Dataset Information

Agreement between An Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test.

ABSTRACT: The validity of an inertial sensor-based motion capture system (IMC) has not been examined within the demands of a sports-specific field movement test. This study examined the validity of an IMC during a field test (VU®) by comparing it to an optical marker-based motion capture system (MMC). Expected accuracy and precision benchmarks were computed by comparing the outcomes of a linear and functional joint fitting model within the MMC. The kinematics from the IMC in sagittal plane demonstrated correlations (r2) between 0.76 and 0.98 with root mean square differences (RMSD) < 5, only the knee bias was within the benchmark. In the frontal plane, r2 ranged between 0.13 and 0.80 with RMSD < 10, while the knee and hip bias was within the benchmark. For the transversal plane, r2 ranged 0.11 to 0.93 with RMSD < 7, while the ankle, knee and hip bias remained within the benchmark. The findings indicate that ankle kinematics are not interchangeable with MMC, that hip flexion and pelvis tilt higher in IMC than MMC, while other measures are comparable to MMC. Higher pelvis tilt/hip flexion in the IMC can be explained by a one sensor tilt estimation, while ankle kinematics demonstrated a considerable level of disagreement, which is likely due to four reasons: A one sensor estimation, sensor/marker attachment, movement artefacts of shoe sole and the ankle model used.

SUBMITTER: Richter C

PROVIDER: S-EPMC7038694 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Agreement between An Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test.

Richter Chris C Daniels Katherine A J KAJ King Enda E Franklyn-Miller Andrew A

Sensors (Basel, Switzerland) 20200204 3

The validity of an inertial sensor-based motion capture system (IMC) has not been examined within the demands of a sports-specific field movement test<b>.</b> This study examined the validity of an IMC during a field test (VU®) by comparing it to an optical marker-based motion capture system (MMC). Expected accuracy and precision benchmarks were computed by comparing the outcomes of a linear and functional joint fitting model within the MMC. The kinematics from the IMC in sagittal plane demonstr ...[more]

PMID: 32033123

Similar Datasets

Project description:Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.

Project description:BackgroundQuestions remain regarding the traditional protocols used in rehabilitation and clearance for return to sports after anterior cruciate ligament reconstruction (ACLR).Purpose/hypothesisTo investigate the impact on injury rates after return to sports by developing and validating a Safer Return to Play Following ACL Reconstruction Checklist consisting of subjective and objective functional tests that can be quickly and easily implemented into a sports medicine practice. It was hypothesized that patients who successfully passed the checklist before returning to sports would experience lower rates of ipsilateral and contralateral knee injuries at a 2-year follow-up as compared with patients who returned to play before completing the checklist.Study designCohort study; Level of evidence, 2.MethodsFirst, a systematic review was performed to generate a list of the most common outcome measures used to assess return to play after ACLR. To refine our checklist, we conducted a survey with an expert panel of 10 medical professionals utilizing the Delphi technique. After the creation of the checklist, validation was performed by prospectively evaluating patients who had undergone ACLR for injury of the ipsilateral or contralateral knee, with a minimum 2-year follow-up.ResultsAfter our systematic review of 60 studies, 7 criteria were included in the final checklist. During the period studied, October 2014 to December 2017, a total of 222 patients met the inclusion criteria and were enrolled in the study. At a minimum 2 years of follow-up, there were 146 patients who successfully passed the checklist and 38 who did not. Overall, 24 (16.4%) patients who had passed the checklist sustained an injury to either knee, as compared with 10 (26.3%) from the group that did not pass the checklist (P = .162). Of the group that passed the checklist, 8 (5.5%) patients sustained an injury to the ipsilateral knee, as compared with 7 (18.4%) in the group that did not pass (P = .017).ConclusionProspective validation of our checklist demonstrated that patients who successfully passed the checklist before returning to play experienced a significantly lower incidence of ipsilateral anterior cruciate ligament injury as compared with patients who did not pass the checklist.

Project description:Measuring motion of the human foot presents a unique challenge due to the large number of closely packed bones with congruent articulating surfaces. Optical motion capture (OMC) and multi-segment models can be used to infer foot motion, but might be affected by soft tissue artifact (STA). Biplanar videoradiography (BVR) is a relatively new tool that allows direct, non-invasive measurement of bone motion using high-speed, dynamic x-ray images to track individual bones. It is unknown whether OMC and BVR can be used interchangeably to analyse multi-segment foot motion. Therefore, the aim of this study was to determine the agreement in kinematic measures of dynamic activities. Nine healthy participants performed three walking and three running trials while BVR was recorded with synchronous OMC. Bone position and orientation was determined through manual scientific-rotoscoping. The OMC and BVR kinematics were co-registered to the same coordinate system, and BVR tracking was used to create virtual markers for comparison to OMC during dynamic trials. Root mean square (RMS) differences in marker positions and joint angles as well as a linear fit method (LFM) was used to compare the outputs of both methods. When comparing BVR and OMC, sagittal plane angles were in good agreement (ankle: R2 = 0.947, 0.939; Medial Longitudinal Arch (MLA) Angle: R2 = 0.713, 0.703, walking and running, respectively). When examining the ankle, there was a moderate agreement between the systems in the frontal plane (R2 = 0.322, 0.452, walking and running, respectively), with a weak to moderate correlation for the transverse plane (R2 = 0.178, 0.326, walking and running, respectively). However, root mean squared error (RMSE) showed angular errors ranging from 1.06 to 8.31° across the planes (frontal: 3.57°, 3.67°, transverse: 4.28°, 4.70°, sagittal: 2.45°, 2.67°, walking and running, respectively). Root mean square (RMS) differences between OMC and BVR marker trajectories were task dependent with the largest differences in the shank (6.0 ± 2.01 mm) for running, and metatarsals (3.97 ± 0.81 mm) for walking. Based on the results, we suggest BVR and OMC provide comparable solutions to foot motion in the sagittal plane, however, interpretations of out-of-plane movement should be made carefully.

Dataset Information

Agreement between An Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test.

Publications

Agreement between An Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets