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--- visual3d:tutorials:events:kinematic_event_detection [2025/07/16 14:19] – [Sample Files] wikisysop
+++ visual3d:tutorials:events:kinematic_event_detection [2025/07/16 16:29] (current) – wikisysop
@@ Line 3: / Line 3: @@
 ===== Overview =====
-This tutorial is a comprehensive walkthrough of a research investigation presented at the Ontario Biomechanics Conference (OBC) 2025.
+This tutorial is a comprehensive walkthrough of a research investigation presented at the Ontario Biomechanics Conference (OBC) 2025. The study, titled "Assessing Different Kinematic Methods of Structuring Gait", explores how gait events can be reliably detected using only kinematic data, without access to lab-based kinetic systems such as force plates or instrumented treadmills.
-The study, titled "Assessing Different Kinematic Methods of Structuring Gait", explores how gait events can be reliably detected using only kinematic data, without access to lab-based kinetic systems such as force plates or instrumented treadmills.
 **Research Question: Are kinematic event detection methods able to reliably structure biomechanical waveforms in the gait cycle?**
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 ===== Introduction ======
-In gait analysis, the ability to structure biomechanical waveforms into gait cycles is needed for analyzing different kinematic signals and calculating various gait measures.
+In gait analysis, the ability to structure biomechanical waveforms into gait cycles is needed for analyzing different kinematic signals and calculating various gait measures. Traditionally, gait cycles are structured using kinetic event detection methods based on force thresholds, which provide an objective and reliable signal for detecting ON (HS) and OFF(TO). However, kinetic data acquisition is not always available and with more in-field data collection it is not feasible.
-Traditionally, gait cycles are structured using kinetic event detection methods based on force thresholds, which provide an objective and reliable signal for detecting ON (HS) and OFF(TO). However, kinetic data acquisition is not always available and with more in-field data collection it is not feasible.
 Given these limitations, researchers have developed a range of kinematic event detection algorithms that rely on marker trajectories or derived joint kinematics to infer gait events. While these methods are promising, their accuracy and robustness - especially under varying walking speeds and among diverse populations- needs validation.