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Table of Contents
Example: Gait Events using Kinematic Data
Gait events refer to meaningful moments during walking movement, particularly Heelstrike (HS)- when the foot makes contact with the ground, and Toe-off (TO)- when the foot lifts off the ground.
These events are typically identified by applying thresholds on ground reaction force (GRF) signals. These signals are acquired from lab-based kinetic equipment such as force plates or instrumented treadmills.
To address this issue, several journal articles have been published which introduce different algorithms that make use of kinematic features to identify these gait events. To serve as an alternative when the kinetic-based method is unavailable.
Overview
This example walks through the application of these kinematic event detection methods into the Visual3D pipeline, in order to produce heelstrike and toe-off events.
It must be noted that these methods vary in their effectiveness on correctly identifying these events.
A research study was conducted to assess the different methods' reliability in structuring gait cycles- where each of these methods were compared to the baseline with an analysis model. A tutorial was developed based off of the study and is available here:
Supporting Tutorial: Assessing Kinematic Methods of Structuring Gait
Outline + Data
A pipeline script was constructed for each detection method by referencing the respective literature. These are available here: [SAMPLE DATA - ZIP FILE].
These scripts were built around detecting gait events in the walking data from the Fukuchi et al. [1] open-source dataset. This presents overground and treadmill walking data from 42 healthy adult participants (24 classified 'younger', 18 'older'.
- External forces were collected using force plates and an instrumented treadmill.
- Results include both raw and processed kinematic and kinetic data in the c3d format.
NOTE: For this example we only use treadmill trials and identify left-side gait events.
Preprocessing
Preprocessing must be performed on the WBDSc3dwithGaitEvents folder within the dataset in order to create participant CMZs, build models, and extract only treadmill trials.
The use of the Preprocessing_Pipeline for this data is detailed further in the tutorial.