## 2 Materials and Methods
### Automated LESS
\begin{table}
\begin{tabular}{l l} \hline
**Step** & **Description** \\ \hline \hline  & Input: F, Frontal view video; L, Lateral view video \\ \hline
1. & Obtain the body part keypoints in each frame in both F and L videos using OpenPose \\ \hline
2. & Impute keypoint positions using linear interpolation when not recognized by OpenPose \\ \hline
3. & Find F key frames IC and KFmax \\ \hline
3.1. & Based on the coordinates of the left and right ankle (both visible in F), find the intersections of the original and rolling window plots [2] for each ankle \\ \hline
3.2. & Calculate the distances between each consecutive intersection point pairs \\ \hline
3.3. & Find the first point of the pair of intersection points with the longest distances for each ankle \\ \hline
3.4. & Identify the first point of the pair of intersection points that has the lowest \(x\) value (i.e., number of frames) as IC \\ \hline
3.5. & Based on the coordinates of the body keypoint, find the intersections of the original and rolling window plots [2] \\ \hline
3.6. & Calculate the distances between each consecutive intersection point pairs \\ \hline
3.7. & Identify the first point of the pair of intersection points with the longest distances as KFmax \\ \hline
4. & Find L key frames IC and KFmax \\ \hline
4.1. & Based on the coordinates of the individual’s right ankle (which is closest to the camera L), find the intersections of the original and rolling window plots \\ \hline
4.2. & Calculate the distances between each consecutive intersection point pairs \\ \hline
4.3. & Identify the first point of the pair of intersection points with the longest distances as IC \\ \hline
4.4. & Based on the coordinates of the body keypoint, find the intersections of the original and rolling window [2] plots \\ \hline
4.5. & Calculate the distances between each consecutive intersection point pairs \\ \hline
4.6. & Identify the first point of the pair of intersection points with the longest distances with upper/positive trend as KFmax \\ \hline
5. & Crop the videos (F and L) according to IC and KFmax key frames. \\ \hline  & Output: F\({}_{\text{}}\) cropped version of frontal view video; L, cropped version of lateral view video \\ \hline \end{tabular}
\end{table} 
Table 2: Algorithm used to detect key frames from the two input videos. Figure 3: OpenPose’s COCO 18-points model keypoint positions (left image) [16] and example of a frontal (middle image) and lateral (right image) view processed video at the maximal knee flexion key frame. Following feature extraction, we then used a machine learning algorithm to predict the LESS score associated with the drop-jump landing videos. To evaluate the predictive effectiveness of the various machine learning algorithms, we generated features for all 320 drop-jump landings in the dataset using the approach described above. It was also noticed that the distribution of the LESS scores in the dataset was imbalanced, with the majority of LESS scores falling in the range 4-6. Given that unbalanced datasets can potentially affect the accuracy of machine learning techniques, we additionally generated a balanced version of the dataset consisting of 153 drop-jump landing trials with at most 20 trials per LESS score. All evaluations of machine learning techniques were applied to both datasets. The machine learning techniques chosen to be evaluated were random forest regression, because it is a state-of-the-art machine learning approach and generally performs well 'out of the box' on most problems in practice; and linear regression, which is a widely understood linear modelling technique. Unlike random forest regression, linear regression produces an interpretable model, but it has the disadvantage of being unable to model interactions between features. Given that the full dataset was imbalanced, we also evaluated a dummy regressor (ZeroR) that simply predicts the mean LESS score from the training data. For the original dataset, this method was expected to have reasonably high accuracy, but lower accuracy for the balanced dataset.