  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.
