Automated Quantification of the Landing Error Scoring System With a Markerless Motion-Capture System

Mauntel, Timothy C.; Padua, Darin A.; Stanley, Laura E.; Frank, Barnett S.; DiStefano, Lindsay J.; Peck, Karen Y.; Cameron, Kenneth L.; Marshall, Stephen W.

doi:10.4085/1062-6050-52.10.12

Cited by 42 publications

(57 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automated system has shown good agreement with the conventional method. 34,48 We believe the differences in data collection methodology between the two research sites are a strength. Both research sites had large, independent sample sizes with similar conclusions.…”

Section: Discussionmentioning

confidence: 99%

“…This automated system is comparable to expert human evaluators for the LESS and produces a score on the same scale as the traditional method of scoring. 34 The average of each participant's three total LESS scores was used for analysis to assess the group average for each sport specialization group.…”

Section: Jump-landing Task (Less)mentioning

confidence: 99%

“…Movement control influences injury risk by minimizing loads on ligaments and other supporting structures. 28 The Landing Error Scoring System (LESS) is one of the most researched and used clinical examinations to assess movement control in active populations 24,[29][30][31][32][33][34][35][36] This tool is associated with injuries in several athletic populations and is able to identify possible high-risk movement patterns and deficits in neuromuscular control. 30,32,37 The central hypothesis is that highly specialized athletes will perform repetitive sport-specific tasks, resulting in the overdevelopment of certain muscle groups and leading to hazardous lower extremity movement patterns that increase the risk of injury.…”

mentioning

confidence: 99%

See 2 more Smart Citations

The Influence of Sport Specialization on Landing Error Scoring System Scores in High School Athletes

Peckham¹,

DiStefano²,

Root³

et al. 2018

Athletic Training & Sports Health Care

Self Cite

View full text Add to dashboard Cite

To evaluate the impact of sport specialization on jump-landing performance in high school athletes as measured by the Landing Error Scoring System (LESS). Methods: Five hundred seventy-four high school athletes participated in a cross-sectional study that evaluated the influence of sport specialization on jump-landing mechanics. The specialization level was assessed via a 3-point classification system (low = 38% of sample, moderate = 31% of sample, and high = 31% of sample). Research site and sex of the athlete were used as covariates in an analysis of covariance for the evaluation of differences in the mean total LESS scores across the three specialization groups, with higher LESS scores indicating poorer jump-landing mechanics. Results: No differences in total LESS scores were observed between the three sport specialization groups when evaluating research sites independently or together and controlling for research site and sex of the athlete (adjusted mean LESS errors: low = 5.6 ± 2.1, moderate = 5.3 ± 1.9, and high = 5.1 ± 2.0; P > .05). Conclusions: Jump-landing mechanics do not appear to be influenced by sport specialization status in high school athletes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Jump-landing Task (Less)mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Sport Specialization on Landing Error Scoring System Scores in High School Athletes

Peckham¹,

DiStefano²,

Root³

et al. 2018

Athletic Training & Sports Health Care

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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]. Dar, Yehiel, and Cale' Benzoor [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.…”

Section: Of 13mentioning

confidence: 99%

The ‘DEEP’ Landing Error Scoring System

et al. 2020

View full text Add to dashboard Cite

The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.

show abstract

Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

Trinidad-Fernández

Cuesta-Vargas

Vaes

et al. 2021

Med Biol Eng Comput

View full text Add to dashboard Cite

A human motion capture system using an RGB-D camera could be a good option to understand the trunk limitations in spondyloarthritis. The aim of this study is to validate a human motion capture system using an RGB-D camera to analyse trunk movement limitations in spondyloarthritis patients. Cross-sectional study was performed where spondyloarthritis patients were diagnosed with a rheumatologist. The RGB-D camera analysed the kinematics of each participant during seven functional tasks based on rheumatologic assessment. The OpenNI2 library collected the depth data, the NiTE2 middleware detected a virtual skeleton and the MRPT library recorded the trunk positions. The gold standard was registered using an inertial measurement unit. The outcome variables were angular displacement, angular velocity and lineal acceleration of the trunk. Criterion validity and the reliability were calculated. Seventeen subjects (54.35 (11.75) years) were measured. The Bending task obtained moderate results in validity (r = 0.55–0.62) and successful results in reliability (ICC = 0.80–0.88) and validity and reliability of angular kinematic results in Chair task were moderate and (r = 0.60–0.74, ICC = 0.61–0.72). The kinematic results in Timed Up and Go test were less consistent. The RGB-D camera was documented to be a reliable tool to assess the movement limitations in spondyloarthritis depending on the functional tasks: Bending task. Chair task needs further research and the TUG analysis was not validated. Graphical abstract Comparation of both systems, required software for camera analysis, outcomes and final results of validity and reliability of each test.

show abstract

Automated Quantification of the Landing Error Scoring System With a Markerless Motion-Capture System

Cited by 42 publications

References 27 publications

The Influence of Sport Specialization on Landing Error Scoring System Scores in High School Athletes

The Influence of Sport Specialization on Landing Error Scoring System Scores in High School Athletes

The ‘DEEP’ Landing Error Scoring System

Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

Contact Info

Product

Resources

About