Longitudinal Evaluation of a 30 Day Therapy Treatment of a Hemiplegic Ankle by Applying a Wearable System and Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00382863" target="_blank" >RIV/68407700:21460/24:00382863 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-62523-7_51" target="_blank" >http://dx.doi.org/10.1007/978-3-031-62523-7_51</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-62523-7_51" target="_blank" >10.1007/978-3-031-62523-7_51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Longitudinal Evaluation of a 30 Day Therapy Treatment of a Hemiplegic Ankle by Applying a Wearable System and Machine Learning

Popis výsledku v původním jazyce

The amalgamation of wearable systems and machine learning are envisioned to considerably augment the acuity and situational awareness of clinicians for the prescription of a rehabilitation strategy. For example, a therapy regimen for improving the rehabilitation status of a subject’s hemiplegic ankle can be monitored by a wearable system, such as through a smartphone equipped with a software application to function as a wearable and wireless gyroscope platform. Using a machine learning algorithm, such as a support vector machine, various longitudinal phases of the therapy prescription can be differentiated to establish the efficacy of the rehabilitation strategy. Considerable classification accuracy relative to the preliminary first day and final phase of a 30 day longitudinal therapy application has been achieved in conjunction with the application of a smartphone as a wearable system capable of providing a gyroscope signal for subsequent machine learning application through a support vector machine. The implications are for the ability to optimize a rehabilitation strategy from a remote context regarding the clinical team and subject. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Název v anglickém jazyce

Longitudinal Evaluation of a 30 Day Therapy Treatment of a Hemiplegic Ankle by Applying a Wearable System and Machine Learning

Popis výsledku anglicky

The amalgamation of wearable systems and machine learning are envisioned to considerably augment the acuity and situational awareness of clinicians for the prescription of a rehabilitation strategy. For example, a therapy regimen for improving the rehabilitation status of a subject’s hemiplegic ankle can be monitored by a wearable system, such as through a smartphone equipped with a software application to function as a wearable and wireless gyroscope platform. Using a machine learning algorithm, such as a support vector machine, various longitudinal phases of the therapy prescription can be differentiated to establish the efficacy of the rehabilitation strategy. Considerable classification accuracy relative to the preliminary first day and final phase of a 30 day longitudinal therapy application has been achieved in conjunction with the application of a smartphone as a wearable system capable of providing a gyroscope signal for subsequent machine learning application through a support vector machine. The implications are for the ability to optimize a rehabilitation strategy from a remote context regarding the clinical team and subject. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Digital Health and Medical Bioengineering. Proceedings of the 11th International Conference on E-Health and Bioengineering, EHB-2023, November 9–10, 2023, Bucharest, Romania – Volume 3: Telemedicine, Biomaterials, Environmental Protection, Medical Imaging, and Biomechanics

ISBN

978-3-031-62523-7

ISSN

1680-0737

e-ISSN

1433-9277

Počet stran výsledku

8

Strana od-do

461-468

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Místo konání akce

Bucuresti

Datum konání akce

9. 11. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001434998400051