Validation of a commercially available markerless motion-capture system for trunk and lower extremity kinematics during a jump-landing assessment

Context: Field-based, portable motion-capture systems can be used to help identify individuals at greater risk of lower extremity injury. Microsoft Kinect-based markerless motion-capture systems meet these requirements; however, until recently, these systems were generally not automated, required substantial data postprocessing, and were not commercially available. Objective: To validate the kinematic measures of a commercially available markerless motion-capture system. Design: Descriptive laboratory study. Setting: Laboratory. Patients or Other Participants: A total of 20 healthy, physically active university students (10 males, 10 females; age ¼ 20.50 6 2.78 years, height ¼ 170.36 6 9.82 cm, mass ¼ 68.38 6 10.07 kg, body mass index ¼ 23.50 6 2.40 kg/m2). Intervention(s): Participants completed 5 jump-landing trials. Kinematic data were simultaneously recorded using Kinect-based markerless and stereophotogrammetric motion-capture systems. Main Outcome Measure(s): Sagittal- and frontal-plane trunk, hip-joint, and knee-joint angles were identified at initial ground contact of the jump landing (IC), for the maximum joint angle during the landing phase of the initial landing (MAX), and for the joint-angle displacement from IC to MAX (DSP). Outliers were removed, and data were averaged across trials. We used intraclass correlation coefficients (ICCs [2,1]) to assess intersystem reliability and the paired-samples t test to examine mean differences (a < .05). Results: Agreement existed between the systems (ICC range ¼1.52 to 0.96; ICC average ¼ 0.58), with 75.00% (n ¼ 24/ 32) of the measures being validated (P < .05). Agreement was better for sagittal- (ICC average ¼ 0.84) than frontal- (ICC average ¼ 0.35) plane measures. Agreement was best for MAX (ICC average ¼ 0.77) compared with IC (ICC average ¼ 0.56) and DSP (ICC average ¼ 0.41) measures. Pairwise comparisons identified differences for 18.75% (6/32) of the measures. Fewer differences were observed for sagittal- (0.00%; 0/15) than for frontal- (35.29%; 6/17) plane measures. Between-systems differences were equivalent for MAX (18.18%; 2/11), DSP (18.18%; 2/11), and IC (20.00%; 2/10) measures. The markerless system underestimated sagittal-plane measures (86.67%; 13/15) and overestimated frontal-plane measures (76.47%; 13/ 17). No trends were observed for overestimating or underestimating IC, MAX, or DSP measures. Conclusions: Moderate agreement existed between markerless and stereophotogrammetric motion-capture systems. Better agreement existed for larger (eg, sagittal-plane, MAX) than for smaller (eg, frontal-plane, IC) joint angles. The DSP angles had the worst agreement. Markerless motion-capture systems may help clinicians identify individuals at greater risk of lower extremity injury

Trunk and lower extremity movement patterns, stress fracture risk factors, and biomarkers of bone turnover in military trainees

Context: Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies. Objective: To determine how SFx risk factors influence bone metabolism. Design: Cross-sectional study. Setting: Military service academy. Patients or Other Participants: Forty-five men (agepre ¼ 18.56 6 1.39 years, heightpre ¼ 176.95 6 7.29 cm, masspre ¼ 77.20 6 9.40 kg; body mass indexpre ¼ 24.68 6 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded. Intervention(s): We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum. Main Outcome Measure(s): A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP: CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP: CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using a ≤ .10 for simple and a ≤ .05 for multiple regression analyses. Results: The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 ¼ 0.47, P ¼ .02) and PINP: CTx-1 ratio (R2 ¼ 0.66, P ¼ .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP: CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes. Conclusions: Stress fracture risk factors accounted for 66% of the PINP: CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies

Automated quantification of the landing error scoring system with a markerless motion-Capture system

Context: The Landing Error Scoring System (LESS) can be used to identify individuals with an elevated risk of lower extremity injury. The limitation of the LESS is that raters identify movement errors from video replay, which is time-consuming and, therefore, may limit its use by clinicians. A markerless motion-capture system may be capable of automating LESS scoring, thereby removing this obstacle. Objective: To determine the reliability of an automated markerless motion-capture system for scoring the LESS. Design: Cross-sectional study. Setting: United States Military Academy. Patients or Other Participants: A total of 57 healthy, physically active individuals (47 men, 10 women; age ¼ 18.6 6 0.6 years, height ¼ 174.5 6 6.7 cm, mass ¼ 75.9 6 9.2 kg). Main Outcome Measure(s): Participants completed 3 jump-landing trials that were recorded by standard video cameras and a depth camera. Their movement quality was evaluated by expert LESS raters (standard video recording) using the LESS rubric and by software that automates LESS scoring (depth-camera data). We recorded an error for a LESS item if it was present on at least 2 of 3 jump-landing trials. We calculated j statistics, prevalence- and bias-adjusted j (PABAK) statistics, and percentage agreement for each LESS item. Interrater reliability was evaluated between the 2 expert rater scores and between a consensus expert score and the markerless motion-capture system score. Results: We observed reliability between the 2 expert LESS raters (average j ¼ 0.45 6 0.35, average PABAK ¼ 0.67 6 0.34; percentage agreement ¼ 0.83 6 0.17). The markerless motion-capture system had similar reliability with consensus expert scores (average j ¼ 0.48 6 0.40, average PABAK ¼ 0.71 6 0.27; percentage agreement ¼ 0.85 6 0.14). However, reliability was poor for 5 LESS items in both LESS score comparisons. Conclusions: A markerless motion-capture system had the same level of reliability as expert LESS raters, suggesting that an automated system can accurately assess movement. Therefore, clinicians can use the markerless motion-capture system to reliably score the LESS without being limited by the time requirements of manual LESS scoring