Advancing our ability to quantify an individual’s habitual motion path and deviation when running

Figures & data

Figure 1. (a) Original method (Trudeau et al., 2019) to calculate a deviation as the difference between run and squat joint angle values at maximum knee flexion. (b) Exemplar joint angles from all movements used to create the habitual movement kernel density plot. This process is completed for all three deviations of interest, with knee internal rotation (TIR) plotted here for visualisation. (c) Exemplar comparison of run and habitual movement data for one participant.

Figure 2. Method for quantifying deviation from one’s habitual motion path when running for an exemplar segment. For all sub-figures, red is running data; blue is habitual movement data. (3a) Quantify average joint angle (θ) from each segment in the run kernel density plot. (3b) Apply weighting to the segment, w, that is the body weight normalised external vGRF at time, t, where θ occurs. (c) Calculate the percent difference, as defined in Equation 3(3) $$\mathit{\text{Deviation}}=\frac{\mathit{\text{WRA}}-\mathit{\text{WOA}}}{\mathit{\text{WRA}}}\text{\hspace{0.33em}*\hspace{0.33em}}100$$(3) , between the weighted run area (WRA) and weighted overlap area (WOA), defined in Equations 1(1) $$\mathit{\text{WRA}}=\sum w_i\text{\hspace{0.33em}*\hspace{0.33em}}\mathit{\text{rs}}{a}_i$$(1) and 2(2) $$\mathit{\text{WOA}}=\sum w_i\text{\hspace{0.33em}*\hspace{0.33em}}\mathit{\text{os}}{a}_i$$(2) , respectively.

Figure 3. Exemplar plots of (a) one runner exhibiting a high deviation when running in the sock shoe (left) and a low deviation (right) as a result of using a shoe that guided them back to their habitual motion path. (b) three different runners exhibiting a high deviation in the sock shoe but with different habitual motion paths to be guided back to that will theoretically require different footwear constructions to reduce deviation based on their unique movement. The first deviation method (Trudeau et al., 2019) did not account for the habitual movement to be guided back to, just the presence of a deviation. For all sub-figures, red is running data; blue is habitual movement data. TIR: knee internal rotation.

Figure 4. Population distribution for deviations of each respective joint angle during the sock run. Eve: ankle eversion; TIR: knee internal rotation; AbAd: knee ab-adduction.

Figure 5. Exemplar individual classified as exhibiting a low deviation when running (left, sock condition), and running in two different footwear of neutral classification. Shoe B (right) is pushing the runner away from their habitual motion path to exhibit a high deviation, despite being classified as neutral like shoe A (Middle). This suggests that wearing the wrong shoe can theoretically increase risk of injury, as a shoe can increase one’s habitual motion path deviation.

Trudeau, M. B., Willwacher, S., Weir, G., Rohr, E., Ertel, C., Bruggemann, G.-P., & Hamill, J. (2019). A novel method for estimating an individual’s deviation from their habitual motion path when running. Footwear Science, 11(3), 135–145. https://doi.org/10.1080/19424280.2019.1615004

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