ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-04152018-203709


Tipo di tesi
Tesi di laurea magistrale
Autore
LIVOLSI, CHIARA
URN
etd-04152018-203709
Titolo
Towards wearable robotic products: analysis of the state of the market and development of a novel gait segmentation method for a portable hip exoskeleton
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
BIONICS ENGINEERING
Relatori
relatore Prof. Vitiello, Nicola
tutor Ing. Conti, Roberto
tutor Crea, Simona
controrelatore Prof. Vozzi, Giovanni
Parole chiave
  • commercial exoskeletons
  • discrete wavelet transform
  • real-time gait monitoring
  • walking assistive strategy
Data inizio appello
03/05/2018
Consultabilità
Non consultabile
Data di rilascio
03/05/2088
Riassunto
The rapid advancement of wearable robotic technology in recent years has promoted technology transfer actions translating research outcomes into market products. Over the last decade, many industry research teams have brought to the market wearable exoskeletons for healthcare and manufacturing applications. Some of them have already reached the commercial market internationally and many others are preparing to do it. The first part of the present thesis has the objective to analyse and classify exoskeletons on the market based on application domains. Distinctive technical features, functional performances and regulatory approvals are highlighted, with the goal to give an overview of the current market scenario and of the most impactful research trends for future prospectives.
The second part of the project thesis is focused on the design of a novel software method for real-time gait segmentation for portable powered hip exoskeleton, developed at Scuola Superiore Sant’Anna. The implemented algorithm automatically extracts biomechanics gait events, i.e. Heel Strike (HS) and Toe Off (TO), with the first objective to make more effective the assistive strategy of the exoskeleton and the secondary objective to improve its performance in the quantitative assessment of the gait. The challenge of this algorithm development was to extract distal foot events (HS and TO), by exploiting only proximal signals of encoders located at the hip joints of the exoskeleton, aiming at enhancing potentiality of the Cognitive Human Robot interface (cHRI) keeping low the complexity of the physical one (pHRI). To develop and implement the novel method a time-frequency analysis was adopted, based on discrete wavelet transforms, that resulted a valid tool for enhancing real-time discriminative characteristics of a signal.
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