A multimodal approach for event detection from lifelogs

This paper analyzes how personal lifelog data which contains biometric, visual, activity data, can be leveraged to detect points in time where the individual is partaking in an eating activity. To answer this question, three artificial neural network models were introduced. Firstly, a image object detection model trained to detect eating related objects using the YOLO framework. Secondly, a feed-forward neural network (FANN) and a Long-Short-Term-Memory (LSTM) neural network model which attempts to detect ‘eating moments’ in the lifelog data. The results show promise, with F1-score and AUC score of 0.489 and 0.796 for the FANN model, and F1-score of 0.74 and AUC score of 0.835 respectively. However, there are clear rooms for improvement on all models. The models and methods introduced can help individuals monitor their nutrition habits so they are empowered to make healthy lifestyle decisions. Additionally, several methods for streamlining event detection in lifelog data are introduced.Masteroppgave i informasjonsvitenskapINFO390MASV-INFOMASV-IK

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis

PRESENT AND FUTURE PERVASIVE HEALTHCARE METHODOLOGIES: INTELLIGENT BODY DEVICES, PROCESSING AND MODELING TO SEARCH FOR NEW CARDIOVASCULAR AND PHYSIOLOGICAL BIOMARKERS

The motivation behind this work comes from the area of pervasive computing technologies for healthcare and wearable healthcare IT systems, an emerging field of research that brings in revolutionary paradigms for computing models in the 21st century. The aim of this thesis is focused on emerging personal health technologies and pattern recognition strategies for early diagnosis and personalized treatment and rehabilitation for individuals with cardiovascular and neurophysiological diseases. Attention was paid to the development of an intelligent system for the automatic classification of cardiac valve disease for screening purposes. Promising results were reported with the possibility to implement a new screening strategy for the diagnosis of cardiac valve disease in developing countries. A novel assistive architecture for the elderly able to non-invasively assess muscle fatigue by surface electromyography using wireless platform during exercise with an ergonomic platform was presented. Finally a wearable chest belt for ECG monitoring to investigate the psycho-physiological effects of the autonomic system and a wearable technology for monitoring of knee kinematics and recognition of ambulatory activities were characterized to evaluate the reliability for clinical purposes of collected data. The potential impact in the clinical arena of this research would be extremely important, since promising data show how such emerging personal technologies and methodologies are effective in several scenarios to early screening and discovery of novel diagnostic and prognostic biomarkers

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz