Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F24%3A43930505" target="_blank" >RIV/60461373:22340/24:43930505 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/24/22/7330" target="_blank" >https://www.mdpi.com/1424-8220/24/22/7330</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s24227330" target="_blank" >10.3390/s24227330</a>

Result language

angličtina

Original language name

Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment

Original language description

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients&apos; physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes.

Czech name

—

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

—

OECD FORD branch

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

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-3210

e-ISSN

1424-8220

Volume of the periodical

24

Issue of the periodical within the volume

22

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

"7330:1"-7330

UT code for WoS article

001366142400001

EID of the result in the Scopus database

2-s2.0-85210558400