Complex activity recognition is a valuable issue in mobile and wearable computing. Since
complex activities are strongly relevant to users' locations, location data can be used in …

This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which
are composed of several repetitive and concurrent short movements having temporal …

This article provides a survey about the state of the art in embedded intelligence (EI)
research for smart cities. Currently, a comprehensive survey for EI research for smart cities is …

The automatic and unobtrusive identification of user activities is one of the most challenging
goals of context-aware computing. This paper discusses and experimentally evaluates …

In this paper, we introduce Mago, a novel system that can infer a person's mode of transport
(MOT) using the Hall-effect magnetic sensor and accelerometer present in most smart …

Machine learning with deep neural networks (DNNs) is widely used for human activity
recognition (HAR) to automatically learn features, identify and analyze activities, and to …

Driven by the rapid progress in mobile sensing and computing, wearable computing has
developed powerful methods for the automatic recognition, categorization, and labeling of …

Wearable sensors and the Internet of Things (IoT) will be two buzzwords that will be heard
commonly in the coming decades. The combination of these two technologies soon will …

Modeling human behavior requires vast quantities of accurately labeled training data, but for
ubiquitous people-aware applications such data is rarely attainable. Even researchers make …

User activity monitoring is a major problem in ambient assisted living, since it requires to
infer new knowledge from collected and fused sensor data while dealing with highly …