  Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and
privacy-preserving alternative to optical cameras and smartphones/wearables for
social distance monitoring in indoor spaces, permitting the recognition of
basic shapes, without revealing the personal details of individuals. In this
work, we demonstrate that an accurate detection of social distance violations
can be achieved processing the raw output of a 8x8 IR array sensor with a
small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be
executed directly on a Microcontroller (MCU)-based sensor node.
  With results on a newly collected open dataset, we show that our best CNN
achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved
by a state-of-the-art deterministic algorithm. Changing the architectural
parameters of the CNN, we obtain a rich Pareto set of models, spanning
70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RG MCU,
these models have a latency of 0.73-5.33ms, with an energy consumption per
inference of 9.38-68.57{\mu}J.
