| Abstract: |
The ability to assess biomechanical gestures is important in both medical and sport fields. Thanks to technological advancements, it is possible to monitor patient movements with high precision using suits and sensors. We discuss the use of the BASAS (bootstrap analysis of speed and angle in squat) algorithm, our solution for evaluating asymmetries and variability in squat. The algorithm bilaterally compares speed and angle movements in a patient during squat with different barbell weights, providing an easily interpretable graphical solution that highlights movement speed, angles, asymmetries and gesture variability. As a by-product it is possible to identify sticking-points in the lift and notice a deflating baloon behavior due to the greater fatigue expressed with increasing weights. The algorithm makes no distributional assumptions about the data generating process, and does not need to consider large sample theory. The algorithm is generalizable to other technical gestures and makes use of advanced statistical methods recently discussed in the biomechanics literature (Pataky et al, 2015). We discuss its limitations, and possible alternatives in contexts where there is a lack of data. |