Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
This addresses the challenge of applying CNNs to sensor data in pervasive computing, though it is incremental as it adapts existing visual domain methods to a new modality.
The paper tackled the problem of limited annotated sensor data for deep learning by transforming 2D sensor data into pressure distribution images to leverage pre-trained CNNs, achieving an 87.66% classification accuracy for footstep detection, which outperformed conventional methods by over 10%.
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%.