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visual3d:tutorials:events:kinematic_event_detection [2025/06/02 14:16] – [Generating and Comparing Gait Cycles Across Methods] 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. 
  
-The study, titled "Assessing Different Kinematic Methods of Structuring Gait", explores how gait events can 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?** **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.
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 **Knee Angle Max Method**: A simpler method that structures cycles based on successive local maxima of the knee flexion signal, using **Event_Maximum** **Knee Angle Max Method**: A simpler method that structures cycles based on successive local maxima of the knee flexion signal, using **Event_Maximum**
  
-Each of these pipeline scripts define events AND compute the cycle duration using this events by placing them in sequences.+Each of these pipeline scripts define events AND compute the cycle duration using this events by placing them in sequences. The table below shows a summary of these methods: 
 + 
 +|**Method Name**|**Events Produced**|**Description**| 
 +|Foot Position - Zeni Method 1|LHS, LTO|Uses anterior-posterior position of the foot.| 
 +|Foot Velocity - Zeni Method 2|LHS, LTO|Identifies local minimum in foot's anterior-posterior velocity/| 
 +|Hip Extension - DeAsha Method|LHS| Points of peak hip extension.| 
 +|Toe Acceleration - Hreljac Method|LHS, LTO|Peak in vertical acceleration of toe marker.| 
 +|Heel Velocity - OConnor Method|LHS, LTO|Identifies local minimum in local heel velocity.| 
 +|Hip Surpassing Heel|LHip_Passes_Heel|Identifies moment the leading hip joint center overtakes the opposite foot.| 
 +|Max Knee Angle|LKnee_Angle_Max|Peak knee flexion-extension angle.|
  
  
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 ===== Exporting Data for Statistical Analysis ===== ===== Exporting Data for Statistical Analysis =====
  
-After processing, gait cycle duration data is exported using **Export_Data_To_ASCIII**. The command extracts mean cycle duration values for each trial and method combination, formatting them into a standardized table like so:+After processing, gait cycle duration data is exported using **Export_Data_To_ASCII**.  
 + 
 +<code> 
 +Export_Data_To_Ascii_File 
 +! /FILE_NAME= 
 +/SIGNAL_TYPES= 
 +/SIGNAL_FOLDER= 
 +! /SIGNAL_NAMES= 
 +! /SIGNAL_COMPONENTS= 
 +! /COMPONENT_SEQUENCE= 
 +! /SIGNAL_PRECISION= 
 +! /EVENT_SEQUENCE= 
 +! /EXCLUDE_EVENTS= 
 +! /USE_POINT_RATE=FALSE 
 +! /NORMALIZE_DATA=FALSE 
 +/NORMALIZE_POINTS= 
 +! /EXPORT_MEAN_AND_STD_DEV=FALSE 
 +! /EXPORT_MEAN_AND_STD_DEV_FOR_METRIC=FALSE 
 +! /USE_P2D_FORMAT=FALSE 
 +! /USE_XML_FORMAT=FALSE 
 +! /USE_JSON_FORMAT=FALSE 
 +! /USE_SHORT_FILENAME=FALSE 
 +! /INCLUDE_TAGS_FOR_XML=FALSE 
 +! /EXPORT_EMPTY_SIGNALS=FALSE 
 +! /EXPORT_WITHOUT_HEADER=FALSE 
 +! /EXPORT_COMPONENT_AS_TEXT=FALSE 
 +! /EXPORT_NAN=FALSE 
 +! /CREATE_FOLDER_PATH=TRUE 
 +! /USE_SCIENTIFIC_NOTATION=FALSE 
 +; 
 +</code> 
 + 
 +The command extracts the METRIC value of cycle durations for each method, for each of the participants. This is then opened in an excel file, and all participants were combined into one XML included in the sample data. The format of the sheet is the following:
  
 |**SUBJECT NO.**|**TRIAL**|**METHOD**|**CYCLE_INSTANCE**|**CYCLE_DURATION**| |**SUBJECT NO.**|**TRIAL**|**METHOD**|**CYCLE_INSTANCE**|**CYCLE_DURATION**|
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 ===== Statistical Evaluation Using Linear Mixed Models ====== ===== Statistical Evaluation Using Linear Mixed Models ======
-To evaluate the agreement between kinematic methods and the kinetic baseline, a Linear Mixed Model (LMM) was applied in Python using **statsmodels**. The dependent variable was cycle duration. The fixed effect was the detection method, while nested random effects were defined for participant and trial. 
  
-LMM was chosen because it can model variability within and between participants and handle repeated measures across conditions. The results were visualized as coefficient plots showing deviations from the kinetic baseline, with confidence intervals indicating statistical significance.+Once all gait events and cycle durations were processed in Visual3D, the data was ready for statistical analysis. To assess how well the different kinematic methods agreed with the kinetic gold standard, we used a statistical approach called a **Linear Mixed Model (LMM)** in Python (via the statsmodels library). 
 + 
 +In this analysis, we looked at the **cycle duration** as the outcome we were measuring. We then compared this outcome across different **event detection methods** (which served as the fixed effect), while accounting for variation across participants and their trials (included as random effects). 
 + 
 +This model was chosen because it is especially good at handling repeated measures, meaning it can recognize that the same person may walk differently across trials, and still lets us isolate whether the method itself is making a difference. 
 + 
 +To visualize the results, we start with a boxplot that shows the cycle durations for one of the more reliable kinematic methods- **LHS Foot Position** (also known as Zeni Method 1)- compared to the **LON Kinetic Baseline**. You'll notice that the distributions of the two are quite similar, suggesting that this method might be reliably mimicking the gold standard. 
 + 
 +However, this figure does not show us the whole story, since it doesn't take into account individual differences between participants and how they vary. 
 + 
 +{{:visual3d:tutorials:events:boxplot_figure.png?600|}} 
 + 
 +That's where the results plot from the Linear Mixed Model comes in. In this graph, we show how each method's average cycle duration compares to the baseline. Each point represents the difference in time between the method and the kinetic baseline, and the horizontal bars are confidence intervals. 
 + 
 +If a method's bar overlaps with the red vertical line at zero, it means there's no **statistically significant difference**- that method produces similar cycle durations to the kinetic baseline. In this case, three methods stood out as being statistically similar to the kinetic reference: 
 +  - LHS Foot Position 
 +  - LTO Foot Position 
 +  - Peak Knee Angle 
 + 
 +These results suggest that those methods can structure gait cycles just as consistently as the gold standard kinetic approach. 
 + 
 +On the other hand, several methods have an asterisk beside their points, meaning they were significantly different from the baseline using a Bonferroni correction. This indicates that while they may still detect events, they do not structure the gait cycle in a way that aligns closely with the kinetic reference.
  
 +{{:visual3d:tutorials:events:final_results_fig.png?600|}}
 ===== Conclusions ===== ===== Conclusions =====
  
visual3d/tutorials/events/kinematic_event_detection.1748873804.txt.gz · Last modified: 2025/06/02 14:16 by wikisysop