Recommendation for the minimum number of steps to analyze when performing the uncontrolled manifold analysis on walking data

The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derive...

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Published inJournal of biomechanics Vol. 85; pp. 218 - 223
Main Authors Rosenblatt, Noah J., Hurt, Christopher P.
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 06.03.2019
Elsevier Limited
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Abstract The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derived measures are thought to depend on the number of data points analyzed, the minimum number needed to reasonably approximate true values of these measures is unknown. For each of two performance variables related to mechanical stability of gait, we evaluated changes in UCM-derived measures when increasing the number of analyzed points, here steps. Fourteen older adults walked on a treadmill while motion capture tracked movement. For each subject, n steps (where n = 2–99) were randomly sampled from the first 100, then used to calculate UCM-derived variables. For each subject, variables were expressed as a percent of the subject-specific value with n = 100 and averaged across 50 simulations. For each n, 95% confidence intervals (CIs) were calculated from group data. The minimum number of steps to “reasonably approximate” a variables was defined as the value of n for which the lower CI was >90% of the value with n = 100. Regardless of performance variable, reasonable approximations of the synergy index were attained with n = 16 steps, whereas n = 50 steps were needed for each of the variance components However, the differences between using 16 steps and 50 steps were small. Collecting 15–20 steps is recommended for a reasonable approximation of the synergy indices considered herein, particularly when data collection is constrained to a limited number of steps.
AbstractList The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derived measures are thought to depend on the number of data points analyzed, the minimum number needed to reasonably approximate true values of these measures is unknown. For each of two performance variables related to mechanical stability of gait, we evaluated changes in UCM-derived measures when increasing the number of analyzed points, here steps. Fourteen older adults walked on a treadmill while motion capture tracked movement. For each subject, n steps (where n = 2–99) were randomly sampled from the first 100, then used to calculate UCM-derived variables. For each subject, variables were expressed as a percent of the subject-specific value with n = 100 and averaged across 50 simulations. For each n, 95% confidence intervals (CIs) were calculated from group data. The minimum number of steps to “reasonably approximate” a variables was defined as the value of n for which the lower CI was >90% of the value with n = 100. Regardless of performance variable, reasonable approximations of the synergy index were attained with n = 16 steps, whereas n = 50 steps were needed for each of the variance components However, the differences between using 16 steps and 50 steps were small. Collecting 15–20 steps is recommended for a reasonable approximation of the synergy indices considered herein, particularly when data collection is constrained to a limited number of steps.
The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derived measures are thought to depend on the number of data points analyzed, the minimum number needed to reasonably approximate true values of these measures is unknown. For each of two performance variables related to mechanical stability of gait, we evaluated changes in UCM-derived measures when increasing the number of analyzed points, here steps. Fourteen older adults walked on a treadmill while motion capture tracked movement. For each subject, n steps (where n = 2-99) were randomly sampled from the first 100, then used to calculate UCM-derived variables. For each subject, variables were expressed as a percent of the subject-specific value with n = 100 and averaged across 50 simulations. For each n, 95% confidence intervals (CIs) were calculated from group data. The minimum number of steps to "reasonably approximate" a variables was defined as the value of n for which the lower CI was >90% of the value with n = 100. Regardless of performance variable, reasonable approximations of the synergy index were attained with n = 16 steps, whereas n = 50 steps were needed for each of the variance components However, the differences between using 16 steps and 50 steps were small. Collecting 15-20 steps is recommended for a reasonable approximation of the synergy indices considered herein, particularly when data collection is constrained to a limited number of steps.The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derived measures are thought to depend on the number of data points analyzed, the minimum number needed to reasonably approximate true values of these measures is unknown. For each of two performance variables related to mechanical stability of gait, we evaluated changes in UCM-derived measures when increasing the number of analyzed points, here steps. Fourteen older adults walked on a treadmill while motion capture tracked movement. For each subject, n steps (where n = 2-99) were randomly sampled from the first 100, then used to calculate UCM-derived variables. For each subject, variables were expressed as a percent of the subject-specific value with n = 100 and averaged across 50 simulations. For each n, 95% confidence intervals (CIs) were calculated from group data. The minimum number of steps to "reasonably approximate" a variables was defined as the value of n for which the lower CI was >90% of the value with n = 100. Regardless of performance variable, reasonable approximations of the synergy index were attained with n = 16 steps, whereas n = 50 steps were needed for each of the variance components However, the differences between using 16 steps and 50 steps were small. Collecting 15-20 steps is recommended for a reasonable approximation of the synergy indices considered herein, particularly when data collection is constrained to a limited number of steps.
The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance, by partitioning variance in those variables into two components then calculating their normalized difference (i.e., the synergy index). Although UCM-derived measures are thought to depend on the number of data points analyzed, the minimum number needed to reasonably approximate true values of these measures is unknown. For each of two performance variables related to mechanical stability of gait, we evaluated changes in UCM-derived measures when increasing the number of analyzed points, here steps. Fourteen older adults walked on a treadmill while motion capture tracked movement. For each subject, n steps (where n =2-99) were randomly sampled from the first 100, then used to calculate UCM-derived variables. For each subject, variables were expressed as a percent of the subject-specific value with n =100 and averaged across 50 simulations. For each n , 95% confidence intervals (CIs) were calculated from group data. The minimum number of steps to “reasonably approximate” a variables was defined as the value of n for which the lower CI was >90% of the value with n =100. Regardless of performance variable, reasonable approximations of the synergy index were attained with n =16 steps, whereas n = steps were needed for each of the variance components However, the differences between using 16 steps and 50 steps were small. Collecting 15-20 steps is recommended for a reasonable approximation of the synergy indices considered herein, particularly when data collection is constrained to a limited number of steps.
Author Rosenblatt, Noah J.
Hurt, Christopher P.
AuthorAffiliation 1 Rosalind Franklin University of Medicine and Science, Center for Lower Extremity Ambulatory Research (CLEAR) at the Dr. William M. Scholl, College of Podiatric Medicine, 3333 Greenbay Road, North Chicago, IL 60064
2 University of Alabama at Birmingham, Department of Physical Therapy
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Keywords Foot trajectory
Synergy index
Stability
Mediolateral
Gait
Center of mass
Older adults
UCM
Language English
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Snippet The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance by...
The uncontrolled manifold (UCM) analysis quantifies the extent to which co-variation among a set of variables facilitates consistent performance, by...
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SubjectTerms Adults
Aged
Biomechanical Phenomena
Center of mass
Confidence intervals
Data acquisition
Data analysis
Data points
Data processing
Exercise Test - methods
Female
Fitness equipment
Foot trajectory
Gait
Humans
Male
Manifolds
Mediolateral
Motion capture
Movement
Older adults
Older people
Stability
Stability analysis
Synergy index
UCM
Variables
Variance
Walking
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Title Recommendation for the minimum number of steps to analyze when performing the uncontrolled manifold analysis on walking data
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