Humans are integral to manufacturing assembly, and workstation design is crucial for optimizing operator productivity. While Virtual Reality (VR) simulations have been used to design workstations, they often lack data acquisition capabilities. Wearable sensors are commonly employed for tracking worker performance and productivity. Enhancing VR simulations with data acquisition system simulations could improve their effectiveness. This research aims to determine if virtual sensors can be substituted for physical sensors by comparing the identification accuracy of the movements they measure. Participants performed a few basic movements in a virtual reality assembly environment while a physical wearable measured acceleration and angular velocity, and a virtual sensor measured position and rotation. The similarity between the identified movements was assessed using a K Nearest Neighbours machine learning algorithm. The movements recorded by the two sensors were found to be similar, supporting the idea that virtual sensors can effectively simulate physical sensors. Simulating sensors in VR can reduce the risks associated with capital investment in developing human workstations. By identifying optimal sensor configurations through virtual testing, appropriate physical sensors can be selected. Previously, workstations could be prototyped in VR; now, wearable sensors can also be simulated in virtual reality.