Analyzing Energy Requirements of Meta-Differential Evolution for Future Wearable Medical Devices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952021" target="_blank" >RIV/49777513:23520/18:43952021 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-10-9023-3_44" target="_blank" >http://dx.doi.org/10.1007/978-981-10-9023-3_44</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-9023-3_44" target="_blank" >10.1007/978-981-10-9023-3_44</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analyzing Energy Requirements of Meta-Differential Evolution for Future Wearable Medical Devices

Popis výsledku v původním jazyce

Recent advances in clinical engineering include development of physiological models to deliver optimized healthcare. Physiological model comprises a number of equations to relate biomedical signals. Each equation contains a set of coefficients. Determining the coefficients is a complex task as the models are non-linear. Therefore, development of the models must be accompanied by a development of methods to determine model coefficients. With the advent of wearable medical devices, we have to consider energy require-ments of the models and the methods. Considering an illustrative case of type-1 diabetes mellitus patients, we already demonstrated that Meta-Differential Evolution outperforms analytical methods, when determining coefficients of gl-cose dynamics. In this paper, we analyze convergence of the Meta-Differential Evolution, running time and associated power consumption on a single board computer with a system-on-a-chip – Cortex-A8 AM335x. Based on the analysis, we recommend splitting the process of determining the coefficients into two phases. First phase determines the initial, per-patient optimized coefficients. Second phase is an energetically efficient update of these coefficients with new, continuously measured signal of the patient. Meta-Differential Evolution searches for optimal coefficients by evolving a number of generations of candidate coefficients, using a number of evolutionary strategies. We demonstrate that the proposed approach significantly reduces the number of candidate coefficients to evaluate, while achieving the desired accuracy. This positively reflects in the lifetime of wearable device’s battery. Specifically, calculating coefficient’s update took 0.05Ws only. It shows the feasibility of using Meta-Differential Evolution with its improved accuracy for blood glucose calculations in a wearable device

Název v anglickém jazyce

Analyzing Energy Requirements of Meta-Differential Evolution for Future Wearable Medical Devices

Popis výsledku anglicky

Recent advances in clinical engineering include development of physiological models to deliver optimized healthcare. Physiological model comprises a number of equations to relate biomedical signals. Each equation contains a set of coefficients. Determining the coefficients is a complex task as the models are non-linear. Therefore, development of the models must be accompanied by a development of methods to determine model coefficients. With the advent of wearable medical devices, we have to consider energy require-ments of the models and the methods. Considering an illustrative case of type-1 diabetes mellitus patients, we already demonstrated that Meta-Differential Evolution outperforms analytical methods, when determining coefficients of gl-cose dynamics. In this paper, we analyze convergence of the Meta-Differential Evolution, running time and associated power consumption on a single board computer with a system-on-a-chip – Cortex-A8 AM335x. Based on the analysis, we recommend splitting the process of determining the coefficients into two phases. First phase determines the initial, per-patient optimized coefficients. Second phase is an energetically efficient update of these coefficients with new, continuously measured signal of the patient. Meta-Differential Evolution searches for optimal coefficients by evolving a number of generations of candidate coefficients, using a number of evolutionary strategies. We demonstrate that the proposed approach significantly reduces the number of candidate coefficients to evaluate, while achieving the desired accuracy. This positively reflects in the lifetime of wearable device’s battery. Specifically, calculating coefficient’s update took 0.05Ws only. It shows the feasibility of using Meta-Differential Evolution with its improved accuracy for blood glucose calculations in a wearable device

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering 2018

ISBN

978-981-10-9022-6

ISSN

1680-0737

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

249-252

Název nakladatele

Springer Nature

Místo vydání

Singapore

Místo konání akce

Funchal, Madeira, Portugal

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000449744300044