  Locomotion in the real world involves unexpected perturbations, and therefore
requires strategies to maintain stability to successfully execute desired
behaviours. Ensuring the safety of locomoting systems therefore necessitates a
quantitative metric for stability. Due to the difficulty of determining the set
of perturbations that induce failure, researchers have used a variety of
features as a proxy to describe stability. This paper utilises recent advances
in dynamical systems theory to develop a personalised, automated framework to
compute the set of perturbations from which a system can avoid failure, which
is known as the basin of stability. The approach tracks human motion to
synthesise a control input that is analysed to measure the basin of stability.
The utility of this analysis is verified on a Sit-to-Stand task performed by 15
individuals. The experiment illustrates that the computed basin of stability
for each individual can successfully differentiate between less and more stable
Sit-to-Stand strategies.
