A multi-sensor, hybrid model- and signal-based control system for powered lower limb prostheses
File(s)
Author(s)
Dimitrov, Hristo
Type
Thesis
Abstract
Humans have come a long way in the efforts for mobility restoration, but, as many primary solutions, these breakthroughs suffer from long-term secondary side effects. Today's prosthetic technology is much more advanced than the early days of wooden substitutes. And yet, when it comes to prosthetic legs, the basic principle still remains the same - attaching a fixed rigid body, albeit with some spring and damper properties, to the residual limb. Since the user has no intrinsic control over it, they adapt to these foreign bodies using compensatory movements. This in term cause long-term complications due to increased loads and wear and tear of the parts used for adaptation. The aim of this dissertation is to address this need and develop a below-knee prosthesis controller, which includes the user in the prosthesis control as a way to bridge the performance gap between below-knee prostheses and their biological counterparts, thus reducing the long-term effects due to compensatory movements. I have first developed an algorithm that predicts desired ankle kinematics and stiffness based on high-density electromyography signals, which was intended to be used as the user's input to the prosthetic controller. This algorithm was successful in providing users with voluntary position and stiffness control, assessed with a virtual target matching task, as well as provide reasonable estimates for kinematics and ability to control stiffness during locomotion. Following this, a prosthetic control strategy, providing consistent cyclical gait oscillations, which are modulated by this user input, was devised. Further technological advancements were needed for the implementation of high-density sensor system with real-time prosthesis users...
Version
Open Access
Date Issued
2022-09-13
Date Awarded
01/12/2022
License URL
Advisor
Farina, Dario
Bull, Anthony
Sponsor
Engineering and Physical Sciences Research Council
Grant Number
EP/R512540/1
Publisher Department
Bioengineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)