Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.

ABSTRACT: Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high q

SUBMITTER: Bach MM 

PROVIDER: S-EPMC9644164 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature