ETD system

Electronic theses and dissertations repository


Tesi etd-09232019-113304

Thesis type
Tesi di laurea magistrale
Development of strategies to improve the robustness and accuracy of a gait phase estimation algorithm for a robotic pelvis orthosis for transfemoral amputees assistance in overground walking
Corso di studi
relatore Prof. Vitiello, Nicola
correlatore Dott.ssa Crea, Simona
controrelatore Prof. Tognetti, Alessandro
Parole chiave
  • controllo di medio livello
  • middle-level control
  • picco dell’angolo di anca
  • hip angle peak
  • esoscheletro
  • exoskeleton
  • event detection
  • ortesi attiva di bacino
  • active pelvis orthosis
  • stima della fase di cammino
  • gait phase estimation
  • synchronization
  • sincronizzazione
Data inizio appello
Secretata d'ufficio
Data di rilascio
Riassunto analitico
In lower limb exoskeletons’ control scenario, synchronization between the delivered assistive torque and user’s locomotion is a crucial issue. This is particularly true when considering subjects with impaired locomotion, such as transfemoral amputees (TFAs). Continuous phase estimation-based control strategies rely on a real-time estimate of the user’s gait phase to enhance synchronization. The primary aim of this work is to improve the robustness and the accuracy of an existing phase estimation-based algorithm for the control of an active pelvis orthosis to assist TFAs during ground-level walking (GLW). The algorithm consists of three subsystems dealing with: event detection, adaptive oscillators-based gait phase estimation and phase error learning. This work focuses on the event detector, which detects the maximum hip flexion angle: the criticalities of the original algorithm were investigated offline and addressed through the implementation of several modifications. Offline simulations on data collected with TFAs performing GLW were performed to evaluate the effectiveness of each of the introduced modifications and of combinations of them. Results show an overall tendency to decrease the variability of the distribution of the errors committed by the peak detection algorithm, which would lead to a more reliable estimated gait phase and thus, to a more regular assistive profile. Future work will regard the online validation of the refined algorithm for peak detection with TFAs.