3rd International Workshop on Deep Learning for Human Activity Recognition

Held in conjunction with IJCAI-21, 21st – 26th August, 2021 in Montreal

1st workshop 2nd workshop


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.

Important Date

May 31, 2021 Submission deadline
July 1, 2021 Acceptance notification
August 21-23, 2021 Conference dates


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

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

Survey for deep learning based HAR

Submission and Registration

Submission Format: The authors should follow IJCAI paper preparation instructions, including page length (e.g. 6 pages + 1 extra page for reference).

Program Schedule




Min Wu


A*STAR, Singapore

Jianfei Yang


BEARS, UC Berkeley, Singapore

Xiaoli Li


Nanyang Technological University/A*STAR, Singapore

Program Committee

Vincent Zheng

Advanced Digital Sciences Center, Singapore

Sinno Pan

Nanyang Technological University, Singapore

Joey Tianyi Zhou

A*STAR, Singapore

Keyu Wu

A*STAR, Singapore

Bing Li

University of New South Wale, Australian

Jinming Xu

Zhejiang University, China

Yuecong Xu

Nanyang Technological University, Singapore

Han Zou

Microsoft, USA

Wei Cui

A*STAR, Singapore

Lu Xiaoxuan

University of Edinburg, UK

Zenglin Shi

University of Amsterdam, Amsterdam

Le Zhang


Karl Surmacz

Zimmer Biomet, UK


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