Real-time adaptive child-robot interaction: Age and gender determination of children based on 3D body metrics

Sandygulova, Anara; Dragone, Mauro; O’Hare, Gregory M. P.

doi:10.1109/roman.2014.6926355

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…However, the recognition of children's handwriting data was only 38%. This is reflected in other works that use adult datasets with child data: state-of-the-art speech recognition technologies (Kennedy et al, 2017 ) did not perform well with child speech, while age and gender determination did not perform well on children's faces (Sandygulova et al, 2014 ). As noted by Asselborn et al ( 2018 ), the quality of handwriting performance can only be evaluated when considering the age and gender of children.…”

Section: Discussion and Limitationsmentioning

confidence: 94%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.

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Section: Discussion and Limitationsmentioning

confidence: 94%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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“…The third game-like activity is dance, where a series of dance movements are learned by the child via the robot. Sandygulova et al [11] presented a robotic system for gathering 3-D body metrics and using them for effectively estimating the gender and age of previouslyunseen children in real-world situations. They evaluated the performance of the systems on 428 children volunteers; they showed that even a small number of biometrics might achieve excellent gender and age estimation results in perceptually challenging environments.…”

Section: Related Workmentioning

confidence: 99%

Gender detection in children’s speech utterances for human-robot interaction

Badr

Hassan²

2022

IJECE

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The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.

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