Event Detection for Active Lower Limb Prosthesis

TL;DR

This study explores how cruciate ligament stretch data from a bicondylar knee model can improve event detection in gait cycles, enhancing control accuracy for active lower limb prostheses.

Contribution

It introduces a novel approach using ligament stretch measurements to predict gait events, potentially improving prosthetic control systems.

Findings

Ligament stretch varies with walking speed, especially around 5% and 80% of gait cycle.

Static features at 90% and 95% of cycle can predict initial contact and foot flat.

Bicondylar knee design enhances event detection accuracy for prosthetic control.

Abstract

Accurate event detection is key to the successful design of semi-passive and powered prosthetics. Kinematically, the natural knee is complex, with translation and rotation components that have a substantial impact on gait characteristics. When simplified to a pin joint, some of this behaviour is lost. This study investigates the role of cruciate ligament stretch in event detection. A bicondylar knee design was used, constrained by analogues of the anterior and posterior cruciate ligaments. This offers the ability to characterize knee kinematics by the stretch of the ligaments. The ligament stretch was recorded using LVDTs parallel to the ligaments of the Russell knee on a bent knee crutch. Which was used to capture data on a treadmill at 3 speeds. This study finds speed dependence within the stretch of the cruciate ligaments, prominently around 5\% and 80\% of the gait cycle for the posterior and anterior. The cycle profile remains consistent with speed; therefore, other static events such as the turning point feature at around 90\% and 95\% of the cycle, for the posterior and anterior, respectively, could be used as a predictive precursor for initial contact. Likewise at 90\% and 95\%, another pair of turning points that in this case could be used to predict foot flat. This concludes that the use of a bicondylar knee design could improve the detection of events during the gait cycle, and therefore could increase the accuracy of subsequent controllers for powered prosthetics.