Author

Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki

Abstract

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%.   [Download]

BibTex

@inproceedings {Singh:Transforming:2017:9072,
	number = {}, 
	month = {}, 
	year = {2017}, 
	title = {Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection}, 
	journal = {}, 
	volume = {}, 
	pages = {}, 
	publisher = {IEEE}, 
	author = {Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki}, 
	keywords = {}
}