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visual3d:tutorials:events:kinematic_event_detection [2025/06/02 20:17] – [Overview - Abstract] wikisysopvisual3d:tutorials:events:kinematic_event_detection [2025/06/02 20:33] (current) – [Workflow Outline] wikisysop
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 ====== Assessing Kinematic Methods of Structuring Gait====== ====== Assessing Kinematic Methods of Structuring Gait======
  
-===== Overview - Abstract =====+===== 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. 
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 **Research Question: Are kinematic event detection methods able to reliably structure biomechanical waveforms in the gait cycle?** **Research Question: Are kinematic event detection methods able to reliably structure biomechanical waveforms in the gait cycle?**
  
-The tutorial has been designed for biomechanics researchers, students, and clinicians interested in alternative gait cycle structuring approaches, especially in field-based or markerless motion capture settings.+The tutorial has been designed for biomechanics researchers, students, and clinicians interested in alternative gait cycle structuring approaches, especially in field-based or markerless motion capture settings. Pipelines for all event detection methods are provided.
  
 ===== Introduction ====== ===== Introduction ======
  
-In gait analysis, the ability to structure biomechanical waveforms into gait cycles is crucial for analyzing joint angles, segment kinematics, and for computing various gait measures like step length and phase durations.+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 ground reaction forces (GRFs), which provide an objective and reliable signal for detecting HS and TO. However, kinetic data acquisition requires expensive equipment and comes with many other downfalls.+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- need validation.+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.
  
 The aim of this tutorial is to present a step-by-step guide for using HAS-Motion software tools, specifically Visual3D, to process gait data and compare several kinematic-based methods against the kinetic-based gold standard. The aim of this tutorial is to present a step-by-step guide for using HAS-Motion software tools, specifically Visual3D, to process gait data and compare several kinematic-based methods against the kinetic-based gold standard.
  
-===== Dataset Description =====+===== Data =====
  
 {{:visual3d:tutorials:events:fukuchi_dataset_article_image.png?600|}} {{:visual3d:tutorials:events:fukuchi_dataset_article_image.png?600|}}
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 This tutorial focuses exclusively on **treadmill trials** and analyzes only **left-side events** (LHS and LTO). This simplifies gait cycle definitions to the interval between two identical events on the same foot, which is essential for consistent waveform normalization.  This tutorial focuses exclusively on **treadmill trials** and analyzes only **left-side events** (LHS and LTO). This simplifies gait cycle definitions to the interval between two identical events on the same foot, which is essential for consistent waveform normalization. 
  
-===== Sample Data Download and Contents ======+===== Sample Files ======
  
 To facilitate learning and application, we provide a premade ZIP archive containing all required files. To facilitate learning and application, we provide a premade ZIP archive containing all required files.
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 ===== Workflow Outline ====== ===== Workflow Outline ======
  
-1. **Preprocessing in Visual3D**+====1. Preprocessing in Visual3D ====
  
 The first stage involves preprocessing all participant data using the **Preprocessing_Pipeline.v3s** script. This step takes treadmill-only trials for each participant and puts them into a single CMZ file and computes joint kinematic data. The first stage involves preprocessing all participant data using the **Preprocessing_Pipeline.v3s** script. This step takes treadmill-only trials for each participant and puts them into a single CMZ file and computes joint kinematic data.
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 During this stage, ground reaction force (GRF) data is used to automatically detect kinetic gait events, establishing a **baseline** for later comparison. Subject-specific anthropometric data (height and weight) are manually input from the provided spreadsheet. During this stage, ground reaction force (GRF) data is used to automatically detect kinetic gait events, establishing a **baseline** for later comparison. Subject-specific anthropometric data (height and weight) are manually input from the provided spreadsheet.
  
-2. **Understanding and Applying Kinematic Method Pipelines**+==== 2. Understanding and Applying Kinematic Method Pipelines ====
  
 The next phase involves applying several published and custom kinematic methods to detect gait events without relying on kinetic data.  The next phase involves applying several published and custom kinematic methods to detect gait events without relying on kinetic data. 
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-3. **Apply and Generate Measures to Compare all Methods**+==== 3. Apply and Generate Measures to Compare all Methods ====
  
 Once individual method pipelines were validated, they were consolidated into a master script (**FinalPipeline_ALL_METHODS_SEQUENCES.v3s**). This pipeline computes all gait event across all methods simultaneously and then defines gait cycles based on those events. Once individual method pipelines were validated, they were consolidated into a master script (**FinalPipeline_ALL_METHODS_SEQUENCES.v3s**). This pipeline computes all gait event across all methods simultaneously and then defines gait cycles based on those events.
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 These durations are stored under the METRIC::PROCESSED:: folder in the application. These durations are stored under the METRIC::PROCESSED:: folder in the application.
  
-4. **Exporting to Python for Statistical Evaluation**+==== 4. Exporting to Python for Statistical Evaluation ====
  
 Finally, the computed cycle durations are exported to an Excel file where each row represents one gait cycle instance. Finally, the computed cycle durations are exported to an Excel file where each row represents one gait cycle instance.
visual3d/tutorials/events/kinematic_event_detection.1748895421.txt.gz · Last modified: 2025/06/02 20:17 by wikisysop