The Runbot: engineering control applied to rehabilitation in spinal cord injury patients

Meng, Lin (2015) The Runbot: engineering control applied to rehabilitation in spinal cord injury patients. PhD thesis, University of Glasgow.

Abstract

Human walking is a complicated interaction among the musculoskeletal system, nervous
system and the environment. An injury affecting the neurological system, such as a spinal
cord injury (SCI) can cause sensor and motor deficits, and can result in a partial or complete
loss of their ambulatory functions. Functional electrical stimulation (FES), a technique to
generate artificial muscle contractions with the application of electrical current, has been
shown to improve the ambulatory ability of patients with an SCI. FES walking systems have
been used as a neural prosthesis to assist patients walking, but further work is needed to
establish a system with reduced engineering complexity which more closely resembles the
pattern of natural walking.
The aim of this thesis was to develop a new FES gait assistance system with a simple and
efficient FES control based on insights from robotic walking models, which can be used in
patients with neuromuscular dysfunction, for example in SCI.
The understanding of human walking is fundamental to develop suitable control strategies.
Limit cycle walkers are capable of walking with reduced mechanical complexity and simple
control. Walking robots based on this principle allow bio-inspired mechanisms to be analysed
and validated in a real environment. The Runbot is a bipedal walker which has been
developed based on models of reflexes in the human central nervous system, without the
need for a precise trajectory algorithm. Instead, the timing of the control pattern is based
on ground contact information. Taking the inspiration of bio-inspired robotic control, two
primary objectives were addressed. Firstly, the development of a new reflexive controller
with the addition of ankle control. Secondly, the development of a new FES walking system
with an FES control model derived from the principles of the robotic control system.
The control model of the original Runbot utilized a model of neuronal firing processes based
on the complexity of the central neural system. As a causal relationship between foot contact
information and muscle activity during human walking has been established, the control
model was simplified using filter functions that transfer the sensory inputs into motor outputs,
based on experimental observations in humans. The transfer functions were applied
to the RunBot II to generate a stable walking pattern. A control system for walking was
created, based on linear transfer functions and ground reaction information. The new control
system also includes ankle control, which has not been considered before. The controller
was validated in experiments with the new RunBot III.
The successful generation of stable walking with the implementation of the novel reflexive
robotic controller indicates that the control system has the potential to be used in controlling
the strategies in neural prosthesis for the retraining of an efficient and effective gait. To aid
of the development of the FES walking system, a reliable and practical gait phase detection
system was firstly developed to provide correct ground contact information and trigger timing
for the control. The reliability of the system was investigated in experiments with ten
able-bodied subjects. Secondly, an automatic FES walking system was implemented, which
can apply stimulation to eight muscles (four in each leg) in synchrony with the user’s walking
activity. The feasibility and effectiveness of this system for gait assistance was demonstrated
with an experiment in seven able-bodied participants.
This thesis addresses the feasibility and effectiveness of applying biomimetic robotic control
principles to FES control. The interaction among robotic control, biology and FES control
in assistive neural prosthesis provides a novel framework to developing an efficient and
effective control system that can be applied in various control applications.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: functional electrical stimulation, gait rehabilitation, robotic control, bio-inspired, human reflexive mechanism
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Supervisor's Name: Porr, Dr. Bernd and Gollee, Dr. Henrik
• Date of Award: 2015
• Depositing User: Miss Lin Meng
• Unique ID: glathesis:2015-7042
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 21 Jan 2016 09:30
• Last Modified: 30 Sep 2016 08:16
• URI: https://theses.gla.ac.uk/id/eprint/7042