دانلود رایگان مقاله انگلیسی یادگیری عمیق برای تشخیص فعالیت مبتنی بر سنسور - الزویر 2018

ABSTRACT

Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

8. Conclusion

Human activity recognition is an important research topic in pattern recognition and pervasive computing. In this paper, we survey the recent advance in deep learning approaches for sensor-based activity recognition. Compared to traditional pattern recognition methods, deep learning reduces the dependency on human-crafted feature extraction and achieves better performance by automatically learning high-level representations of the sensor data. We highlight the recent progress in three important categories: sensor modality, deep model, and application. Subsequently, we summarize and discuss the surveyed research in detail. Finally, several grand challenges and feasible solutions are presented for future research.