  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%.
