There is a growing need for touch-free interaction with public utilities such as coffeemakers and vending machines that will help prevent the spread of diseases such as COVID-19. One solution is the integration of embedded gesture recognition systems relying on ambient light. However, existing work so far is found to be inefficient in terms of size, cost and recognisable gestures. This research is part of the development of a smaller and more economical machine learning-powered gesture recognition system using only 3 photodiodes and an Arduino microcontroller. The goal is to design the software for sensor reading, gesture detection and data preprocessing. The resulting receiver samples at 100 Hz, uses an adaptable threshold for identifying gesture endpoints and a mix of FFT,
maximum division and Linear Interpolation for signal processing. It is evaluated in two lighting conditions on two distinct gestures and is found to provide a
good trade-off between simplicity, real-time processing within milliseconds and robustness against environmental changes. This is achieved with a small RAM memory footprint of only 2 KB and independence of classification backend. The existing design
can be further improved in the future through software optimisation and extended environment dynamics support.