The Influence of Walking Speed on Gait Patterns During Upslope Walking

Han, Shuyang

doi:10.1166/jmihi.2015.1354

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Figure 2 shows the average waveforms of lower-limb joint angles at low, intermediate, and high walking speeds on the level and on the steepest positive and negative slopes. The joint motion pattern observed on slope walking in this study shows good agreement with results obtained in other studies (24,30,49,57). Compared with level walking, the major kinematic changes in uphill walking occur at FC, where the hip, knee, and ankle joints are more flexed.…”

Section: General Gait Parameters and Limb Kinematicssupporting

confidence: 90%

Kinematic patterns while walking on a slope at different speeds

Dewolf

Ivanenko

Zelik

et al. 2018

Journal of Applied Physiology

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During walking, the elevation angles of the thigh, shank, and foot (i.e., the angle between the segment and the vertical) covary along a characteristic loop constrained on a plane. Here, we investigate how the shape of the loop and the orientation of the plane, which reflect the intersegmental coordination, change with the slope of the terrain and the speed of progression. Ten subjects walked on an inclined treadmill at different slopes (between −9° and +9°) and speeds (from 0.56 to 2.22 m/s). A principal component analysis was performed on the covariance matrix of the thigh, shank, and foot elevation angles. At each slope and speed, the variance accounted for by the two principal components was >99%, indicating that the planar covariation is maintained. The two principal components can be associated to the limb orientation (PC1*) and the limb length (PC2*). At low walking speeds, changes in the intersegmental coordination across slopes are characterized mainly by a change in the orientation of the covariation plane and in PC2* and to a lesser extent, by a change in PC1*. As speed increases, changes in the intersegmental coordination across slopes are more related to a change in PC1 *, with limited changes in the orientation of the plane and in PC 2*. Our results show that the kinematic patterns highly depend on both slope and speed. NEW & NOTEWORTHY In this paper, changes in the lower-limb intersegmental coordination during walking with slope and speed are linked to changes in the trajectory of the body center of mass. Modifications in the kinematic pattern with slope depend on speed: at slow speeds, the net vertical displacement of the body during each step is related to changes in limb length and orientation. When speed increases, the vertical displacement is mostly related to a change in limb orientation.

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Section: General Gait Parameters and Limb Kinematicssupporting

confidence: 90%

Kinematic patterns while walking on a slope at different speeds

Dewolf

Ivanenko

Zelik

et al. 2018

Journal of Applied Physiology

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“…Furthermore, it was reported before that distance-based features might work better than angle-based features, in particular when the number of subjects is relatively low [37]. Joint angles are also prone to changes in the walking speed [48], [49]. We also observe that regardless of the feature type, both length-based and vector-based features perform better after joint correction filtering.…”

Section: Performance Comparisonsupporting

confidence: 60%

GlidarCo: gait recognition by 3D skeleton estimation and biometric feature correction of flash lidar data

Sadeghzadehyazdi¹,

Batabyal²,

Dhar³

et al. 2019

Preprint

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Gait recognition using noninvasively acquired data has been attracting an increasing interest in the last decade. Among various modalities of data sources, it is experimentally found that the data involving skeletal representation are amenable for reliable feature compaction and fast processing. Model-based gait recognition methods that exploit features from a fitted model, like skeleton, are recognized for their view and scale-invariant properties. We propose a model-based gait recognition method, using sequences recorded by a single flash lidar. Existing state-of-the-art model-based approaches that exploit features from high quality skeletal data collected by Kinect and Mocap are limited to controlled laboratory environments. The performance of conventional research efforts is negatively affected by poor data quality. We address the problem of gait recognition under challenging scenarios, such as lower quality and noisy imaging process of lidar, that degrades the performance of state-of-the-art skeleton-based systems. We present GlidarCo to attain high accuracy on gait recognition under the described conditions. A filtering mechanism corrects faulty skeleton joint measurements, and robust statistics are integrated to conventional feature moments to encode the dynamic of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the efficacy of the proposed methodology in improving gait recognition given noisy low resolution lidar data.

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