Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge

Roeck, Joris De; Houcke, Jan Van; Almeida, Diogo; Galibarov, Pavel E.; Roeck, Lynn De; Audenaert, Emmanuel

doi:10.3389/fbioe.2020.00233

Cited by 11 publications

(11 citation statements)

References 40 publications

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“…The study was approved by the Ghent University Hospital Ethics Committee and informed consent was obtained from all participants. As demonstrated in previous work on lunge dynamics, a minimal sample size of 50 is required to reproduce biomechanical waveform data at a population covering level ( De Roeck et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Statistical shape modeling enables to describe individualized bone geometry more precisely than consensus bone geometry or linearly scaled generic bone models ( Audenaert et al, 2019a ; Cerveri et al, 2020 ; Nolte et al, 2020 ). Similarly, statistical modeling of kinematics by non-linear methods as well as improvements in curve alignment methods during the pre-processing phase, might provide more reliable and stronger correlations between human anatomy and motion as opposed to previous reports ( Freedman and Sheehan, 2013 ; Moissenet et al, 2019 ; De Roeck et al, 2020 ). Nevertheless, acquiring perfect kinematic data in an ethically responsible way remains a sticking point.…”

Section: Introductionmentioning

confidence: 95%

“…In contrast, skin-mounted marker motion capturing does not cause any associated hazards and therefore is the standard for healthy cohorts to date. However, the accuracy of marker-based or optoelectronic motion capture systems is affected by soft tissue artifacts ( Andersen et al, 2009 ; Leardini et al, 2017 ; Begon et al, 2018 ; Galvin et al, 2018 ; Niu et al, 2018 ; Van Houcke et al, 2019 ; De Roeck et al, 2020 ). Several approaches have been developed to deal with these errors, such as multibody kinematics optimization methods ( Lu and O’Connor, 1999 ; Andersen et al, 2009 ; Leardini et al, 2017 ; Begon et al, 2018 ) or by combining the motion tracking system with ultrasound ( Niu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Roeck

Duquesne

Houcke

et al. 2021

Front. Bioeng. Biotechnol.

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Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics.Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated.Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°.Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Roeck

Duquesne

Houcke

et al. 2021

Front. Bioeng. Biotechnol.

Self Cite

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“…The musculoskeletal modeling and simulation of squatting were based on the demo provided by AnyBody Technology (Wu et al, 2020). The parameters in the musculoskeletal model were modified according to the subjects' body (De Roeck et al, 2020): The time period of a squat cycle and frames per second for simulation were set to 3 s and 30, respectively, minimum and maximum knee flexion angle of a squat cycle were set as 10 • and 135 • , squat distance between toe medial nodes shoulder width ratio and squat angle foot rotation were set to 1.6 and 5 • separately.…”

Section: Simulationmentioning

confidence: 99%

A Semi-active Exoskeleton Based on EMGs Reduces Muscle Fatigue When Squatting

Wang

Chen

et al. 2021

Front. Neurorobot.

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In dynamic manufacturing and warehousing environments, the work scene made it impossible for workers to sit, so workers suffer from muscle fatigue of the lower limb caused by standing or squatting for a long period of time. In this paper, a semi-active exoskeleton used to reduce the muscle fatigue of the lower limb was designed and evaluated. (i) Background: The advantages and disadvantages of assistive exoskeletons developed for industrial purposes were introduced. (ii) Simulation: The process of squatting was simulated in the AnyBody.7.1 software, the result showed that muscle activity of the gluteus maximus, rectus femoris, vastus medialis, vastus lateralis, vastus intermedius, and erector spinae increased with increasing of knee flexion angle. (iii) Design: The exoskeleton was designed with three working modes: rigid-support mode, elastic-support mode and follow mode. Rigid-support mode was suitable for scenes where the squatting posture is stable, while elastic-support mode was beneficial for working environments where the height of squatting varied frequently.The working environments were identified intelligently based on the EMGs of the gluteus maximus, and quadriceps, and the motor was controlled to switch the working mode between rigid-support mode and elastic-support mode. In follow mode, the exoskeleton moves freely with users without interfering with activities such as walking, ascending and descending stairs. (iv) Experiments: Three sets of experiments were conducted to evaluate the effect of exoskeleton. Experiment one was conducted to measure the surface electromyography signal (EMGs) in both condition of with and without exoskeleton, the root mean square of EMGs amplitude of soleus, vastus lateralis, vastus medialis, gastrocnemius, vastus intermedius, rectus femoris, gluteus maximus, and erector spinae were reduced by 98.5, 97.89, 80.09, 77.27, 96.73, 94.17, 70.71, and 36.32%, respectively, with the assistance of the exoskeleton. The purpose of experiment two was aimed to measure the plantar pressure with and without exoskeleton. With exoskeleton, the percentage of weight through subject's feet was reduced by 63.94, 64.52, and 65.61% respectively at 60°, 90°, and 120° of knee flexion angle, compared to the condition of without exoskeleton. Experiment three was purposed to measure the metabolic cost at a speed of 4 and 5 km/h with and without exoskeleton. Experiment results showed that the average additional metabolic cost introduced by exoskeleton was 2.525 and 2.85%. It indicated that the exoskeleton would not interfere with the movement of the wearer Seriously in follow mode. (v) Conclusion: The exoskeleton not only effectively reduced muscle fatigue, but also avoided interfering with the free movement of the wearer.

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“…Moreover, the demand for a sense of balance is added compared to the squat because it performs a movement [13]. In addition, due to the movement of the center of gravity (COG) on the ground, the lunge regulates the external force applied to the body between movements, and many muscles are mobilized to maintain stability [14]. Therefore, the types of lunges selected for exercise design for various purposes are classified into the forward lunge, backward lunge, and side lunge according to the direction of the lower limb to be moved [13].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of the Biomechanical Factors in Range of Motion, Muscle Activity, and Vertical Ground Reaction Force between a Forward Lunge and Backward Lunge

Park

Huang

Song

et al. 2021

PTRS

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The purpose of this study was to examined the kinematic relationship and differences through the range of motion (ROM), muscle activity, and vertical ground reaction force (VGRF) during forward and backward lunge movements, which are effective in improving muscle strength and balance ability of the lower extremities, and to provide clinical information on more efficient lunge movements. Design: Cross-sectional study Methods: Fifteen adult males who met the selection criteria were tested for their dominant feet.Forward and backward lunges were then performed, and the ROM, muscle activity, and VGRF were measured for kinematic analysis during the lunge movement.The differences betweenthe forward lunge and backward lunge intervention were examined using a paired t-test. Results: A significant increase in the ROM of the knee and ankle was observed during the forward and backward lunges (p＜0.05). In addition, in terms of the muscle activity, the peak values of the vastus medialis oblique (VMO) and VGRF also showed a significant increase in the forward lunge compared to the backward lunge (p＜0.05). Conclusions: This study showed an increase in VGRF peak value, knee and ankle ROM, and VMO muscle activity during forward lunge. Based on these results, it is considered necessary to apply differently depending on the direction of progress in consideration of the musculoskeletal situation and physical ability during the lunge movement.

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Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge

Cited by 11 publications

References 40 publications

Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

A Semi-active Exoskeleton Based on EMGs Reduces Muscle Fatigue When Squatting

Comparative Study of the Biomechanical Factors in Range of Motion, Muscle Activity, and Vertical Ground Reaction Force between a Forward Lunge and Backward Lunge

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