How to choose and interpret similarity indices to quantify the variability in gait joint kinematics

Abstract

Repeatability and reproducibility indices are often used in gait analysis to validate models and assess patients in their follow-up. When comparing joint kinematics, their interpretation can be ambiguous due to a lack of understanding of the exact sources of their variations. This paper studied four indices (Root Mean Square Deviation, Mean Absolute Variability, Coefficient of Multiple Correlation, and Linear Fit Method) in relation to five confusing-factors: joints' range of motion, sample-by-sample amplitude variability, offset, time shift and curve shape. A first simulation was conducted to test the mathematics behind each index. A second simulation tested the influence of the curve shape on the indices using a Fourier's decomposition. The Coefficient of Multiple Correlation and the Linear Fit method Coefficients were independent from the range of motion. Different Coefficients of Multiple Correlation were found among different joints, leading to misinterpretation of the results. The Linear Fit Method coefficients should not be adopted when time shift increases. Root Mean Square Deviation and Mean Absolute Variability were sensitive to all the confusing-factors. The Linear Fit Method coefficients seemed to be the most suitable to assess gait data variability, complemented with Root Mean Square Deviation or Mean Absolute Variability as measurements of data dispersion.

Inter-laboratory and inter-operator reproducibility in gait analysis measurements in pediatric subjects

“…Although EMG curves showed similar patterns, they were affected by a delay between the activation peaks of the muscle signals (Figure 3), probably due to the high stride to stride variability inherent to the EMG data. Thus, the observed lower values of the determination coefficient R 2 are most likely ascribed to the time shift between the curves, which invalidated the hypothesis of linearity relationship between the curves (Di Marco et al 2018). Moreover, the EMG intra-subject repeatability and interoperator reproducibility were comparable, suggesting that the physiological muscle activation variability predominates on the difficulty of accurately placing surface electrodes, which is also particularly challenging in the pediatric population (Granata et al 2005).…”

Observer-Agent Kinematic Similarity Facilitates Action Intention Decoding

“…The Linear Fit Method (LFM 49 ), complemented with the Root Mean Square Error (RMSE 50 ), were selected to assess the waveform curve similarity 51 . LFM calculates the linear regression between the dataset under investigation, returning information about the scaling factor (a 1 ), the weighted average offset (a 0 ), and the trueness of the linear relation between them (R 2 ).…”

An image-based kinematic model of the tibiotalar and subtalar joints and its application to gait analysis in children with Juvenile Idiopathic Arthritis

“…The following kinematic parameters were also calculated to investigate the correlation with the : area under the curves of the tibiotalar and subtalar joint angles, maximum plantarflexion (PF) and dorsiflexion (DF) angles, maximum inversion (Inv) and eversion (Ev) angles, and joint ROM. Furthermore, the asymmetry between the left and right foot kinematics was quantified using the Root Mean Square Deviation (RMSD) and Mean Absolute Variability (MAV) (Di Marco et al, 2018),…”