Kinematic Analysis of a Six-Degrees-of-Freedom Model Based on ISB Recommendation: A Repeatability Analysis and Comparison with Conventional Gait Model

Żuk, Magdalena; Pezowicz, Celina

doi:10.1155/2015/503713

Cited by 35 publications

(15 citation statements)

References 28 publications

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“…A virtual segment method was used to calibrate actual joint angles according to the recommendation of Visual 3D [33]. Joint angles were filtered using a 4th order Butterworth filter with a cutoff frequency of 6 Hz, following previous recommendation [34].…”

Section: Methodsmentioning

confidence: 99%

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Gholami

Rezaei

Cuthbert

et al. 2019

Sensors

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Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

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Section: Methodsmentioning

confidence: 99%

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Gholami

Rezaei

Cuthbert

et al. 2019

Sensors

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“…The Helen HayesDavis marker set (HH) was used to follow center-of-gravity displacements. 10 Jump height was given by the difference between the sacrum marker height in upright position before execution and the apex of the airborne phase. estimate additional random error associated with motion-capture on a given jump attempt), and the residual.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of Jump-Mat Systems for Measuring Jump Height

Pueo¹,

Lipińska²,

Jiménez-Olmedo³

et al. 2017

International Journal of Sports Physiology and Performance

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word count: 250 Text-only word count: 3407Number of figures and tables: 2 "Accuracy of Jump-Mat Systems for Measuring Jump Height" by Pueo B et al. International Journal of Sports Physiology and Performance © 2016 Human Kinetics, Inc. AbstractPurpose: Vertical-jump tests are commonly used to evaluate lower-limb power of athletes and non-athletes. Several types of equipment are available for this purpose. Here we compared the error of measurement of two jump-mat systems (Chronojump-Boscosystem and Globus Ergo Tester) with that of a motion-capture system as a criterion. Additionally we determined the modifying effect of foot length on jump height. Methods: Thirty-one young adult males alternated four countermovement jumps with four squat jumps. Mean jump height and standard deviations representing technical error of measurement arising from each device and variability arising from the subjects themselves were estimated with a novel mixed model and evaluated via standardization and magnitude-based inference. Results: The jump-mat systems produced nearly identical measures of jump height (differences in means and in technical errors of measurement 1 mm). Countermovement and squat-jump height were both 13.6 cm higher with motion capture (90% confidence limits ±0.3 cm), but this very large difference was reduced to small unclear differences when adjusted to a foot length of zero. Variability in countermovement and squat-jump height arising from the subjects was small (1.1 and 1.5 cm respectively, 90% confidence limits ±0.3 cm); technical error of motion capture was similar in magnitude (1.7 and 1.6 cm, ±0.3 and ±0.4 cm), while that of the jump mats was similar or smaller (1.2 and 0.3 cm, ±0.5 and ±0.9 cm). Conclusions: The jump-mat systems provide trustworthy measurements for monitoring changes in jump height. Foot length can explain the substantially higher jump height observed with motion capture.

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“…The five cycles from Vicon and IMUs were synchronized using the positive peak value in the sagittal and frontal planes. The raw data were filtered by a fourth-order Butterworth lowpass filter with a cut-off frequency of 6 Hz, following the recommendation of previous studies [18], to attenuate unwanted noise. Data analysis was performed in Matlab software for running for both sagittal and frontal planes of movement.…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

Measurement of Ankle Joint Movements using IMUs during Running

Kim¹,

Hong²,

Oh³

et al. 2021

Preprint

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Gait analysis has historically been implemented in laboratory settings with expensive instruments; however, recently, wearable sensors have allowed the integration into clinical applications and use in daily activities. Previous studies have shown poor validity of ankle joints using inertial measurement units (IMUs), especially for small movement ranges. The purpose of this study was to validate the ability of commercially available IMUs to accurately measure the ankle joint angles during running. Ten healthy subjects participated in the study. Validation was performed by comparing the ankle joint angles measured using the wearable device with those obtained using the gold standard motion capture system during running. Reliability was evaluated using the intraclass correlation coefficient and standard error of measurement, whereas validity was evaluated using Pearson coefficient correlation method. Day-to-day reliability was excellent in the two planes for ankle joints. Validity was good in both sagittal and frontal planes for ankle joints. The results suggested that the developed device might be used as an alternative tool to the 3D motion capture system.

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Kinematic Analysis of a Six-Degrees-of-Freedom Model Based on ISB Recommendation: A Repeatability Analysis and Comparison with Conventional Gait Model

Cited by 35 publications

References 28 publications

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Accuracy of Jump-Mat Systems for Measuring Jump Height

Measurement of Ankle Joint Movements using IMUs during Running

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