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Tesi etd-06282022-182424


Tipo di tesi
Tesi di laurea magistrale
Autore
DI SALVO, GIULIA
URN
etd-06282022-182424
Titolo
Development and validation of an EMG-based intention detection algorithm for assisting reaching movements by using an upper-limb exoskeleton
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
BIONICS ENGINEERING
Relatori
relatore Prof. Vitiello, Nicola
relatore Dott.ssa Crea, Simona
Parole chiave
  • electromyography
  • Gaussian mixture model
  • high-level controller
  • intention recognition
  • muscle synergy
  • upper-limb exoskeleton
Data inizio appello
15/07/2022
Consultabilità
Non consultabile
Data di rilascio
15/07/2092
Riassunto
Several common diseases, such as stroke, multiple sclerosis, and spinal cord injuries, lead to upper-limb impairments, affecting the ability to perform activities of daily living. Among robotic devices, upper-limb exoskeletons have been found effective in the rehabilitation of impaired subjects, helping them to improve their residual capabilities. The aim of this work is to develop an electromyography (EMG)-based control strategy for using upper-limb exoskeletons during rehabilitative exercises consisting in planar reaching tasks. In this thesis, an EMG-based high-level control algorithm for user’s movement onset and intention detection is presented, describing its real-time implementation and human-in-the-loop testing. The movement intention detection algorithm consisted of a muscle synergy-based prediction algorithm, implementing an evidence accumulation approach. In particular, the activation coefficients of muscle synergies were used as inputs of a Gaussian Mixture Model (GMM)-based classifier that estimated the reaching movement’s direction. The synergies were extracted on the real-time processed EMG-signals, acquired using a custom electromyograph. The algorithm was tested on seven healthy subjects performing planar reaching tasks towards eight targets, while wearing the NESM-gamma shoulder-elbow exoskeleton. The muscle synergies and the GMMs were subject-specific and were computed during the training phase of the algorithm. The results showed an overall accuracy, averaged over participants, of about 60%; most of the classification errors were obtained on directions adjacent to the actual one.
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