In the construction industry, construction machinery are an important factor in the overall productivity and efficiency of a worksite. Thus, emphasis is put on the monitoring of actions conducted by construction machinery. This was traditionally done manually by humans, which is a timeconsuming and laborious task. Automatic action recognition of construction machinery is therefore needed. The field of action recognition is predominantly occupied by Deep Learning approaches and several previous works focused on adapting such approaches for construction machinery. However, the issue of obtaining training data is particularly troublesome for construction machinery. Our previous work proposed a Deep Learning method for learning an action recognition model from training data generated in a simulator using video filters but the precise contributions of the introduced video filter were unclear. The purpose of this study is therefore to clarify the effects of video filters for learning an action recognition model for construction machinery from simulated training data.