Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning
This work addresses the problem of enabling adaptive human-robot interaction in construction, though it is incremental as it applies an existing transfer learning approach to a specific domain.
The paper tackled the lack of robustness and generalizability in human activity recognition for construction workers by proposing a transfer learning method that requires significantly less data and compute time, achieving comparable or better classification accuracy on manual material handling tasks.
Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers' activities. HAR has many applications in human-robot interaction research to enable robots' understanding of human counterparts' activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction.