Smart-watch-based construction worker activity recognition with hand-held power tools

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.