LAUSR

The use of capacitive sensors in the automotive context opens new possibilities in the development of new interfaces for machine interaction with the vehicle occupants. Large smart surfaces with gesture recognition will possibly be part of such new interfaces. However, the data processing cost of such new sensors should be maintained at a minimum while increasing the complexity of their gesture recognition accuracy by using modern deep-learning approaches. In this paper, we introduce the use of Bayesian optimization with execution platform constraints to implement accurate gesture recognition sensors based on 1D capacitive sensor arrays. Various RNN-based designs are implemented and optimized for their execution on embedded automotive microcontrollers. We show that LSTM and GRU-based designs are especially adequate achieving an average recall of the gesture classes over 95% in less than 100 optimization steps.