Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection

Monit Shah Singh; Vinaychandran Pondenkandath; Bo Zhou; Paul Lukowicz; Marcus Liwicki
In: The International Joint Conference on Neural Networks. International Joint Conference on Neural Networks (IJCNN-2017), May 14-19, Anchorage, Alaska, USA, IEEE, 2017.


Convolutional Neural Networks (CNNs) have be- come 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%.

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