DTA

Digital Theses Archive

 

Tesi etd-03292023-185842

Type of thesis
Dottorato
Author
FILOSA, MARIANGELA
URN
etd-03292023-185842
Title
Haptic sensing and feedback of biomechanical information
Scientific disciplinary sector
ING-IND/34
Course
Istituto di Biorobotica - PHD IN BIOROBOTICA
Committee
relatore Prof. ODDO, CALOGERO MARIA
Membro Dott.ssa LO PRESTI, DANIELA
Keywords
  • Nessuna parola chiave trovata
Exam session start date
26/06/2023;
Availability
parziale
Abstract
Human movements result from the complex motor control mechanisms directed by the central nervous system, the skillful supervisor of actions. Such actions can occur voluntarily, as when moving the hand to grasp an object, or involuntarily. This is the case of either external or internal events perturbating the human organism’s stability. Specialized neural structures tune action enactment to successfully accomplish a task while preserving the body homeostasis. In this process, perception is paramount as, through peripheral information, the central neural units can check the ongoing operations and adjust them when needed. The interoceptive, proprioceptive and exteroceptive pathways are, hence, continuously interrogated and alert the central units in the event of abnormal conditions. In this process, the great variety of peripheral mechanoreceptors has a central role. Indeed, thanks to their sensitivity to mechanical cues, they provide a wealth of biomechanical information, hence, the disruption of these pathways can negatively impact the overall health status. In these cases, technologically advanced tools could help to restore biomechanical functions or, more simply, to notify about potential biomechanical changes. In this perspective, haptic devices resembling human physiological perceptual abilities are herein discussed for some use cases. Artificial mechano-interoceptors along with deep-learning approaches achieved the encoding of respiratory biomechanics. Fiber Bragg Grating sensors embedded into artificial soft skins mimicked the functions of exteroceptors and proprioceptors. In this regard, touch stimuli were encoded through deep learning or neuromorphic algorithms. Finally, gait events were decoded into vibrotactile cues to promote the recognition of terrain unevenness through sensory substitution. These achievements are the starting point for the development of life-changing enabling technologies for impaired people. The haptic encoding and/or decoding of biomechanical information paves the way to sophisticated sensory restoration systems to elicit natural-like sensations in bionic limb applications and prosthetics.
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