  Low-resolution infrared (IR) Sensors combined with machine learning (ML) can
be leveraged to implement privacy-preserving social distance monitoring
solutions in indoor spaces. However, the need of executing these applications
on Internet of Things (IoT) edge nodes makes energy consumption critical. In
this work, we propose an energy-efficient adaptive inference solution
consisting of the cascade of a simple wake-up trigger and a 8-bit quantized
Convolutional Neural Network (CNN), which is only invoked for
difficult-to-classify frames. Deploying such adaptive system on a IoT
Microcontroller, we show that, when processing the output of a 8x8
low-resolution IR sensor, we are able to reduce the energy consumption by
37-57% with respect to a static CNN-based approach, with an accuracy drop of
less than 2% (83% balanced accuracy).
