Recovery Method for Missing Sensor Data in Multi-Sensor Based Walking Recognition System

Missing data is a major challenge in activity recognition and becomes an increasingly important study. Most current research on activity recognition is based on multiple sensors, which may bring the problem of missing data for one or more sensors. This paper presents Multi-source Denoising AutoEncoder (MSDA) for filling missing sensor data in walking recognition system based on previously proposed Hierarchical AdaBoost. The recovery method uses Denoising AutoEncoder to build a neural network which output is the embedding of features that include missing sensors, so that it can predict the features for missing sensor data. The experimental results show that MSDA can not only improve the recognition accuracy, but has higher performance comparing with other missing data processing method such as EM-PCA and filling missing data with a special value.