2nd International Workshop on Deep Learning for Human Activity Recognition
Held in conjunction with IJCAI-PRICAI 2020, January 2021, Japan


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

September 15, 2020 May 20, 2020; May 1, 2020 Submission deadline
September 30, 2020 June 1, 2020 Acceptance notification
January 4-10, 2021 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.

The proceedings have been pulished in Springer CCIS book sereis (CCIS, volume 1370) with the Link


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

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). Submission Link

IJCAI-20 Registration is open. Registration Link

Program Schedule

Time Zone: UTC

12:00AM--12:10AM Welcome from Organizers

Oral 1: Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes
Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Ioannis Kanellos and Benoit LeDuc

Oral 2: Single Run Action Detector over Video Stream - A Privacy Preserving Approach
Anbumalar Saravanan, Justin Sanchez, Hassan Ghasemzadeh, Aurelia Macabasco-O'Connell and Hamed Tabkhi

Oral 3: Personalization Models for Human Activity Recognition With Distribution Matching-Based Metrics
Huy Thong Nguyen, Hyeokhyen Kwon, Harish Haresamudram, Andrew Peterson and Thomas Ploetz

Oral 4: Efficacy of Model Fine-Tuning for Personalized Dynamic Gesture Recognition
Junyao Guo, Unmesh Kurup and Mohak Shah

Oral 5: Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Shaoqing Yuan, Parminder Bhatia, Busra Celikkaya, Haiyang Liu and Kyunghwan Choi

01:50AM--02:20AM Tea Break

Oral 6: ARID: A New Dataset for Recognizing Action in the Dark
Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin and Simon See

Oral 7: Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Takumi Watanabe, Hiroki Takahashi, Goh Sato, Yusuke Iwasawa, Yutaka Matsuo and Ikuko Eguchi Yairi

Oral 8: Towards Data Augmentation and Interpretation on Sensor-based Fine-grained Hand Activity Recognition
Jinqi Luo, Xiang Li and Rabih Younes

Oral 9: Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition
Gautham Krishna Gudur and Satheesh Kumar Perepu

Oral 10: Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark
Reem Abdel-Salam, Rana Mostafa and Mayada Hadhood



Xiaoli Li

Nanyang Technological University/A*STAR, Singapore


Min Wu

A*STAR, Singapore


Zhenghua Chen

A*STAR, Singapore


Le Zhang

A*STAR, Singapore


Program Committee

Ming-Ming Cheng

Nankai Univerisity, P.R.C

Xi Peng

Sichuan University, P.R.C

Vincent Zheng

Advanced Digital Sciences Center, Singapore

Sinno Pan

Nanyang Technological University, Singapore

Joey Tianyi Zhou

A*STAR, Singapore

Zhang Wenyu

Cornell University, USA

Jinming Xu

Purdue University, USA

Zou Han

University of California, Berkeley, USA

Lu Xiaoxuan

University of Oxford, UK

Zenglin Shi

University of Amsterdam, Amsterdam

Peilin Zhao

Tencent AI Lab, P.R.C

Karl Surmacz

McLaren Applied Technologies, UK


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