Assessing the accuracy of in-stadium and portable multi-camera markerless motion capture for baseball pitching kinematics and kinetics

Basketball requires high lower limb performance. Assessing jump biomechanics is vital for enhancing performance and injury prevention. Marker-based (MB) systems are common but limited. In recent years, Markerless (ML) motion capture systems have gradually become emerging tools in sports biomechanics research due to their characteristic of not requiring physical marker points. However, their specific application and verification in basketball events are still relatively limited. Purpose: In this study, lower limb kinematics and kinetics estimated by MB and ML motion capture systems during jumps were compared. Methods: Twelve subjects performed the standing vertical jump (SVJ), standing long jump (SLJ) and running vertical jump (RVJ) tests. Data was collected using 10 infrared cameras, 6 high-resolution cameras and two force platforms via Vicon Nexus software. Markerless motion capture calculated sagittal plane angles, torque and power of the Hip, Knee and Ankle joints via Theia3D software, with these parameters also collected by the marker-based Vicon system. Both systems' '64ata were then processed in Visual3D. We analyzed the correlation coefficient (r), root mean square difference (RMSD), and maximum/minimum errors, as well as using statistical parametric mapping (SPM) to compare temporal patterns between groups and determine specific moments where significant differences occurred. Results: SLJ capture was slightly inferior in both systems. SPM analysis of the sagittal plane showed significant differences only at the hip joint. Joint angle RMSD was < 8.2°, torque RMSD < 0.41 N·M/kg, and power RMSD < 1.76 W/kg. Conclusions: The ML system accurately captures knee and ankle joints in the sagittal plane but shows significant differences in hip measurement and certain movements, requiring further validation.

Markerless motion capture (MMC) is a newer technology that utilizes advanced computer vision and machine learning algorithms to evaluate human movement. Traditional marker-based motion capture (MBC) systems can be time consuming for both participant and researcher and setup can often take more time than collection. Further, MMC allows athletes to perform in a more familiar environment without the potential error introduced by the laboratory environment. However, it is imperative to examine its accuracy in comparison to the gold-standard of conventional MBC. PURPOSE: To determine interrater reliability between a marker-based and markerless motion capture systems. METHODS: 15 collegiate basketball players (8 females, height: 180.8 ± 14.0 cm, mass: 80.8 ± 19.0 kg) participated in this study. 3 double leg drop vertical jumps (DVJ) and 3 maximum single leg vertical countermovement hops on each limb were performed. Limb dominance (Dom vs NDom) was self-reported through survey. Standard MBC techniques were utilized to track markers throughout the execution of each task. A MMC system collected data concurrently and used advanced computer vision and machine learning algorithms to estimate the pose of individuals during dynamic movements without the need of markers. Hip joint centers were estimated from each system and used to determine the lowest and highest vertical displacement during each trial. Intraclass correlation coefficient (ICC[3,1]) was used to determine the interrater reliability between systems. ICC and 95% confidence interval were used for interpretation. RESULTS: Interrater reliability between MMC and MBC was excellent for DOM side single leg jumps (MMC 47.8 ± 10.6 cm, MBC 47.1 ± 10.8 cm, ICC = 0.986 [95 CI 0.966, 0.994]), NDom side single leg jumps (MMC 48.4 ± 10.3 cm, MBC 47.2 ± 10.7 cm, ICC = 0.983 [95 CI 0.958, 0.993]), and double leg drop vertical jumps (MMC 67.7 ± 11.1 cm, MBC 66.9 ± 12.1 cm, ICC = 0.982 [95 CI 0.956, 0.993]). The root mean square error between systems was less than 2.3 cm (Dom: 1.8 cm, NDom: 2.2 cm, DVJ: 2.3 cm). CONCLUSIONS: Markerless motion capture techniques to calculate vertical jump displacement had excellent agreement to marker-based motion capture during both single leg and double leg tasks. Future studies should compare additional kinematic and kinetic variables between systems.

Validity and Reliability of Upper Limb Kinematic Assessment Using a Markerless Motion Capture (MMC) System: A Pilot Study

Markerless motion capture (MMC) shows promise for examining human movement across many domains because of its nonintrusive nature and negligible per-subject setup time. However, published MMC systems typically require specific hardware. This validation study compared lower-body joint kinematics from Ergomechanic, a hardware-agnostic pose model-based MMC system, to an established marker-based motion capture (MBMC) system. Static trial data from eighteen people were used to register MMC keypoints within a widely used musculoskeletal model. The registered model was used to calculate joint kinematics for static pose, squatting, and walking trials. A novel perturbation analysis estimated the contributions to differences in MBMC and MMC approaches to measurement disparities. Very good (0.87-1.0) correlations between the systems were calculated for ankle, knee, and hip flexion-extension angles. Good (0.70-0.86) correlations were found for hip external-internal and abduction-adduction. Pelvis and lumbar spine angles had a wider range of correlation results (-0.06 to 0.95), likely due to the few MMC keypoints in these body regions. Relative contributions from the perturbation analysis were (i) 75% from variations in MMC data relative to MBMC; (ii) 8% because MMC keypoints (26) < MBMC markers (67); and (iii) 3% from differences in musculoskeletal model scaling. These results validate Ergomechanic for leg kinematics during standing, walking, and squatting. Further, they suggest system improvements for pelvis and torso kinematics and provide new insights into the sources of known differences between MMC and MBMC measurements.

Agreement between a markerless and a marker-based motion capture systems for balance related quantities

Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research has examined ML systems’ estimation of the lower extremity joint moments and powers. This study aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems. Methods: Sixteen volunteers ran on a treadmill for 120 s at 3.58 m/s. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Greater peak magnitudes for hip extension and flexion moments, knee flexion moment, and ankle plantarflexion moment, along with their joint powers, were observed in the ML system compared to an MB system (p < 0.0001). For example, greater hip extension (MB: 1.42 ± 0.29 vs. ML: 2.27 ± 0.45) and knee flexion (MB: −0.74 vs. ML: −1.17 nm/kg) moments were observed in the late swing phase. Additionally, the ML system’s estimations resulted in significantly smaller peak magnitudes for knee extension moment, along with the knee production power (p < 0.0001). Conclusions: These observations indicate that inconsistent estimates of joint center position and segment center of mass between the two systems may cause differences in the lower extremity joint moments and powers. However, with the progression of pose estimation in the markerless system, future applications can be promising.

BackgroundInvestigations into the possible associations between early in life motor function and later in life musculoskeletal health, will require easily obtainable, valid, and reliable measures of gross motor function and kinematics. Marker-based motion capture systems provide reasonably valid and reliable measures, but recordings are restricted to expensive lab environments. Markerless motion capture systems can provide measures of gross motor function and kinematics outside of lab environments and with minimal interference to the subjects being investigated. It is, however, unknown if these measures are sufficiently valid and reliable in young children to warrant further use. This study aims to document the concurrent validity of a markerless motion capture system: “The Captury.”MethodMeasures of gross motor function and lower extremity kinematics from 14 preschool children (age between three and 6 years) performing a series of squats and standing broad jumps were recorded by a marker-based (Vicon) and a markerless (The Captury) motion capture system simultaneously, in December 2015. Measurement differences between the two systems were examined for the following variables: jump length, jump height, hip flexion, knee flexion, ankle dorsi flexion, knee varus, knee to hip separation distance ratio (KHR), ankle to hip separation distance ratio (AHR), frontal plane projection angle, frontal plane knee angle (FPKA), and frontal plane knee deviation (FPKD). Measurement differences between the systems were expressed in terms of root mean square errors, mean differences, limits of agreement (LOA), and intraclass correlations of absolute agreement (ICC (2,1) A) and consistency of agreement.ResultsMeasurement differences between the two systems varied depending on the variables. Agreement and reliability ranged from acceptable for e.g. jump height [LOA: − 3.8 cm to 2.2 cm; ICC (2,1) A: 0.91] to unacceptable for knee varus [LOA: − 33° to 19°; ICC (2,1) A: 0.29].ConclusionsThe measurements by the markerless motion capture system “The Captury” cannot be considered interchangeable with the Vicon measures, but our results suggest that this system can produce estimates of jump length, jump height, KHR, AHR, knee flexion, FPKA, and FPKD, with acceptable levels of agreement and reliability. These variables are promising for use in future research but require further investigation of their clinimetric properties.

This study sought to compare and validate baseball pitching mechanics, including joint angles and spatiotemporal parameters, from a single camera markerless motion capture solution with a 3D optical marker-based system. Ten healthy pitchers threw 2–3 maximum effort fastballs while concurrently using marker-based optical capture and pitchAITM (markerless) motion capture. Time-series measures were compared using R-squared (r2), and root mean square error (RMSE). Discrete kinematic measures at foot plant, maximal shoulder external rotation, and ball release, plus four spatiotemporal parameters were evaluated using descriptive statistics, Bland-Altman analyses, Pearson’s correlation coefficients, p-values, r2, and RMSE. For time-series angles, r2 ranged from 0.69 (glove arm shoulder external rotation) to 0.98 (trunk and pelvis rotation), and RMSE ranged from 4.37° (trunk lateral tilt) to 20.78° (glove arm shoulder external rotation). Bias for individual joint angle and spatiotemporal parameters ranged from −11.31 (glove arm shoulder horizontal abduction; MER) to 12.01 (ball visible). RMSE was 3.62 m/s for arm speed, 5.75% height for stride length and 21.75 ms for the ball visible metric. pitchAITM can be recommended as a markerless alternative to marker-based motion capture for quantifying pitching kinematics. A database of pitchAITM ranges should be established for comparison between systems.

Validation of upper extremity kinematics using Markerless motion capture

Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the validity of kinematic and kinetic parameters derived from ML for lower limb joints requires further validation in populations engaged in high-intensity jumping sports. The purpose of this study was to compare lower limb kinematic and kinetic estimates between marker-based (MB) and ML motion capture systems during jumps. Fourteen male Division I movement collegiate athletes performed a minimum of three squat jumps (SJ), drop jumps (DJ), and countermovement jumps (CMJ) in a fixed sequence. The movements were synchronized using ten infrared cameras, six high-resolution cameras, and two force measurement platforms, all controlled by Vicon Nexus software. Motion data were collected, and the angles, moments, and power at the hip, knee, and ankle joints were calculated using Theia3D software. These results were then compared with those obtained from the Vicon system. Comparative analyses included Pearson correlation coefficients (r), root mean square differences (RMSD), extreme error values, and statistical parametric mapping (SPM).SPM analysis of the three movements in the sagittal plane revealed significant differences in hip joint angles, with joint angle RMSD ≤ 5.6°, hip joint moments RMSD ≤ 0.26 N·M/kg, and power RMSD ≤ 2.12 W/kg showing considerable variation, though not reaching statistical significance. ML systems demonstrate high measurement accuracy in estimating knee and ankle kinematics and kinetics in the sagittal plane during these conventional jump tests; however, the accuracy of hip joint kinematic measurements in the sagittal plane requires further validation.

Motion capture systems are a key tool for performing quantitative analysis and evaluation of complex in movements sports. The prospect of the development and practical application of markerless motion capture tecnology in applied biomechanics increases research interest regarding the features of using such systems, as well as evaluat ing their accuracy and reliability in comparison with marker-based motion capture systems, which are the gold standard nowadays. Markerless motion capture systems have incomparable advantages over marker-based ones.In particular, significantly reduced time costs for the registration and data processing procedures, since registration is represented by filming a video from different angles, and processing is accompanied by the use of software algorithms for tracking the silhouette of an athlete using a presetted computer avatar. However, there is still no clear answer regarding the accuracy and reliability of the data recorded using markerless motion capture systems in relation to specific sports movements. Thus, the purpose of the presented work is to assess the statistical relationship of the data based on the correlation analysis of biomechanical variables on the example of a golf swing recorded using the Qualisys motion capture system and Simi Reality Motion markerless motion capture system.

Motion capture technology is quickly evolving, providing researchers, clinicians, and coaches with more access to biomechanics data. Markerless motion capture and inertial measurement units (IMUs) are continually developing biomechanics tools that need validation for dynamic movements before widespread use in applied settings. This study evaluated the validity of a markerless motion capture, IMU, and red, green, blue, and depth (RGBD) camera system as compared with marker-based motion capture during countermovement jumps, overhead squats, lunges, and runs with cuts. Thirty adults were recruited for this study (sex: 18 females, 12 males; age: 25.4 ± 8.6 yrs; height: 1.71 ± 0.08 m; weight: 71.6 ± 11.5 kg). Data were collected simultaneously with four motion capture technologies (i.e., Vicon, marker-based; Theia/Optitrack, markerless; APDM Opals, IMUs; and Vald HumanTrak, RGBD camera). System validity for lower and upper body joint angles was evaluated using bias, root mean squared error (RMSE), precision, maximum absolute error, and intraclass correlation coefficients. System usability was descriptively analyzed. Overall, markerless motion capture had the highest validity (sagittal plane RMSE: 3.20°-15.66°; frontal plane RMSE: 2.12°-9.14°; transverse plane RMSE: 3.160°-56.61°), followed by the IMU system (sagittal plane RMSE: 8.11°-28.37°; frontal plane RMSE: 3.26°-16.98°; transverse plane RMSE: 5.08°-116.75°), and lastly the RGBD system (sagittal plane bias: 0.55°-129.48°; frontal plane bias: 1.35°-52.06°). Markerless motion capture and IMUs have moderate validity for joint kinematics, whereas the RGBD system did not have adequate validity. Markerless systems have lower data processing time, require moderate technical expertise, but have high data storage size. IMUs are easier to use, can collect data in any location, but require participant set-up. Overall, individuals using motion capture should consider the specific movements, testing locations, and technical expertise available before selecting a system.

Comparison of kinematics and kinetics between OpenCap and a marker-based motion capture system in cycling.

BackgroundThree-dimensional (3D) motion analysis is an advanced tool used to quantify movement patterns in adults with chronic stroke and children with cerebral palsy. However, gold-standard marker-based systems have limitations for implementation in clinical settings. Markerless motion capture using Theia3D may provide a more accessible and clinically feasible alternative, but its accuracy is unknown in clinical populations. The purpose of this study was to quantify kinematic differences between marker-based and markerless motion capture systems in individuals with gait impairments.MethodsThree adults with chronic stroke and three children with cerebral palsy completed overground walking trials while marker-based and markerless motion capture data were synchronously recorded. Time-series waveforms of 3D ankle, knee, hip, and trunk angles were stride normalized and compared. Root mean squared error, maximum peak, minimum peak, and range of motion were used to assess discrete point differences. Pearson’s correlation and coefficient of multiple correlation were computed to assess similarity between the time series joint angle waveforms from both systems.ResultsThis study demonstrates that markerless motion capture using Theia3D produces good agreement with marker-based in the measurement of gait kinematics at most joints and anatomical planes in individuals with chronic stroke and cerebral palsy.ConclusionsThis is the first investigation to study the feasibility of Theia3D markerless motion capture for use in chronic stroke and cerebral palsy gait analysis. Our results indicate that markerless motion capture may be an acceptable tool to measure gait kinematics in clinical populations to provide clinicians with objective movement assessment data.

Knee motion is altered in overuse injuries and chronic diseases like osteoarthritis. Yet, relying on marker-based motion capture in the lab limits our understanding of how knee motion continuously impacts joint health in the real world. Markerless optical motion capture improves clinical suitability, but space and time constraints remain barriers toward real-world assessments. Inertial measurement unit (IMU) sensors have enabled continuous motion tracking outside the lab. In this study, we recorded thigh and shank-worn IMU data concurrently with marker-based and markerless optical motion capture on 10 healthy adults, who performed various daily living and exercise movements. We developed an IMU virtual alignment and data fusion paradigm to estimate knee flexion angle during each movement. We compared IMU-based estimate against marker-based and markerless motion capture using Pearson correlation (Rxy) and root-mean-square difference (RMSD). IMU-estimated knee flexion angle strongly correlated with motion capture (Rxy ≥ 0.9). RMSDs were small for slower movements like walking, stairs, and squats (RMSD = 4.4° – 6.0°) while larger during faster movements like running and jumping (RMSD = 5.4° – 9.4°). Our findings show that wearable IMUs track knee flexion with similar accuracy to optical motion capture during daily living activities typical to older adults, highlighting their potential for monitoring real-world mobility in knees with chronic diseases. Conversely, it remains inconclusive whether IMUs accurately track dynamic knee motion relevant to athletic injuries. Future research should seek best practice for IMU wearing and mitigate practical pitfalls to secure high-fidelity data, for identifying clinically meaningful real-world biomarkers of knee mobility.