Human activity recognition (HAR) can be used for a number of 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 workshop intends to prompt state-of-the-art approaches on deep learning for human activity recognition. The organizers invite researchers to participate and submit their research papers in the Deep Learning for Human Activity Recognition Workshop.
|September 30, 2020 ||Submission deadline|
|June 1, 2020||Acceptance notification|
|July 11-13, 2020||Conference date|
Due to COVID 19, the main conference has been postponed to January 2021, thus we will also extend the submission to give more time for authors. Thanks.
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
Submission Format: The authors should follow IJCAI paper preparation instructions, including page length (e.g. 6 pages + 1 extra page for reference). Submission Link
Nanyang Technological University/A*STAR, Singaporexlli@i2r.a-star.edu.sg
Nankai Univerisity, P.R.C
Sichuan University, P.R.C
Advanced Digital Sciences Center, Singapore
Nanyang Technological University, Singapore
Cornell University, USA
Purdue University, USA
University of California, Berkeley, USA
University of Oxford, UK
University of Amsterdam, Amsterdam
Tencent AI Lab, P.R.C
McLaren Applied Technologies, UK