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A Versatile Annotated Dataset for Multimodal Locomotion Analytics with Mobile Devices

Authors:

Hristijan Gjoreski,

Mathias Ciliberto,

Francisco Javier Ordoñez Morales,

Daniel Roggen,

Sami Mekki,

Stefan ValentinAuthors Info & Claims

SenSys '17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

Article No.: 61, Pages 1 - 2

https://doi.org/10.1145/3131672.3136976

Published: 06 November 2017 Publication History

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Abstract

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.

References

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Y. Zheng, X. Xie, W. Ma. GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. IEEE Data Eng. Bull. (2010), 32--39.

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S. Wang, C. Chen, and J. Ma. Accelerometer based transportation mode recognition on mobile phones. Procs. APWCS, (2010), 44--46.

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M. C., Yu, T. Yu, S.C. Wang, C.J. Lin, E.Y. Chang. Big data small footprint: the design of a low-power classifier for detecting transportation modes. Procs. VLDB Endowment, (2014), 1429--1440.

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[4]

HUAWEI Mate 9: http://consumer.huawei.com/en/phones/mate9/specs/

Google Scholar

[5]

M. Ciliberto, F.J. Morales, H. Gjoreski, D. Roggen, S. Mekki, S. Valentin. High reliability Android application for multidevice multimodal mobile data acquisition and annotation. Proc. SenSys, (2017), to appear.

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D. Bannach, K. Kunze, J. Weppner, and Paul Lukowicz. Integrated tool chain for recording and handling large, multimodal context recognition data sets. UbiComp '10 Adjunct, (2010), 357--358.

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Cited By

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Hoelzemann AVan Laerhoven K(2024)A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensorsFrontiers in Computer Science10.3389/fcomp.2024.13797886Online publication date: 18-Jul-2024
https://doi.org/10.3389/fcomp.2024.1379788
Hinkle LMetsis V(2023)An LLVM-Inspired Framework for Unified Processing of Multimodal Time-Series DataProceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3594806.3594812(91-94)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3594806.3594812
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
Show More Cited By

Index Terms

A Versatile Annotated Dataset for Multimodal Locomotion Analytics with Mobile Devices
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor applications and deployments

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Published In

SenSys '17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

November 2017

490 pages

ISBN:9781450354592

DOI:10.1145/3131672

Editor:
Rasit Eskicioglu
University of Manitoba, Canada

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 the author(s) 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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2017

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Short-paper
Research
Refereed limited

Conference

SenSys '17

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SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems

November 6 - 8, 2017

Delft, Netherlands

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Overall Acceptance Rate 174 of 867 submissions, 20%

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The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

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Cited By

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Hoelzemann AVan Laerhoven K(2024)A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensorsFrontiers in Computer Science10.3389/fcomp.2024.13797886Online publication date: 18-Jul-2024
https://doi.org/10.3389/fcomp.2024.1379788
Hinkle LMetsis V(2023)An LLVM-Inspired Framework for Unified Processing of Multimodal Time-Series DataProceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3594806.3594812(91-94)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3594806.3594812
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
Hamidi MOsmani A(2021)Domain models for data sources integration in HARNeurocomputing10.1016/j.neucom.2020.06.138444(244-259)Online publication date: Jul-2021
https://doi.org/10.1016/j.neucom.2020.06.138
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
Hamidi MOsmani A(2021)Data Generation Process Modeling for Activity RecognitionMachine Learning and Knowledge Discovery in Databases: Applied Data Science Track10.1007/978-3-030-67667-4_23(374-390)Online publication date: 25-Feb-2021
https://doi.org/10.1007/978-3-030-67667-4_23
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
Zhu YZhao FChen RHarle RFarrahi KLane N(2019)Applying 1D sensor DenseNet to Sussex-Huawei locomotion-transportation recognition challengeAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3345571(873-877)Online publication date: 9-Sep-2019
https://doi.org/10.1145/3341162.3345571
Gjoreski MKalabakov SLuštrek MGams MGjoreski HHarle RFarrahi KLane N(2019)Cross-dataset deep transfer learning for activity recognitionAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3344865(714-718)Online publication date: 9-Sep-2019
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Alkurd RAbuAlhaol IYanikomeroglu H(2019)Dataset Modeling for Data-Driven AI-Based Personalized Wireless NetworksICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761211(1-6)Online publication date: May-2019
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