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

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. OpenLESS is an open-source tool that uses a markerless motion capture system to automate the LESS using 3D kinematics.

Objective: To describe the development of 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.

Participants: 92 total adult participants from three distinct cohorts: a healthy university student cohort (12 males, 14 females; 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 males, 19 females; age=21.4±5.7 years, height=173.5±12.5 cm; mass=73.9±13.1 kg; median 33 months post surgery), and a field-based athlete cohort (39 females; age=25.0±4.7 years, height=165.0±7.1cm; mass=63.5±8.6kg).

Main outcome measures(s): The OpenLESS software interprets movement quality from kinematics captured by markerless motion capture. Validity and reliability were assessed using intraclass correlation coefficients (ICC), standard error of measure (SEM), and minimal detectable change (MDC).

Results: OpenLESS agreed well with expert rater LESS scores for healthy (ICC2,k=0.79) and clinically relevant, post-ACLR cohorts (ICC2,k=0.88). The automated OpenLESS system reduced scoring time, processing all 353 trials in under 25 minutes compared to the 35 hours (~6 minutes per trial) required for expert rater scoring. When tested outside laboratory conditions, OpenLESS showed excellent reliability across repeated sessions (ICC2,k>0.89), with a SEM of 0.98 errors and MDC of 2.72 errors.

Conclusions: 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.