Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …

The ubiquity of smartphones and the growth of computing resources, such as connectivity,
processing, portability, and power of sensing, have greatly changed people's lives. Today …

We propose a one-dimensional (1D) Convolutional Neural Network (CNN)-based method
for recognizing human activity using triaxial accelerometer data collected from users' …

There has been an upsurge recently in investigating machine learning techniques for activity
recognition (AR) problems as they have been very effective in extracting and learning …

Activity detection and classification are very important for autonomous monitoring of humans
for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors …

Human-centered computing is an emerging research field that aims to understand human
behavior and integrate users and their social context with computer systems. One of the …

Physical activity recognition using embedded sensors has enabled many context-aware
applications in different areas, such as healthcare. Initially, one or more dedicated wearable …

Signal segmentation is a crucial stage in the activity recognition process; however, this has
been rarely and vaguely characterized so far. Windowing approaches are normally used for …

Activity recognition plays an essential role in bridging the gap between the low-level sensor
data and the high-level applications in ambient-assisted living systems. With the aim to …

Smart mobile devices are being widely used to identify and track human behaviors in simple
and complex daily activities. The evolution of wearable sensing technologies pertaining to …