Repeatability and reproducibility of joint kinematics can be assessed through Similarity Indices (SI) quantifying their pattern variability. These include: Coefficient of Multiple Correlation (CMC) [1]; Mean Absolute Variability (MAV) [2]; and Linear Fit Method (LFM) [3], which accounts for scaling (a1), offset (a0) and truthfulness of the linear model between the curves (R2). Among gait cycles, the intra-subject variability for a given joint is due to physiological fluctuations of the range of motion (ROM) and time shift. SIs might be differently affected for each joint, due to their different ROMs, and by marker positioning, leading to offsets among gait curves. This paper aims to investigate the effects that each of these sources of curve variability has on the SIs, in order to provide indications on which is the most suitable for the assessment of gait similarity.
Choosing a similarity index to quantify gait data variability
Di Marco, Roberto
;
2016-01-01
Abstract
Repeatability and reproducibility of joint kinematics can be assessed through Similarity Indices (SI) quantifying their pattern variability. These include: Coefficient of Multiple Correlation (CMC) [1]; Mean Absolute Variability (MAV) [2]; and Linear Fit Method (LFM) [3], which accounts for scaling (a1), offset (a0) and truthfulness of the linear model between the curves (R2). Among gait cycles, the intra-subject variability for a given joint is due to physiological fluctuations of the range of motion (ROM) and time shift. SIs might be differently affected for each joint, due to their different ROMs, and by marker positioning, leading to offsets among gait curves. This paper aims to investigate the effects that each of these sources of curve variability has on the SIs, in order to provide indications on which is the most suitable for the assessment of gait similarity.
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