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Real-time Integrated Human Activity Recognition System based on Multimodal User Understanding

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Jong-Seok LeeAuthors Info & Claims

IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages 89 - 90

https://doi.org/10.1145/3379336.3381482

Published: 17 March 2020 Publication History

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Abstract

This paper presents our real-time human activity recognition system that understands human behavior using multimodal sensor data at multiple levels. Our system consists of a multimodal data acquisition framework and a user understanding algorithm including user identification, activity recognition, and health monitoring components.

References

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Jun-Ho Choi and Jong-Seok Lee. 2019. EmbraceNet: A robust deep learning architecture for multimodal classification. Information Fusion 51 (2019), 259--270.

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Cited By

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Lee LLin Z(2024)Danger, Nuisance, Disregard: Analyzing User-Generated Videos for Augmented Reality Gameplay on Hand-held DevicesProceedings of the ACM on Human-Computer Interaction10.1145/36770638:CHI PLAY(1-33)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1145/3677063
Yun SLim Y(2023)Potential and Challenges of DIY Smart Homes with an ML-intensive Camera SensorProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581462(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581462
Lee HHwang JPark HChoi JLee J(2022)Classifying Gas Data Measured Under Multiple Conditions Using Deep LearningIEEE Access10.1109/ACCESS.2022.318561310(68138-68150)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3185613

Index Terms

Real-time Integrated Human Activity Recognition System based on Multimodal User Understanding
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
  2. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces

March 2020

153 pages

ISBN:9781450375139

DOI:10.1145/3379336

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 17 March 2020

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Ministry of Science ICT and Future Planning

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IUI '20

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SIGAI
SIGCHI

IUI '20: 25th International Conference on Intelligent User Interfaces

March 17 - 20, 2020

Cagliari, Italy

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Lee LLin Z(2024)Danger, Nuisance, Disregard: Analyzing User-Generated Videos for Augmented Reality Gameplay on Hand-held DevicesProceedings of the ACM on Human-Computer Interaction10.1145/36770638:CHI PLAY(1-33)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1145/3677063
Yun SLim Y(2023)Potential and Challenges of DIY Smart Homes with an ML-intensive Camera SensorProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581462(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581462
Lee HHwang JPark HChoi JLee J(2022)Classifying Gas Data Measured Under Multiple Conditions Using Deep LearningIEEE Access10.1109/ACCESS.2022.318561310(68138-68150)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3185613

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A tutorial on human activity recognition using body-worn inertial sensors

Time handling for real-time progressive activity recognition

Human Activity Recognition Using Gait Pattern

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A tutorial on human activity recognition using body-worn inertial sensors

Time handling for real-time progressive activity recognition

Human Activity Recognition Using Gait Pattern

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