How humans walk: Bout duration, steps per bout, and rest duration

Orendurff, Michael S.

doi:10.1682/jrrd.2007.11.0197

Cited by 266 publications

(237 citation statements)

References 43 publications

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“…Klute et al found that activity of persons with TFA was characterized by frequent, short bouts of slow steps (i.e., <17 steps/min) and that longer periods of activity (i.e., >15 min of consecutive steps) were uncommon [41]. This is similar to the frequency and length of bouts undertaken by nondisabled adults [63]. Although more information is certainly needed, it appears that differences in activity between persons with TFA and those without amputation may be more readily measured using outcomes such as level and variability of step activity than frequency and intensity of activity bouts.…”

Section: Discussionmentioning

confidence: 88%

Long-term activity in and among persons with transfemoral amputation

Halsne¹,

Waddingham²,

Hafner³

2013

JRRD

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Abstract-Although physical limitations associated with transfemoral amputation (TFA) have been studied in laboratory settings, little is known about habitual activity within free-living environments. A retrospective analysis of 12 mo of step activity data was performed to quantify activity levels, variations, and patterns in 17 adults with unilateral TFA. Yearly, seasonal, and monthly average daily step counts and coefficients of variation (CoVs) were examined to characterize mobility. Analysis by Medicare Functional Classification Level (MFCL) was performed to explore relationships between clinical classification and performance. Subjects averaged 1,540 prosthetic steps/day, and activity generally increased with MFCL. Activity between MFCL-2 and -3 subjects was not significantly different, suggesting that ability to engage in habitual physical activity may be similar for these groups. Relative variation (CoV) was 0.65 across subjects but was lower for those with higher activity levels. No significant differences in CoV by group were detected. Marked seasonal and monthly patterns in activity were identified. Warmer seasons and months generally promoted higher activity, but peak temperatures and humidity depressed activity. Results suggest that persons with TFA are greatly limited in regards to activity. Further, large variations within and between subjects may challenge the interpretation of step activity gathered over short periods of time.

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Section: Discussionmentioning

confidence: 88%

Long-term activity in and among persons with transfemoral amputation

Halsne¹,

Waddingham²,

Hafner³

2013

JRRD

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“…This is because the central nervous system will have to evaluate the effect of a change in each parameter on energetic cost in order to intelligently search the parameter space. But most walking bouts are relatively short, lasting less than 30 seconds (Orendurff et al, 2008), leaving little time for multidimensional optimization. Thus, it seems reasonable that realtime optimization is reserved for a small number of gait parameters that have the most meaningful short-term effects on energetic cost and that are free to change while still fulfilling the task constraints.…”

Section: Discussionmentioning

confidence: 99%

Fast and slow processes underlie the selection of both step frequency and walking speed

Pagliara

Snaterse

Donelan

2014

Journal of Experimental Biology

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People prefer gaits that minimize their energetic cost. Research focused on step frequency selection suggests that a fast predictive process and a slower optimization process underlie this energy optimization. Our purpose in this study was to test whether the mechanisms controlling step frequency selection are used more generally to select one of the most relevant characteristics of walking -preferred speed. To accomplish this, we contrasted the dynamic adjustments in speed following perturbations to step frequency against the dynamic adjustments in step frequency following perturbations to speed. Despite the use of different perturbations and contexts, we found that the responses were very similar. In both experiments, subjects responded to perturbations by first rapidly changing their speed or step frequency towards their preferred pattern, and then slowly adjusting their gait to converge onto their preferred pattern. We measured similar response times for both the fast processes (1.4±0.3 versus 2.7±0.6 s) and the slow processes (74.2±25.4 versus 79.7±20.2 s). We also found that the fast process, although quite variable in amplitude, dominated the adjustments in both speed and step frequency. These distinct but complementary experiments demonstrate that people appear to rely heavily on prediction to rapidly select the most relevant aspects of their preferred gait and then gradually fine-tune that selection, perhaps using direct optimization of energetic cost.

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“…By integration, the typical percentages of error over the walking distance are between 2.5% and 5.0% [16]. Other studies face the interpretation of 3D movements of the COM with more complex models [17][18][19][20][21]. Actually, it is possible to measure hCOM without carry out a double integration.…”

Section: Theoretical Modelmentioning

confidence: 99%

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

2018

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A model based on a harmonic oscillator describing human walking and balance with the sinusoidal trajectory of the center of mass of a subject during gait is presented. This model allows overcoming the traditional drift due to the double integration of raw acceleration data. The protocol uses a single 3D accelerometer worn at the pelvis level. The system computes the spatiotemporal gait and balance parameters when the subject is walking with or without aids. An incremental methodological approach is proposed and followed in the implementation and accuracy assessment. Eleven healthy subjects have participated to the study performing 6 trials over a fixed linear walking path at a self-selected speed. For reference, the protocol has imposed the execution of 52 steps whose length has been fixed at 60 cm. Different processing methods have been implemented and tested. The model identifies steps, walking time, and stepping frequency with an excellent reliability (absolute percentage accuracy error < 5%). When the information about the expected step length is given to the model, the percentage error in the measure of walking distance and speed is 3.25%. Without this input, this error rises to 4.95%, while for the anthropometric method is 3.68%.

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How humans walk: Bout duration, steps per bout, and rest duration

Cited by 266 publications

References 43 publications

Long-term activity in and among persons with transfemoral amputation

Long-term activity in and among persons with transfemoral amputation

Fast and slow processes underlie the selection of both step frequency and walking speed

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

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