Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks

Journal of Biomechanics, Год журнала: 2024, Номер 171, С. 112200 - 112200

Опубликована: Июнь 1, 2024

Язык: Английский

---

Comparing lab and field agility kinematics in young talented female football players: Implications for ACL injury prevention

European Journal of Sport Science, Год журнала: 2022, Номер 23(5), С. 859 - 868

Опубликована: Апрель 11, 2022

Modifiable (biomechanical and neuromuscular) anterior cruciate ligament (ACL) injury risk factors have been identified in laboratory settings. These were subsequently used ACL prevention measures. Due to the lack of ecological validity, use on-field data screening is increasingly advocated. Though, kinematic differences between settings never investigated. The aim present study was investigate lower-limb kinematics female footballers during agility movements performed both football field environments. Twenty-eight healthy young talented (soccer) players (14.9 ± 0.9 years) participated. Lower-limb joint collected through wearable inertial sensors (Xsens Link) three conditions: (1) setting unanticipated sidestep cutting at 40-50°; on pitch (2) football-specific exercises (F-EX) (3) games (F-GAME). A hierarchical two-level random effect model Statistical Parametric Mapping compare among conditions. Waveform consistency investigated Pearson's correlation coefficient standardized z-score vector. In-lab differed from ones, while latter similar overall shape peaks. Lower sagittal plane range motion, greater ankle eversion, pelvic rotation found for (p < 0.044). largest landing weight acceptance. biomechanical lab suggest application context-related adaptations implications strategies.HighlightsTalented youth showed kinematical condition thus adopting a motor strategy.Lower field. Such pertain mechanism strategies.Preventative training should support adoption non-linear learning stimulate self-organization adaptability.It recommended test an environment improve subsequent primary programmes.

Язык: Английский

---

Fifty years of performance‐related sports biomechanics research

Journal of Biomechanics, Год журнала: 2023, Номер 155, С. 111666 - 111666

Опубликована: Май 27, 2023

Over the past fifty years there has been considerable development in motion analysis systems and computer simulation modelling of sports movements while relevance importance functional variability technique become increasingly recognised. Technical developments for experimental work have led to increased, still increasing, subject numbers. Increased subjects per study give better statistical power, ability utilise different data analyses, thus determination more subtle nuanced factors. The overall number studies also increased massively. Most actions sport can, have, studied at some level with even challenging ones, such as player on impacts, having developing research. Computer models ranged from simple (one or two segment) very complex musculoskeletal used parameters ranging generic individual-specific. Simple given insights into key mechanics movement individual-specific model optimisations improve athlete performance. Our depth understanding techniques across a wide range sports. In future is likely be use markerless capture, parameters, consideration motor control aspects technique.

Язык: Английский

---

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

npj Digital Medicine, Год журнала: 2023, Номер 6(1)

Опубликована: Март 18, 2023

Abstract Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate risk rehabilitation progress after ACLR; however, lab-based measurements expensive inaccessible to most people. Portable sensors such as wearables cameras be deployed during sporting activities, in clinics, patient homes. Although many portable sensing approaches have demonstrated promising results various assessments related injury, they not yet been widely adopted tools for out-of-lab assessment. The purpose of this review is summarize research on applied ACLR offer our perspectives new opportunities future development. We identified 49 original articles ACL-related assessment; the common modalities were inertial measurement units, depth cameras, RGB cameras. studies combined with direct feature extraction, physics-based modeling, or machine learning estimate a range parameters (e.g., knee kinematics kinetics) jump-landing tasks, cutting, squats, gait. Many reviewed depict proof-of-concept methods potential clinical applications including screening, prevention training, By synthesizing these results, we describe important that exist validation existing approaches, using sophisticated modeling techniques, standardization data collection, creation large benchmark datasets. If successful, advances will enable widespread use portable-sensing identify factors, mitigate high-risk movements prior optimize paradigms.

Язык: Английский

---

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Sensors, Год журнала: 2023, Номер 23(9), С. 4229 - 4229

Опубликована: Апрель 24, 2023

Abnormal posture or movement is generally the indicator of musculoskeletal injuries diseases. Mechanical forces dominate injury and recovery processes tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition force (typically represented by ground reaction force, joint force/torque, muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose present study to summarize recent achievements in application IMUs biomechanics, with an emphasis mechanical estimation. methodology adopted such applications, including pre-processing, noise suppression, classification models, force/torque corresponding effects, are reviewed. extent applications daily assessment, disease diagnosis, rehabilitation, exoskeleton control strategy development illustrated discussed. More importantly, technical feasibility opportunities prediction using IMU-based devices indicated highlighted. With novel adaptive networks deep accurate can become a research field worthy further attention.

Язык: Английский

---

IMU and Smartphone Camera Fusion for Knee Adduction and Knee Flexion Moment Estimation During Walking

IEEE Transactions on Industrial Informatics, Год журнала: 2022, Номер 19(2), С. 1445 - 1455

Опубликована: Июль 11, 2022

Wearable sensing and computer vision could move biomechanics from specialized laboratories to natural environments, but better algorithms are needed extract meaningful outcomes these emerging modalities. In this article, we present new models for estimating biomechanical outcomes—the knee adduction moment (KAM) flexion (KFM)—from fusion of smartphone cameras wearable inertial measurement units (IMUs) among young healthy nonobese males. A deep learning model was developed features, fuse multimodal data, estimate KAM KFM. Walking data 17 subjects were recorded with eight IMUs two cameras. The that used IMU-camera significantly more accurate than those using or alone. root-mean-square errors the 0.49 $\%\;\mathbf {BW}\cdot \mathbf {BH}$ 0.66 KFM estimation, which lower clinically significant thresholds. With larger diverse enable assessment moments in clinics homes.

Язык: Английский

---

Validation of OpenCap on lower extremity kinematics during functional tasks

Journal of Biomechanics, Год журнала: 2025, Номер unknown, С. 112602 - 112602

Опубликована: Фев. 1, 2025

Язык: Английский

---

Effects of Inertial Measurement Unit Location on the Validity of Vertical Acceleration Time-Series Data and Jump Height in Countermovement Jumping

Signals, Год журнала: 2025, Номер 6(1), С. 11 - 11

Опубликована: Март 3, 2025

Inertial measurement units (IMUs) are an example of practical technology for measuring countermovement jump (CMJ) performance, but there is a need to enhance their validity. One potential strategy achieve this advancing the literature on IMU placement. Many studies opt position single anatomical landmarks rather than determining placement based anthropometric principles, despite knowledge that linear mechanics act through segmental centers mass human body. The purpose study was evaluate impact positioning sensors approximate trunk and lower-extremity validity vertical acceleration measurements height (JH) estimation during CMJs. Thirty young adults (female n = 10, 21.3 (3.8) years, 166.1 (4.1) cm, 67.6 (11.3) kg; male 20, 22.0 (2.6) 179.2 (6.4) 83.5 (17.1) kg) from university setting participated in study. Seven IMUs were positioned at trunk, thighs, shanks, feet. Using data these sensors, 15 whole-body center models developed, including 1-, 2-, 3-, 4-segment configurations derived three lower-body segments. root mean square error (RMSE) calculated each model by comparing its against obtained force platform. JH estimates using take-off velocity method compared across platform systematic bias. RMSE values best-performing analyzed main effects one-way analyses variance. best performing 2-segment (trunk shanks; 2.1 ± 1.3 m × s−2) 3-segment (trunk, feet; 2.0 1.2 returned significantly lower 1- segment (trunk; 3.0 1.4 (p 0.021–0.041). No bias detected between those 0.91–0.99). Positioning multiple improved time-series findings highlight importance anthropometric-based enhancing accuracy without introducing

Язык: Английский

---

Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network

Arthroplasty Today, Год журнала: 2025, Номер 33, С. 101656 - 101656

Опубликована: Апрель 15, 2025

Язык: Английский

---

Real-Time Ground Reaction Force and Knee Extension Moment Estimation During Drop Landings Via Modular LSTM Modeling and Wearable IMUs

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(7), С. 3222 - 3233

Опубликована: Апрель 27, 2023

This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- double-leg drop landings via wearable inertial measurement units (IMUs) machine learning. A real-time, modular LSTM model with four sub-deep neural networks was developed to estimate vGRF KEM. Sixteen subjects wore eight IMUs on the chest, waist, right left thighs, shanks, feet performed landing trials. Ground embedded plates an optical motion capture system were used for training evaluation. During single-leg landings, accuracy KEM R2 = 0.88 ± 0.12 0.84 0.14, respectively, 0.85 0.11 0.12, respectively. The best estimations optimal unit number (130) require placed selected locations landings. a leg only needs five leg's shank, thigh, foot. proposed LSTM-based optimally-configurable can accurately in relatively low computational cost tasks. investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening intervention programs.

Язык: Английский

---