Explainable Convolutional Neural Networks for Multi-Sensor Data
Gonie Ahn, Jae Hyeong Yoo, Sang Hoon Lee, Seoung Bum Kim · Journal of Korean Institute of Industrial Engineers · 2019
In this paper, we propose a new convolutional neural networks (CNNs) structure to overcome the interpretation problem posed by a series of the nonlinear transformation processes in CNNs. Convolutional neural networks (CNNs) have been successfully used in various applications including images, signals, and texts. Despite their superior performance in various tasks, the main limitation of CNNs is the lack of interpretability. Recently, deep neural network models including CNNs have been effectively used for the analysis of multi-sensor data. Although the original characteristics of deep neural network models offer high accuracy in classification for activity recognition, they are difficult to identify the critical sensors that play a significant role in discrimination of various equipment conditions. The proposed CNNs offers good performance and interpretability simultaneously. The effectiveness and applicability of the proposed method were demonstrated by the real sensor data collected from construction equipment for activity recognition.