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The construction industry still suffers from a low degree of transparency and bears health and safety risks for workers. A well-known disease is the Hand-Arm Vibration Syndrome, which results from the use of high-vibration tools. The recognition of worker activities to calculate a worker's exposure to vibration while operating tools in different modes has received little attention so far. This paper presents a machine-learning-based approach to recognize worker activities involving high-vibration hand-held power tools using acceleration data recorded with a smart-watch. The data is collected in a laboratory utilizing a combi-hammer in various tool settings. A Decision Tree and a Convolutional Pooling Long Short Term Memory (ConvPoolLSTM) model are developed to recognize worker activities on different levels. The ConvPoolLSTM architecture achieves accuracies between 89.1% and 96.1%. This research contributes towards the development of a more accurate, automated and low-cost monitoring system for vibration exposure time calculation and work process monitoring. • Machine learning models can recognize worker activities including power tools. • Collected data includes 202 min of smart-watch sensor data in various settings. • Models show high performance to recognize activities on three levels of details. • The ConvPoolLSTM model achieves up to 96.1% accuracy for tool runtime recognition. • The models can enable automated vibration time calculation on tool usage mode level. [ABSTRACT FROM AUTHOR]

Understanding the state, behavior, and surrounding context of construction workers is essential to effective project management and control. Exploiting the integrated sensors of ubiquitous mobile phones offers an unprecedented opportunity for an automated approach to workers' activity recognition. In addition, machine learning (ML) methodologies provide the complementary computational part of the process. In this paper, smartphones are used in an unobtrusive way to capture body movements by collecting data using embedded accelerometer and gyroscope sensors. Construction activities of various types have been simulated and collected data are used to train five different types of ML algorithms. Activity recognition accuracy analysis has been performed for all the different categories of activities and ML classifiers in user-dependent and -independent ways. Results indicate that neural networks outperform other classifiers by offering an accuracy ranging from 87% to 97% for user-dependent and 62% to 96% for user-independent categories. [ABSTRACT FROM AUTHOR]