The Relationships Between Length of Stride, Step Frequency, Time of Swing and Speed of Walking for Children and Adults

Grieve, D.W.; Gear, Ruth J.

doi:10.1080/00140136608964399

Cited by 352 publications

(191 citation statements)

References 6 publications

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“…In this study, it was demonstrated that the SCI have a limited capacity to adapt to changes in speed. In the normal population, the adaptation to increases in the walking speed is generally obtained by increasing the cadence (stride frequency) and the stride length simultaneously [11][12][13][14][15][16] and there is a curvilinear relation between the cycle duration and the walking speed in normal subjects. [15][16][17] This relation was also observed in the present study.…”

Section: Discussionmentioning

confidence: 99%

“…This adaptation is generally obtained by increasing the cadence (stride frequency) and the stride length simultaneously, [11][12][13][14][15][16][17] although it is possible to adapt somewhat by modifying only one aspect. 15 The adaptation to higher walking speeds also includes greater joint angular excursions, [17][18][19][20][21] and changes in the timing or in the amplitude of the activity of muscles controlling lower limb joints.…”

Section: Introductionmentioning

confidence: 99%

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Treadmill walking in incomplete spinal-cord-injured subjects: 1. Adaptation to changes in speed

2003

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Walking in spinal-cord-injured (SCI) subjects is usually achieved at a lower speed than in normal subjects. Study design/methods: Time and distance parameters, angular displacements of lower limbs and electromyographic (EMG) activity were measured for seven normal and seven SCI subjects at several walking speeds. Analyses of variance were used for comparing groups and speeds. Objectives: First, to measure the adaptability of SCI subjects' walking pattern to different speeds (0.1-1.0 m/s), and to compare it to that of normal subjects. Second, to characterize SCI subjects' walking pattern as compared to that of normal subjects at a matched treadmill speed (0.3 m/s). Setting: University-Based Human Gait Laboratory, Montreal, Canada. Results: SCI subjects' pattern adapted to a limited range of speeds. Longer cycle duration, flexed knee at foot contact, increased hip joint flexion at foot contact and during swing, and altered coordination of hip and knee joints were found for the SCI group. At all speeds, duration of muscle activity was longer in the SCI group and the increase in amplitude of soleus EMG activity at higher speeds was not specific to push-off. The importance of matching the walking speed of SCI and normal subjects in order to differentiate the features that are a consequence of SCI subjects' reduced walking speed rather than a direct consequence of the injury is demonstrated. Conclusions: All SCI subjects could adapt to a narrow range of speeds and only three could reach the maximal tested speed. This limited maximal speed seems to be a consequence of SCI subjects having reached their limit in increasing stride length and not being able to increase stride frequency further. This limitation in increasing stride frequency is likely because of the altered neural drive. Sponsorship: Neuroscience Network of the Canadian Centre of Excellence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Treadmill walking in incomplete spinal-cord-injured subjects: 1. Adaptation to changes in speed

2003

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“…Perhaps the most frequently studied differences are those associated with age [6,7]. However, numerous other normative studies have been undertaken to determine the effects of such variables such as walking speed, height or leg length, footwear, and curb clearance, to mention but a few [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Basic gait parameters: A comparison of reference data for normal subjects 20 to 29 years of age from Kuwait and Scandinavia

Al‐Obaidi

Wall²,

Al-Yaqoub³

et al. 2003

JRRD

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Abstract-This study obtained measurements of the spatiotemporal gait parameters of healthy young adult Kuwaiti subjects from both genders and compared the data to those collected in a similar study performed in Sweden. Thirty healthy subjects volunteered to participate in the study (which included being asked to walk at their "free," "slow," and "fast" selfselected speeds). We collected the spatiotemporal gait data using an automated system. Descriptive statistics were calculated for each variable measured at each walking condition. The data were then compared to those from the Swedish study. The results indicate several significant differences between Kuwaiti and Swedish subjects in their manner of walking. These results suggest a need to include data from subjects with diverse cultural backgrounds when a database on normal gait is developed or a need to limit the results of the database to a specified ethnic population.

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“…2) Mapping frequency spectrum of signals to speeds: A linear [16], probabilistic model mapping the frequency content of walk to speed was assumed. This was achieved using Bayesian Linear Regression (BLR), a parametric, probabilistic interpretation of linear regression.…”

Section: Estimating Walking Speed From Inertial Sensorsmentioning

confidence: 99%

A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Vathsangam

Tulsyan

Sukhatme

2011

2011 International Conference on Body Sensor Networks

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Abstract-Indoor localization is a promising area with applications in in-home monitoring and tracking. Fingerprinting and propagation model-based WiFi localization techniques have limited spatial resolution because of grid or graph-based representations. An alternative is to incorporate dynamics models based on real-time sensing of human movement and fuse these with WiFi measurements. We present a data-driven dynamic model that tracks the inherent periodicity in walking and converts this representation into velocity. This model however is prone to drift. We correct this drift with WiFi measurements to obtain a combined position estimate. Our approach records and fuses human body movement with WiFi positioning using a single mobile phone. We characterize the movement model to obtain an estimate of error predictions. The movement model showed a best case average RMS prediction error of .25 m/s. We also present a preliminary study characterizing combined system performance across straight line and L-shaped trajectories. The framework showed lower errors across the L-shaped trajectories (mean error ≈ 4.8 m using movement sensing versus a mean error ≈ 6 m without movement sensing) because of the ability assess the validity of a WiFi measurement. Higher errors were observed across the straight line trajectory due to imprecise trajectories.

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The Relationships Between Length of Stride, Step Frequency, Time of Swing and Speed of Walking for Children and Adults

Cited by 352 publications

References 6 publications

Treadmill walking in incomplete spinal-cord-injured subjects: 1. Adaptation to changes in speed

Treadmill walking in incomplete spinal-cord-injured subjects: 1. Adaptation to changes in speed

Basic gait parameters: A comparison of reference data for normal subjects 20 to 29 years of age from Kuwait and Scandinavia

A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

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