Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F23%3A00369906" target="_blank" >RIV/68407700:21460/23:00369906 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/23:00369906

Result on the web

<a href="https://doi.org/10.3390/jpm13050874" target="_blank" >https://doi.org/10.3390/jpm13050874</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/jpm13050874" target="_blank" >10.3390/jpm13050874</a>

Result language

angličtina

Original language name

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

Original language description

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases.

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

<a href="/en/project/LTAIZ19008" target="_blank" >LTAIZ19008: Enhancing Robotic Physiotherapeutic Treatments using Machine Learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of Personalized Medicine

ISSN

2075-4426

e-ISSN

2075-4426

Volume of the periodical

13

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

32

Pages from-to

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UT code for WoS article

001020278300001

EID of the result in the Scopus database

2-s2.0-85160297216