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Optical motion capture accuracy is task-dependent in assessing wrist motion.

ABSTRACT: Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA). This study examined STA during wrist motion by evaluating the agreement between OMC and biplanar videoradiography (BVR). Nine subjects completed 7 different wrist motion tasks: doorknob rotation to capture supination and pronation, radial-ulnar deviation, flexion-extension, circumduction, hammering, and pitcher pouring. BVR and OMC captured the motion simultaneously. Wrist kinematics were quantified using helical motion parameters of rotation and translation, and Bland-Altman analysis quantified the mean difference (bias) and 95% limit of agreement (LOA). The rotational bias of doorknob pronation, a median bias of -4.9°, was significantly larger than the flexion-extension (0.7°, p < 0.05) and radial-ulnar deviation (1.8°, p < 0.01) tasks. The rotational LOA range was significantly smaller in the flexion-extension task (5.9°) compared to pitcher (11.6°, p < 0.05) and doorknob pronation (17.9°, p < 0.05) tasks. The translation bias did not differ between tasks. The translation LOA range was significantly larger in circumduction (9.8°) compared to the radial-ulnar deviation (6.3°, p < 0.05) and pitcher (3.4°, p < 0.05) tasks. While OMC technology has a wide-range of successful applications, we demonstrated it has relatively poor agreement with BVR in tracking wrist motion, and that the agreement depends on the nature and direction of wrist motion.

SUBMITTER: McHugh B

PROVIDER: S-EPMC8089049 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Optical motion capture accuracy is task-dependent in assessing wrist motion.

McHugh Brian B Akhbari Bardiya B Morton Amy M AM Moore Douglas C DC Crisco Joseph J JJ

Journal of biomechanics 20210306

Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA). This study examined STA during wrist motion by evaluating the agreement between OMC and biplanar videoradiography (BVR). Nine subjects completed 7 different wrist motion tasks: doorknob rotation to capture supination and pronation, radial-ulnar deviation, f ...[more]

PMID: 33752132

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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'.

Project description:BackgroundInertial Measurement Unit (IMU)-based wearable sensors have found common use to track arm activity in daily life. However, classifying a high number of arm motions with single IMU-based systems still remains a challenging task. This paper explores the possibility to increase the classification accuracy of these systems by incorporating a thermal sensor. Increasing the number of arm motions that can be classified is relevant to increasing applicability of single-device wearable systems for a variety of applications, including activity monitoring for athletes, gesture control for video games, and motion classification for physical rehabilitation patients. This study explores whether a thermal sensor can increase the classification accuracy of a single-device motion classification system when evaluated with healthy participants. The motions performed are reproductions of exercises described in established rehabilitation protocols.MethodsA single wrist-mounted device was built with an inertial sensor and a thermal sensor. This device was worn on the wrist, was battery powered, and transmitted data over Bluetooth to computer during recording. A LabVIEW Graphical User Interface (GUI) instructed the user to complete 24 different arm motions in a pre-randomized order. The received data were pre-processed, and secondary features were calculated on these data. These features were processed with Principal Component Analysis (PCA) for dimensionality reduction and then several machine learning models were applied to select the optimal model based on speed and accuracy. To test the effectiveness of the scheme, 11 healthy subjects participated in the trials.ResultsAverage personalized classification model accuracies of 93.55% were obtained for 11 healthy participants. Generalized model accuracies of 82.5% indicated that the device can classify arm motions on a user without prior training. The addition of a thermal sensor significantly increased classification accuracy of a single wrist-mounted inertial device, from 75 to 93.55%, (F(1,20)?=?90.53, p?=?7.25e-09).ConclusionThis study found that the addition of the thermal sensor improved the classification accuracy of 24 arm motions from 75 to 93.55% for a single-device system. Our results provide evidence that a single device can be used to classify a relatively large number of arm motions from arm rehabilitation protocols. While this study provides a conceptual proof-of-concept with a healthy population, additional investigation is required to evaluate the performance of this system for specific applications, such as activity classification for physically affected stroke survivors undergoing home-based rehabilitation.

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Optical motion capture accuracy is task-dependent in assessing wrist motion.

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Optical motion capture accuracy is task-dependent in assessing wrist motion.

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