Facial Expression Recognition has demonstrated significant potential in biomedical research area: evaluation of emotional well-being, support in non-verbal communication, control of Human-Machine Interfaces (HMI) and assistance in rehabilitative procedures. While computer vision is currently the dominant approach for facial expression recognition, recent research has shown increasing surface ElectroMyoGraphy (sEMG) use. sEMG is a non-invasive technique to acquire the electrical signals generated by skeletal muscles during contraction by applying non-invasive electrodes on the skin. Several parameters can be extracted from sEMG signals, obtaining accurate and direct measures of muscle activity suitable to digital processing, e.g. machine learning (ML). This thesis presents an implementation of a low-power prototype for facial expression recognition based on the Averaged Threshold Crossing (ATC) technique applied to facial sEMG signals.