Embedded identification of surface based on multirate sensor fusion with deep neural network

Abstract

In this letter, we propose a multivariate time-series classification system that fuses multirate sensor measurements within the latent space of a deep neural network. In our network, the system identifies the surface category based on audio and inertial measurements generated from the surface impact, each of which has a different sampling rate and resolution in nature. We investigate the feasibility of categorizing ten different everyday surfaces using a proposed convolutional neural network, which is trained in an end-to-end manner. To validate our approach, we developed an embedded system and collected 60 000 data samples under a variety of conditions. The experimental results obtained exhibit a test accuracy for a blind test dataset of 93%, taking less than 300 ms for end-to-end classification in an embedded machine environment. We conclude this letter with a discussion of the results and future direction of research.