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A Comparative Analysis of the Impact of Features on Human Activity Recognition with Smartphone Sensors

Published: 17 October 2017 Publication History

Abstract

The recognition of users' physical activities through data analysis of smartphone inertial sensors has aided the development of several solutions in different domains such as transportation and healthcare. Mostly of these solutions have been supported by the cloud communication technologies due to the need of using accurate classification models. In an attempt to solve problems related to the smartphone orientation (e.g. landscape) in the user's body, new types of features classified as orientation independent have arisen in the last years. In this context, this paper presents an extensive comparative study between all the features mapped in literature derived from inertial sensors. A number of experiments were carried out using two databases containing data from 30 users. Results showed that the new orientation independent features proposed in literature cannot discriminate properly between the users' activities using the inertial sensors. In addition, this paper provides an extensive analysis of these type of features and a tool that implements all methodological process of human activity recognition based on smartphones.

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Cited By

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  • (2024)Human Activity Recognition Using Machine Learning2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10688067(1-5)Online publication date: 5-Jun-2024
  • (2023)TinyML optimization for activity classification on the resource-constrained body sensor BI-Vital2023 IEEE 19th International Conference on Body Sensor Networks (BSN)10.1109/BSN58485.2023.10330937(1-4)Online publication date: 9-Oct-2023
  • (2022)Coupling of Dimensionality Reduction and Stacking Ensemble Learning for Smartphone-Based Human Activity RecognitionInternational Journal of E-Services and Mobile Applications10.4018/IJESMA.30026714:1(1-15)Online publication date: 1-Jan-2022
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cover image ACM Other conferences
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
October 2017
522 pages
ISBN:9781450350969
DOI:10.1145/3126858
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].

Sponsors

  • SBC: Brazilian Computer Society
  • 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: 17 October 2017

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Author Tags

  1. feature extraction
  2. human activity recognition
  3. inertial sensors
  4. smartphone

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  • Research-article

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Webmedia '17
Sponsor:
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '17: Brazilian Symposium on Multimedia and the Web
October 17 - 20, 2017
RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2024)Human Activity Recognition Using Machine Learning2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10688067(1-5)Online publication date: 5-Jun-2024
  • (2023)TinyML optimization for activity classification on the resource-constrained body sensor BI-Vital2023 IEEE 19th International Conference on Body Sensor Networks (BSN)10.1109/BSN58485.2023.10330937(1-4)Online publication date: 9-Oct-2023
  • (2022)Coupling of Dimensionality Reduction and Stacking Ensemble Learning for Smartphone-Based Human Activity RecognitionInternational Journal of E-Services and Mobile Applications10.4018/IJESMA.30026714:1(1-15)Online publication date: 1-Jan-2022
  • (2022)Alpine Skiing Activity Recognition Using Smartphone’s IMUsSensors10.3390/s2215592222:15(5922)Online publication date: 8-Aug-2022
  • (2022)Investigating the Impact of Information Sharing in Human Activity RecognitionSensors10.3390/s2206228022:6(2280)Online publication date: 16-Mar-2022
  • (2022)Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural NetworksACM Transactions on Embedded Computing Systems10.1145/354281921:4(1-28)Online publication date: 23-Aug-2022
  • (2022)Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)10.1109/PRIME55000.2022.9816745(173-176)Online publication date: 12-Jun-2022
  • (2022)A multi-scale feature extraction fusion model for human activity recognitionScientific Reports10.1038/s41598-022-24887-y12:1Online publication date: 30-Nov-2022
  • (2021)The Variegated Applications of Deep Learning Techniques in Human Activity RecognitionProceedings of the 2021 Thirteenth International Conference on Contemporary Computing10.1145/3474124.3474156(223-233)Online publication date: 5-Aug-2021
  • (2021)Application of Machine Learning Classifiers for Predicting Human Activity2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT52856.2021.9532572(39-44)Online publication date: 27-Jul-2021
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