Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10259012" target="_blank" >RIV/61989100:27730/25:10259012 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-025-19728-7" target="_blank" >https://www.nature.com/articles/s41598-025-19728-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-19728-7" target="_blank" >10.1038/s41598-025-19728-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals

Popis výsledku v původním jazyce

With the growing integration of social robots into pediatric environments, understanding and monitoring child-robot interaction has become increasingly important. Toward the advancement of biomechanical monitoring systems for pediatric applications, this study presents an innovative approach employing stacked Deep Neural Networks (DNNs) for the objective monitoring of interaction dynamics between children and social robots. The study focuses on quantitatively analyzing behaviors exhibited by children towards four types of social robots, each equipped with accelerometers and gyroscopes. These sensors capture vibration signals and angular displacements, translating them into statistical features-Kurtosis (K) for accelerometer data and Signal Magnitude Area (SMA) for gyroscope data. This transformation facilitates an objective analysis of the interaction dynamics. The model demonstrates high efficacy with accuracy, precision, recall, and F1 scores of 0.941, 0.94, 0.941, and 0.939, respectively. While the initial emphasis of this research is on the development of the stacked DNN model, the study also sets the stage for future applications in real-time mobile monitoring and biomedical robotics. This research contributes to the understanding of child-robot interactions by setting an objective and developmental stage-appropriate perspective, and paves the way for advancements in interactive technologies within developmental contexts.

Název v anglickém jazyce

Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals

Popis výsledku anglicky

With the growing integration of social robots into pediatric environments, understanding and monitoring child-robot interaction has become increasingly important. Toward the advancement of biomechanical monitoring systems for pediatric applications, this study presents an innovative approach employing stacked Deep Neural Networks (DNNs) for the objective monitoring of interaction dynamics between children and social robots. The study focuses on quantitatively analyzing behaviors exhibited by children towards four types of social robots, each equipped with accelerometers and gyroscopes. These sensors capture vibration signals and angular displacements, translating them into statistical features-Kurtosis (K) for accelerometer data and Signal Magnitude Area (SMA) for gyroscope data. This transformation facilitates an objective analysis of the interaction dynamics. The model demonstrates high efficacy with accuracy, precision, recall, and F1 scores of 0.941, 0.94, 0.941, and 0.939, respectively. While the initial emphasis of this research is on the development of the stacked DNN model, the study also sets the stage for future applications in real-time mobile monitoring and biomedical robotics. This research contributes to the understanding of child-robot interactions by setting an objective and developmental stage-appropriate perspective, and paves the way for advancements in interactive technologies within developmental contexts.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001596281700011

EID výsledku v databázi Scopus

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