Open Source, Open Science: Development of the Open Landing Error Scoring System as the Automated Landing Error Scoring System

Jeffrey A Turner; Elaine Taylor Reiche; Matthew T Hartshorne; Connor C Lee; Joanna M Blodgett; Darin A Padua

doi:10.4085/1062-6050-0666.24

. 2025 Aug 26;60(8):590–599. doi: 10.4085/1062-6050-0666.24

Open Source, Open Science: Development of the Open Landing Error Scoring System as the Automated Landing Error Scoring System

Jeffrey A Turner ^*,^✉, Elaine Taylor Reiche ^*, Matthew T Hartshorne ^*, Connor C Lee ^*, Joanna M Blodgett ^†, Darin A Padua ^*

PMCID: PMC12418479 PMID: 40235174

Abstract

Context

The Open Landing Error Scoring System (OpenLESS) is a novel tool for automating the LESS to assess lower extremity movement quality during a jump-landing task. With the growing use of clinical measures to monitor outcomes and limited time during clinical visits, there is a need for automated systems. The OpenLESS is an open-source tool that uses a markerless motion-capture system to automate the LESS using 3-dimensional kinematics.

Objective

To describe the development of the OpenLESS, examine its validity against expert-rater LESS scores in healthy and clinical cohorts, and assess its intersession reliability in an athlete cohort.

Design

Cross-sectional study.

Patients or Other Participants

Ninety-two adult participants from 3 distinct cohorts: a healthy university student cohort (12 men, 14 women, age = 23.0 ± 3.8 years, height = 171.9 ± 8.3 cm, mass = 75.4 ± 18.9 kg), a post–anterior cruciate ligament reconstruction (ACLR) cohort (8 men, 19 women, age = 21.4 ± 5.7 years, height = 173.5 ± 12.5 cm, mass = 73.9 ± 13.1 kg, median 33 months postsurgery), and a field-based athlete cohort (39 women, age = 25.0 ± 4.7 years, height = 165.0 ± 7.1 cm, mass = 63.5 ± 8.6 kg).

Main Outcome Measure(s)

The OpenLESS software interprets movement quality from kinematics captured by markerless motion capture. Validity and reliability were assessed using intraclass correlation coefficients (ICCs), standard error of measurement, and minimal detectable change.

Results

The OpenLESS agreed well with expert-rater LESS scores for healthy (ICC_2,_k = 0.79) and clinically relevant post-ACLR cohorts (ICC_2,_k = 0.88). The automated OpenLESS system reduced scoring time, processing all 353 trials in under 25 minutes compared with the 35 hours (approximately 6 minutes per trial) required for expert-rater scoring. When tested outside laboratory conditions, the OpenLESS showed excellent reliability across repeated sessions (ICC_2,_k > 0.89), with a standard error of measurement of 0.98 errors and minimal detectable change of 2.72 errors.

Conclusions

The OpenLESS is a promising, efficient tool for automated jump-landing assessment, demonstrating good validity in healthy and post-ACLR populations and excellent field reliability, addressing the need for objective movement analysis.

Keywords: markerless motion capture, movement assessment, clinical motion analysis, anterior cruciate ligament

Key Points

The Open Landing Error Scoring System (OpenLESS) accurately detected jump-landing initial contact and toe-off events (intraclass correlation coefficient [ICC] > 0.99) using markerless motion capture, validating its use as an alternative to laboratory-based force plate measurements.
The automated scoring system showed good agreement with expert raters in healthy (ICC = 0.79) and post–anterior cruciate ligament reconstruction (ICC = 0.88) populations.
The OpenLESS demonstrated good to excellent test-retest reliability (ICC = 0.89) across multiple testing sessions, with minimal score variation, supporting its utility for longitudinal movement assessment.

Functional movement screening is a well-established component for assessing lower extremity injury risk in clinical and athletic populations.^1–3 The National Athletic Trainers’ Association’s position statement and the American Physical Therapy Association’s practice guidelines have recommended applying movement quality assessments to identify individuals with heightened injury risk, enabling the implementation of targeted prevention strategies such as strength training, flexibility exercises, and movement retraining.²^,⁴ Although laboratory-based optical 3-dimensional (3D) motion-capture systems are considered the gold standard for quantifying biomechanical risk factors,⁵ their substantial time and financial (up to $150 000 per system) requirements render them impractical for large-scale injury risk screening programs.⁶ As such, there is a growing need for efficient, field-based functional assessment tools that can be readily deployed to identify high-risk movement patterns within clinical and athletic populations.

The Landing Error Scoring System (LESS), initially developed to screen at-risk individuals for noncontact injuries, has become a widely accepted clinical tool for evaluating jump-landing mechanics.³^,^7–9 Video is used to capture jump landings and subsequently graded on 17 criteria over 3 jump-landing trials.⁸ A maximal score of 19 errors can be reached for exceptionally poor performances, with a score of <5 errors considered to be good (ie, low risk). The LESS has been validated against 3D motion capture,⁸^,¹⁰^,¹¹ and high LESS scores have been associated with movement patterns linked to larger injury risk, such as anterior cruciate ligament (ACL) injuries, including smaller flexion at the hip and knee, larger knee valgus and internal rotation moments, and elevated anterior tibial shear forces upon landing from a jump.⁸^,¹¹^,¹² Supporting its clinical relevance, prospective studies have shown that youth soccer athletes who perform better (fewer errors) on the LESS have a lower risk of ACL injury.⁹

Building upon this foundation, Mauntel et al subsequently developed an automated grading system for an expanded LESS version, using a depth camera and Kinect sensor (Microsoft Corp) with proprietary machine learning algorithms to calculate the relevant kinematic variables.¹³ This automated system reliably estimated kinematics during drop–vertical-jump assessments¹³ and demonstrated moderate agreement against the gold standard 3D motion-capture approach.¹⁴ Advancements in deep learning computer vision and markerless motion capture have enabled further efforts to automate LESS scoring.¹⁵ Although this prior work demonstrated the feasibility of machine learning–based LESS assessment, the proprietary nature of the underlying code and methods limited broader accessibility. In contrast, open-source solutions leveraging frameworks like OpenPose¹⁶ and HRNet¹⁷ offer a more scalable path to bringing efficient, automated movement-quality assessments into clinical practice.⁵

Given the growing emphasis on efficient, cost-effective assessment tools within clinical practice, automating the LESS represents a promising avenue to expand the utility and accessibility of this validated movement quality screening.³^,⁷ Prior efforts to automate LESS scoring have demonstrated the technical feasibility of this approach, but the proprietary nature of these systems has limited their widespread adoption.^13–15 In contrast, open-source frameworks leveraging markerless motion capture, such as OpenCap (Stanford University), offer a more scalable path to integrating automated LESS assessments into clinical and athletic settings.⁶^,¹⁸

The primary aim of the present study was to develop and evaluate the validity and reliability of the Open Landing Error Scoring System (OpenLESS), an automated scoring system for the LESS using open-source software and low-cost markerless motion capture (OpenCap). To demonstrate the clinical utility of this approach, the OpenLESS was validated against expert-rater scores in healthy and post–ACL reconstruction (ACLR) populations, with intersession reliability examined in an amateur athlete cohort. We hypothesized that the automated scoring software (Supplemental File 1) using markerless motion capture would be a valid and reliable version of the LESS.

METHODS

Design

This secondary analysis included 3 different cohorts from repeated-measures and cross-sectional observational studies to assess the measurement properties of an automated pipeline for scoring the LESS using a portable, low-cost, markerless motion-capture system. To assess validity, we compared OpenLESS scores with expert-rater LESS scores in a healthy cohort and a post-ACLR cohort. Reliability of the OpenLESS was assessed in a field-based athlete cohort across up to 4 visits over a month. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, ensuring comprehensive reporting and transparency (Figure 1).¹⁹

Strengthening the Reporting of Observational Studies in Epidemiology flow diagram. Abbreviations: ACLR, anterior cruciate ligament reconstruction; OpenLESS, Open Landing Error Scoring System.

Participants

Participants across all cohorts were adults and provided written informed consent. The healthy cohort consisted of 26 university students (12 men, 14 women) with no history of lower extremity surgery or injuries in the last 6 months, approved by the University of North Carolina at Chapel Hill’s institutional review board (23-1507). The post-ACLR cohort included 27 individuals (8 men, 19 women) 6 to 72 months post–ACLR surgery, approved by the University of North Carolina at Chapel Hill (institutional review board 24-1263; 22-1599). Both healthy and post-ACLR participants completed a single session in the biomechanics laboratory, where they performed the LESS jump landing while their movements were recorded using a markerless motion-capture system.

The field-based athlete cohort comprised 39 women (18 amateur soccer players, 10 university athletes from ball and nonball sports, and 11 recreational weight lifters) with no current lower extremity injuries, approved by University College London (REC Project ID: 25759/001). Athlete participants were assessed outside a laboratory environment in a variety of spaces (soccer pitch [grass], athletic field [turf], and an indoor recreation center) across up to 4 consecutive sessions. Each athlete was tested in the same environment for all sessions, during which they performed the LESS jump landing while their movements were recorded using a markerless motion-capture system. To be included in analysis, participants needed to attend at least 2 of the 4 potential sessions, which could be nonconsecutive.

Testing Procedures

The Task: LESS Jump Landing

All cohorts’ participants performed the double-leg jump-landing rebound task under markerless motion capture, referred to as the jump-landing task (Figure 2).⁸ Participants jumped from a 30-cm box to a landing spot 50% of their height in front, landed, and then performed a maximal vertical jump. After verbal instruction, up to 3 practice trials were allowed, followed by the real trials, with 3 successful trials collected for analysis. Trial success was determined if the participant (1) jumped and landed correctly, (2) jumped vertically during the maximal jump, and (3) completed the task without losing balance. Participants wore their preferred footwear, and the validation cohorts were required to wear tight-fitting clothing, whereas the field-based athlete cohort was allowed to wear their usual exercise clothing.²⁰

Landing Error Scoring System jump-landing task. The figure was adapted from Turner et al³⁰ with permission and includes key timepoints represented by illustration and OpenCap still frames. A, Initial ground contact following jump from starting 30 cm box. B, End of landing phase indicated by the moment of peak knee flexion angle. C, Take-off (toe-off) from the ground to complete rebound vertical jump.

The area of interest for scoring the movement quality with the LESS was the first landing of the jump-landing task.⁸^,¹³ The first landing was defined as the stance phase bounded by the moment of initial foot contact with the ground to take off (ie, toe-off). The stance phase was divided into a braking phase and a propulsion phase. The braking phase was defined as the time interval from the feet contacting the ground (Figure 2A) to the lowest point of the braking phase before upward movement (identified by peak knee flexion; Figure 2B). The propulsion phase was defined as the time interval from the lowest point of the braking phase before upward movement (Figure 2B) to the feet taking off from the ground (Figure 2C). The lowest point of the braking phase before upward movement (Figure 2B) was considered as the transition from eccentric to concentric movement.²¹

Expert-Rater LESS Grading

The original LESS evaluated 17 specific movement characteristics during the jump-landing task, with items scored at initial ground contact (Figure 2A), during the braking phase (Figure 2A and 2B), and at peak knee flexion (Figure 2B).⁸^,⁹ Each item was scored dichotomously (0 or 1) or categorically (0, 1, or 2) based on the presence or absence of movement errors, such that an error from either limb resulted in an error for that item, with a total possible score ranging from 0 to 19 errors. The scoring criteria included the assessment of sagittal and frontal plane positioning of the trunk, hips, and knees and additional items for overall movement quality and symmetry.⁸^,⁹ The LESS has demonstrated good interrater (intraclass correlation coefficient [ICC]_2,_k = 0.84, standard error of measurement [SEM] = 0.71) and intersession (ICC_2,_k = 0.81, SEM = 0.81) reliability, with higher scores indicating more aberrant movement patterns that may increase injury risk.⁸^,¹⁰ We used a modified version of the LESS, expanded to 19 items by Mauntel et al to include 2 additional asymmetrical landing characteristics, that demonstrated strong agreement between an automated grading tool and expert raters.¹³

Two expert clinicians (a licensed physical therapist and an athletic trainer, each with >10 years of orthopaedic and sports medicine experience) independently scored the jump-landing trials. Both raters completed standardized training and reliability testing using a 60-trial test set developed by the original LESS author, demonstrating excellent interrater reliability (ICC > 0.9).⁸ The physical therapist assessed the healthy cohort, and the athletic trainer assessed the ACLR cohort.

The System: OpenCap Markerless Motion Capture

The 3D kinematics of the trunk and lower extremities were collected using 2 smartphones (iPhone 12 SE; Apple Inc) running OpenCap version 0.3 markerless motion-capture software.²² OpenCap has been validated against optoelectronic 3D motion-capture systems.⁶^,¹⁸^,²³ The smartphones were positioned at a standardized distance of 3 m from the landing area, 1.5 m off the ground, and at 45° angles to the target area (Figure 3). We followed OpenCap’s best-practice guidelines⁶ for smartphone setup, calibration with a 720- × 540-mm checkerboard, and the recording of a static trial. Markerless motion capture was sampled at 60 Hz for the post-ACLR and field-based athlete cohorts and 240 Hz for the healthy cohort using the default pose estimation algorithm (HRnet)¹⁷ via OpenCap’s cloud-based software (version 0.3).⁶ Two additional smartphones were used in the post-ACLR and healthy validation cohorts to record the sagittal and frontal plane camera views for expert-rater LESS grading.⁸ Further technical details can be found in the development and validation paper by Uhlrich et al.⁶

OpenCap markerless motion capture and force plate setup.

Vertical ground reaction forces were collected on a subsample of 12 participants within the post-ACLR cohort for validating event timing. The vertical ground reaction force data were sampled at a frequency of 2400 Hz from 2 embedded force plates (FP406020, Bertec Corp) and synchronized to the timing of the markerless motion-capture system. The force plates were set up with a Cartesian coordinate system with axes defined as z-axis vertical, x-axis anterior-posterior, and y-axis medial-lateral. Before each participant began their jump-landing trials, the force plates were zeroed, and their mass was recorded while they stood equally on both force plates.

The Pipeline: OpenLESS Automated Scoring

After jump-landing trials were recorded, raw trial data were automatically uploaded and processed over the cloud via OpenCap’s web interface. OpenCap’s automated processing suite included data extraction, pose estimation, time synchronization, and 3D anatomical marker set derivation.⁶ The 3D kinematics were then computed from the derived marker trajectories using inverse kinematics²⁴^,²⁵ and a musculoskeletal model with biomechanical constraints.⁶^,²⁶^,²⁷ The resulting musculoskeletal model included 33 degrees of freedom, with 15 used in our analysis: 3 for the trunk, 3 per hip, 1 per knee, and 2 per ankle.⁶^,²⁶^,²⁷ The final outputs of interest from the automated cloud processing included the marker (.trc) files containing 3D anatomical marker trajectories and motion (.mot) files containing 3D joint angles.

A custom Python (version 3.10.12) pipeline, OpenLESS, was developed for reading, signal processing, and extracting kinematic variables of interest from OpenCap, then algorithmically scoring the LESS based on jump-landing movement quality (Figure 4). The OpenLESS Python script is provided in Supplemental File 1. Before entry into the OpenLESS pipeline, each trial OpenCap video recording was inspected for completeness of the entirety of movement, and kinematic waveforms were assessed for biological plausibility. Trials were removed if there were any errors in cloud processing, pose estimation, and/or if the trial exuded excessive noise outside of expectation.⁶^,¹⁸^,²³

Open Landing Error Scoring System (OpenLESS) automated Landing Error Scoring System pipeline.

After eligibility was determined, the marker trajectory and kinematic data were entered into the custom OpenLESS pipeline, which performed the following steps: (1) marker trajectories and kinematics were filtered with a fourth-order, 12-Hz low-pass Butterworth filter; (2) stance phase was identified by determining key events for initial ground contact and rebound jump take off; (3) initial ground contact was identified as the first global minimum of the great-toe marker trajectory in the vertical axis (y-axis); (4) rebound jump take off was identified as the second global minimum of the great-toe marker trajectory in the vertical axis; (5) a quality-control step was incorporated, wherein the automatically detected key events outlining the contact phase were plotted for manual inspection to ensure accuracy before proceeding with grading; (6) the lowest point of the braking phase before upward movement was determined by the frame where knee flexion angle was at its peak; and (7) 3D marker trajectories and joint angles at both initial contact and the lowest point of the braking phase were extracted.

The OpenLESS then used the joint trajectories and kinematics at the event times of interest (Figure 2A and 2B) to score each item of the expanded 19-item LESS (22 possible errors) based on clinically and literature-informed cutoffs.³^,⁸^,⁹^,¹²^,¹³^,^28–30 Identical to the expert-rater LESS, an error present on either limb results in an error for that associated LESS item. The 2 additional OpenLESS items capture asymmetric foot-landing and weight-shift patterns, recently identified as clinically relevant movement characteristics.¹³^,²⁹ Technical details for scoring each LESS item are made available in Supplemental File 2. Data cleaning, processing, and LESS scoring were performed in Python (version 3.10.12) using the SciPy (version 1.5.4), pandas (version 2.2.2), and NumPy (version 2.0) packages.

Statistical Analysis

Statistical analyses were conducted using R statistical software (version 4.4.1; R Core Team 2024). The normality of LESS scores was evaluated through visual inspection of histograms and quantile-quantile plots. Descriptive statistics are presented as mean ± SD or median (interquartile range) if not normally distributed. Expert-rater LESS and automated OpenLESS scores were calculated as the mean score across all successful jump-landing trials within each participant.

Intraclass correlation coefficients with 95% CIs were calculated using the psych package (version 2.6.4.26) to evaluate concurrent validity and reliability.³¹^,³² A 2-way, random-effects model for an absolute agreement based on single ratings (ICC_2,1) was used for event timing, and average ratings (ICC_2,k) were used for LESS scores. For reliability, a linear mixed-effects model was used to calculate ICC_2,k, optimizing the use of available information while accounting for variability across time points. Intraclass correlation coefficient values were interpreted according to established guidelines: poor (0.0–0.5), moderate (0.5–0.75), good (0.75–0.9), and excellent (0.9–1.0).³²

To further assess the validity of OpenLESS-derived scores compared with expert-rater LESS scores, Pearson correlation coefficient (R) and Bland-Altman limits of agreement (LoAs) were computed using the stats (version 4.2.3) and blandr (version 0.5.1) packages.³¹ Pearson correlations were categorized as per Portney and Watkins: ≤0.25 (little/no), 0.25–0.50 (low/fair), 0.50–0.75 (moderate/good), and ≥0.75 (strong) relationship.³²

Reliability measurement error was quantified using the SEM, calculated as SEM = SD $\sqrt{1 - ICC}$ , where SD represents the pooled SD of test and retest scores.³¹ The minimal detectable change (MDC) was calculated as MDC = 1.96 × $\sqrt{2}$ × SEM.³¹ The SEM and MDC were expressed in the units of the measure (number of LESS errors).

RESULTS

The healthy cohort used in the validation arm consisted of 26 individuals (12 men, 14 women, age = 23.0 ± 3.8 years, height = 171.9 ± 8.3 cm, mass = 75.4 ± 18.9 kg). The post-ACLR cohort used in the validation arm consisted of 27 individuals (8 men, 19 women, age = 21.4 ± 5.7 years, height = 173.5 ± 12.5 cm, mass = 73.9 ± 13.1 kg) who were 6 to 72 months post–ACLR surgery (median: 33.0 [interquartile range: 50.5] months postsurgery; International Knee Documentation Committee score = 83.2 ± 14.3). The field-based athlete cohort used in the reliability arm consisted of 39 recreationally athletic women (18 amateur soccer players, 10 university athletes from ball and nonball sports, 11 recreational weight lifters; age = 25.0 ± 4.7 years, height = 165.0 ± 7.1 cm, mass = 63.5 ± 8.6 kg).

A small percentage of OpenCap-recorded trials (5.2%; 33 of 639) could not be processed through the OpenLESS pipeline due to factors such as excessive noise in the kinematic data, incomplete capture of the jump-landing movement, or failure to detect key events. However, because all participants contributed multiple trials and their LESS scores were averaged within individuals for validity and reliability analyses (ICC_2,_k), this was unlikely to have affected our study’s findings.

Event Detection

The OpenLESS event detection pipeline, using OpenCap kinematics and trajectories, demonstrated excellent validity in identifying initial ground contact and toe-off events when compared with force plate measurements across 98 jump-landing trials (ICC_2,1 > 0.99, P < .001; Supplemental File 3).

Criterion Validity

Analysis of the healthy, college-aged cohort revealed good agreement between expert-rater assessment and the automated OpenLESS pipeline for total LESS scores (ICC_2,_k = 0.79, P < .001; Table 1). Box-whisker plots are presented in Figure 5A to display the range of values from both LESS scoring methods (Expert and OpenLESS). The Bland-Altman analysis (Figure 5C) estimated a mean bias of 0.35 (95% CI = −0.05, 0.75), indicating a small but nonsignificant systematic difference between the 2 methods (t = 1.80, P = .08). The LoA ranged from −1.59 (95% CI = −2.29, −0.90) to 2.30 (95% CI = 1.60, 2.99), representing that 95% of the differences between measurements fell within this range.

Table 1.

Criterion Validity of the OpenLESS in the Healthy and Post-ACLR Cohorts^a

Validation Cohort	No. of Participants	Rater	Mean ± SD	ICC_2,_k (95% CI)	R Value
Healthy	26	OpenLESS	2.78 ± 1.38	0.79 (0.55, 0.91)^b	0.70^b
Healthy	26	Expert	3.13 ± 1.09	0.79 (0.55, 0.91)^b	0.70^b
Post-ACLR	27	OpenLESS	5.50 ± 3.47	0.88 (0.55, 0.96)^b	0.86^b
Post-ACLR	27	Expert	7.01 ± 3.71	0.88 (0.55, 0.96)^b	0.86^b

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Abbreviations: ACLR, anterior cruciate ligament reconstruction; ICC, intraclass correlation coefficient; OpenLESS, Open Landing Error Scoring System; R, Pearson correlation coefficient.

Averaged composite LESS scores computed from the automated OpenLESS compared against an expert rater in a healthy (12 men and 14 women) and post-ACLR (8 men and 19 women) sample. Criterion validity was calculated using a linear mixed-effects model with the ICC function in the psych package (version 2.6.4.26) of R statistical software.

Statistically significant, P value < .05.

A, Healthy cohort box-and-whisker plot of Landing Error Scoring System (LESS) scores by both methods. B, Post–anterior cruciate ligament reconstruction (ACLR) cohort box-and-whisker plot of LESS scores by both methods. C, Healthy cohort Bland-Altman plot of OpenLESS compared with the expert rater. D, Post-ACLR cohort Bland-Altman plot of OpenLESS compared with the expert rater. The x-axis “Mean of Measurements” refers to the mean score from the 2 rating systems (OpenLESS and expert). The y-axis “Difference of Measurements” refers to the difference in score between the 2 rating systems—that is, expert rater minus OpenLESS. Mean bias is represented by the central dashed line, whereas the upper and lower dashed lines represent the upper and lower limits of agreement. 95% CIs are indicated by the shaded regions surrounding mean bias and the limits of agreement.

Good agreement was observed in the clinically relevant post-ACLR cohort (ICC_2,_k = 0.88, P < .001; Table 1). Box-whisker plots are presented in Figure 5B to display the range of values from both LESS scoring methods (Expert and OpenLESS). In the Bland-Altman analysis (Figure 5D), a significant (t = 4.06, P < .001), directionally similar systematic bias was observed in the post-ACLR cohort, where OpenLESS consistently scored lower than the expert rater with a mean bias of 1.51 errors (95% CI = 0.74, 2.27). The LoA ranged from −2.28 (95% CI = −3.60, −0.96) to 5.30 (95% CI = 3.97, 6.62).

The automated OpenLESS pipeline completed grading for all 353 validation trials (242 healthy, 119 post-ACLR) in under 25 minutes. This included batch downloading motion data from OpenCap’s web platform, organizing files, processing them through the automated pipeline, and performing a manual quality check of event time plots before finalizing LESS scores. In contrast, expert-rater grading required 5 to 7 minutes per trial, totaling over 35 hours to complete all 353 validation trials.

Intersession Reliability

A repeated-measures assessment of the OpenLESS score was conducted across 4 visits, with 17 participants attending all 4 visits, 19 attending 3, and 3 attending 2 visits. Mean OpenLESS scores demonstrated minimal variation, with 5.35 errors at visit 1, 5.12 at visit 2, 5.87 at visit 3, and 5.00 at visit 4 (Figure 6). The OpenLESS demonstrated good to excellent intersession reliability (ICC_2,_k = 0.89, P < .001; Table 2). Measurement error metrics indicated an SEM of 0.98 and an MDC of 2.72 OpenLESS errors.

Field-based athlete cohort Landing Error Scoring System (LESS) scores graded by OpenLESS. Box-and-whisker plots with jittered athlete LESS scores across the 4 repeated visits.

Table 2.

Intersession Reliability of the OpenLESS in the Field-Based Athlete Reliability Cohort^a

Session	Participants	Mean ± SD	ICC_2,_k
Session	Participants	Mean ± SD	Value (95% CI)	SEM	MDC
1	39	5.35 ± 2.84	0.89 (0.81, 0.93)^b	0.98	2.72
2	35	5.12 ± 3.20
3	34	5.87 ± 2.44
4	23	5.00 ± 2.65

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Abbreviations: ICC, intraclass correlation coefficient; OpenLESS, Open Landing Error Scoring System; MDC, minimal detectable change; SEM, standard error of measurement.

Averaged composite LESS scores computed from the automated OpenLESS from up to 4 visits (each 7 days apart) in 39 female athletes, all athletes contributed at least 2 visits. Intersession reliability was calculated using a linear mixed-effects model with the ICC function in the psych package (version 2.6.4.26) of R statistical software.

Statistically significant, P value < .05.

DISCUSSION

This study developed and validated the OpenLESS (Supplemental File 1), an automated scoring system for the LESS that leverages markerless motion-capture technology to enhance the efficiency and scalability of movement quality assessment. The OpenLESS demonstrated good agreement with expert raters across healthy and post-ACLR cohorts while showing excellent intersession reliability in a field-based athletic cohort. The kinematic-based event detection algorithm for identifying the landing phase also showed excellent validity against gold-standard force plate measurements. Across all cohorts, participant heights ranged from 153 cm to 192 cm and weights from 52 kg to 99 kg, offering insight into OpenLESS’s applicability across diverse body sizes. Building upon previous efforts to automate movement quality assessments,²^,³ the OpenLESS provides clinicians and researchers with an efficient, accessible tool for capturing and grading jump-landing mechanics.

A fundamental validation step for the OpenLESS pipeline was establishing the accuracy of its event detection algorithm, which processes markerless motion data from OpenCap to identify key jump-landing events (initial contact and toe-off). We compared OpenLESS’s automated event detection against gold-standard force plate measurements in 98 trials from 12 post-ACLR participants. The analysis revealed near-perfect agreement (ICC_2,1 > 0.99) between OpenLESS and force plate event timing, validating our approach to automated event detection using markerless motion-capture data. This high level of agreement demonstrates that the OpenLESS can reliably identify key biomechanical events without requiring specialized laboratory equipment.

The OpenLESS demonstrated good agreement with expert raters in both healthy and post-ACLR cohorts, though with a consistent, systematic bias toward slightly lower scores, particularly in the post-ACLR group. Our observed healthy-cohort bias (−0.3 errors) contrasts with earlier automated systems, such as the depth camera approach of Mauntel et al, which demonstrated higher scores (+1.2 errors) compared with expert raters.¹³ These differences likely reflect technologic advances, as the OpenLESS uses dual high-speed smartphone cameras rather than single-perspective depth sensing.¹⁴ The presence of bias likely reflects differences between algorithmic and human movement-quality assessment thresholds, a pattern commonly observed in automated kinematic analysis systems.³³ Notably, the agreement between the OpenLESS and expert raters (ICC_2,_k = 0.79–0.88) closely parallels the documented interrater reliability among expert raters themselves (ICC = 0.81–0.93), suggesting that OpenLESS’s scoring variability falls within acceptable clinical limits.⁸^,¹⁰^,³⁴ This reliability is particularly significant given that small sample sizes often constrain biomechanical studies investigating injury risk and surgical outcomes due to resource limitations.³⁵ The automated nature of the OpenLESS could help address these constraints by enabling larger-scale assessments while maintaining measurement consistency.

The OpenLESS demonstrated robust scoring consistency across multiple testing environments, including an outdoor soccer pitch (grass), athletic field (turf), and indoor recreation center, validating its utility for movement quality assessment beyond traditional laboratory settings. Score stability was evident across visits with acceptable measurement error.¹⁰^,³¹ This temporal stability is essential for monitoring interventions and rehabilitation progress, for which reliable baseline measurements enable detection of meaningful change.³⁶ Whereas traditional LESS scoring can be subject to rater variability,¹⁰ the OpenLESS achieved high intersession reliability (ICC_2,_k = 0.89), enhancing precision for longitudinal and postintervention assessments. This precision aligns with the growing emphasis on evidence-based injury prevention protocols.^1–4 The minimal measurement error further establishes the OpenLESS for use in clinical trials and rehabilitation, in which repeated assessments are essential for evaluating improvements and the effectiveness of intervention programs.

The assessment of OpenLESS’s measurement properties across 3 distinct participant populations demonstrates its robust clinical utility and versatility in real-world settings, directly addressing critical needs in lower extremity injury risk prevention and intervention.^1–4 Unlike traditional research that prioritizes internal validity through strict inclusion/exclusion criteria,³⁶ our approach deliberately embraced ecologic validity by testing diverse populations and environments. We validated the OpenLESS not only in healthy individuals but also in people post-ACLR, providing evidence of its effectiveness in clinically relevant populations. Our reliability testing further emphasized real-world applicability by conducting assessments outside laboratory environments and allowing participants to wear their typical exercise footwear and clothing—conditions known to challenge markerless motion-capture systems but essential for clinical implementation.⁵^,³⁷

OpenCap, the foundation of our system, has proven its versatility across multiple applications, from analyzing gait in neurologic disorders³⁸ to assessing lower extremity kinematics during cycling³⁹ and evaluating whole-body movement during dynamic balance tasks.⁴⁰ By building the OpenLESS as an open-source tool on this established platform, we provide researchers and clinicians with an adaptable framework that can be customized for various dynamic activities and populations, while maintaining measurement precision in real-world conditions. This accessibility directly responds to the documented need for reliable movement assessment tools that can be deployed beyond laboratory settings.¹³^,³⁷ The combination of OpenCap and the OpenLESS creates new opportunities for larger-scale studies and widespread clinical implementation of sophisticated biomechanical analysis, potentially transforming how we approach movement screening and injury prevention across diverse populations.⁶^,³⁷^,⁴¹

Whereas previous approaches have used machine learning to predict LESS total scores based on expert grader patterns, the OpenLESS takes a fundamentally different approach.¹⁵ Rather than replicating human scoring patterns, OpenLESS directly processes motion-capture data to evaluate LESS items using categorical criteria, aligning with the original LESS development methodology.⁸^,⁹ This transparent approach, combined with accessible source code, addresses the implementation limitations often encountered with proprietary assessment tools.

Biomechanical injury risk assessments have faced criticism for their narrow focus.⁴² Injury risk, both primary and secondary, involves multiple factors beyond physical function and performance metrics.⁴³^,⁴⁴ Therefore, the OpenLESS should be integrated within comprehensive biopsychosocial assessments for complete performance characterization.⁴⁵ Although the OpenLESS effectively identifies aberrant movement patterns consistent with the original LESS, such as less hip and knee flexion during landing, several limitations warrant consideration.⁸^,⁹

The OpenLESS demonstrated a small systematic bias toward lower scores than expert raters, with a greater bias observed in the post-ACLR cohort. A post hoc analysis of item-level errors in this cohort revealed high agreement for sagittal plane movements but moderate agreement for medial knee (valgus) errors, with the OpenLESS detecting fewer errors than the expert rater at initial contact (23 vs 10 errors, Cohen κ = 0.41) and lowest center of mass (43 vs 27 errors, Cohen κ = 0.42). These discrepancies may stem from differences in scoring criteria, markerless motion-capture limitations in frontal and transverse plane estimations, or potential rater bias due to lack of blinding. Additionally, the MDC across sessions was slightly larger for the OpenLESS (2.72 errors) compared with the intersession MDC reported by Hanzlíková and Hébert-Losier’s systematic review of expert-rater LESS grading (2.25 errors), though this comparison should be interpreted cautiously due to the difference in sample sizes between our study (n = 39) and the prior reliability study (n = 13).¹⁰

Beyond measurement considerations, several methodologic limitations should be noted. The use of different sampling frequencies across cohorts (240 Hz for laboratory-based and 60 Hz for field-based testing) due to varying internet connectivity conditions may have influenced motion-capture quality, though this effect was not directly assessed. Although our validity cohorts included both male and female participants, the reliability analyses were conducted exclusively in female athletes, limiting the generalizability of the reliability findings across the sexes. Additionally, we were unable to assess OpenLESS validity in the field-based cohort due to the absence of standard LESS camera views required for expert rating. Although we anticipate similar agreement given that only background conditions and sampling frequency differed from laboratory testing, this assumption requires verification. Nevertheless, the field-based cohort’s data remain valuable, demonstrating OpenLESS’s capacity for longitudinal monitoring in real-world settings. These findings lay the groundwork for expanded validation studies examining OpenLESS performance across diverse clinical and athletic populations in field-based settings.

Future researchers should continue leveraging open-source tools like OpenCap to enhance accessibility and automate additional clinical and return-to-activity assessments, including cutting-movement assessment scores and single-leg LESS variations. Priority areas for future research include validating the OpenLESS in other clinically relevant populations and expanding validation studies to larger healthy cohorts, with a particular focus on intervention responsiveness. Encouraging open-source tools not only increases accessibility but also empowers clinicians and researchers of all experience levels to use and adapt these methods for diverse clinical and research applications.

CONCLUSIONS

The OpenLESS provides a comprehensive, automated pipeline for jump-landing assessment, encompassing data processing, event detection, and standardized LESS scoring based on established operational definitions. The system demonstrates robust reliability and clinical utility across diverse settings, offering time-efficient movement quality assessment without specialized laboratory equipment. Initial validation in healthy college-aged and post-ACLR populations supports the OpenLESS as a promising tool for democratizing evidence-based injury risk screening. By reducing barriers to implementation while maintaining measurement precision, the OpenLESS advances the field toward more accessible, standardized biomechanical assessment.

Supplementary Material

Turner_666-24_Supp_File1.docx^{(80.5KB, docx)}

Turner_666-24_Supp_File2.docx^{(41.3KB, docx)}

Turner_666-24_Supp_File3.docx^{(40.3KB, docx)}

SUPPLEMENTAL MATERIAL

Supplemental File 1. Open Landing Error Scoring System Python Pipeline.

Supplemental File 2. Open Landing Error Scoring System Operational Definitions.

Supplemental File 3. Jump Landing Key Event Detection With OpenCap Markerless Motion Capture.

REFERENCES

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Turner_666-24_Supp_File1.docx^{(80.5KB, docx)}

Turner_666-24_Supp_File2.docx^{(41.3KB, docx)}

Turner_666-24_Supp_File3.docx^{(40.3KB, docx)}

[i1938-162X-60-8-590-b01] 1.Hewett TE, Bates NA. Preventive biomechanics: a paradigm shift with a translational approach to injury prevention. Am J Sports Med. 2017;45(11):2654–2664. 10.1177/0363546516686080 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b02] 2.Padua DA, DiStefano LJ, Hewett TE, et al. National Athletic Trainers’ Association position statement: prevention of anterior cruciate ligament injury. J Athl Train. 2018;53(1):5–19. 10.4085/1062-6050-99-16 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b03] 3.Whittaker JL, Booysen N, de la Motte S, et al. Predicting sport and occupational lower extremity injury risk through movement quality screening: a systematic review. Br J Sports Med. 2017;51(7):580–585. 10.1136/bjsports-2016-096760 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b04] 4.Logerstedt DS, Scalzitti D, Risberg MA, et al. Knee stability and movement coordination impairments: knee ligament sprain revision 2017. J Orthop Sports Phys Ther. 2017;47(11):A1–A47. 10.2519/jospt.2017.0303 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b05] 5.Colyer SL, Evans M, Cosker DP, Salo AIT. A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med Open. 2018;4(1):24. 10.1186/s40798-018-0139-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b06] 6.Uhlrich SD, Falisse A, Kidziński Ł, et al. OpenCap: human movement dynamics from smartphone videos. PLoS Comput Biol. 2023;19(10):e1011462. 10.1371/journal.pcbi.1011462 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b07] 7.Lally EM, Ericksen H, Earl-Boehm J. Measurement properties of clinically accessible movement assessment tools for analyzing jump landings: a systematic review. J Sport Rehabil. 2022;31(4):465–475. 10.1123/jsr.2021-0288 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b08] 8.Padua DA, Marshall SW, Boling MC, Thigpen CA, Garrett WE, II,, Beutler AI. The Landing Error Scoring System (LESS) is a valid and reliable clinical assessment tool of jump-landing biomechanics: the JUMP-ACL study. Am J Sports Med. 2009;37(10):1996–2002. 10.1177/0363546509343200 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b09] 9.Padua DA, DiStefano LJ, Beutler AI, de la Motte SJ, DiStefano MJ, Marshall SW. The Landing Error Scoring System as a screening tool for an anterior cruciate ligament injury-prevention program in elite-youth soccer athletes. J Athl Train. 2015;50(6):589–595. 10.4085/1062-6050-50.1.10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b10] 10.Hanzlíková I, Hébert-Losier K. Is the Landing Error Scoring System reliable and valid? A systematic review. Sports Health. 2020;12(2):181–188. 10.1177/1941738119886593 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b11] 11.Onate J, Cortes N, Welch C, Van Lunen BL. Expert versus novice interrater reliability and criterion validity of the Landing Error Scoring System. J Sport Rehabil. 2010;19(1):41–56. 10.1123/jsr.19.1.41 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b12] 12.Padua DA, Distefano LJ. Sagittal plane knee biomechanics and vertical ground reaction forces are modified following ACL injury prevention programs: a systematic review. Sports Health. 2009;1(2):165–173. 10.1177/1941738108330971 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b13] 13.Mauntel TC, Padua DA, Stanley LE, et al. Automated quantification of the Landing Error Scoring System with a markerless motion-capture system. J Athl Train. 2017;52(11):1002–1009. 10.4085/1062-6050-52.10.12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b14] 14.Mauntel TC, Cameron KL, Pietrosimone B, Marshall SW, Hackney AC, Padua DA. Validation of a commercially available markerless motion-capture system for trunk and lower extremity kinematics during a jump-landing assessment. J Athl Train. 2021;56(2):177–190. 10.4085/1062-6050-0023.20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b15] 15.Hébert-Losier K, Hanzlíková I, Zheng C, Streeter L, Mayo M. The “DEEP” Landing Error Scoring System. Appl Sci. 2020;10(3):892. 10.3390/app10030892 [DOI] [Google Scholar]

[i1938-162X-60-8-590-b16] 16.Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y. OpenPose: realtime multi-person 2D Pose estimation using Part Affinity Fields. IEEE Trans Pattern Anal Mach Intell. 2021;43(1):172–186. 10.1109/TPAMI.2019.2929257 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b17] 17.Sun K, Xiao B, Liu D, Wang J. Deep high-resolution representation learning for human pose estimation. ArXiv. Preprint posted online February 25, 2019. 10.48550/arXiv.1902.09212 [DOI] [Google Scholar]

[i1938-162X-60-8-590-b18] 18.Turner JA, Chaaban CR, Padua DA. Validation of OpenCap: a low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks. J Biomech. 2024;171:112200. 10.1016/j.jbiomech.2024.112200 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b19] 19.Vandenbroucke JP von Elm E Altman DG et al. ; STROBE Initiative . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297. 10.1371/journal.pmed.0040297 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b20] 20.Horsak B, Prock K, Krondorfer P, Siragy T, Simonlehner M, Dumphart B. Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: implications for data post-processing and analysis. J Biomech. 2024;166:112049. 10.1016/j.jbiomech.2024.112049 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b21] 21.Peng HT. Changes in biomechanical properties during drop jumps of incremental height. J Strength Cond Res. 2011;25(9):2510–2518. 10.1519/JSC.0b013e318201bcb3 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b22] 22.OpenCap—musculoskeletal forces from smartphone videos. OpenCap. 2022. Accessed July 6, 2024. https://www.opencap.ai/

[i1938-162X-60-8-590-b23] 23.Horsak B, Eichmann A, Lauer K, et al. Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait. J Biomech. 2023;159:111801. 10.1016/j.jbiomech.2023.111801 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b24] 24.Delp SL, Anderson FC, Arnold AS, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng. 2007;54(11):1940–1950. 10.1109/TBME.2007.901024 [DOI] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b25] 25.Seth A, Hicks JL, Uchida TK, et al. OpenSim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol. 2018;14(7):e1006223. 10.1371/journal.pcbi.1006223 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b26] 26.Lai AKM, Arnold AS, Wakeling JM. Why are antagonist muscles co-activated in my simulation? A musculoskeletal model for analysing human locomotor tasks. Ann Biomed Eng. 2017;45(12):2762–2774. 10.1007/s10439-017-1920-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b27] 27.Rajagopal A, Dembia CL, DeMers MS, Delp DD, Hicks JL, Delp SL. Full-body musculoskeletal model for muscle-driven simulation of human gait. IEEE Trans Biomed Eng. 2016;63(10):2068–2079. 10.1109/TBME.2016.2586891 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b28] 28.Bishop C, Jordan M, Torres-Ronda L, et al. Selecting metrics that matter: comparing the use of the countermovement jump for performance profiling, neuromuscular fatigue monitoring, and injury rehabilitation testing. Strength Cond J. 2023;45(5):545–553. 10.1519/SSC.0000000000000772 [DOI] [Google Scholar]

[i1938-162X-60-8-590-b29] 29.Eckard TG, Miraldi SFP, Peck KY, et al. Automated Landing Error Scoring System performance and the risk of bone stress injury in military trainees. J Athl Train. 2021;57(4):334–340. 10.4085/1062-6050-0263.21 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b30] 30.Turner JA, Hartshorne M, Cameron KL, Padua D. Jump landing kinematics: establishing normative ranges for male and female athletes. J Athl Train. Published online April 18, 2024. 10.4085/1062-6050-0006.24 [DOI] [PMC free article] [PubMed] [Google Scholar]

[i1938-162X-60-8-590-b31] 31.de Vet HCW, Terwee CB, Mokkink LB, Knol DL. Measurement in Medicine: A Practical Guide. Cambridge University Press; 2011. 10.1017/CBO9780511996214 [DOI] [Google Scholar]

[i1938-162X-60-8-590-b32] 32.Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. 3rd ed. Pearson/Prentice Hall; 2009. [Google Scholar]

[i1938-162X-60-8-590-b33] 33.Breithaupt K, Hare D. Automated test assembly. In: Drasgow F, ed. Technology and Testing. Routledge; 2015:128–141. [Google Scholar]

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PERMALINK

Open Source, Open Science: Development of the Open Landing Error Scoring System as the Automated Landing Error Scoring System

Jeffrey A Turner, PT, DPT, PhD

Elaine Taylor Reiche, PhD, LAT, ATC, CSCS

Matthew T Hartshorne, PT, DPT

Connor C Lee, BA

Joanna M Blodgett, PhD

Darin A Padua, PhD, ATC

Abstract

Context

Objective

Design

Patients or Other Participants

Main Outcome Measure(s)

Results

Conclusions

Key Points

METHODS

Design

Figure 1.

Participants

Testing Procedures

The Task: LESS Jump Landing

Figure 2.

Expert-Rater LESS Grading

The System: OpenCap Markerless Motion Capture

Figure 3.

The Pipeline: OpenLESS Automated Scoring

Figure 4.

Statistical Analysis

RESULTS

Event Detection

Criterion Validity

Table 1.

Figure 5.

Intersession Reliability

Figure 6.

Table 2.

DISCUSSION

CONCLUSIONS

Supplementary Material

SUPPLEMENTAL MATERIAL

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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