Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running.

ABSTRACT: The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and

SUBMITTER: Rivadulla A 

PROVIDER: S-EPMC8351929 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature