Development of an optical motion-capture system for 3D gait analysis

Mihradi, Sandro; Ferryanto, F.; Dirgantara, Tatacipta; Mahyuddin, Andi Isra

doi:10.1109/icici-bme.2011.6108633

Cited by 18 publications

(10 citation statements)

References 4 publications

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“…Currently, a marker-based technique of optical motion capture is the most common technique used in motion analysis because of its relatively high accuracy [1][2]. The marker-based optical motion capture system is widely used in human movement analysis, mainly in gait analysis [3][4]. Moreover, the marker-based optical motion capture system is also used in swimming movement analysis.…”

Section: Introductionmentioning

confidence: 99%

Markerless Optical Motion Capture System for Asymmetrical Swimming Stroke

Ferryanto

Mahyuddin

Nakashima

2022

J. Eng. Technol. Sci.

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This work presents the development of a markerless optical motion capture system of the front-crawl swimming stroke. The system only uses one underwater camera to record swimming motion in the sagittal plane. The participant in this experiment was a swimmer who is active in the university’s swimming club. The recorded images were then segmented to obtain silhouettes of the participant by a Gaussian Mixture Model. One of the swimming images was employed to generate a human body model that consists of 15 segments. The silhouette and model of the participant were subjected to an image matching process. The shape of the body segment was used as the feature in the image matching. The model was transformed to estimate the pose of the participant. The intraclass correlation coefficient between the results of the developed system and references were evaluated. In general, all body segments, except head and trunk, had a correlation coefficient higher than 0.95. Then, dynamics analysis by SWUM was conducted based on the joint angle acquired by the present work. The simulation implied that the developed system was suitable for daily training of athletes and coaches due to its simplicity and accuracy.

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Section: Introductionmentioning

confidence: 99%

Markerless Optical Motion Capture System for Asymmetrical Swimming Stroke

Ferryanto

Mahyuddin

Nakashima

2022

J. Eng. Technol. Sci.

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“…To capture the quality of gait, a significant research effort has been made towards achieving a comprehensive understanding of spatio-temporal gait characteristics, such as motion capture systems [27], [28], pressure mats [29], [30], and wearable inertial sensors [31]- [33]. Compared to traditional subjective visual observations by therapists, instrumented assessments allow for a more objective assessment of gait characteristics [34].…”

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confidence: 99%

Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

Wu¹,

Maurenbrecher²,

Schaer³

et al. 2021

Preprint

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<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

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“…Mechanical instruments [4]- [14], marker-based optical systems [15]- [20], force sensing walkways [21]- [25], and electromyography (EMG) [25]- [28] allow detailed quantification of movement and the cause of motion. Fig.…”

Section: A Sensing Technologiesmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

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Development of an optical motion-capture system for 3D gait analysis

Cited by 18 publications

References 4 publications

Markerless Optical Motion Capture System for Asymmetrical Swimming Stroke

Markerless Optical Motion Capture System for Asymmetrical Swimming Stroke

Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

Wearable Sensing for Solid Biomechanics

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