We tackle the problem of Egocentric Human-Object Interaction (EHOI) detection in an industrial domain. To overcome the lack of public datasets in this context, we propose a pipeline and a tool able to generate synthetic images of EHOIs paired with several annotations and data signals. Using the proposed pipeline, we present EgoISM-HOI a new multimodal dataset composed of synthetic EHOI images in an industrial environment with rich annotations of hands and objects. To demonstrate the utility of synthetic data, we designed an EHOI detection method that uses the different multimodal signals available within our dataset. Our study shows that exploiting synthetic data to pre-train the proposed system significantly improves performance when tested on real-world data. Additional experiments show that the proposed approach outperforms classic baseline approaches based on state-of-the-art class agnostic methods.