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Computer vision and pattern recognition approaches have been applied to determine unsafe behaviors on construction sites. Such approaches have been reliant on the computation of artificially complex image features that utilize a cumbersome parameter re-adjustment process. The creation of image features that can recognize unsafe actions, however, poses a significant research challenge on construction sites. This due to the prevailing complexity of spatio-temporal features, lighting, and the array of viewpoints that are required to identify an unsafe action. Considering these challenges, a new hybrid deep learning model that integrates a convolution neural network (CNN) and long short-term memory (LSTM) that automatically recognizes workers' unsafe actions is developed. The proposed hybrid deep learning model is used to: (1) identify unsafe actions; (2) collect motion data and site videos; (3) extract the …