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Automated, real-time, and reliable equipment activity identification on construction sites can help to minimize idle times, improve operational efficiencies, and reduce emissions. Many previous efforts in activity identification have explored different machine learning algorithms that use time-series sensor data collected from inertial measurement units mounted on the equipment. However, machine learning algorithms requires large volume of training data collection from the field, as inadequate and smaller amounts of data results in model overfitting. This study proposes an automatic and real-time activity recognition framework by using data from multiple IMUs attached to equipment's moving and articulated parts. In doing so, first a time-series data augmentation technique called window-warping (WW) is introduced to generate synthetic training data from a smaller volume of field-collected data. Two supervised machine learning algorithms, artificial neural network (ANN), and K-nearest neighbour (KNN) were trained and evaluated using the augmented training data to identify equipment activity. The developed data augmentation methodology is validated using a case study of an earthmoving excavator. The results show the potential for using time-series data augmentation in training machine learning algorithms for construction equipment activity recognition using minimal data collected from the field.

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