3 research outputs found
Bio-inspired design and validation of the Efficient Lockable Spring Ankle (ELSA) prosthesis
Over the last decade, active lower-limb prostheses demonstrated their ability to restore a physiological gait for transfemoral amputees by supplying the required positive energy balance during daily life locomotion activities. However, the added-value of such devices is significantly impacted by their limited energetic autonomy, excessive weight and cost, thus preventing their full appropriation by the users. There is thus a strong incentive to produce active yet affordable, lightweight and energy efficient devices. To address these issues, we developed the ELSA (Efficient Lockable Spring Ankle) prosthesis embedding both a lockable parallel spring and a series elastic actuator, tailored to the walking dynamics of a sound ankle. The first contribution of this paper concerns the developement of a bio-inspired, lightweight and stiffness-adjustable parallel spring, comprising an energy efficient ratchet and pawl mechanism with servo actuation. The second contribution is the addition of a complementary rope-driven series elastic actuator to generate the active push-off. The system produces a sound ankle torque pattern during flat ground walking. Up to 50% of the peak torque is generated passively at a negligible energetic cost (0.1 J/stride). By design, the total system is lightweight (1.2kg) and low cost
Exoskeleton-assisted locomotion: design, control and evaluation of wearable robotic devices
Assistive robotic devices such as exoskeletons and prosthetic limbs have great
potential as tools for both augmentation and rehabilitation. However, due to
the complexity of controlling these devices, especially in unstructured environments
where factors such as walking speed and incline can vary rapidly, it is
uncommon to see exoskeletons outside of a clinical or research setting. Prostheses,
whilst more common, are typically passive, which limits their ability
to match the push off forces associated with healthy gait.
Motivated by modern techniques for controlling legged robots, this thesis
motivates the pursuit of an optimisation-based approach to the control and
design of exoskeletons. We identify a number of open problems within the
field, namely (1) how to model the dynamic interaction between a human
subject and an attached exoskeleton; (2) identifying the appropriate metric
or combination of metrics to optimise for in exoskeleton-assisted locomotion;
and (3) how to account for changes in human walking style induced by the
presence of external assistive forces. This thesis details attempts to solve each
of these problems.
We present a methodology for expressing human-exoskeleton system models
as a combination of musculoskeletal models, exoskeleton inertial parameters
and constraint forces. A specific human-exoskeleton model is detailed,
along with a range of methods for modelling the interaction forces which occur
at the attachment points between the human and exoskeleton agents. Experimental
motion data is analysed using musculoskeletal modelling software
(OpenSim) to quantify the effect that each of these interaction models, which
represent various degrees of approximation, have on the resulting humanexoskeleton
dynamics.
Applying exoskeleton assistance is inherently a shared control problem.
The overall goal is not to achieve a prescribed motion at any cost, or to do
so while minimising exoskeleton joint torques, but rather to enhance aspects
of the assisted humans motions; for example, increasing energy efficiency or
stability. Therefore, in order to optimise exoskeleton control patterns we must
first consider what it means for the resultant gait patterns to be optimal, or
even good. We present a detailed analysis of exoskeleton-assisted walking in
healthy subjects, with a particular focus on identifying those metrics which are
invariant to changes in walking condition (e.g. walking speed or incline). We
posit that such metrics, which exhibit strong invariance properties, are good
candidates for the objective function of an optimisation-based controller.
Human walking strategies are unique and complex, and the problem of
predicting the effect of exoskeleton assistance on a subjects gait pattern is a
challenging one. In recent years, success has been had by methods which
aim to learn suitable assistance strategies directly from a subject, via a process
known as human-in-the-loop optimisation. We present a novel humanin-
the-loop framework which utilises musculoskeletal modelling to make the
learning process more time-efficient. Our method is evaluated on a number of
subjects walking on a treadmill with exoskeleton assistance. In addition, we
also explore how human-in-the-loop optimisation can be used to inform the
design of exoskeletons to enhance their assistive capabilities.
Overall, these contributions represent a step towards enabling the wider
usage of exoskeletons and other assistive robotic devices, which could lead to
significant improvements to quality of life for many