Person Attribution in a Multi-Technician LabURL copied
This is the system's hardest problem. The state machine requires a technician ID with every instrument pickup event; without it, the system cannot know whose wash window to check. If two technicians are working adjacent benches and one picks up an instrument, the RFID reader at that bench must associate the pickup with a specific person.
Wristband RFID solves this at the bench level: the reader detects both the wristband tag and the instrument tag in the same read cycle, producing a (person, instrument) tuple. The challenge is ensuring read geometry — the wristband must enter the same read field as the instrument. Bench-mounted RFID antennas with well-chosen placement (pointing upward from the bench surface, not sideways) reliably achieve this.
Camera-based person tracking is the fallback. A person detection model (YOLO-class, running on an edge GPU) can assign instrument pickup events to bounding box tracks. This works well in single-occupancy scenes and degrades gracefully in two-person scenes. Beyond two people at a shared bench, occlusion becomes a genuine risk: one technician reaching across another will break the track.
The recommendation is to deploy wristband RFID as the primary attribution mechanism and use the camera track only for validation. A mismatch between the two signals should surface as a data quality alert, not a violation, and be reviewed manually.