Effect of tibia marker placement on knee joint kinematic analysis

Wen, Yu-Hui; Huang, Hongshi; Yu, Yuanyuan; Zhang, Si; Yang, Jie; Ao, Yingfang; Xia, Shihong

doi:10.1016/j.gaitpost.2017.11.020

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…In acknowledgment of this, we included sample size as an extra degree of freedom in our analysis. We chose sample sizes of n = 10, n = 25 and n = 50, as these represent the low, mid and upper ranges of sample sizes typically used in biomechanical studies (Besier, Lloyd, and Ackland 2003;Ithurburn et al 2017;Sankey et al 2015;Wen et al 2018). The sensitivity analysis procedure outlined above was repeated for each variable in data sets A, B and C, at each sample size, resulting in a total of nine sensitivity analyses.…”

Section: Sensitivity Analysismentioning

confidence: 99%

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

McFadden

Daniels

Strike

2020

Journal of Biomechanics

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The conventional gait model (CGM) refers to several closely related biomechanical models used in the objective analysis of human motion. Their use has become popular in the analysis of change of direction tasks to inform best practice in the prevention and rehabilitation of anterior cruciate ligament injury. As externally-placed markers define segment axes origins and orientations, kinematic and kinetic outputs from the CGM are sensitive to marker placement. The aim of this investigation was to quantify the sensitivity of lower extremity kinematics and knee moments to systematic differences in marker placement across the stance phase of a change of direction task. Systematic anterior/posterior displacements were applied to the lateral thigh, femoral epicondyle and tibia markers in software. One-dimensional statistical parametric mapping was used to determine the effect of marker placement across the entire stance phase of a 90° change of direction task. Marker placement error within previously reported inter-tester variability ranges caused significant differences in knee abduction moment, hip rotation angle, knee rotation angle, ankle rotation angle and ankle abduction angle across various periods of stance. Discrete measures of these variables have been associated with increased frontal plane knee loading during change of direction, considered a key mechanism of anterior cruciate ligament injury. Systematic differences in marker placement may lead to incorrect group statistical inferences in such discrete measures.

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Section: Sensitivity Analysismentioning

confidence: 99%

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

McFadden

Daniels

Strike

2020

Journal of Biomechanics

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“…The processing of temporal patterns and kinematics has been the most common type of study. The methods used to research gait analysis are presented by Wen Y. et al [6]. This paper compares knee kinematics based on different In phase IV, the two parts of the leg begin to move in the same direction.…”

Section: Related Workmentioning

confidence: 99%

Human Signature Identification Using IoT Technology and Gait Recognition

et al. 2021

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This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones.

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“…However, body segment composition depends entirely on the locations of attached markers in the 3D motion analysis system (Andersen et al, 2010;Labbe et al, 2008;Schulz and Kimmel, 2010;Cereatti et al, 2006). A number of studies have shown that marker misplacement and inaccuracies have a significant impact on gait kinematic measurements (Croce et al, 2005;Stagni et al, 2006;Jensen et al, 2016;Wen et al, 2018;Mickaël et al, 2020). Consequently, there are questions regarding the validity and reliability of measurements obtained Sentong WANG*, Yunan CAI*, Kazunori HASE**, Kazuo UCHIDA***, Daisuke KONDO***, Tsuguya SAITOU*** and Susumu OTA****…”

Section: Introductionmentioning

confidence: 99%

Estimation of knee joint angle during gait cycle using inertial measurement unit sensors: a method of sensor-to-clinical bone calibration on the lower limb skeletal model

Wang

Cai

Hase

et al. 2022

JBSE

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This study describes a new calibration procedure that provides a simple and accurate method to place and align an inertial measurement unit (IMU) sensor with the relative clinical bone frame on a developed lower limb skeletal model. The calibration can be easily replicated without the need for additional tools and, more importantly, it is independent of the joint attachment position. The proposed method was validated under realistic gait tests using eight healthy subjects in a motion capture system environment. Calculated results showed that the joint angles were correctly measured after applying the calibration method. Therefore, the proposed calibration procedure is an interesting alternative to solve the alignment problem when using IMU sensors for gait analysis.

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Effect of tibia marker placement on knee joint kinematic analysis

Cited by 11 publications

References 37 publications

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

Human Signature Identification Using IoT Technology and Gait Recognition

Estimation of knee joint angle during gait cycle using inertial measurement unit sensors: a method of sensor-to-clinical bone calibration on the lower limb skeletal model

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