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HASC-IPSC: indoor pedestrian sensing corpus with a balance of gender and age for indoor positioning and floor-plan generation researches

Authors:

Katsuhiko Kaji,

Hodaka Watanabe,

Ryoji Ban,

Nobuo KawaguchiAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

Pages 605 - 610

https://doi.org/10.1145/2494091.2495981

Published: 08 September 2013 Publication History

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UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

HASC-IPSC: indoor pedestrian sensing corpus with a balance of gender and age for indoor positioning and floor-plan generation researches

Pages 605 - 610

Abstract
References

Abstract

Up till now, the majority of researches related to location estimation and floor plan creation have used different kinds of data and there has simply been no technique to compare the relative advantages and disadvantages. We collected indoor pedestrian sensing data of 100 people with a balance of gender and age. The data is part of the HASC corpus, free to use for research purposes.

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Cited By

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Logacjov ABach KKongsvold ABårdstu HMork P(2021)HARTH: A Human Activity Recognition Dataset for Machine LearningSensors10.3390/s2123785321:23(7853)Online publication date: 25-Nov-2021
https://doi.org/10.3390/s21237853
Antar AAhmed MAhad M(2021)Sensor-Based Human Activity and Behavior ComputingVision, Sensing and Analytics: Integrative Approaches10.1007/978-3-030-75490-7_6(147-176)Online publication date: 6-Jun-2021
https://doi.org/10.1007/978-3-030-75490-7_6
Ahad MAntar AAhmed MAhad MAntar AAhmed M(2020)Sensor-Based Benchmark Datasets: Comparison and AnalysisIoT Sensor-Based Activity Recognition10.1007/978-3-030-51379-5_6(95-121)Online publication date: 31-Jul-2020
https://doi.org/10.1007/978-3-030-51379-5_6
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Index Terms

HASC-IPSC: indoor pedestrian sensing corpus with a balance of gender and age for indoor positioning and floor-plan generation researches
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

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UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

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

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Cited By

View all

Logacjov ABach KKongsvold ABårdstu HMork P(2021)HARTH: A Human Activity Recognition Dataset for Machine LearningSensors10.3390/s2123785321:23(7853)Online publication date: 25-Nov-2021
https://doi.org/10.3390/s21237853
Antar AAhmed MAhad M(2021)Sensor-Based Human Activity and Behavior ComputingVision, Sensing and Analytics: Integrative Approaches10.1007/978-3-030-75490-7_6(147-176)Online publication date: 6-Jun-2021
https://doi.org/10.1007/978-3-030-75490-7_6
Ahad MAntar AAhmed MAhad MAntar AAhmed M(2020)Sensor-Based Benchmark Datasets: Comparison and AnalysisIoT Sensor-Based Activity Recognition10.1007/978-3-030-51379-5_6(95-121)Online publication date: 31-Jul-2020
https://doi.org/10.1007/978-3-030-51379-5_6
Bayev AChistyakov IDerevyankin AGartseev INikulin APikhletsky M(2019)RuDaCoP: The Dataset for Smartphone-based Intellectual Pedestrian Navigation2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN.2019.8911823(1-8)Online publication date: Sep-2019
https://doi.org/10.1109/IPIN.2019.8911823
Das Antar AAhmed MAhad M(2019)Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)10.1109/ICIEV.2019.8858508(134-139)Online publication date: May-2019
https://doi.org/10.1109/ICIEV.2019.8858508
Nozaki JHiroi KKaji KKawaguchi N(2019)Compensation Scheme for PDR Using Component-Wise Error ModelsHuman Activity Sensing10.1007/978-3-030-13001-5_3(29-46)Online publication date: 10-Sep-2019
https://doi.org/10.1007/978-3-030-13001-5_3
Sugimoto SIto NNaito KChujo NMizuno TKaji K(2018)Partial Matching Estimation Method of Walking Trajectories for Generating Indoor Pedestrian Networks2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)10.23919/ICMU.2018.8653585(1-6)Online publication date: Oct-2018
https://doi.org/10.23919/ICMU.2018.8653585
Yotsuya KIto NNaito KChujo NMizuno TKaji K(2018)Method to Improve Accuracy of Indoor PDR Trajectories Using a Large Amount of Trajectories2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)10.23919/ICMU.2018.8653262(1-6)Online publication date: Oct-2018
https://doi.org/10.23919/ICMU.2018.8653262
Bayındır L(2017)A survey of people-centric sensing studies utilizing mobile phone sensorsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-1704469:4(421-448)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/AIS-170446
Ichino HKaji KSakurada KHiroi KKawaguchi NLukowicz PKrüger ABulling ALim YPatel S(2016)HASC-PAC2016Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct10.1145/2968219.2968277(705-714)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2968219.2968277
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HASC-PAC2016: large scale human pedestrian activity corpus and its baseline recognition

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