Real-time, automatic detection of smoking behavior could lead to novel measurement tools
for smoking research and" just-in-time" interventions that may help people quit, reducing …

This poster presents an integrated framework to enable using standard non-sequential
machine learning tools for accurate multi-modal activity recognition. Our framework contains …

Low-cost inertial and motion sensors embedded on smartphones have provided a new
platform for dynamic activity pattern inference. In this research, a comparison has been …

Individuals spend a majority of their time in their home or workplace and for many, these
places are our sanctuaries. As society and technology advance there is a growing interest in …

This paper explores the possibility of using low-level activity spotting for daily routine
recognition. Using occurrence statistics of low-level activities and simple classifiers based …

Methods, systems, and techniques for facilitating cognitive assessment are provided.
Example embodiments provide a Cognitive Assessment Facilitator System CAFS, which …

Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes,
and other sensors empower users to track real‐time health data continuously, have made it …

Data mining is one hot orientation in today's research field. Human activity recognition is
meaningful in our daily living and is a significant aspect in data mining. Most previously …

In the past decades, activity recognition has aroused a great interest for the research groups
majoring in context-awareness computing and human behaviours monitoring. However, the …

The emergence of data-driven demand analysis has led to the increased use of generative
modelling to learn the probabilistic dependencies between random variables. Although their …