Unsupervised Embedded Gesture Recognition Based on Multi-objective NAS and Capacitive Sensing

Gesture recognition has become pervasive in many interactive environments. Recognition based on Neural Networks often reaches higher recognition rates than competing methods at a cost of a higher computational complexity that becomes very challenging in low resource computing platforms such as microcontrollers. New optimization methodologies, such as quantization and Neural Architecture Search are steps forward for the development of embeddable networks. In addition, as neural networks are commonly used in a supervised fashion, labeling tends to include bias in the model. Unsupervised methods allow for performing tasks as classification without depending on labeling. In this work, we present an embedded and unsupervised gesture recognition system, composed of a neural network autoencoder and K-Means clustering algorithm and optimized through a state-of-the-art multi- objective NAS. The present method allows for a method to develop, deploy and perform unsupervised classification in low resource embedded devices.