The use of technical refinement is playing an important role in the process of optimizing training efficiency and improving results of athletes. Therefore, we aim to develop an online monitoring and feedback tool to follow the swimming training using a single waterproofed and wireless motion sensor attached on the swimmers center of body (back). In this paper, as the first step, we investigated the feature quantities to discriminate four swimming styles using the data of a single three-axis accelerometer. We found that there was a possibility that the combination of simple feature quantities could be used to discriminate four swimming styles.

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Cited By

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Wang G(2024)RL-CWtrans Net: multimodal swimming coaching driven via robot visionFrontiers in Neurorobotics10.3389/fnbot.2024.143918818Online publication date: 14-Aug-2024
https://doi.org/10.3389/fnbot.2024.1439188
Ehab MMohamed HAhmed MHammad MElMasry NAtia ATruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)ISwimCoach: A Smart Coach guiding System for Assisting Swimmers Free Style StrokesCompanion Publication of the 2020 International Conference on Multimodal Interaction10.1145/3395035.3425314(265-269)Online publication date: 25-Oct-2020
https://dl.acm.org/doi/10.1145/3395035.3425314
Tarasevicius DSerackis A(2020)Deep Learning Model for Sensor based Swimming Style Recognition2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)10.1109/eStream50540.2020.9108849(1-4)Online publication date: Apr-2020
https://doi.org/10.1109/eStream50540.2020.9108849

Index Terms

Toward classification of swimming style by using underwater wireless accelerometer data
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Published In

September 2015

1626 pages

ISBN:9781450335751

DOI:10.1145/2800835

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 07 September 2015

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UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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Wang G(2024)RL-CWtrans Net: multimodal swimming coaching driven via robot visionFrontiers in Neurorobotics10.3389/fnbot.2024.143918818Online publication date: 14-Aug-2024
https://doi.org/10.3389/fnbot.2024.1439188
Ehab MMohamed HAhmed MHammad MElMasry NAtia ATruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)ISwimCoach: A Smart Coach guiding System for Assisting Swimmers Free Style StrokesCompanion Publication of the 2020 International Conference on Multimodal Interaction10.1145/3395035.3425314(265-269)Online publication date: 25-Oct-2020
https://dl.acm.org/doi/10.1145/3395035.3425314
Tarasevicius DSerackis A(2020)Deep Learning Model for Sensor based Swimming Style Recognition2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)10.1109/eStream50540.2020.9108849(1-4)Online publication date: Apr-2020
https://doi.org/10.1109/eStream50540.2020.9108849
Omae YTakahashi H(2017)Feature Selection Algorithm Considering Trial and Individual Differences for Machine Learning of Human Activity RecognitionJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2017.p081321:5(813-824)Online publication date: 20-Sep-2017
https://doi.org/10.20965/jaciii.2017.p0813

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Gait determination and activity measurement in horses using an accelerometer

Estimation of beautiful gait using an accelerometer

A Method for Estimating Distance Actually Swam using Wearable Accelerometer and Gyroscope

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