Detection and localization of activities in a human-centric manufacturing assembly operation
will help improve manufacturing process optimization. Through the human-in-loop …

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 …

Multi-sensor-based wearable human activity recognition (WHAR) is a research hotspot in
the field of ubiquitous computing. Extracting effective features from multi-sensor data is …

Human activity recognition (HAR) based on sensing data from wearable and mobile devices
has become an active research area in ubiquitous computing, and it envisions a wide range …

This paper presents a robust unsupervised method for recognizing factory work using
sensor data from body-worn acceleration sensors. In line-production systems, each factory …

Human activity recognition (HAR) has become an interesting topic in healthcare. This
application is important in various domains, such as health monitoring, supporting elders …

Protective behavior exhibited by people with chronic pain (CP) during physical activities is
very informative to understanding their physical and emotional states. Existing automatic …

The power of end-to-end deep learning techniques to automatically learn latent high-level
features from raw signals has been demonstrated in numerous application fields, however …

Manufacturing industries strive to improve production efficiency and product quality by
deploying advanced sensing and control systems. Wearable sensors are emerging as a …