A novel wireless toy for measuring infants' bimanual actions

Serio, S.M.; Assaf, Tareq; Cecchi, Francesca; Laschi, Cecilia; Dario, P.

doi:10.1109/biorob.2012.6290773

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Previous work has shown us how sensors can be integrated into a toy that is meant for infant handling. Serio and others successfully developed novel wireless toys to measure infants’ uni-manual and bi-manual actions ( Serio et al, 2012 ; Serio et al, 2013 ). The previous Ailu Toy for the PANDA Gym was embedded with an inertial measuring unit (IMU), pressure sensing soft hands, and force sensing resistors for the face and ears.…”

Section: Methodsmentioning

confidence: 99%

Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

Udayagiri,

Yin,

Cai

et al. 2024

Front. Robot. AI

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Introduction: It is crucial to identify neurodevelopmental disorders in infants early on for timely intervention to improve their long-term outcomes. Combining natural play with quantitative measurements of developmental milestones can be an effective way to swiftly and efficiently detect infants who are at risk of neurodevelopmental delays. Clinical studies have established differences in toy interaction behaviors between full-term infants and pre-term infants who are at risk for cerebral palsy and other developmental disorders.Methods: The proposed toy aims to improve the quantitative assessment of infant-toy interactions and fully automate the process of detecting those infants at risk of developing motor delays. This paper describes the design and development of a toy that uniquely utilizes a collection of soft lossy force sensors which are developed using optical fibers to gather play interaction data from infants laying supine in a gym. An example interaction database was created by having 15 adults complete a total of 2480 interactions with the toy consisting of 620 touches, 620 punches—“kick substitute,” 620 weak grasps and 620 strong grasps.Results: The data is analyzed for patterns of interaction with the toy face using a machine learning model developed to classify the four interactions present in the database. Results indicate that the configuration of 6 soft force sensors on the face created unique activation patterns.Discussion: The machine learning algorithm was able to identify the distinct action types from the data, suggesting the potential usability of the toy. Next steps involve sensorizing the entire toy and testing with infants.

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Section: Methodsmentioning

confidence: 99%

Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

Udayagiri,

Yin,

Cai

et al. 2024

Front. Robot. AI

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“…A sensorized wireless toy has been developed with embedded MEMS pressure sensors and audio/visual feedback, etc. [ 35 ]. In the study of Serio et al , infants showed a good grade of acceptance to such kind of sensorized toys, as confirmed by the results of preliminary tests that involved nine healthy infants; in particular: (i) the dimensions matched infants’ anthropometrics; (ii) the device was robust and safe; (iii) the acquired signals were in the expected range and (iv) the wireless communication was stable [ 36 ].…”

Section: Mems-based Sensor Technologies For Human Centred Applicatmentioning

confidence: 99%

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

Ciuti

Ricotti

Menciassi

et al. 2015

Sensors

131

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Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

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A novel wireless toy for measuring infants' bimanual actions

Cited by 2 publications

References 15 publications

Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

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