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

TL;DR

This paper introduces a method to convert sensor data into images to leverage pre-trained CNNs for improved gait classification, achieving higher accuracy than traditional methods.

Contribution

It proposes three strategies for transforming 2D sensor data into images and demonstrates the effectiveness of transfer learning with pre-trained CNNs for sensor data analysis.

Findings

Achieved 87.66% classification accuracy on gait dataset.

Outperformed conventional machine learning methods by over 10%.

Validated the effectiveness of image-based transfer learning for sensor data.

Abstract

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%.