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visual3d:tutorials:knowledge_discovery:assesing_stability_during_gait [2024/11/15 19:07] – wikisysopvisual3d:tutorials:knowledge_discovery:assesing_stability_during_gait [2024/11/15 19:14] (current) – wikisysop
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 The most common way that the literature addresses stability in standing and walking is through the Margin Of Stability (MoS). When the extrapolated center of mass (xCoM) is within the Base of Support (BoS), the MoS is positive, indicating that the system is stable. If MoS is negative, this would indicate that the xCoM outside the BoS, and the system is unstable [1]. In this interpretation, it is unclear where the XCoM is positioned when we are given a negative or positive MoS. The subject could be beyond the boundary of stability to the left, right, forward, or behind, and the MoS value as it is presented does not describe where the limit is crossed. Additionally, MoS values are often averaged over strides, which does not provide information about stride-to-stride variability and also assumes that the trajectory of a stride is independent of past or future strides [2]. These limitations make it difficult to interpret the MoS as a measure of stability during walking. It is important that we explore alternative ways of organizing and displaying MoS values that can overcome some of these limitations - which we will do in this tutorial! The most common way that the literature addresses stability in standing and walking is through the Margin Of Stability (MoS). When the extrapolated center of mass (xCoM) is within the Base of Support (BoS), the MoS is positive, indicating that the system is stable. If MoS is negative, this would indicate that the xCoM outside the BoS, and the system is unstable [1]. In this interpretation, it is unclear where the XCoM is positioned when we are given a negative or positive MoS. The subject could be beyond the boundary of stability to the left, right, forward, or behind, and the MoS value as it is presented does not describe where the limit is crossed. Additionally, MoS values are often averaged over strides, which does not provide information about stride-to-stride variability and also assumes that the trajectory of a stride is independent of past or future strides [2]. These limitations make it difficult to interpret the MoS as a measure of stability during walking. It is important that we explore alternative ways of organizing and displaying MoS values that can overcome some of these limitations - which we will do in this tutorial!
  
-{{:MoS_Intro.png}}+{{:MoS_Intro.png?400}}
  
 ==== Data ==== ==== Data ====
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     * Open the meta commands subfolder     * Open the meta commands subfolder
     * select **Model_Landmarks_For_MoS** and run pipeline     * select **Model_Landmarks_For_MoS** and run pipeline
 +{{ :Inputs.png?300}}
 You will first be prompted to provide the name of the targets for the following markers: Left heel (l_heel), right heel (r_heel), left mid toes (l_met23), right mid toes (r_met23), left fifth metatarsal (l_met5), right fifth metatarsal (l_met5), left first metatarsal (l_met1), and right first metatarsal (l_met1). **FOR THIS TUTORIAL**, the marker-based data targets have the same names as the parameters they are being set to, and in the dialog you can type these same names as the value entry. This is shown in the figure below. **IF** you are applying this method to a **DIFFERENT MODEL**, make sure to check what your target signals are named for these markers. You will first be prompted to provide the name of the targets for the following markers: Left heel (l_heel), right heel (r_heel), left mid toes (l_met23), right mid toes (r_met23), left fifth metatarsal (l_met5), right fifth metatarsal (l_met5), left first metatarsal (l_met1), and right first metatarsal (l_met1). **FOR THIS TUTORIAL**, the marker-based data targets have the same names as the parameters they are being set to, and in the dialog you can type these same names as the value entry. This is shown in the figure below. **IF** you are applying this method to a **DIFFERENT MODEL**, make sure to check what your target signals are named for these markers.
  
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 All motion files in Signals and Events should now have a model applied, with blue markers on the feet showing the applied landmarks. In the figure, landmarks have been checked to show you which have been added from the pipeline, you do not need to do this yourself. All motion files in Signals and Events should now have a model applied, with blue markers on the feet showing the applied landmarks. In the figure, landmarks have been checked to show you which have been added from the pipeline, you do not need to do this yourself.
  
-{{:Inputs.png?500}} {{:Landmarks.png?600}}+{{:Landmarks.png?700}}
  
 === To apply landmarks to markeless data: === === To apply landmarks to markeless data: ===
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 ==== Base of Support ==== ==== Base of Support ====
  
-{{:BoSSingle.png}}+{{:BoSSingle.png?400}}
  
-{{:BoSDouble.png}}+{{:BoSDouble.png?500}}
  
 The BoS uses the AP and ML bounds of the feet from the landmarks we applied to the model. At each instant in the motion trial, the base of support is defined by four boundaries in single or double stance. The BoS uses the AP and ML bounds of the feet from the landmarks we applied to the model. At each instant in the motion trial, the base of support is defined by four boundaries in single or double stance.
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 ===== Margin Of Stability: New Definition ===== ===== Margin Of Stability: New Definition =====
  
-{{:NewBoS.png}}+{{:NewBoS.png?600}}
  
 This section will use the pipeline: **Find_MoS_New.v3s** This pipeline builds off of the previous section. You must run **Find_MoS_Original.v3s** first! This section will use the pipeline: **Find_MoS_New.v3s** This pipeline builds off of the previous section. You must run **Find_MoS_Original.v3s** first!
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 ===== Further MoS Analysis ===== ===== Further MoS Analysis =====
  
-{{:NormalizedMoSSigs.png}}+{{:NormalizedMoSSigs.png?400}}
  
 In the **MOS_NEW** derived folder, you will see several normalized signals for each left or right stride of the selected walking trial. This was done to plot several strides on top of eachother. Here we only have a few strides, but analyzing these MoS signals normalized between heel strikes may be a more useful way to assess a subject's stability. If we consider that the MoS is being classified as ‘unstable’ for the majority of the stride, but we know the subject walked normally and did not fall, we might be able to detect moments of actual instability through outlier MoS strides. {{:NormalizedMoSPlot.png}} In the **MOS_NEW** derived folder, you will see several normalized signals for each left or right stride of the selected walking trial. This was done to plot several strides on top of eachother. Here we only have a few strides, but analyzing these MoS signals normalized between heel strikes may be a more useful way to assess a subject's stability. If we consider that the MoS is being classified as ‘unstable’ for the majority of the stride, but we know the subject walked normally and did not fall, we might be able to detect moments of actual instability through outlier MoS strides. {{:NormalizedMoSPlot.png}}
visual3d/tutorials/knowledge_discovery/assesing_stability_during_gait.1731697669.txt.gz · Last modified: 2024/11/15 19:07 by wikisysop