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%.
In REF , pressure sensor data was converted into pressure distribution images, then CNN was used for transfer learning on the converted imagery data.
Transforming sensor data to the image domain for deep learning — An application to footstep detection
2017 International Joint Conference on Neural Networks (IJCNN)
2017 International Joint Conference on Neural Networks (IJCNN)
Computer Science