Deep Learning for Human Activity Recognition

  in Special Issue   Posted on June 15, 2019

Information for the Special Issue

Submission Deadline: Sun 01 Sep 2019
Journal Impact Factor : 4.438
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

Aims & Scope:

Human activity recognition (HAR) can benefit various applications, such as health-care services and smart home applications. Many sensors have been utilized for human activity recognition, such as wearable sensors, smartphones, radio frequency (RF) sensors (WiFi, RFID), LED light sensors, cameras, etc. Owing to the rapid development of wireless sensor network, a large amount of data has been collected for the recognition of human activities with different kind of sensors. Conventional shallow learning algorithms, such as support vector machine and random forest, require to manually extract some representative features from large and noisy sensory data. However, manual feature engineering requires export knowledge and will inevitably miss implicit features.

Recently, deep learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn representative features from massive data. This technology can be a good candidate for human activity recognition. Some initial attempts can be found in the literature. However, many challenging research problems in terms of accuracy, device heterogeneous, environment changes, etc. remain unsolved.

This special collection intends to prompt state-of-the-art approaches on deep learning for human activity recognition. All submitted papers will be peer-reviewed and selected based on both their quality and relevance.

Potential topics include but are not limited to:

  • Device-based HAR using deep learning
  • Device-free HAR using deep learning
  • Image based HAR using deep learning
  • Light sensor based HAR using deep learning
  • Sensor fusion for HAR using deep learning
  • Fusion of shallow models with deep networks for HAR
  • Device heterogeneous for device-based HAR
  • Environment changes for device-free HAR
  • Transfer Learning for HAR
  • Online Learning for HAR
  • Semi-supervised Learning for HAR
  • Survey for deep learning based HAR

Deadline for submissions:

September 1, 2019

Lead Guest Editor
Xiaoli Li, Institute for Infocomm Research (I2R) A*STAR, Singapore

Guest Editor
Peilin Zhao, Tencent AI Lab, China

Guest Editor
Min Wu, Institute for Infocomm Research (I2R) A*STAR, Singapore

Guest Editor
Zhenghua Chen, Institute for Infocomm Research (I2R) A*STAR, Singapore

Guest Editor

Le Zhang, Institute for Infocomm Research (I2R) A*STAR, Singapore

Closed Special Issues