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HASC2011corpus: towards the common ground of human activity recognition

Authors:

Nobuhiko NishioAuthors Info & Claims

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

Pages 571 - 572

https://doi.org/10.1145/2030112.2030218

Published: 17 September 2011 Publication History

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Abstract

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.

References

[1]

Nobuo Kawaguchi, Nobuhiro Ogawa, Yohei Iwasaki, Katsuhiko Kaji, Tsutomu Terada, Kazuya Murao, Sozo Inoue, Yoshihiro Kawahara, Yasuyuki Sumi and Nobuhiko Nishio, HASC Challenge: Gathering Large Scale Human Activity Corpus for the Real-World Activity Understandings, ACM Augmented Human (AH2011), pp. 27:1--27:5 (2011).

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[2]

HASC Website: http://hasc.jp/e.

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[3]

Nobuhiro Ogawa, Katsuhiko Kaji, Nobuo Kawaguchi, Effects of Number of Subjects on Activity Recognition - Findings from HASC2010corpus -, International Workshop on Frontiers in Activity Recognition using Pervasive Sensing(IWFAR2011), pp. 48--51(2011).

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[4]

Yuichi Hattori, Sozo Inoue, Go Hirakawa, A Large Scale Gathering System for Activity Data with Mobile Sensors, IEEE International Symposium on Wearable Computers (ISWC2011),pp.97--100(2011).

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Cited By

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Prabpon NHomsud KVatiwutipong P(2024)Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard functionStatistics and Computing10.1007/s11222-023-10375-434:2Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1007/s11222-023-10375-4
Takenaka KKondo KHasegawa T(2023)Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity RecognitionSensors10.3390/s2320844923:20(8449)Online publication date: 13-Oct-2023
https://doi.org/10.3390/s23208449
Murao KEnokibori YGjoreski HLago POkita TSiirtola PHiroi KScholl PCiliberto MUrano K(2023)11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3605106(773-776)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3605106
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Index Terms

HASC2011corpus: towards the common ground of human activity recognition
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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Information & Contributors

Information

Published In

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

September 2011

668 pages

ISBN:9781450306300

DOI:10.1145/2030112

General Chairs:
James Landay
University of Washington, USA & Microsoft Research Asia, China
,
Yuanchun Shi
Tsinghua University, China
,
Program Chairs:
Donald J. Patterson
University of California, Irvine, USA
,
Yvonne Rogers
Open University, UK
,
Xing Xie
Microsoft Research Asia, China

In-Cooperation

University of Florida: University of Florida
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2011

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Ubicomp '11

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 17 - 21, 2011

Beijing, China

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

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Prabpon NHomsud KVatiwutipong P(2024)Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard functionStatistics and Computing10.1007/s11222-023-10375-434:2Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1007/s11222-023-10375-4
Takenaka KKondo KHasegawa T(2023)Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity RecognitionSensors10.3390/s2320844923:20(8449)Online publication date: 13-Oct-2023
https://doi.org/10.3390/s23208449
Murao KEnokibori YGjoreski HLago POkita TSiirtola PHiroi KScholl PCiliberto MUrano K(2023)11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3605106(773-776)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3605106
Bao JLi YZhu MWang S(2023)Bayesian Nonparametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming AnalysisIEEE Transactions on Signal Processing10.1109/TSP.2023.332665071(3968-3982)Online publication date: 2023
https://doi.org/10.1109/TSP.2023.3326650
Cao ZSeeuws NDe Vos MBertrand A(2023)A Novel Loss for Change Point Detection Models With Time-Invariant RepresentationsIEEE Signal Processing Letters10.1109/LSP.2023.333625830(1737-1741)Online publication date: 2023
https://doi.org/10.1109/LSP.2023.3336258
Coşkun KTümer B(2023)Learning under concept drift and non-stationary noise: Introduction of the concept of persistenceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106363123(106363)Online publication date: Aug-2023
https://doi.org/10.1016/j.engappai.2023.106363
Zhang SLi YZhang SShahabi FXia SDeng YAlshurafa N(2022)Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on AdvancesSensors10.3390/s2204147622:4(1476)Online publication date: 14-Feb-2022
https://doi.org/10.3390/s22041476
Murao KEnokibori YGjoreski HLago POkita3 TSiirtola PHiroi KScholl PCiliberto MUrano K(2022)10th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers10.1145/3544793.3560377(321-323)Online publication date: 11-Sep-2022
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Fereidoonian FAhmadi-Pajouh M(2022)A Personalized Deep Neural Network to Recognize Human Activities in Healthy Subjects2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)10.1109/ICBME57741.2022.10052893(325-332)Online publication date: 21-Dec-2022
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Wu SHuang TLi Y(2022)A rehabilitation activity monitoring method based on Shallow-CNN2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995387(2482-2489)Online publication date: 6-Dec-2022
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