Process-aware Human Activity Recognition
This work addresses the limitation of overlooking contextual information in HAR algorithms, which is an incremental improvement for applications like production workflows or daily routines.
The paper tackled the problem of human activity recognition (HAR) by incorporating contextual process information to enhance performance, achieving better accuracy and Macro F1-score compared to baseline models.
Humans naturally follow distinct patterns when conducting their daily activities, which are driven by established practices and processes, such as production workflows, social norms and daily routines. Human activity recognition (HAR) algorithms usually use neural networks or machine learning techniques to analyse inherent relationships within the data. However, these approaches often overlook the contextual information in which the data are generated, potentially limiting their effectiveness. We propose a novel approach that incorporates process information from context to enhance the HAR performance. Specifically, we align probabilistic events generated by machine learning models with process models derived from contextual information. This alignment adaptively weighs these two sources of information to optimise HAR accuracy. Our experiments demonstrate that our approach achieves better accuracy and Macro F1-score compared to baseline models.