Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training

Author:

Franchi de’ Cavalieri Mattia,Filogna Silvia,Martini Giada,Beani Elena,Maselli Martina,Cianchetti Matteo,Dubbini Nevio,Cioni Giovanni,Sgandurra GiuseppinaORCID,Artese Claudia,Barzacchi Veronica,Cecchi Alessandra,Cervo Marta,Cioni Maria Luce,Dani Carlo,Dario Paolo,Di Galante Marco,Faraguna Ugo,Fiorini Patrizio,Fortini Viola,Giampietri Matteo,Giustini Simona,Lunardi Clara,Mannari Irene,Menici Valentina,Padrini Letizia,Paternoster Filomena,Rizzi Riccardo,

Abstract

Abstract Background Nowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants’ motor activity. Materials and methods Starting from data acquired by accelerometers placed on infants’ wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models. Conclusions Despite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models. Trial registration number: ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.

Funder

ministero della salute

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Rehabilitation

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