Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training.
Accelerometers
CareToy system
Cerebral palsy
Early detection
Early intervention
Functional data analysis
Infant’s activity
Tele-rehabilitation
Upper limb movements
Wearable sensors for healthcare
Journal
Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233
Informations de publication
Date de publication:
06 05 2023
06 05 2023
Historique:
received:
30
06
2021
accepted:
20
04
2023
medline:
8
5
2023
pubmed:
7
5
2023
entrez:
6
5
2023
Statut:
epublish
Résumé
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. 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. 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. ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.
Sections du résumé
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.
Identifiants
pubmed: 37149595
doi: 10.1186/s12984-023-01182-z
pii: 10.1186/s12984-023-01182-z
pmc: PMC10164332
doi:
Banques de données
ClinicalTrials.gov
['NCT03211533', 'NCT03234959']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
62Investigateurs
Claudia Artese
(C)
Veronica Barzacchi
(V)
Alessandra Cecchi
(A)
Marta Cervo
(M)
Maria Luce Cioni
(ML)
Carlo Dani
(C)
Paolo Dario
(P)
Marco Di Galante
(M)
Ugo Faraguna
(U)
Patrizio Fiorini
(P)
Viola Fortini
(V)
Matteo Giampietri
(M)
Simona Giustini
(S)
Clara Lunardi
(C)
Irene Mannari
(I)
Valentina Menici
(V)
Letizia Padrini
(L)
Filomena Paternoster
(F)
Riccardo Rizzi
(R)
Informations de copyright
© 2023. The Author(s).
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