short-paper

A Comparative Study on Fitness Activity Recognition

Authors:

Igor Lopes de Faria,

Vaninha VieiraAuthors Info & Claims

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

Pages 327 - 330

https://doi.org/10.1145/3243082.3267452

Published: 16 October 2018 Publication History

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Abstract

Human Activity Recognition (RAH) has become a field of high interest and relevance in the Context Awareness area. When knowing what the user is doing a system can provide good information to increase the quality of the delivered data. Commercial smartwatches and smartphones contain several sensors that can sense an userś movement and help identifying which activity an user is performing in a certain moment. In this paper we compare the smartwatch and the smartphone on human activity recognition, observing accuracy and comfort while users are practicing fitness activities. While analyzing the results of the comparative experiment executed between a Smartphone and a Smartwatch, the second one showed to be a good choice on a qualitative and a quantitative way which can form the basis of new a fitness application, including applications that automatically track the activities done.

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Cited By

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Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645091
Bera TAcharjya P(2023)An Investigation Into the Use of Deep Learning to Recognize Human ActivityAI and Its Convergence With Communication Technologies10.4018/978-1-6684-7702-1.ch008(212-262)Online publication date: 15-Sep-2023
https://doi.org/10.4018/978-1-6684-7702-1.ch008
Staab SBröning LLuderschmidt JMartin L(2023)Live Classification of Similar Arm Motion Sequences Using SmartwatchesHuman Aspects of IT for the Aged Population10.1007/978-3-031-34917-1_25(357-376)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-34917-1_25
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Index Terms

A Comparative Study on Fitness Activity Recognition
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Mobile devices

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Information & Contributors

Information

Published In

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

October 2018

437 pages

ISBN:9781450358675

DOI:10.1145/3243082

General Chairs:
Manoel Carvalho Marques Neto
IFBA
,
Renato Lima Novais
IFBA
,
Carlos Ferraz
UFPE
,
Windson Viana
UFC

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

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SBC: Brazilian Computer Society
SIGMM: ACM Special Interest Group on Multimedia
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2018

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WebMedia '18

WebMedia '18: Brazilian Symposium on Multimedia and the Web

October 16 - 19, 2018

BA, Salvador, Brazil

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WebMedia '18 Paper Acceptance Rate 37 of 111 submissions, 33%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all

Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645091
Bera TAcharjya P(2023)An Investigation Into the Use of Deep Learning to Recognize Human ActivityAI and Its Convergence With Communication Technologies10.4018/978-1-6684-7702-1.ch008(212-262)Online publication date: 15-Sep-2023
https://doi.org/10.4018/978-1-6684-7702-1.ch008
Staab SBröning LLuderschmidt JMartin L(2023)Live Classification of Similar Arm Motion Sequences Using SmartwatchesHuman Aspects of IT for the Aged Population10.1007/978-3-031-34917-1_25(357-376)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-34917-1_25
Pham QNguyen DNguyen TNguyen TNguyen DPham DTran TLe TVu H(2022)A study on skeleton-based action recognition and its application to physical exercise recognitionProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568639(239-246)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3568562.3568639
Staab SBröning LLuderschmidt JMartin L(2022)Activity Recognition over Temporal Distance using Supervised Learning in the Context of Dementia DiagnosticsProceedings of Mensch und Computer 202210.1145/3543758.3543948(169-181)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3543948
Pham QNguyen VNguyen TNguyen DNguyen DPham DVu HLe T(2022)Automatic recognition and assessment of physical exercises from RGB images2022 IEEE Ninth International Conference on Communications and Electronics (ICCE)10.1109/ICCE55644.2022.9852094(349-354)Online publication date: 27-Jul-2022
https://doi.org/10.1109/ICCE55644.2022.9852094

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