The raw kinematic and kinetic data were processed with Track Manager and Visual3D software (C-Motion). A fourth-order Butterworth low-pass filter with a cut-off frequency of 40 Hz (kinetics) and 12 Hz (kinematics) was used to filter overground walking trials.11 (link),23 ,31 ,67 (link) The values of dependent variables were determined from the first step of each successful trial for a particular footwear condition. All variables were calculated as a mean of the first (absorption) and the second (propulsion) 50% of the stance phase. Kinematic variables were obtained from 3-dimensional knee angles (sagittal plane: knee flexion; frontal plane: knee adduction; transverse plane: knee internal rotation). Kinetic variables were obtained from 3-dimensional knee net moments and selected loading characteristics (sagittal plane: flexion moment, anteroposterior force; frontal plane: adduction moment, mediolateral force; transverse plane: internal rotation moment, vGRF). In addition, the scalar power from all planes was calculated. All force data were normalized to body mass. All dependent variables curves were normalized into 101 points (0%-100% stance phase) for statistical parametric mapping (SPM) analysis.48 (link)
Track manager
Manufactured by C-Motion
Sourced in United States
Track Manager is a software tool designed to facilitate the management and analysis of motion capture data. It provides a centralized interface for organizing, visualizing, and processing motion data collected from various sources.
Automatically generated - may contain errors
Lab products found in correlation
3 protocols using track manager
1
3D Biomechanics of Overground Walking
2
Knee Adduction Moment Analysis
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Dynamic trials were digitized using Qualisys Track Manager then exported as C3D files to Visual 3D (C-Motion, Germantown, USA). Ground reaction force and kinematic data were smoothed using cut-off frequencies of 50 and 12 Hz with a low-pass Butterworth 4th order zero-lag filter (Sinclair, 2014 ). Knee joint kinetics were computed using Newton–Euler inverse-dynamics, allowing net knee joint moments to be calculated. Medial tibiofemoral compartment loading was examined using the KAM during the stance phase.
The KAM magnitude was normalized by dividing by body mass (Nm/kg). Following this KAM data for all participants in each footwear during the stance phase was extracted and time normalized to 101 data points. In addition, the KAM instantaneous load rate (Nm/kg/s) was determined the maximum slope in the KAM computed between each frame.
The KAM magnitude was normalized by dividing by body mass (Nm/kg). Following this KAM data for all participants in each footwear during the stance phase was extracted and time normalized to 101 data points. In addition, the KAM instantaneous load rate (Nm/kg/s) was determined the maximum slope in the KAM computed between each frame.
3
Kinematic Analysis of Knee Movements
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Maximum knee angle, minimum knee angle and knee joint range of motion (ROM) in all three planes of motion for both the loading (eccentric) phase of the movement and the entire (eccentric, hold and concentric) movement were measured. Data were processed in Qualisys Track Manager and then exported as C3D files to Visual3D (CMotion Inc., USA) software in order to quantify kinematic parameters, smoothed with a low pass Butterworth 6Hz filter, allowing recognition of significant events within the data creating a report. All data was averaged within subjects prior to statistical analysis.
The distribution of the data was assessed for normality using the Shapiro-Wilk test and found to be suitable for parametric statistical testing. A paired t-test was used to compare pre and post intervention data, with a Bonferroni correction applied, including data analysed using absolute error (AE) for knee flexion. Statistical significance was set at P<.05. All statistical analysis was conducted using SPSS version 21.0 (SPSS, Inc., Chicago, USA).
The distribution of the data was assessed for normality using the Shapiro-Wilk test and found to be suitable for parametric statistical testing. A paired t-test was used to compare pre and post intervention data, with a Bonferroni correction applied, including data analysed using absolute error (AE) for knee flexion. Statistical significance was set at P<.05. All statistical analysis was conducted using SPSS version 21.0 (SPSS, Inc., Chicago, USA).
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