LSTM-CNN architecture for construction activity recognition using optimal positioning of wearables

Enhancing construction worker performance, safety, and project management through automated activity classification is a promising endeavor. By extracting activity-level information, this technology provides valuable insights for informed decision-making, facilitating project schedule adjustments, efficient resource management, and improved construction site control. Previous studies in this domain focused on basic activities, neglecting optimal sensor placement and no regard for worker comfort. This paper extends beyond existing research, encompassing a broader range of complex construction activities and surpassing current methods. Utilizing unobtrusive wearables like a smartwatch and smartphone, the study determines optimal sensor positions (dominant/nondominant wrist, dominant/nondominant leg pocket). Notably, it introduces a novel deep neural network structure, merging long short-term memory (LSTM) and convolutional layers, offering an innovative solution for automated activity classification tasks in the construction industry. This model extracts activity features automatically reducing the need for manual feature engineering and performs classification with few model parameters indicating efficiency in terms of computational resources and memory requirements making the model more suitable for real-time applications and deployment on resource-constrained devices. By leveraging the strengths of both convolutional layers and LSTM, this approach offers a powerful and efficient solution for activity classification tasks. An experimental study was carried out to recognize four different activities: manual excavation, rebar stirrups, cement plastering, and bar binding. These were performed by four subjects (three males and one female) for 30 s each with different positions of smartwatch and smartphone producing 24,080 data points. Results indicate the optimal positioning of wearables to be smartwatch on dominant hand and smartphone on opposite leg pocket because of a balanced and effective coverage of the relevant movements and contextual information yielding 98.18% accuracy, 98.20% precision, 98.17% recall, and F1 score of 98.17%.