Geomagnetism has recently attracted considerable attention for indoor localization due to its
pervasiveness and independence from extra infrastructure. Its location signature has been …

Indoor human tracking is fundamental to many real-world applications such as security
surveillance, behavioral analysis, and elderly care. Previous solutions usually require …

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity
labels. To solve this problem, transfer learning leverages the labeled samples from the …

Human activity recognition plays an important role in people's daily life. However, it is often
expensive and time-consuming to acquire sufficient labeled activity data. To solve this …

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …

Smartphone-based indoor localization systems are increasingly needed in various types of
applications. This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi …

Localization is one of the current challenges in indoor navigation research. The
conventional global positioning system (GPS) is affected by weak signal strengths due to …

Few-shot learning is a technique to learn a model with a very small amount of labeled
training data by transferring knowledge from relevant tasks. In this paper, a few-shot learning …

This paper presents a multifunctional interdisciplinary framework that makes four scientific
contributions towards the development of personalized ambient assisted living (AAL), with a …

Indoor localization is of great significance to a wide range of applications in the era of mobile
computing. The maturity of the computer vision techniques and the ubiquity of embedded …