Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning

TL;DR

This paper introduces a transfer learning approach for human activity recognition in construction, enabling robots to better understand worker actions with less data and computational effort, enhancing collaboration.

Contribution

It develops a transfer learning method that adapts a pre-trained video model for construction activity recognition, improving robustness and efficiency over existing approaches.

Findings

Achieved accurate recognition of construction activities from YouTube videos.

Reduced data and compute requirements for activity classification.

Demonstrated robustness and adaptability in real-world scenarios.

Abstract

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.