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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-08252021-103138


Tipo di tesi
Tesi di laurea magistrale
Autore
LORENZONI, DARIO
URN
etd-08252021-103138
Titolo
A Convolutional Neural Network approach for frailty status assessment using a wrist-worn device during gait
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Relatori
relatore Avvenuti, Marco
relatore Cola, Guglielmo
relatore Minici, Domenico
Parole chiave
  • Continuous Wavelet Transform
  • Convolutional Neural Networks
  • Frailty
  • Gait Analysis
  • Wearable sensor
Data inizio appello
24/09/2021
Consultabilità
Non consultabile
Data di rilascio
24/09/2091
Riassunto
Wearable sensors have become particularly appealing for evaluating health-related condition, due to their capacity to continuously monitor the users. Moreover, costs and dimensions of these devices enable the analysis in non-clinical settings. The geriatric syndrome of frailty is one of the greatest challenges facing the worldwide aging population. In this thesis, a system that exploits a wrist-worn sensor-derived signal will be designed and implemented. In particular, signals recorded during gait are pre-processed and given as input to Continuous Wavelet Transform. Finally, outputs from wavelet are used to train and test a Convolutional Neural Network to distinguish subjects according to their frailty status.
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