short-paper

Wearable sensors for recognizing individuals undertaking daily activities

Authors:

Seham Abd Elkader,

Michael Barlow,

Erandi LakshikaAuthors Info & Claims

ISWC '18: Proceedings of the 2018 ACM International Symposium on Wearable Computers

Pages 64 - 67

https://doi.org/10.1145/3267242.3267245

Published: 08 October 2018 Publication History

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Abstract

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition while undertaking their normal daily activities. In this paper, we focus on utilizing motion information of known daily activities gathered from wearable sensors for recognizing the person. The analysis of the results demonstrates that different fundamental classification factors have an impact on person recognition success-rates. Furthermore, the results of comparison among subjects prove that some subjects have high classification results and can be easily identifiable compared to other subjects which have high confusability rates. Lastly, a significant improvement in subject classification success rate was found for activities with little or no movement which can successfully distinguish among persons and hence producing higher classification results compared to activities with large movement.

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Cited By

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Laporte MGjoreski MLangheinrich M(2023)LAUREATEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108927:3(1-41)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610892
Alsafery WRana OPerera C(2023)Sensing within Smart Buildings: A SurveyACM Computing Surveys10.1145/359660055:13s(1-35)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3596600
Luo FKhan SHuang YWu K(2023)Activity-Based Person Identification Using Multimodal Wearable Sensor DataIEEE Internet of Things Journal10.1109/JIOT.2022.320908410:2(1711-1723)Online publication date: 15-Jan-2023
https://doi.org/10.1109/JIOT.2022.3209084
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Index Terms

Wearable sensors for recognizing individuals undertaking daily activities
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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Information

Published In

ISWC '18: Proceedings of the 2018 ACM International Symposium on Wearable Computers

October 2018

307 pages

ISBN:9781450359672

DOI:10.1145/3267242

General Chairs:
Kai Kunze
Keio University
,
Rajesh K. Balan
Singapore Management University
,
Youngki Lee
Singapore Management University
,
Program Chairs:
Michael Beigl
Karlsruhe Institute of Technology
,
Roshan Peiris
Keio University

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2018

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UbiComp '18

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UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Laporte MGjoreski MLangheinrich M(2023)LAUREATEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108927:3(1-41)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610892
Alsafery WRana OPerera C(2023)Sensing within Smart Buildings: A SurveyACM Computing Surveys10.1145/359660055:13s(1-35)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3596600
Luo FKhan SHuang YWu K(2023)Activity-Based Person Identification Using Multimodal Wearable Sensor DataIEEE Internet of Things Journal10.1109/JIOT.2022.320908410:2(1711-1723)Online publication date: 15-Jan-2023
https://doi.org/10.1109/JIOT.2022.3209084
Dietrich AJain KGutjahr GSteffes BSorge C(2023)I recognize you by your stepsComputers and Security10.1016/j.cose.2022.102994124:COnline publication date: 1-Jan-2023
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Ermshaus ASchäfer PBagnall AGuyet TIfrim GLemaire VLeser ULeverger CMalinowski S(2023)Human Activity Segmentation Challenge @ ECML/PKDD’23Advanced Analytics and Learning on Temporal Data10.1007/978-3-031-49896-1_1(3-13)Online publication date: 20-Dec-2023
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Nair NMoya Rueda FReining CFink G(2023)Multi-Channel Time-Series Person and Soft-Biometric IdentificationPattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges10.1007/978-3-031-37660-3_18(256-272)Online publication date: 30-Jul-2023
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Sanchez Guinea AHeinrich SMühlhäuser M(2022)Activity-Free User Identification Using Wearables Based on Vision TechniquesSensors10.3390/s2219736822:19(7368)Online publication date: 28-Sep-2022
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Elkader SBarlow MLakshika E(2021)Subject and Activity-wise Performance for Activity Independent Person Recognition2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9660017(1-8)Online publication date: 5-Dec-2021
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