Personal and ubiquitous sensing technologies such as smartphones have allowed the
continuous collection of data in an unobtrusive manner. Machine learning methods have …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

Abstract Human Activity Recognition (HAR) has become a crucial element for smart
healthcare applications due to the fast adoption of wearable sensors and mobile …

Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

With the current advancement in the Internet, there has been a growing demand for building
intelligent and smart systems that can efficiently address the detection of health‐related …

As the age profile of many societies continues to increase, in addition to the increasing
population of people affected by chronic diseases, including diabetes, cardiovascular …

Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …

Recognizing human physical activities using wireless sensor networks has attracted
significant research interest due to its broad range of applications, such as healthcare …

We propose a method for supervised learning with multiple sets of features (“views”). The
multiview problem is especially important in biology and medicine, where “-omics” data …

Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement
classic methods of mental health assessment and monitoring. This research area proposes …