Evolving consumer demands and market trends have led to businesses increasingly
embracing a production approach that prioritizes flexibility and customization. Consequently …

Workers' behavior should be analyzed to improve their efficiency and that of cell production
systems. However, traditional approaches and supervised learning methods are time …

Extraction of complex temporal patterns, such as human behaviors, from time series data is
a challenging yet important problem. The double articulation analyzer has been previously …

Complex human activities can be decomposed into primitive activities (PAs) that happen
sequentially but may vary in order or frequency among different observation sequences. The …

This paper presents an unsupervised method for recognizing assembly work done by factory
workers by using wearable sensor data. Such assembly work is a common part of line …

Real-time action detection and feedback systems are needed to reduce the load on
assembly line workers. Segmenting motion based on a Gaussian process hidden semi …

Trajectory-based human activity recognition aims at understanding human behaviors in
video sequences, which is important for intelligent surveillance. Some existing approaches …

Humans recognize perceived continuous information by dividing it into significant segments
such as words and unit motions. We believe that such unsupervised segmentation is also an …

Hidden Markov Models (HMMs) are a stochastic signal modeling formalism that is actively
used in the machine learning community for a wide range of applications such as speech …

Abstract Applying Human Action Recognition (HAR) in manufacturing site to recognize the
human assembling tasks, representing as repetitions of human actions, is an emerging …