Computational protocol: Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot

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Protocol publication

[…] Using the force plate as the gold standard reference for stride characterisation timing, accuracy of the computed parameters (i.e. hoof‐on/off, toe‐on/off, heel‐on/off) was calculated as the difference in milliseconds between the IMU or motion capture generated data and the data from the force plate and precision as the s.d. of the accuracy. A positive accuracy indicates an overestimation (i.e. IMU or motion capture detection of event later than force plate) of the parameter calculated by the IMU or motion capture system and a negative accuracy indicates an underestimation of that parameter (i.e. IMU or motion capture detection of event before force plate). Performance of the algorithms was judged based on primarily demonstrating the best precision (closest to zero) combined with the best accuracy (closest to zero).For further comparison of the algorithms’ performance, the accuracy of IMU stance duration was determined as described above for hoof‐on/off, with the force plate calculated stance duration as a reference and with stance duration defined as the time between hoof‐on and the subsequent hoof‐off. Combination of algorithms for stance duration calculation were also evaluated and tested to identify the combination that obtained the best overall accuracy and precision. For the motion capture data, stance duration was calculated for both limbs and gaits, based on four possible combinations (i.e. toe‐on toe‐off; toe‐on heel‐off; heel‐on heel‐off and heel‐on toe‐off). Accuracy and precision of motion capture stance duration were calculated as described above for the IMU. The percentage of error in stance duration was calculated with the accuracy as a percentage of the force plate measured stance duration. If any of the IMU or motion capture algorithms failed to perform detection of an event (e.g. hoof‐on/off), the calculation of that specific event for that trial was not included in the final calculations. This was verified in a number of trials in our experiment for algorithms 1, 3 and algorithm combination (n = 2; n = 3, respectively) and in eight events for the motion capture detection. However, the remaining calculated parameters not related to that specific event for that trial were kept.Open software (R version 3.2.3) was used for statistical analysis using the package ‘nlme’ (version 3.1–121) for linear mixed effects model and ‘BlandAltmanLeh’ (version 0.1.0) for calculation of IMU and motion capture stance duration limits of agreement. For statistical comparison of the 4 different IMU algorithms and the algorithm combination, the calculated square root transformed absolute accuracy for the stance duration was used as the outcome variable. Horse Id was used as random effect to account for the correlated observations within horse; explanatory variables are algorithm, gait, limb and the interaction between limb and algorithm. A constant variance function (varIdent) for algorithms was added to the model to take the different variances between algorithms into account. Model adequacy (normality and constancy of variance) was confirmed using visualisation of the scatter plot residuals vs. fitted values and explanatory variables respectively and QQ‐plots. The Akaike's information criterion was used to select the best model. […]

Pipeline specifications

Software tools nlme, lme4
Application Mathematical modeling
Organisms Equus caballus