Abstract

Abstract

Abstract

Abstract
Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques. © 2019, Springer Nature Switzerland AG.

Abstract
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

Abstract
Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.