Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography

Cui, Weiling; Wang, Changjiang; Chen, Weiyi; Guo, Yuan; Jia, Yang; Du, Weijin; Wang, Chenyan

doi:10.3390/app9235245

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…Previous research on KAM in TIG has been inconclusive [78,[83][84][85][86]] so these results were not unsurprising. However, a recent study that used musculoskeletal modeling with TIG found no change in JRF even with a decrease in KAM so our results are consistent with their findings [39].…”

Section: Discussionsupporting

confidence: 92%

“…A study in healthy controls using TIG without RTB showed a statistically significant reduction in first peak KAM but found no reduction in JRF as estimated by a musculoskeletal model [39]. While some studies have found that KAM may provide a reasonable indicator for the JRF at the first peak of stance, the relationship between KAM and the joint contact force is not as strong for the second peak of stance [20,34].…”

Section: Introductionmentioning

confidence: 99%

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Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Prebble

Wei

Martin

et al. 2022

Journal of Biomechanical Engineering

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Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT) and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for a normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data was used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via OpenSim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. Statistically significant effects from baseline were found for the medial second peak as well as the first and second peaks in the lateral compartment. While there was a decrease in JRF in the medial compartment during the loading phase of gait for both TIG and LTL the differences were not statistically significant. MKT showed an increasing JRF in the medial compartment but was also not significant.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Prebble

Wei

Martin

et al. 2022

Journal of Biomechanical Engineering

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“…In our abnormal gait database 2 , 18 healthy volunteers (16 male and 2 female) were recruited and instructed to walk normally and imitate four pathological gait patterns (i.e., toe-in, toe-out, supination, and pronation), following the settings of previous simulation based works [58], [59], [60]. The recruited subjects were with no lower-limb injury history and did not undergo any joint instability during the course of the past six months.…”

Section: A Data Collectionmentioning

confidence: 99%

Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition

Guo

Deligianni

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are therefore prone to overfitting and the models derived cannot generalize well to new subjects. Furthermore, there is limited availability of abnormal gait data obtained from precise Motion Capture (Mocap) systems because of regulatory issues and slow adaptation of new technologies in health care. On the other hand, data captured from markerless vision sensors or wearable sensors can be obtained in home environments but noises from such devices may prevent effective extraction of relevant features. To address these challenges, we propose a cascade of deep architectures that can encode cross-modal and cross-subject transfer for abnormal gait recognition. Cross-modal transfer maps noisy data obtained from RGBD and wearable sensors to accurate four-dimensional (4D) representations of the lower limb and joints obtained from the Mocap system. Subsequently, cross-subject transfer allows to disentangle subject-specific from abnormal pattern-specific gait features based on a multi-encoder autoencoder architecture. To validate the proposed methodology, we obtained multi-modal gait data based on a multi-camera motion capture system along with synchronized recordings of Electromyography (EMG) data and 4D skeleton data extracted from a single RGBD camera. Classification accuracy was improved significantly in both Mocap and noisy modalities.

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“…2, the Subjects (6 males, 2 females) were instructed to present different walking styles on a treadmill by supplementary wedged insoles or imitation. It follows the experimental settings similar to existing gait research [2], [36], [37], [38]. In total, five Conditions were considered 1 Experiments were approved by the College Ethics Committee of Imperial College London with the reference No.…”

Section: Multi-view Lower-limb Pose Dataset a Real-world Datasetsmentioning

confidence: 99%

Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation

Gu¹,

Yang²,

Zhang³

et al. 2021

Preprint

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Accurate estimation of three-dimensional human skeletons from depth images can provide important metrics for healthcare applications, especially for biomechanical gait analysis. However, there exist inherent problems associated with depth images captured from a single view. The collected data is greatly affected by occlusions where only partial surface data can be recorded. Furthermore, depth images of human body exhibit heterogeneous characteristics with viewpoint changes, and the estimated poses under local coordinate systems are expected to go through equivariant rotations. Most existing pose estimation models are sensitive to both issues. To address this, we propose a novel approach for cross-view generalization with an occlusion-invariant semi-supervised learning framework built upon a novel rotation-equivariant backbone. Our model was trained with real-world data from a single view and unlabelled synthetic data from multiple views. It can generalize well on the real-world data from all the other unseen views. Our approach has shown superior performance on gait analysis on our ICL-Gait dataset compared to other state-of-the-arts and it can produce more convincing keypoints on ITOP dataset, than its provided "ground truth".

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Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography

Cited by 15 publications

References 39 publications

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition

Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation

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