  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.
