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Activity Simulation from Signals

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UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Pages 59 - 60

https://doi.org/10.1145/3460418.3479275

Published: 24 September 2021 Publication History

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Abstract

Sensor-based human activity recognition technology has been used for estimating human action based on the sensor data. In this paper, we propose a new paradigm to render the human activity on a screen instead of classifying the activity among the activity labels. We could built this mockup of a simulator, combining our previous translation tool between signals [2] with the motion rendering systems [3]. We faced two problems which decrease the simulation ability a lot. We proposed two algorithms to increase the performance of this simulator in this preliminary work.

References

[1]

G. Kurillo R. Vidal F. Ofli, R. Chaudhryand R. Bajcsy. 2013. Berkeley MHAD: A Comprehensive Multimodal Human Action Database. In Proceedings of the IEEE Workshop on Applications on Computer Vision (WACV) (2013).

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[2]

Tsuyoshi Okita and Sozo Inoue. 2018. Activity Recognition: Translation across Sensor Modalities Using Deep Learning. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers Adjunct. 1462–1471.

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[3]

Yu Rong, Takaaki Shiratori, and Hanbyul Joo. 2020. FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration. arXiv preprint arXiv:2008.08324(2020).

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Published In

September 2021

711 pages

ISBN:9781450384612

DOI:10.1145/3460418

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: 24 September 2021

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UbiComp '21

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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 21 - 26, 2021

Virtual, USA

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Annotating smart environment sensor data for activity learning

Bootstrapping activity modeling for ambient assisted living

Using wearable activity type detection to improve physical activity energy expenditure estimation

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