Process-aware Human Activity Recognition

TL;DR

This paper introduces a process-aware approach to human activity recognition that combines machine learning with contextual process models, improving accuracy by leveraging process information.

Contribution

It presents a novel method that aligns probabilistic ML events with process models, enhancing HAR performance by integrating contextual information.

Findings

Improved accuracy over baseline models

Higher Macro F1-score achieved

Effective integration of process context

Abstract

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.