Transfer learning has been demonstrated to be effective for many real-world applications as
it exploits knowledge present in labeled training data from a source domain to enhance a …

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 …

There are huge differences in the layouts and numbers of sensors in different smart home
environments. Daily activities performed by residents trigger a variety of sensor event …

Human activity recognition requires the efforts to build a generalizable model using the
training datasets with the hope to achieve good performance in test datasets. However, in …

Sensor-based human activity recognition (HAR) has been an active research area, owing to
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …

Sensor-based human activity recognition (HAR) has been an active research area for many
years, resulting in practical applications in smart environments, assisted living, fitness …

Recognizing human activities seamlessly and ubiquitously is now closer than ever given the
myriad of sensors readily deployed on and around users. However, the training of …

Heterogeneity of features and lack of correspondence between data points of different
domains are the two primary challenges while performing feature transfer. In this paper, we …

Building contextual models for new “smart” environments is not considered cost effective if
data for model training must be collected from scratch. It is more practical to transfer as much …

Recent smart home applications enhance the quality of people's home experiences by
detecting their daily activities and providing them services that make their daily life more …