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sift:tutorials:bathuniversity:statistical_power_analysis_changing_gradients_and_speeds

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sift:tutorials:bathuniversity:statistical_power_analysis_changing_gradients_and_speeds [2026/08/26 13:56] – ↷ Page moved and renamed from sift:tutorials:a_statistical_power_analysis_on_lower_limb_kinematic_comparisons_across_changing_gradients_and_speeds to sift:tutorials:bathuniversity:statistical_power_analysis_changing_gradients_and_speeds wikisysopsift:tutorials:bathuniversity:statistical_power_analysis_changing_gradients_and_speeds [2026/08/26 13:56] (current) – ↷ Links adapted because of a move operation wikisysop
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 Interpolate: add SIGNAL_TYPES to TARGET Interpolate: add SIGNAL_TYPES to TARGET
  
-{{.bathuniversity_project:pasted:20260626-134155.png}}+{{..:bathuniversity_project:pasted:20260626-134155.png}}
  
 Lowpass Filter: add SIGNAL_TYPES to TARGET and SIGNAL_FOLDER to PROCESSED Lowpass Filter: add SIGNAL_TYPES to TARGET and SIGNAL_FOLDER to PROCESSED
  
-{{.bathuniversity_project:pasted:20260626-134454.png}}+{{..:bathuniversity_project:pasted:20260626-134454.png}}
  
 Use Processed Analog: add USE_PROCESSED to TRUE Use Processed Analog: add USE_PROCESSED to TRUE
  
-{{.bathuniversity_project:pasted:20260626-134718.png}}+{{..:bathuniversity_project:pasted:20260626-134718.png}}
  
 Lowpass Filter: add SIGNAL_TYPES to ANALOG Lowpass Filter: add SIGNAL_TYPES to ANALOG
  
-{{.bathuniversity_project:pasted:20260626-134701.png}}+{{..:bathuniversity_project:pasted:20260626-134701.png}}
  
 FP Auto Baseline: add FP_NUMBER to 1+2 FP Auto Baseline: add FP_NUMBER to 1+2
  
-{{.bathuniversity_project:pasted:20260626-134936.png}} +{{..:bathuniversity_project:pasted:20260626-134936.png}} 
  
 Modify force platform parameters: click edit and select get current C3D parameters Modify force platform parameters: click edit and select get current C3D parameters
  
-{{.bathuniversity_project:pasted:20260626-135115.png}}+{{..:bathuniversity_project:pasted:20260626-135115.png}}
  
 Recalc: default parameters Recalc: default parameters
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 Assign tags to file: each trial had two sets of tags applied, one for the gradient/type (uphill run, flat run, downhill run, walk) and speed (slow, norm, fixed, fast). Tags were made seperately and applied using a file mask such as *uphillrun*. This finds any .c3d files that have "uphillrun" in the file name. Assign tags to file: each trial had two sets of tags applied, one for the gradient/type (uphill run, flat run, downhill run, walk) and speed (slow, norm, fixed, fast). Tags were made seperately and applied using a file mask such as *uphillrun*. This finds any .c3d files that have "uphillrun" in the file name.
  
-{{.bathuniversity_project:pasted:20260626-140049.png}}+{{..:bathuniversity_project:pasted:20260626-140049.png}}
  
 Compute model based data: similar to assigning tags, computing model based data was computed individually for the left and right side as well for the metrics that you want to calculated, Add RESULT_NAME as "the name of metric" such as Left_Knee_Angle, add SUBJECT_TAG as ALL_SUBJECTS, add FUNCTION as "type of metric" such as Joint Angle (chosen from list under model based item properties), add SEGMENT as "the proximal segment" such as Left Shank (LSK) , add REFERENCE_SEGMENT as "distal segment" such as Left Thigh (LTH), add RESOLUTION_COORDINATE_SYSTEM, for left side only add NEGATEY and NEGATEZ as TRUE (as it is opposite to global coordinate system) Compute model based data: similar to assigning tags, computing model based data was computed individually for the left and right side as well for the metrics that you want to calculated, Add RESULT_NAME as "the name of metric" such as Left_Knee_Angle, add SUBJECT_TAG as ALL_SUBJECTS, add FUNCTION as "type of metric" such as Joint Angle (chosen from list under model based item properties), add SEGMENT as "the proximal segment" such as Left Shank (LSK) , add REFERENCE_SEGMENT as "distal segment" such as Left Thigh (LTH), add RESOLUTION_COORDINATE_SYSTEM, for left side only add NEGATEY and NEGATEZ as TRUE (as it is opposite to global coordinate system)
  
-{{.bathuniversity_project:pasted:20260626-141125.png}}+{{..:bathuniversity_project:pasted:20260626-141125.png}}
  
 Save pipeline as .vs3 and save workspace .cmz Save pipeline as .vs3 and save workspace .cmz
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 To repeat the steps from Visual3D for all the participants, this can be completed in Sift by selecting Load Data and the correct Library path.  To repeat the steps from Visual3D for all the participants, this can be completed in Sift by selecting Load Data and the correct Library path. 
  
-{{.bathuniversity_project:pasted:20260819-181458.png}}+{{..:bathuniversity_project:pasted:20260819-181458.png}}
  
 Each participant should have their own folder with all the .c3d files for each movement.  Each participant should have their own folder with all the .c3d files for each movement. 
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 This is done by opening the Explore tab on the left and select Query Builder. Follow along [[sift:tutorials:openbiomechanics_project:refine_queries_with_metadata|Refine Queries]] by selecting Auto-Populate Queries through refinements of tags. To add multiple tags within the groups, edit the conditions and go under the refinement tab to select **Refine using tag** and **Use AND Logic** and select the speed and gradient tag to create as one group.  This is done by opening the Explore tab on the left and select Query Builder. Follow along [[sift:tutorials:openbiomechanics_project:refine_queries_with_metadata|Refine Queries]] by selecting Auto-Populate Queries through refinements of tags. To add multiple tags within the groups, edit the conditions and go under the refinement tab to select **Refine using tag** and **Use AND Logic** and select the speed and gradient tag to create as one group. 
  
-{{.bathuniversity_project:pasted:20260729-142020.png}}+{{..:bathuniversity_project:pasted:20260729-142020.png}}
  
 For example, we assessed the knee joint angle in the X direction (flexion/extension) for each of the trials as separate groups. This allowed us to select multiple groups to compare either changing speed (ex. Downhill Fast, Downhill Slow and Downhill Norm) or changing gradient (Downhill Slow, Uphill Slow, Flat Slow). Each workspace within these groups represents an individual participant (CMZ file). For example, we assessed the knee joint angle in the X direction (flexion/extension) for each of the trials as separate groups. This allowed us to select multiple groups to compare either changing speed (ex. Downhill Fast, Downhill Slow and Downhill Norm) or changing gradient (Downhill Slow, Uphill Slow, Flat Slow). Each workspace within these groups represents an individual participant (CMZ file).
  
-{{.bathuniversity_project:pasted:20260819-194918.png}}+{{..:bathuniversity_project:pasted:20260819-194918.png}}
  
 Here is the query builder file used in this tutorial: [[https://has-motion.com/wiki/doku.php?ns=sift%3Atutorials%3Abathuniversity_project%3A&image=sift%3Atutorials%3Abathuniversity_project%3Arun_kneexja_queries.zip&do=media&tab_files=files&tab_details=view|Queries.q3d]] to match the groups above. Here is the query builder file used in this tutorial: [[https://has-motion.com/wiki/doku.php?ns=sift%3Atutorials%3Abathuniversity_project%3A&image=sift%3Atutorials%3Abathuniversity_project%3Arun_kneexja_queries.zip&do=media&tab_files=files&tab_details=view|Queries.q3d]] to match the groups above.
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 If there are any traces that appear visually as outliers, they can be removed within the graph. To clean the data, start by selecting the trial(s) that had issues and right click -> Exclude Trace (raw data) to remove any incorrect trials. If there are any traces that appear visually as outliers, they can be removed within the graph. To clean the data, start by selecting the trial(s) that had issues and right click -> Exclude Trace (raw data) to remove any incorrect trials.
  
-{{.bathuniversity_project:pasted:20260729-142203.png}}+{{..:bathuniversity_project:pasted:20260729-142203.png}}
  
 Alternative anomaly and outlier detection methods can be found from the [[https://has-motion.com/wiki/doku.php?id=sift:sift_overview:anomaly_detection|Anomaly Detection]] wiki page.  Alternative anomaly and outlier detection methods can be found from the [[https://has-motion.com/wiki/doku.php?id=sift:sift_overview:anomaly_detection|Anomaly Detection]] wiki page. 
  
  
-{{.bathuniversity_project:pasted:20260729-142746.png}} -> {{.bathuniversity_project:pasted:20260729-142821.png}}+{{..:bathuniversity_project:pasted:20260729-142746.png}} -> {{..:bathuniversity_project:pasted:20260729-142821.png}}
  
 Once data is cleaned you can update CMZ data. This is shown through the [[sift:tutorials:clean_your_data|Clean Data]] tutorial page to update a clean dataset. This is done by clicking the Update CMZ Files which will open this dialog: Once data is cleaned you can update CMZ data. This is shown through the [[sift:tutorials:clean_your_data|Clean Data]] tutorial page to update a clean dataset. This is done by clicking the Update CMZ Files which will open this dialog:
  
-{{.bathuniversity_project:pasted:20260728-172835.png}}+{{..:bathuniversity_project:pasted:20260728-172835.png}}
  
 Select Excluded Traces and Add Event to Exclude Signals which is defined as BAD. This will tag the bad data helping to exclude those signals that are defined as "BAD". The click Run Pipeline which will update the dataset.  Select Excluded Traces and Add Event to Exclude Signals which is defined as BAD. This will tag the bad data helping to exclude those signals that are defined as "BAD". The click Run Pipeline which will update the dataset. 
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 Results will popup Results will popup
  
-{{.bathuniversity_project:pasted:20260729-154029.png}}+{{..:bathuniversity_project:pasted:20260729-154029.png}}
  
 Results of PCA can be interpreted as explained in [[sift:application:analyse_page|Analysis]] page Results of PCA can be interpreted as explained in [[sift:application:analyse_page|Analysis]] page
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    - Set Use Workspace Mean    - Set Use Workspace Mean
  
-{{.bathuniversity_project:pasted:20260729-153053.png}}+{{..:bathuniversity_project:pasted:20260729-153053.png}}
  
 Head to Statistics and select Compute SPM Head to Statistics and select Compute SPM
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    - Create SPM    - Create SPM
  
-{{.bathuniversity_project:pasted:20260729-154850.png}}+{{..:bathuniversity_project:pasted:20260729-154850.png}}
  
 Results of SPM can be interpreted as explained in [[sift:application:analyse_page|Analysis]] page Results of SPM can be interpreted as explained in [[sift:application:analyse_page|Analysis]] page
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 ===== Does variation of speed or gradient have a larger effect on changes in knee joint kinematics? ===== ===== Does variation of speed or gradient have a larger effect on changes in knee joint kinematics? =====
 | Changing Speed - Fast vs Slow | Changing Gradients - Downhill vs Uphill | | Changing Speed - Fast vs Slow | Changing Gradients - Downhill vs Uphill |
-| {{.bathuniversity_project:pasted:20260729-171527.png}} | {{.bathuniversity_project:pasted:20260729-171605.png}} |+| {{..:bathuniversity_project:pasted:20260729-171527.png}} | {{..:bathuniversity_project:pasted:20260729-171605.png}} |
 | Large variance in PC1 | Variance in both PC1 and PC2 | | Large variance in PC1 | Variance in both PC1 and PC2 |
-| {{.bathuniversity_project:pasted:20260729-181848.png}} | {{.bathuniversity_project:pasted:20260729-172935.png}} |  +| {{..:bathuniversity_project:pasted:20260729-181848.png}} | {{..:bathuniversity_project:pasted:20260729-172935.png}} |  
-| {{.bathuniversity_project:pasted:20260729-182259.png}} | {{.bathuniversity_project:pasted:20260729-182400.png}} |+| {{..:bathuniversity_project:pasted:20260729-182259.png}} | {{..:bathuniversity_project:pasted:20260729-182400.png}} |
 | Highest variance at the peak flexion angle during the stance phase | Highest variance during the swing phase | | Highest variance at the peak flexion angle during the stance phase | Highest variance during the swing phase |
  
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 Changing Gradients - Running slow pace uphill vs downhill Changing Gradients - Running slow pace uphill vs downhill
 | PC1 | PC2 | | PC1 | PC2 |
-| {{.bathuniversity_project:pasted:20260729-182733.png}} | {{.bathuniversity_project:pasted:20260729-182641.png}} | +| {{..:bathuniversity_project:pasted:20260729-182733.png}} | {{..:bathuniversity_project:pasted:20260729-182641.png}} | 
  
 ====== Analysis in Jiku ====== ====== Analysis in Jiku ======
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    - Choose ImportedDatum in the dropdown selection    - Choose ImportedDatum in the dropdown selection
  
-{{.bathuniversity_project:pasted:20260729-184850.png}}+{{..:bathuniversity_project:pasted:20260729-184850.png}}
  
 Head to Analysis Head to Analysis
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    - Lead Seed at 0, and select Seed Lock. This is the initial number used to start the random number generator. Keeping it the same means the software will create the exact same random noise and graphs again.     - Lead Seed at 0, and select Seed Lock. This is the initial number used to start the random number generator. Keeping it the same means the software will create the exact same random noise and graphs again. 
    - Change sample size mode to Absolute    - Change sample size mode to Absolute
-   - Use Sift Workspace to determine how many samples are used. Choose {{.bathuniversity_project:pasted:20260729-194755.png}} icon in Sift and change **Display type for excluded data** to Fraction. Add all the denominators of the fractions for each of the workspace to determine the total number of samples used. +   - Use Sift Workspace to determine how many samples are used. Choose {{..:bathuniversity_project:pasted:20260729-194755.png}} icon in Sift and change **Display type for excluded data** to Fraction. Add all the denominators of the fractions for each of the workspace to determine the total number of samples used. 
  
-{{.bathuniversity_project:pasted:20260729-194908.png}} +{{..:bathuniversity_project:pasted:20260729-194908.png}} 
  
 Example. For one participant comparing running downhill at fast (Group A) vs slow (Group B) pace, Power1D will look like this. Click Run to simulate data.  Example. For one participant comparing running downhill at fast (Group A) vs slow (Group B) pace, Power1D will look like this. Click Run to simulate data. 
  
-{{.bathuniversity_project:pasted:20260729-195231.png}}+{{..:bathuniversity_project:pasted:20260729-195231.png}}
  
  
 This will popup Results This will popup Results
  
-{{.bathuniversity_project:pasted:20260729-195403.png}}+{{..:bathuniversity_project:pasted:20260729-195403.png}}
  
 and Distribution and Distribution
  
-{{.bathuniversity_project:pasted:20260729-195414.png}}+{{..:bathuniversity_project:pasted:20260729-195414.png}}
  
 **Results** indicate the power at each point across the domain. Domain represents time or cycle %. Non zero power means true effect is detectable at both maximal values and neighbouring values. Omnibus power represents the probability that the null hypothesis will be rejected in a large number of experiments. This is shown as the proportion of the h1 distribution that lies above the critical value.  **Results** indicate the power at each point across the domain. Domain represents time or cycle %. Non zero power means true effect is detectable at both maximal values and neighbouring values. Omnibus power represents the probability that the null hypothesis will be rejected in a large number of experiments. This is shown as the proportion of the h1 distribution that lies above the critical value. 
sift/tutorials/bathuniversity/statistical_power_analysis_changing_gradients_and_speeds.1787752583.txt.gz · Last modified: by wikisysop