## 4 Discussion
In previous research, depth sensor technology has been used to automate LESS scoring [13; 15]. Comparisons between automated and expert clinicians indicate a mean difference of 1.20 errors [15], mean absolute difference of 1.13 errors [13], intra-class correlation of 0.80 [16], and percentage agreement of the individual items ranging from 55-100% [13; 15]. These research findings are comparable to our lowest mean absolute error (1.23), greatest correlation (\(r=0.63\)), and agreement in risk classification (sensitivity 0.82, specificity 0.77) between actual and predicted scores from the cross validation experiments using random forest regression. In contrast to the PhysiMax system [13; 15], our approach did not require the clinician to add the overall impression manually (no. 17 in Table 1) given that the LESS items were not scored one-by-one. Although the lack of individual-item scores might be perceived as a limitation of the deep LESS approach; no subjective rating from the clinician or hardware other than a handheld camera or smart portable device are required. Furthermore, only the final LESS score has shown predictive value in terms of injury risk [12]; hence, the individual items are of lesser clinical value. The better accuracy achieved by random forest can be explained by the fact that the features (angles, distances, and ratios) are likely correlated and related in a non-linear manner. Decision tree ensembles in general are better able to cope with correlated variables and model non-linear patterns [25]. Linear regression, on the other hand, achieves optimal results when the predictor variables are independent and do not interact. We also foresee a possibility of processing the raw video images themselves and attempting direct deep learning-based classification with minimal pre-processing. Such an approach would obviate the need to use OpenPose or a similar pose-tracking tool. However, taking such an approach would be challenging because of the lack of training data relative to size of datasets usually used to train deep image recognisers. Another significant disadvantage of the proposed approach is that deep learning needs GPU-based acceleration hardware, and is therefore currently unable to process videos independently on consumer smartphones. That said; the rapidly increasing computational power of consumer smartphones and the current trend in research of compressing deep models [26] so that they run efficiently on mobile devices should solve this problem in the next few years. One of the main concerns in clinical screening tools are their subjective nature and reliance on visual observations to estimate angles, which are challenging to quantify accurately [27; 28]. During the LESS, a small kinematic difference (e.g., knee angle 29\({}^{\circ}\), 1--error present; knee angle 30\({}^{\circ}\), 0--error absent) can result in poor agreement between raters and between clinical LESS scores and motion capture scores. Recent technological advances have allowed the more objective quantification of human motion using wearable technology [29; 30]. Inertial measurement units are able to measure linear and angular motion of individual body segments and centre of mass, and are proposed as more accurate means of identifying risky movement patterns than through visual observations [31]. Although inertial measurement units are relatively inexpensive; they are not commonly used in clinical environments and an expert is still needed to process and interpret data signals. The automated scoring process here developed using standard video recordings offers an alternative solution that can possibly improve consistency of LESS ratings, removing the subjective interpretation of the task. Moving forward, the reliability of deep LESS scores, validity of OpenPose derived data during the dynamic double-leg drop-landing task, and predictive ability of the method need empirical support.