporating the demographic data with age groups divided into: 20-35 years, 36-50 years, and 51-65 years. The Pan & Tompkins algorithm was applied for ECG feature extraction, while statistical analysis was employed for both GSR and EMG signals. Following data pre-processing, an early fusion approach was implemented in all unimodal and multimodal experiments encompassing binary (no pain vs. 1 pain) and multiclass tasks (all pain levels, no pain vs. pain levels). The Long Short-Term Memory (LSTM) approach demonstrated superior performance in pain intensity classification compared to Support Vector Machines (SVM) and Decision Trees, whether applied in unimodal or multimodal tasks. Galvanic skin response emerged as the most effective standalone modality, while the combination of ECG, EMG, and GSR proved to be the most ef fective pair of fused modalities. The combination of all physiological signals resulted in further improvement in our outcomes, achieving 81.75% accuracy in the binary classification task of no pain (BL) versus the highest level of pain (PA4) and 86.05% in the female population of the same classification task. It is noteworthy that in machine learning experiments, the polynomial kernel consistently yielded higher accuracy values in both binary and multiclass classification tasks. To the best of our knowledge, our study reported the highest accuracy (82.83%) in gender-age-based experiments using all physiological data from the BioVid dataset. This performance was observed in a binary task distinguishing between baseline and the highest intensity of pain within the female population aged 20-35.
  • Μικρογραφία εικόνας
    Τεκμήριο
    Χειμερινός τουρισμός.
    (Τ.Ε.Ι. Κρήτης, Σχολή Διοίκησης και Οικονομίας (Σ.Δ.Ο), Τμήμα Διοίκησης Επιχειρήσεων (Ηράκλειο), 2014-12-01T10:08:53Z) Παυλίδου, Ελισάβετ; Pavlidou, Elisavet
    Η παρούσα εργασία αποτελεί μια εισαγωγική σπουδή στην κατανόηση του Χειμερινού Τουρισμού και των ευνοϊκών αποτελεσμάτων από την ανάπτυξή του. Αποτελείται από έξι κεφάλαια των οποίων δομικά στοιχεία είναι η εισαγωγή, η ερμηνεία και η ιστορική αναδρομή των κεντρικών τους θεμάτων καθώς και η περιγραφή της τρέχουσας κατάστασης στη Χώρα μας. Η εργασία ξεκινά με μια γενική εισαγωγή στον Τουρισμό και το Τουριστικό φαινόμενο και συνεχίζεται με την παρουσίαση των διάφορων μορφών τουρισμού με βάση τις φυσικές και τις πολιτιστικές ανάγκες των ταξιδιωτών. Ακολουθεί αναλυτικότερη παρουσίαση του χειμερινού τουρισμού, της ιστορίας, των μορφών και των δραστηριοτήτων που λαμβάνουν χώρα κατά την διάρκεια του. Επίσης παρουσιάζεται ο χιονοδρομικός τουρισμός, ως κυρίαρχη μορφή τουρισμού κατά την διάρκεια του χειμώνα, οι δραστηριότητες του και οι λεπτομέρειες που οδηγούν σε ασφαλή πραγματοποίηση αυτών. Εν συνεχεία παρουσιάζεται η αγορά του χιονοδρομικού τουρισμού και η υφιστάμενη στη Χώρα κατάστασή του. Συγκρίνεται η μορφή αυτή με τη γενική τουριστική εικόνα της χώρας και αναλύονται οι υποδομές αλλά και τα προβλήματα της σύγχρονης χιονοδρομίας. Παράλληλα παρουσιάζονται ο κοινωνικός τουρισμός και πρακτικές κοινωνικού τουρισμού στη Χώρα. Η σχέση του χρονομεριστικού τουρισμού με τον χειμερινό τουρισμό και με την Ελληνική πραγματικότητα. Εν κατακλείδα συνοψίζονται τα συμπεράσματα αλλά και οι προτάσεις περαιτέρω ανάπτυξης και ενίσχυσης αυτής της μορφής τουρισμού.