Many activities in laboratories at Purdue require user movement that cannot be carefully orchestrated or planned out, e.g., in our hardware, manufacturing, or propulsion labs. In such environments, it is challenging for users to consciously maintain the required safe social distance. This project provides a technical approach to proactively monitor the distance between users utilizing the Bluetooth transmission-reception signal strength (RSSI). We use a lightweight machine learning model to map the signal strength to the distance and infer the direction of motion between any two users. The technology builds on a long line of research in the area of wireless signals, some of which has been carried out in our lab. It is lightweight (can be easily carried as a lanyard worn by users), low cost (less than $15 when produced in bulk), privacy preserving (no data need to be shared to any other organizations), proactive (provides warning messages prior to approaching unsafe distance). We have shown its effectiveness in our preliminary experiments.