Smartwatch-Based Human Staff Activity Classification: A Use-Case Study in Internal Logistics Systems Utilizing AGVs

Recent advancements in the domain of human activity recognition (HAR) are increasingly aimed at developing methodologies, approaches, and models for real-time, multi-step activity recognition and analysis. This work presents a smartwatch-based approach for complex, real-time HAR that is applicable but not limited to internal logistics systems that use autonomous guided vehicles. A distributed smartwatch-based data collection system was developed, and a dataset was gathered and published, containing readings from human staff representatives executing activity sequences representing typical internal logistics tasks. A HAR-specific, pre-trained DenseNet121 was used for basic activity classification, achieving an F1-score of 91.01%. For multi-step activity classification, we compared models based on CNN, LSTM, BiLSTM, GRU, and BiGRU as meta-classifiers, employing different dataset-splitting strategies and models' configurations. The best-performing CNN-based model achieved an F1-score of 87.44% using the shared dataset utilization approach. Despite the challenges faced, the adaptability of the proposed approach suggests that it can be integrated into an intelligent enterprise management system to provide a robust and flexible HAR framework that enhances production efficiency.