Abstract
The Human Activity Recognition (HAR) research area showed great advances in the last decade, achieving excellent prediction performances and great applicability, which is reflected on the wearable sensors market adoption. However, most of the research effort concentrated on an adult target population. When considering a younger population of infants or children, currently available HAR solution based on wearable devices are not applicable anymore. In this paper we present an HAR based solution targeting infants, based on a non-intrusive and privacy-preserving measurement methodology which allows the preservation of children behaviour and the collection of objective data (particularly important for clinical observation purposes). The proposed solution, based on the usage of a set of smart toys (AutoPlay toys-set) achieves great performances in the recognition of a set of 12 toy-activity pairs, reaching accuracy values up to \(96\%\). These results pave the way to a broad application of the presented methodology on objective analysis of humans motor skills.
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Acknowledgements
We would like to acknowledge first of all Emmanuelle Rossini for the definition of the idea at the base of the AutoPlay approach. We acknowledge Dr. Gian Paolo Ramelli and his neuropediatric team of the EOC Hospital in Bellinzona for supporting us in the real use case scenario data collection. We acknowledge Franesca Faraci, Alessandro Puiatti, all the SUPSI DTI and DEASS collaborators of the AutoPlay team which contributed to the collection of the real use case scenarios data, Pepe Hiller and Hape Toys for the AutoPlay toys design and production. Last but not least, we would like to thank all the families and children which trust our project and which agreed to collaborate with us.
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Bonomi, N., Papandrea, M. (2022). Non-intrusive and Privacy Preserving Activity Recognition System for Infants Exploiting Smart Toys. In: Spinsante, S., Silva, B., Goleva, R. (eds) IoT Technologies for Health Care. HealthyIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-030-99197-5_1
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