Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing. A major reason for this is the limited amount of annotated training data. In this paper, we propose the idea of leveraging the discriminative power of pre-trained deep CNNs on 2-dimensional sensor data by transforming the sensor modality to the visual domain. By three proposed strategies, 2D sensor output is converted into pressure distribution imageries. Then we utilize a pre-trained CNN for transfer learning on the converted imagery data. We evaluate our method on a gait dataset of floor surface pressure mapping. We obtain a classification accuracy of 87.66%, which outperforms the conventional machine learning methods by over 10%.
@article{arxiv.1701.01077,
title = {Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection},
author = {Monit Shah Singh and Vinaychandran Pondenkandath and Bo Zhou and Paul Lukowicz and Marcus Liwicki},
journal= {arXiv preprint arXiv:1701.01077},
year = {2017}
}
Comments
8 pages, 8 figures, Published in IJCNN, 2017 Copyright: IEEE 2017 DOI: 10.1109/IJCNN.2017.7966182