Biomechanical effects of three types of foot orthoses in individuals with posterior tibial tendon dysfunction

Chicoine, Dominic; Bouchard, Marc; Laurendeau, Simon; Moisan, Gabriel; Belzile, Étienne L.; Corbeil, Philippe

doi:10.1016/j.gaitpost.2020.11.001

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…As hypothesised, there was no significant change in MG or TA with FOs, which has been reported previously (Barn et al, 2013;Chicoine et al, 2020;Mills et al, 2012;Murley and Bird, 2006;Murley et al, 2010;Telfer et al, 2013a). In one study FOs increased PL activity (Murley et al, 2010) and although not significant, a small to moderate effect was found in the present study for increased PL activity with FOs.…”

Section: Emgsupporting

confidence: 79%

“…Foot orthoses (FOs) alter external joint moments (Chicoine et al, 2020;Hart et al, 2020;Nester et al, 2003;Sweeney, 2016;Telfer et al, 2013b), but it is unclear how this affects muscle activation during walking. If FOs reduced the external eversion moment in early stance, less force might be required from tibialis posterior (TP) to resist eversion third law.…”

Section: Introductionmentioning

confidence: 99%

“…Specific aspects of medial arch FOs geometry may have different mechanisms that exert a therapeutic effect. For example, medial heel wedges or external rearfoot posting, could decrease external eversion moments from early stance (Chicoine et al, 2020;Hart et al, 2020;Nester et al, 2003;Telfer et al, 2013b), which may accompany decreased TP EMG amplitude. In mid-late stance increased height of FOs in the medial arch may reduce TP EMG amplitude due to reduced need for support of medial arch structures.…”

Section: Introductionmentioning

confidence: 99%

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The immediate effects of foot orthosis geometry on lower limb muscle activity and foot biomechanics

Reeves

Jones

Liu

et al. 2021

Journal of Biomechanics

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Section: Emgsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The immediate effects of foot orthosis geometry on lower limb muscle activity and foot biomechanics

Reeves

Jones

Liu

et al. 2021

Journal of Biomechanics

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“…When the arch is missing when standing, but not the arch, it is called a flexible flat foot. This posture causes the talus to slide past the calcaneus' inner side and contact the ground, which is called flat metatarsal foot [27]. This feature can be reflected in the shape of the footprint.…”

Section: Figure1 the Experiments On Plantar Pressure Dataset Acquisimentioning

confidence: 99%

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

Han

Wang

et al. 2021

Preprint

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The number of layers of deep learning (DL) increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based DNN model was applied to address these issues recently. This paper improved the RN and developed a rectified linear unit (ReLU) based conditional generative adversarial nets (cGAN) to classify plantar pressure images. A foot scan system collected the plantar pressure images, in which normal (N), planus (PL), and talipes equinovarus feet (TE) data-sets were acquired subsequently. The 9-foot types named N, PL, TE, N-PL, N-TE, PL-N, PL-TE, TE-N, and TE-PL were classified using the proposed DNN models, named resident network-based conditional generative adversarial nets (RNcGAN). It improved the RN structure firstly and the cGAN system hereafter. In the classification of plantar pressure images, the pixel-level state matrix can be direct as an input, which is different from the previous image classification task with image reduction and feature extraction. cGAN can directly output the pixels of the image without any simplification. Finally, the model achieved better results in the evaluation indicators of accuracy (AC), sensitivity (SE), and F1-measurement (F1) by comparing to artificial neural networks (ANN), k-nearest neighbor (kNN), Fast Region-based Convolution Neural Network (Fast R-CNN), visual geometry group (VGG16), scaled-conjugate-gradient convolution neural networks (SCG-CNN), GoogleNet, AlexNet, ResNet-50-177, and Inception-v3. The final prediction of class accuracy is 95.17%. Foot type classification is vital for producing comfortable shoes in the industry.

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“…The extensor and flexor muscle groups of the HJ cooperate with each other to jointly control the activities of various joints of the human lower limb, and elastic tissues such as ligaments in the human. HJ can assist the muscle tissue to support the movement of the human body [ 5 ]. Previous findings have pointed out that during walking, when the body's auxiliary joints show positive work, they can significantly reduce body EC [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Construction and Simulation of Biomechanical Model of Human Hip Joint Muscle-Tendon Assisted by Elastic External Tendon by Hill Muscle Model

Luo

Cai

et al. 2022

Computational Intelligence and Neuroscience

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Based on the Hill muscle model (HMM), a biomechanical model of human hip muscle tendon assisted by elastic external tendon (EET) was preliminarily established to investigate and analyze the biomechanical transition between the hip joint (HJ) and related muscle tendons. Using the HMM, the optimal muscle fiber length and muscle force scaling variables were introduced by means of constrained optimization problems and were optimized. The optimized HMM was constructed with human parameters of 170 cm and 70 kg. The biomechanical model simulation test of the hip muscle tendon was performed in the automatic dynamic analysis of mechanical systems (ADAMS) software to analyze and optimize the changes in the root mean square error (RMSE), biological moment, muscle moment distribution coefficient (MDC), muscle moment, muscle force, muscle power, and mechanical work of the activation curves of the hip major muscle, iliopsoas muscle, rectus femoris muscle, and hamstring muscle under analyzing the optimized HMM and under different EET auxiliary stiffnesses from the joint moment level, joint level, and muscle level, respectively. It was found that the trends of the output joint moment of the optimized HMM and the biological moment of the human HJ were basically the same, r2 = 0.883 and RMSE = 0.18 Nm/kg, and the average metabolizable energy consumption of the HJ was (243.77 ± 1.59) J. In the range of 35%∼65% of gait cycle (GC), the auxiliary moment showed a significant downward trend with the increase of EET stiffness, when the EET stiffness of the human body was less than 200 Nm/rad, the biological moment of the human HJ gradually decreased with the increase of EET stiffness, and the MDC of the iliopsoas and hamstring muscles gradually decreased; when the EET stiffness was greater than 200 Nm/rad, the increase of the total moment of the extensor muscles significantly increased, the MDC of the gluteus maximus and rectus muscles gradually increased, and the gluteus maximus and hamstring muscle moments and muscle forces gradually increased; the results show that the optimized muscle model based on Hill can reflect the law of human movement and complete the simulation test of HJ movements, which provides a new idea for the analysis of energy migration in the musculoskeletal system of the lower limb.

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Biomechanical effects of three types of foot orthoses in individuals with posterior tibial tendon dysfunction

Cited by 18 publications

References 28 publications

The immediate effects of foot orthosis geometry on lower limb muscle activity and foot biomechanics

The immediate effects of foot orthosis geometry on lower limb muscle activity and foot biomechanics

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

Construction and Simulation of Biomechanical Model of Human Hip Joint Muscle-Tendon Assisted by Elastic External Tendon by Hill Muscle Model

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