This paper demonstrates a laboratory setup to replay previously collected experimental human motion data in real-time, allowing data re-purposing for activity recognition. The setup proposed is to examine the suitability of simulating motion by replaying a previ-ously collected data using an Electrodynamic Shaker - a table that can generate motion based on pre-recorded accelerometer traces. Building upon the information gathered from the sensor input, ac-curate continuous motion data from human dynamic movements can be recreated. By reusing data from past projects with novel or alternative sensor attachments, new datasets are created gen-erating activity data for movement recognition without requiring volunteers or time consuming data collection. This demo illustrates the potential for accurate low-cost in-house motion data collection without requiring field experiments.