Discrimination of cycling patterns using accelerometric data and deep learning techniques

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

Discrimination of cycling patterns using accelerometric data and deep learning techniques

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Name of the periodical

NEURAL COMPUTING &amp; APPLICATIONS

ISSN

0941-0643

e-ISSN

—

Volume of the periodical

Neuveden

Issue of the periodical within the volume

Neuveden

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1-11

UT code for WoS article

000590534800007

EID of the result in the Scopus database

2-s2.0-85096301452