Dataset Information

Measurements of cervical range of motion using an optical motion capture system: Repeatability and validity.

ABSTRACT: The current study aimed to assess the repeatability and validity of cervical range of motion (CROM) measurements using an optical motion capture system (OMCS), compared with a CROM device. A total of 20 healthy volunteers were selected and enrolled in the current study after informed consent was received. The motion of the cervical spine in all directions was measured using the OMCS and CROM devices. Reproducibility of data was assessed using the intra-group correlation coefficient (ICC), standard error of measurement (SEM) and minimum detectable change (MDC). Validity was assessed using the coefficient of determination (R2) in combination with Pearson's correlation coefficient. Bland-Altman plot were presented for the two measurement methods. The range of motion (ROM) was measured by using the OMCS and the CROM device during the same session. Both procedures evidenced high ICCs [OMCS: ICC (1,2) =0.802-0.981; CROM device: ICC (1,2) =0.768-0.948], low SEM values (OMCS: 0.98°-1.38°; CROM device: 1.04°-2.45°) and low MDC values (OMCS: 2.72°-3.81°; CROM device: 2.89°-6.78°). A high R2 (0.568-0.882) and Pearson's correlation coefficient (0.753-0.939) were determined. The Bland-Altman plots demonstrated that most of the data were within the 95% consistency limit. In summary, the OMCS has good repeatability and validity when measuring CROM and is an effective way to evaluate cervical vertebral range of motion.

SUBMITTER: Feng M

PROVIDER: S-EPMC6862302 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Measurements of cervical range of motion using an optical motion capture system: Repeatability and validity.

Feng Minshan M Liang Long L Sun Wu W Liu Guang Wei GW Yin Xunlu X Han Tao T Wei Xu X Zhu Liguo L

Experimental and therapeutic medicine 20191015 6

The current study aimed to assess the repeatability and validity of cervical range of motion (CROM) measurements using an optical motion capture system (OMCS), compared with a CROM device. A total of 20 healthy volunteers were selected and enrolled in the current study after informed consent was received. The motion of the cervical spine in all directions was measured using the OMCS and CROM devices. Reproducibility of data was assessed using the intra-group correlation coefficient (ICC), standa ...[more]

PMID: 31777530

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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'.

Dataset Information

Measurements of cervical range of motion using an optical motion capture system: Repeatability and validity.

Publications

Measurements of cervical range of motion using an optical motion capture system: Repeatability and validity.

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