## 1 Introduction  
Lower-extremity injuries due to physical activities have devastating short-term and long-term consequences to the health and wellbeing of individuals [1, 2] and burden societies worldwide [3, 4]. Non-contact injuries account for approximately 20% of injuries in game situations and 37% of injuries in training situations [5]. Non-contact injuries in sport and recreation are the ones of most practical interest to coaches and clinicians as preventable through neuromuscular training programs [6]. The mechanism of non-contact lower-extremity injuries and their underlying risk factors have been linked with 'risky' movement patterns [7, 8], such as knee valgus and stiff landings. 3D motion analysis systems, which provide gold-standard measures for the objective quantification of human motion noninvasively, can readily identify altered movement patterns and biomechanical control. However, conventional 3D motion analysis using infrared systems requires a considerable financial outlay and an expert-user, in addition to time and space to perform the analysis. These constraints limit its practical application and use for large-scale screening of injury risk factors in physically active individuals. As a countermeasure and to reduce technological requirements, various clinician-led movement screens have been developed [9]. Even though these clinician-led screens reduce the financial costs and space requirements compared to 3D motion analysis, they nonetheless require expert clinicians and dedicated time for testing and scoring, limiting their widespread use. For instance, the Functional Movement ScreenTM takes 12 to 15 min and the Tuck jump assessment takes 12 min to administer and score for one individual [9]. The Landing Error Scoring System (LESS) is one movement screen with demonstrated reliability [10, 11] and validity [11, 12]. Clinicians evaluate 2D video recordings from three double-leg drop-jump landing tasks per individual to detect'movement errors' linked to non-contact anterior cruciate ligament (ACL) and other lower-extremity injury mechanisms [10]. The LESS consists of 17 items (Table 1), with the total number or possible errors ranging from 0 (best) to 17 (worst). Greater scores hence indicate more movement errors, poorer landing biomechanics, and greater relative risk of sustaining non-contact lower-extremity injuries. In a prospective study, [PERSON] et al. [12] determined that scoring 5 or more errors on the LESS was associated with a 10.7 times greater relative risk of sustaining a non-contact ACL injury in youth soccer players (sensitivity 0.86, specificity 0.64). The total testing time (including set up) takes ~5 min with 3 to 4 min for a trained rater to score the three drop-jump landing trials of one individual once downloaded to a computer [10]. A few of the drawbacks of the LESS is the subjective nature of the assessment, requirement for an expert-rater, and need to view videos at a later stage [13, 14]. In recent years, researchers have striven to automate the LESS to streamline the process using depth sensor cameras [13, 15]. [PERSON], [PERSON], and [PERSON] [13] introduced the PhysiMax system (PhysiMax Technologies Ltd., Tel Aviv, Israel) to automate LESS scoring using a personal computer, 3D Microsoft Kinect, and motion analysis software that requires limited clinical input. Their results indicated high consensus between clinician and PhysiMax LESS scores (intra-class correlation, ICC = 0.80, mean absolute difference 1.13 errors), although the clinician manually inputted the overall impression item (no. 17, Table 1). Despite the automated quantification of the LESS using markerless motion capture using depth cameras provides time-cost saving benefits, there are still additional hardware-software expenditures to consider. Deep-learning-based computer vision technologies enable the automatic identification and quantification of human motion without the need for depth sensor cameras. Numerous such systems are currently being developed.