Viability of automating the landing error scoring system using inertial measurement units

The Landing Error Scoring System (LESS) is an assessment tool used for identifying movement patterns linked with non-contact anterior cruciate ligament injuries during a double-leg jump-landing; however, the LESS is scored by experts using 2D video recordings, which limits large-scale screening. This study explores the viability of using inertial measurement unit (IMU) data to automate scoring of 17 LESS items, using conventional machine learning model architecture. Forty healthy participants completed six jumps each, and raw movement data from three IMU sensors placed on the sacrum and medial-inferior aspects of the tibias were processed and segmented into the key phases of the double-leg jump-landing. A total of 218 jumps were used to train supervised machine learning models using various subsets of the processed dataset, including extracted temporal and statistical features. Performance metrics were extracted, and the best performing models for each scoring item were evaluated against a majority-class baseline classifier (ZeroR). Results indicated moderate improvements over ZeroR, ranging from 0.3 % to 22.5 %, with some models demonstrating limited recognition of the minority class. These findings provide a foundation for future research in IMU-based LESS automation, and improving the accessibility of movement screening tools.