Unveiling the Potential of Machine Learning in Activity Recognition for Industry 4.0

This paper presents a comprehensive overview of Machine Learning’s (ML) pivotal role in Activity Recognition (AR) within the Industry 4.0 framework. Positioned at the forefront of the Fourth Industrial Revolution, data-centric, automated technologies are forging new pathways for transformative enhancements in manufacturing and industrial procedures. Central to these advancements is the synergy between ML and AR, playing a crucial role in this evolutionary process. We conduct an extensive review of recent developments in ML applications to AR, particularly in industrial settings. The paper begins with an introductory discussion on AR, highlighting its significance and potential benefits for Industry 4.0, such as augmented efficiency, predictive maintenance, and improved safety measures. The core emphasis of our study is on examining a variety of ML algorithms, ranging from supervised learning approaches like Decision Trees and Support Vector Machines to unsupervised techniques such as clustering, along with cutting-edge progressions in Deep Learning (DL). We meticulously assess the suitability, strengths, and limitations of these methodologies within the AR domain in Industry 4.0. Moreover, the paper includes an analysis of real-world instances where ML-enhanced AR has markedly boosted operational performance in industrial contexts. We also address the current challenges and prospective directions in employing ML for AR in Industry 4.0, including managing high-dimensional data, ensuring the interpretability of models, and adapting to the dynamic cybersecurity environment. In summary, this succinct survey offers a holistic view of ML’s impact in industrial AR, sparking further inquiry and practical implementations in this rapidly growing field.