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Convolutional Neural Networks (CNNs) have become the state-of-the-art invarious computer vision tasks, but they are still premature for most sensordata, especially in pervasive and wearable computing. A major reason for thisis the limited amount of annotated training data. In this paper, we propose theidea of leveraging the discriminative power of pre-trained deep CNNs on2-dimensional sensor data by transforming the sensor modality to the visualdomain. By three proposed strategies, 2D sensor output is converted intopressure distribution imageries. Then we utilize a pre-trained CNN for transferlearning on the converted imagery data. We evaluate our method on a gaitdataset of floor surface pressure mapping. We obtain a classification accuracyof 87.66%, which outperforms the conventional machine learning methods by over10%.

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