In today's connected society, many people rely on mHealth and self-tracking (ST) technology
to help them adopt healthier habits with a focus on breaking their sedentary lifestyle and …

Detecting anomalies in time-varying multivariate data is crucial in various industries for the
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …

We propose a knowledge model for capturing dietary preferences and personal context to
provide personalized dietary recommendations. We develop a knowledge model called the …

The term personal knowledge graph (PKG) has been broadly used to refer to structured
representation of information about a given user, primarily in the form of entities that are …

With an increased interest in the production of personal health technologies designed to
track user data (eg, nutrient intake, step counts), there is now more opportunity than ever to …

The age distribution has changed in Europe over the last decade. The group of 45 year-olds
and above has increased and the median age in the EU is estimated to increase by 4.5 …

Adaptive Brownian bridge-based aggregation (ABBA)(Elsworth & Güttel, 2020a) is a
symbolic time series representation approach that is applicable to general time series. It is …

Diabetes has become one of the most relevant concerns of the century due to the large
number of users who are affected by it. Devices that control blood glucose are very diverse …

Despite recent advances in digital health solutions and machine learning, personal health
applications that aim to modify health behaviors are still limited in their ability to offer more …

In recent years, the use of contrastive learning methods for time series classification has
shown promising results. State-of-the-art approaches leverage self-supervised contrastive …