  Smart factories leverage advanced technologies to optimize manufacturing
processes and enhance efficiency. Implementing worker tracking systems,
primarily through camera-based methods, ensures accurate monitoring. However,
concerns about worker privacy and technology protection make it necessary to
explore alternative approaches. We propose a non-visual, scalable solution
using Bluetooth Low Energy (BLE) and ultrasound coordinates. BLE position
estimation offers a very low-power and cost-effective solution, as the
technology is available on smartphones and is scalable due to the large number
of smartphone users, facilitating worker localization and safety protocol
transmission. Ultrasound signals provide faster response times and higher
accuracy but require custom hardware, increasing costs. To combine the benefits
of both modalities, we employ knowledge distillation (KD) from ultrasound
signals to BLE RSSI data. Once the student model is trained, the model only
takes as inputs the BLE-RSSI data for inference, retaining the advantages of
ubiquity and low cost of BLE RSSI. We tested our approach using data from an
experiment with twelve participants in a smart factory test bed environment. We
obtained an increase of 11.79% in the F1-score compared to the baseline (target
model without KD and trained with BLE-RSSI data only).
