User-Age Classification Using Touch Gestures on Smartphones

Al-Showarah, Suleyman; Al-Jawad, Naseer; Sellahewa, Harin

doi:10.4038/ijms.v2i1.57

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In a complementary case of our study [14], authors showed high classification rates between young adults (20-50 years) and older adults (70+ years) based on touch gestures, demonstrating differences in neuromotor skills during human ageing while our work studies differences between undeveloped neuromotor skills in children and total maturity in young adults.…”

Section: Introductionsupporting

confidence: 61%

Active detection of age groups based on touch interaction

et al. 2018

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This article studies user classification into children and adults according to their interaction with touchscreen devices. The authors analyse the performance of two sets of features derived from the sigma-lognormal theory of rapid human movements and global characterisation of touchscreen interaction. The authors propose an active detection approach aimed to continuously monitor the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The authors have used support vector machines algorithm to classify the resulting features into age groups. The sets of features are fused at the score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotorinspired features to classify age groups according to the interaction with touch devices. In the active detection setup , the authors' method is able to identify a child using only four gestures in average.

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Section: Introductionsupporting

confidence: 61%

Active detection of age groups based on touch interaction

et al. 2018

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“…This section analyses quantitatively one of the many different potential applications of ChildCIdb. In particular, we focus on the popular task of children age group detection based on the interaction with mobile devices [7], [9], [10], [33]. Due to the large volume of information captured in ChildCIdb, we focus in this section only on the analysis of the Test 6 (Drawing Test) based on the way children colour a tree.…”

Section: Example Application: Age Detectionmentioning

confidence: 99%

Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection

Tolosana,

Ruiz-Garcia,

Vera-Rodriguez

et al. 2021

Preprint

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We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and ehealth, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task.

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