Due to the ever-growing powers in sensing, computing, communicating and storing, mobile
devices (eg, smartphone, smartwatch, smart glasses) become ubiquitous and an …

Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …

Human activity recognition (HAR) using inertial motion data has gained a lot of momentum
in recent years both in research and industrial applications. From the abstract perspective …

End-to-end deep learning models are increasingly applied to safety-critical human activity
recognition (HAR) applications, eg, healthcare monitoring and smart home control, to reduce …

This study presents a new neural network model for recognizing manual works using body-
worn accelerometers in industrial settings, named Lightweight Ordered-work Segmentation …

Unlike human daily activities, existing publicly available sensor datasets for work activity
recognition in industrial domains are limited by difficulties in collecting realistic data as close …

The automated assessment and analysis of employee activity in a manufacturing enterprise,
operating in accordance with the concept of Industry 4.0, is essential for a quick and precise …

The development of a machine-learning-based human activity recognition (HAR) system
using body-worn sensors is mainly composed of three phases: data collection, model …

Accurate recognition of facial expressions and emotional gestures is promising to
understand the audience's feedback and engagement on the entertainment content. Existing …

This study presents a new method for recognizing complex human activities in a logistical
domain, such as packaging, using acceleration data from a body-worn sensor. Recognition …