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Discrimination of cycling patterns using accelerometric data and deep learning techniques

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526346" target="_blank" >RIV/70883521:28140/20:63526346 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/60461373:22340/20:43920990 RIV/00216208:11150/21:10438922 RIV/00179906:_____/21:10438922 RIV/68407700:21730/21:00347478

  • Výsledek na webu

    <a href="https://link.springer.com/article/10.1007/s00521-020-05504-3" target="_blank" >https://link.springer.com/article/10.1007/s00521-020-05504-3</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00521-020-05504-3" target="_blank" >10.1007/s00521-020-05504-3</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Discrimination of cycling patterns using accelerometric data and deep learning techniques

  • Popis výsledku v původním jazyce

    The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well.

  • Název v anglickém jazyce

    Discrimination of cycling patterns using accelerometric data and deep learning techniques

  • Popis výsledku anglicky

    The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Návaznosti výsledku

  • Projekt

    Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

  • Návaznosti

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    NEURAL COMPUTING &amp; APPLICATIONS

  • ISSN

    0941-0643

  • e-ISSN

  • Svazek periodika

    Neuveden

  • Číslo periodika v rámci svazku

    Neuveden

  • Stát vydavatele periodika

    GB - Spojené království Velké Británie a Severního Irska

  • Počet stran výsledku

    11

  • Strana od-do

    1-11

  • Kód UT WoS článku

    000590534800007

  • EID výsledku v databázi Scopus

    2-s2.0-85096301452