Effectiveness of an E-learning System for Emergency Signs and CPR Emergency Preparedness in Marathon Events: A Comparative Study

This study investigates the implementation, effectiveness, and impact of a unique e-learning system designed specifically for emergency signs and cardiopulmonary resuscitation (CPR) emergency preparedness in marathon events. Our approach introduces the first e-learning system specifically designed for marathon events. It delivers engaging content, including infographic stories, expert lectures, and interactive modules, to provide registered runners with comprehensive knowledge of first aid and emergency signs for CPR. To evaluate the e-learning application, we conducted a comparative experiment during the CMU (Chiang Mai University) marathon with 9,761 participants. We used pre- and post-tests, as well as a survey questionnaire. The results showed significant improvements in participants’ CPR knowledge across all educational backgrounds. The integration of e-learning into the registration process contributed to a safer marathon environment, as participants felt more confident in handling emergencies. Approximately 85% of participants expressed a willingness to recommend the e-learning system. This increased confidence among participants in handling emergencies benefits both runners and marathon organizers by enhancing safety measures and emergency response during events. In conclusion, our findings strongly support the integration of e-learning into the registration process for marathon events. Recommendations based on our research include providing comprehensive guidelines for other marathon events, instilling stakeholder confidence, and emphasizing the suitability of e-learning for medium- to largescale events. However, caution is advised for smaller events due to potential complexities and costs. Additionally, we suggest limiting the validity of e-certificates to ensure that participants have up-to-date CPR knowledge

The timing of strike-slip shear along the Ranong and Khlong Marui faults, Thailand

The timing of shear along many important strike-slip faults in Southeast Asia, such as the Ailao Shan-Red River, Mae Ping and Three Pagodas faults, is poorly understood. We present 40Ar/39Ar, U-Pb SHRIMP and microstructural data from the Ranong and Khlong Marui faults of Thailand to show that they experienced a major period of ductile dextral shear during the middle Eocene (48–40 Ma, centered on 44 Ma) which followed two phases of dextral shear along the Ranong Fault, before the Late Cretaceous (>81 Ma) and between the late Paleocene and early Eocene (59–49 Ma). Many of the sheared rocks were part of a pre-kinematic crystalline basement complex, which partially melted and was intruded by Late Cretaceous (81–71 Ma) and early Eocene (48 Ma) tin-bearing granites. Middle Eocene dextral shear at temperatures of ~300–500°C formed extensive mylonite belts through these rocks and was synchronous with granitoid vein emplacement. Dextral shear along the Ranong and Khlong Marui faults occurred at the same time as sinistral shear along the Mae Ping and Three Pagodas faults of northern Thailand, a result of India-Burma coupling in advance of India-Asia collision. In the late Eocene (<37 Ma) the Ranong and Khlong Marui faults were reactivated as curved sinistral branches of the Mae Ping and Three Pagodas faults, which were accommodating lateral extrusion during India-Asia collision and Himalayan orogenesis

The structural evolution of Tertiary sedimentary basins in southern Thailand and their relationship to the Khlong Marui fault

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automatic traffic analysis in video sequences

Cette thèse s’inscrit dans le contexte de l’analyse vidéo du trafic routier. Dans certaines grandes villes, des centaines de caméras produisent de très grandes quantités de données, impossible à manipuler sans traitement automatique. Notre principal objectif est d'aider les opérateurs humains en analysant automatiquement les données vidéo. Pour aider les contrôleurs de la circulation à prendre leurs décisions, il est important de connaître en temps réel, l'état du trafic (nombre de véhicules et vitesse des véhicules sur chaque segment de voie), mais aussi de disposer de statistiques temporelles tout au long de la journée, de la semaine, de la saison ou de l'année. Les caméras ont été déployées depuis longtemps pour le trafic et pour d'autres fins de surveillance, car elles fournissent une source d'information riche pour la compréhension humaine. L'analyse vidéo peut désormais apporter une valeur ajoutée aux caméras en extrayant automatiquement des informations pertinentes. De cette façon, la vision par ordinateur et l'analyse vidéo deviennent de plus en plus importantes pour les systèmes de transport intelligents (intelligent transport systems : ITSs). L’une des problématiques abordées dans cette thèse est liée au comptage automatique de véhicules. Pour être utile, un système de surveillance vidéo doit être entièrement automatique et capable de fournir, en temps réel, l'information qui concerne le comportement de l'objet dans la scène. Nous pouvons obtenir ces renseignements sur la détection et le suivi des objets en mouvement dans les vidéos, ce qui a été un domaine largement étudié. Néanmoins, la plupart des systèmes d'analyse automatique par vidéo ont des difficultés à gérer les situations particulières. Aujourd'hui, il existe de nombreux défis à résoudre tels que les occultations entre les différents objets, les arrêts longs, les changements de luminosité, etc… qui conduisent à des trajectoires incomplètes. Dans la chaîne de traitements que nous proposons, nous nous sommes concentrés sur l'extraction automatique de statistiques globales dans les scènes de vidéosurveillance routière. Notre chaîne de traitements est constituée par les étapes suivantes : premièrement, nous avons évalué différentes techniques de segmentation de vidéos et de détection d'objets en mouvement. Nous avons choisi une méthode de segmentation basée sur une version paramétrique du mélange de gaussiennes appliquée sur une hiérarchie de blocs, méthode qui est considérée actuellement comme l'un des meilleurs procédés pour la détection d'objets en mouvement. Nous avons proposé une nouvelle méthodologie pour choisir les valeurs optimales des paramètres d’un algorithme permettant d’améliorer la segmentation d’objets en utilisant des opérations morphologiques. Nous nous sommes intéressés aux différents critères permettant d’évaluer la qualité d’une segmentation, résultant d’un compromis entre une bonne détection des objets en mouvement, et un faible nombre de fausses détections, par exemple causées par des changements d’illumination, des reflets ou des bruits d’acquisition. Deuxièmement, nous effectuons une classification des objets, basée sur les descripteurs de Fourier, et nous utilisons ces descripteurs pour éliminer les objets de type piétons ou autres et ne conserver que les véhicules. Troisièmement, nous utilisons un modèle de mouvement et un descripteur basé sur les couleurs dominantes pour effectuer le suivi des objets extraits. En raison des difficultés mentionnées ci-dessus, nous obtenons des trajectoires incomplètes, qui donneraient une information de comptage erronée si elles étaient exploitées directement. Nous proposons donc d’agréger les données partielles des trajectoires incomplètes et de construire une information globale sur la circulation des véhicules dans la scène. Notre approche permet la détection des points d’entrée et de sortie dans les séquences d’images. Nous avons testé nos algorithmes sur des données privées provenant...This thesis is written in the context of video traffic analysis. In several big cities, hundreds of cameras produce very large amounts of data, impossible to handle without automatic processing. Our main goal is to help human operators by automatically analyzing video data. To help traffic controllers make decisions, it is important to know the traffic status in real time (number of vehicles and vehicle speed on each path), but also to dispose of traffic statistics along the day, week, season or year. The cameras have been deployed for a long time for traffic and other monitoring purposes, because they provide a rich source of information for human comprehension. Video analysis can automatically extract relevant information. Computer vision and video analysis are becoming more and more important for Intelligent Transport Systems (ITSs). One of the issues addressed in this thesis is related to automatic vehicle counting. In order to be useful, a video surveillance system must be fully automatic and capable of providing, in real time, information concerning the behavior of the objects in the scene. We can get this information by detection and tracking of moving objects in videos, a widely studied field. However, most automated video analysis systems do not easily manage particular situations.Today, there are many challenges to be solved, such as occlusions between different objects, long stops of an object in the scene, luminosity changes, etc., leading to incomplete trajectories of moving objects detected in the scene. We have concentrated our work on the automatic extraction of global statistics in the scenes. Our workflow consists of the following steps: first, we evaluated different methods of video segmentation and detection of moving objects. We have chosen a segmentation method based on a parametric version of the Mixture of Gaussians, applied to a hierarchy of blocks, which is currently considered one of the best methods for the detection of moving objects. We proposed a new methodology to choose the optimal parameter values of an algorithm to improve object segmentation by using morphological operations. We were interested in the different criteria for evaluating the segmentation quality, resulting from a compromise between a good detection of moving objects, and a low number of false detections, for example caused by illumination changes, reflections or acquisition noises. Secondly, we performed an objects classification, based on Fourier descriptors, and we use these descriptors to eliminate pedestrian or other objects and retain only vehicles. Third, we use a motion model and a descriptor based on the dominant colors to track the extracted objects. Because of the difficulties mentioned above, we obtain incomplete trajectories, which, exploited as they are, give incorrect counting information. We therefore proposed to aggregate the partial data of the incomplete trajectories and to construct a global information on the vehicles circulation in the scene. Our approach allows to detect input and output points in image sequences. We tested our algorithms on private data from the traffic control center in Chiang Mai City, Thailand, as well as on MIT public video data. On this last dataset, we compared the performance of our algorithms with previously published articles using the same data. In several situations, we illustrate the improvements made by our method in terms of location of input / output zones, and in terms of vehicle counting

A Framework of Developing Mobile Gamification to Improve User Engagement of Physical Activity: A Case Study of Location-Based Augmented Reality Mobile Game for Promoting Physical Health

This study presents the framework of developing mobile gamification for conducting application on health promotion. The method is composed of four phases adapted from the spiral model: objectives determination for enhancement of positive health; core game flow and mechanics definition; development, test and evaluation; and the next iteration plan. To evaluate the frame-work, we developed Camt comic run application to provide a practical method to select the suitable game elements (leaderboard, score point, map progress bar, inventory and randomness) and validation by Game Experience Questionnaire (GEQ): Four weeks with 40 participants were to investigate the outcome of the application which was divided into two stages. Week 1 and Week 2 were the baseline stage collecting behavioral information of participants.  In the second stage – Week 3 and Week 4 – the participants were divided into two groups:  the ones who use our application and those who don’t. The results showed that gamification drove the engagement and motivation of participants who had not reached the standard guideline. The data showed significance mostly in participants who had less physical activity than physical activity guidelines at least 150 min/week and average increase physical activity rate from baseline

A new pixel-based quality measure for segmentation algorithms integrating precision, recall and specificity

International audienceThere are several approaches for performance evaluation of image processing algorithms in video-based surveillance systems: Precision/ Recall, Receiver Operator Characteristics (ROC), F-measure, Jaccard Coefficient, etc. These measures can be used to find good values for input parameters of image segmentation algorithms. Different measures can give different values of these parameters, considered as optimal by one criterion, but not for another. Most of the times, the measures are expressed as a compromise to be found between two of the three aspects that are important for a quality assessment: precision, recall and specificity. In this paper, we propose a new 3-dimensional measure Dprs), which takes into account all of the three aspects. It can be considered as a 3D generalization of 2D ROC analysis and Precision/Recall curves. To estimate the impact of parameters on the quality of the segmentation, we study the behavior of this measure and compare it with several classical measures. Both objective and subjective evaluations confirm that our new measure allows to determine more stable parameters than classical criteria, and to obtain better segmentations of images