Held in conjunction with IJCAI-24, 3rd – 9th August, 2024 in Jeju, Korea
1st workshop 2nd workshop 3rd workshopHuman 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.
May 9, 2024 | Submission deadline |
June 4, 2024 | Acceptance notification |
August 3-9, 2024 | Conference dates |
As requested by authors, we will extend the submission to May 9 to give more time for authors. The time zone is the same as the main conference.
Potential topics include but are not limited to
Foundation models for HAR
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
Transfer Learning for HAR
Federated Learning for HAR
Reinforcement Learning for HAR
Online Learning for HAR
Self-supervised Learning for HAR
Semi-supervised Learning for HAR
Submission Format: The authors should follow IJCAI paper preparation instructions, including page length (e.g. 7 pages + 2 extra page for reference). The paper follows double-blind review.
At least one author of each accepted paper *must* travel to the IJCAI venue in person, and that multiple submissions of the same paper to more IJCAI workshops are forbidden.
Sign up is required for submission. Submission Link.
09:00--09:30
Welcome and round introduction of participants
09:30--10:15
Invited Keynote I (Prof. Dr. Paul Lukowicz, DFKI, Germany)
10:15—11:15
Oral Presentations and Panel Discussion I (3-4 contributions)
11:15--11:30
Coffee Break
11:30--12:15
Invited Keynote II (TBD)
12:15--13:15
Oral Presentations and Panel Discussion II (3-4 contributions)
13:15--14:30
Lunch
14:30--15:30
Oral Presentations and Panel Discussion III (3-4 contributions)
15:30--17:00
Moderated round table discussions
17:00--
Feedback, reflection, and concluding remarks
End of the Section
Advanced Digital Sciences Center, Singapore
The Chinese University of Hong Kong
A*STAR, Singapore
University of New South Wale, Australian
Zhejiang University, China
National University of Singapore
Microsoft, USA
A*STAR, Singapore
University of Edinburg, UK
University of Electronic Science and Technology of China
DFKI, Germany
DFKI, Germany
DFKI, Germany
Karlsruhe Institute of Technology
Aalto University
Co-organizer