FRAME-LEVEL PROXIMITY AND TOUCH RECOGNITION USING CAPACITIVE SENSING AND SEMI-SUPERVISED SEQUENTIAL MODELING

This paper demonstrates a semi-supervised learning approach to frame-level proximity and touch recognition with machine learning algorithms for sequential modeling. We focus on capacitive sensing, which is employable in low cost embedded devices and provides high sensing capability. We optimize our models to run with minimum complexity to enable the use of state-of-the-art machine learning models in low cost embedded devices. We evaluate two different models, either based on recurrent neural networks (RNN) with gated recurrent units or hidden markov models (HMM). We show that the developed models are capable of a robust proximity and touch recognition invariant to interference factors. However, the RNN model outperforms the HMM model reaching a superior frame-level recognition accuracy of 97.1% on a challenging set of touches containing multiple interference factors like the use of gloves with different materials and invalid touches, where the test persons swiped over the sensor.