Cooling fan controlled by embedded vision system

The HMI (human machine interaction) systems are widely used to control machines and variety of devices. Currently the HMI solutions, based on touch screens are almost commonly used in many domains, however the number of devices, which interaction with the user is based on speech recognition or user gesture recognition increases systematically. The paper focuses on the electromechanical system, which applies gestures and handwritten digits to control the speed of the DC cooling fan. The system crucial elements are the AVR microcontroller and the developer board, equipped with the embedded supercomputer NVIDIA Jetson TX1. To create the software part of the system artificial intelligence algorithms and deep neural networks were applied. The paper describes the complete routine of data preprocessing, deep neural network training and testing with the use of the GPU Tesla K20 and with the use of the DIGITS (Deep Learning GPU Training System), deployment of the trained model on Jetson TX1 board and the system execution. The system enables to control the fan through the two gestures (“stone”, ”paper”) or through four handwritten digits.