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Explainable Convolutional Neural Networks for Multi-Sensor Data

In this paper, a novel deep learning architecture is proposed to overcome problems in existing method by enabling effective activity recognition of construction equipment. Recently, the deep learning method for multi-sensor data has been widely used. Even though the original characteristics of deep learning model offers high accuracy in classification for activity recognition, it is difficult to recognize which sensor is the most critical for sensor selection. Thus, the proposed method offers a deep learning model with not only high accuracy, but also explainability, which can greatly aid sensor selection. This leads to avoiding any redundant sensor adoption and therefore, minimize the classification cost. The superiority and effectiveness of the proposed method is demonstrated by the real collected sensor data which was applied in construction equipment to solve activity recognition problem.