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other:inspect3d:tutorials:treadmill_walking_in_healthy_individuals [2025/01/17 18:11] – wikisysopother:inspect3d:tutorials:treadmill_walking_in_healthy_individuals [2025/01/20 19:58] (current) – [Visual3D Processing] wikisysop
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 Visual3D and Inspect3D can be used to automate the processing of large batches of clinical data. This tutorial provides an example of how to do so using a publicly available data set. Visual3D and Inspect3D can be used to automate the processing of large batches of clinical data. This tutorial provides an example of how to do so using a publicly available data set.
  
-Fukuchi et al.[[https://peerj.com/articles/4640/|[1]]] published a gold standard public data set [[https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711|[2]]] of marker-based motion capture to describe healthy individuals walking overground and on a treadmill. Although the authors present a number of descriptive analyses, the effect of using handrail support on gait biomechanics during treadmill walking in this data set has yet to be addressed.+[[https://peerj.com/articles/4640/|Fukuchi et al. [1]]] published a gold standard public data set available [[https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711|here [2]]] of marker-based motion capture to describe healthy individuals walking overground and on a treadmill. Although the authors present a number of descriptive analyses, the effect of using handrail support on gait biomechanics during treadmill walking in this data set has yet to be addressed.
  
 This tutorial is designed to demonstrate how to use Inspect3D to compare joint angles between two groups of older adults: those who used a handrail and those who did not. This tutorial is designed to demonstrate how to use Inspect3D to compare joint angles between two groups of older adults: those who used a handrail and those who did not.
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 {{ :Intro1.jpg}} {{ :Intro1.jpg}}
  
-The Fukuchi et al. data set includes 3D kinematic data for 26 lower limb and pelvic markers and ground reaction force plate data for 42 subjects walking at 8 different speeds. Motion capture data was sampled at 150 Hz and force plate data was sampled at 300 Hz. Metadata in these trials included anthropometric measurements such as mass, height, leg length, leg dominance, gender, handrail support, and walking speed. For this tutorial only a subset of the data was used, which included the trials with older adults (subjects 25-42) walking at a single comfortable control speed (T05).+The Fukuchi et al. data set includes 3D kinematic data for 26 lower limb and pelvic markers, and ground reaction force plate data for 42 subjects walking at 8 different speeds. Motion capture data was sampled at 150 Hz and force plate data was sampled at 300 Hz. Metadata in these trials included anthropometric measurements such as mass, height, leg length, leg dominance, gender, handrail support, and walking speed. For this tutorial only a subset of the data was used, which included the trials with older adults (subjects aged 25-42) walking at a single comfortable control speed (T05).
  
   * **Fukuchi et. al. subject data:** From the publicly available data files, download WBDSc3d.zip for subject .c3d files, and WBDSinfo.xlsx for the metadata [[https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711|[3]]]   * **Fukuchi et. al. subject data:** From the publicly available data files, download WBDSc3d.zip for subject .c3d files, and WBDSinfo.xlsx for the metadata [[https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711|[3]]]
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   * **Inspect3D Color Palette (FukuchiPalette.xml):** Premade color template used for generating images in this tutorial.   * **Inspect3D Color Palette (FukuchiPalette.xml):** Premade color template used for generating images in this tutorial.
  
-Table 1 below shows the tags used in this tutorial. These will be used in later steps.+**Table 1** below shows the tags used in this tutorial. These will be used in later steps.
  
-Table 1: Tag Definitions+__**Table 1: Tag Definitions**__
 |Tag                        |Definition                                               | |Tag                        |Definition                                               |
 |SLOW, CONTROL, FAST        |Walking speeds.                                          | |SLOW, CONTROL, FAST        |Walking speeds.                                          |
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 ==== Visual3D Processing ==== ==== Visual3D Processing ====
 +This tutorial will be started by processing the raw data provided by Fukuchi et al. using the Visual3D Pipeline feature.
  
-If you want to skip to the Inspect3D analysis portion of this tutorial, ensure you have downloaded the **"I3D_Tutorial_Treadmill_Walking.zip"** folder and then skip to the header called **Loading Data into Inspect3D**. If you want to use or modify the pre-made pipeline, continue with the steps below. You also need to have downloaded the original data from Fukuchi et al.[[https://peerj.com/articles/4640/|[4]]]+If you want to skip to the Inspect3D analysis portion of this tutorial, ensure that you have downloaded the **"I3D_Tutorial_Treadmill_Walking.zip"** folder and then skip to the header called **Loading Data into Inspect3D**. If you want to use or modify the pre-made pipeline, continue with the steps below. You also need to have downloaded the original data from Fukuchi et al.[[https://peerj.com/articles/4640/|[4]]]
  
-This tutorial uses the Pipeline feature of Visual3D to process the raw data provided by Fukuchi et al. The benefit of the pipeline feature is that it can be used to automate repeated steps and reduces the amount of time it takes to work with large data sets. If you are unfamiliar with using the Visual3D workspace, take some time to review the [[visual3d:tutorials:knowledge_discovery:typical_processing_session|Typical Processing Session Tutorial]] and the [[visual3d:tutorials:pipeline:command_pipeline|Command Pipeline Tutorial]].+The benefit of the pipeline feature is that it can be used to automate repeated steps and reduces the amount of time it takes to work with large data sets. If you are unfamiliar with using the Visual3D workspace, take some time to review the [[visual3d:tutorials:knowledge_discovery:typical_processing_session|Typical Processing Session Tutorial]] and the [[visual3d:tutorials:pipeline:command_pipeline|Command Pipeline Tutorial]].
  
-{{ :processing1.png }}+We can now begin the tutorial:
  
 **1. Download .c3d files** **1. Download .c3d files**
  
-Fukuchi et al. has a folder labelled **WBDSc3dWithGaitEvents**, ensure that you are using the .c3d files contained in this folder.+Fukuchi et al. has a folder labelled **WBDSc3dWithGaitEvents**, ensure that you are using the .c3d files contained in this folder.  
 + 
 +**NOTE:** The processed data files after the Visual3D processing are available in the **I3D_Tutorial_Treadmill_Walking.zip** for you to use in the Inspect3D portion of the tutorial.
  
 **2. Subject by Subject Pipeline Edits**  **2. Subject by Subject Pipeline Edits** 
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 {{ :LoadingData2.png }} {{ :LoadingData2.png }}
  
-  - Select {{:I3DLoadLibrary.png?20}} **Load Library** and set **CMO Library Path** to the folder where your .cmz and .c3d files are located. Ensure you are using the files from the **WBDSc3dWithGaitEvents** folder. Then select **Load** and exit the window.+  - Select {{:I3DLoadLibrary.png?20}} **Load Library** and select browse to set the file path to the folder where your .cmz and .c3d files are located. Ensure you are using the files from the **processed_workspaces** folder. Then select **Load** and exit the window.
   - Open the {{:I3DGroups.png?20}}**Group Definitions** tab and select {{:I3DOpenGroupDef.png?20}}**Load Group Definitions**. Once prompted, open the **group_definitions.q3d** query file.   - Open the {{:I3DGroups.png?20}}**Group Definitions** tab and select {{:I3DOpenGroupDef.png?20}}**Load Group Definitions**. Once prompted, open the **group_definitions.q3d** query file.
   - Select **Calculate All Groups**. Groups will be divided up based on right and left pelvis, hip, knee and ankle joint angles in x, y, and z axis.   - Select **Calculate All Groups**. Groups will be divided up based on right and left pelvis, hip, knee and ankle joint angles in x, y, and z axis.
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 === Ankle === === Ankle ===
  
-For this study, to look specifically at one joint 6 figures can be laid out in a 3x2 grid. To do so go to the {{:I3DShowOptions.png}} **[[Other:Inspect3D:Documentation:Dialogs:Load_Library_Dialogue#Plotting_options|Options]]** tab and change the number of rows and columns. This allows for easy comparison between the different angle changes in different directions. We plotted the left and right ankle angles in abduction-adduction, internal-external rotation, and flexion-extension to compare signals between railing and non-railing walking conditions. Visual analysis showed very similar trajectories over a gait cycle, however holding the railing (dark red) appeared to have greater variability during stance for left angle in abduction-adduction and internal-external rotation. For the right leg they overall show similar trajectories. {{:ankleresult.png}}+For this study, to look specifically at one joint 6 figures can be laid out in a 3x2 grid. To do so go to the {{:I3DShowOptions.png?20}} **[[Other:Inspect3D:Documentation:Dialogs:Load_Library_Dialogue#Plotting_options|Options]]** tab and change the number of rows and columns. This allows for easy comparison between the different angle changes in different directions. We plotted the left and right ankle angles in abduction-adduction, internal-external rotation, and flexion-extension to compare signals between railing and non-railing walking conditions. Visual analysis showed very similar trajectories over a gait cycle, however holding the railing (dark red) appeared to have greater variability during stance for left angle in abduction-adduction and internal-external rotation. For the right leg they overall show similar trajectories.  
 +{{ :ankleresult.png?600 }}
  
 === Knee === === Knee ===
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 We plotted the left knee angle signals over a gait cycle, including the mean and standard deviations for both railing and non-railing groups. For the coordinate system used in the Fukuchi et. al. data set, the Z-axis is aligned in the medial/lateral direction, and so the knee angle about the Z-axis describes the flexion/extension rotation. In this analysis, we can see that there is some amount of distinction in the mean values between those holding a railing, and those not holding a railing, particularly during stance (0-45%) and late-swing phase (90%+). It appears that those holding the rail had a higher mean joint angle in both those instances. We plotted the left knee angle signals over a gait cycle, including the mean and standard deviations for both railing and non-railing groups. For the coordinate system used in the Fukuchi et. al. data set, the Z-axis is aligned in the medial/lateral direction, and so the knee angle about the Z-axis describes the flexion/extension rotation. In this analysis, we can see that there is some amount of distinction in the mean values between those holding a railing, and those not holding a railing, particularly during stance (0-45%) and late-swing phase (90%+). It appears that those holding the rail had a higher mean joint angle in both those instances.
  
-{{:KNEE1.jpg}}+{{ :KNEE1.jpg?600 }}
  
 We then performed a PCA analysis on the knee data. Through this we can see that the underlying gait cycle signal is structured, and 95% variance can be explained by the first four principal components. We then performed a PCA analysis on the knee data. Through this we can see that the underlying gait cycle signal is structured, and 95% variance can be explained by the first four principal components.
  
-{{:KNEE3.jpg}}+{{ :KNEE3.jpg?600 }}
  
 Returning to our visualisation analysis on the knee, we know that there are potential differences between the knee angles of those who used the railing, and those who did not. If possible, we want to be able to classify subjects as having used the railing, and not having used it. By looking at the **Group Scores** tab, that is the average values of each principal component on each group, we see that the standard errors of PC1 and PC2 do not overlap, suggesting PC1 and PC2 can best discriminate between groups. PC4 for example, has standard errors that do overlap, signifying that it could not be used to discriminate the groups. We can graph both PC1 and PC2 through the **Workspace Scores** tab, and see that we have a nearly linearly separable dataset. Returning to our visualisation analysis on the knee, we know that there are potential differences between the knee angles of those who used the railing, and those who did not. If possible, we want to be able to classify subjects as having used the railing, and not having used it. By looking at the **Group Scores** tab, that is the average values of each principal component on each group, we see that the standard errors of PC1 and PC2 do not overlap, suggesting PC1 and PC2 can best discriminate between groups. PC4 for example, has standard errors that do overlap, signifying that it could not be used to discriminate the groups. We can graph both PC1 and PC2 through the **Workspace Scores** tab, and see that we have a nearly linearly separable dataset.
  
 \\ \\
-{{:KNEE4.jpg}}{{:KNEE6.jpg}}+{{ :KNEE4.jpg?600 }}{{ :KNEE6.jpg?600 }}
  
 We can then look more closely at PC1 and PC2, and see why this may make sense. Looking back at the mean signal trace graph, we see that there is a notable difference in the joint angle between the 2 groups at the beginning and end of the gait cycle, and PC1 in particular may represent this. We can see by plotting the vector of PC1 that in general, the value of PC4 is smaller in the middle, while it is a large value at both the beginning and end of the gait cycle. Since we can visually see that the subjects who held the rail generally had higher angles at these points in the cycle, we would expect, and in fact do see, large values of PC1. While this is not a perfect separation of the two groups, the addition of PC2 explains the variance, again at the very start where we see the most difference, and around 60-75% where there is slight variance. We can then look more closely at PC1 and PC2, and see why this may make sense. Looking back at the mean signal trace graph, we see that there is a notable difference in the joint angle between the 2 groups at the beginning and end of the gait cycle, and PC1 in particular may represent this. We can see by plotting the vector of PC1 that in general, the value of PC4 is smaller in the middle, while it is a large value at both the beginning and end of the gait cycle. Since we can visually see that the subjects who held the rail generally had higher angles at these points in the cycle, we would expect, and in fact do see, large values of PC1. While this is not a perfect separation of the two groups, the addition of PC2 explains the variance, again at the very start where we see the most difference, and around 60-75% where there is slight variance.
  
-{{:KNEE7.jpg}}{{:KNEE8.jpg}}+{{ :KNEE7.jpg?600 }}{{ :KNEE8.jpg?600 }}
  
 === Hip === === Hip ===
other/inspect3d/tutorials/treadmill_walking_in_healthy_individuals.1737137510.txt.gz · Last modified: 2025/01/17 18:11 by wikisysop