This paper presents a novel framework that includes an inhomogeneous (time-variant)
Hidden Markov Model (HMM) and learning from data concepts. The framework either …

People-centric sensing and user state recognition can provide rich contextual information for
various mobile applications and services. However, continuously capturing this contextual …

This book proposes novel context-inferring algorithms and generic framework designs that
can help readers enhance existing tradeoffs in mobile sensing, especially between accuracy …

The ever increasing technological advances in embedded systems engineering, together
with the proliferation of small-size sensor designs and deployment, have enabled smart …

New-generation mobile devices will inevitably be employed within the realm of ubiquitous
sensing. In particular, smartphones have been increasingly used for human activity …

The machine recognition of user trajectories and activities is fundamental to devise context-
aware applications for support and monitoring in daily life. So far, tracking and activity …

Monitoring context depends on continuous collection of raw data from sensors which are
either embedded in smart mobile devices or worn by the user. However, continuous sensing …

Mobile device based sensing provides a vast variety of user contextual information. To
continuously acquire, classify and reason any context through built-in sensors cause mobile …

Human activity recognition (HAR) essentially uses (past) sensor data or complex context
information for inferring the activities a user performs in his daily tasks. HAR has been …

Human context recognition (HCR) from on-body sensor networks is an important and
challenging task for many healthcare applications because it offers continuous monitoring …