Assessment and validation of a simple automated method for the detection of gait events and intervals

Ghoussayni, Salim; Stevens, Christopher J.; Durham, Sally; Ewins, David

doi:10.1016/j.gaitpost.2003.10.001

Cited by 199 publications

(193 citation statements)

References 9 publications

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“…The average of three repetitions was calculated; from this average, it could be calculated the maximal LD, angular excursion -AE (maximal minus minimal angular displacement) of each task and the integral of the EMG activity (IEMG) during the stance and balance gait phases. These phases were defined by visual inspection (Ghoussayni et al, 2004) taking into account the analysis of the linear displacement of the marks of the ankle and the tiptoe (Figure 2). …”

Section: Data Processingmentioning

confidence: 99%

Kinematic and electromyographic analysis of school children gait with and without load in the backpack

et al. 2018

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Introduction: Excessive load on the backpacks can lead to musculoskeletal injuries and gait alterations. The objective of this study was to analyze the electromyographic (EMG) activity in association to the kinematic during the stance and balance phases of gait performed with and without the use of the backpack. Methods: Twelve volunteers have executed a gait cycle in 3 tasks: without the school backpack (SM), with a backpack with load equivalent to 10% (M10) and 20% (M20) of the body weight (BW). It was evaluated the ankle, knee and hip angular excursion (AE), linear displacement (LD) of the toe and ankle, knee, hip, shoulder, head and EMG activity of the tibialis anterior (TA), vastus medialis (VM), rectus abdominis (RA), gastrocnemius lateral (GL), biceps femoris (BF) and spinal erector (EE) muscles. Results: In the LD in the stance phase there was an AP increase for the toe and ankle, hip and head (p<0.043). In the balance phase for VT (vertical) direction, the shoulder presented a smaller displacement as well as the toe, ankle, knee, hip, shoulder and head in the ML direction. In the AP direction, the toe displacement was smaller as well as the ankle (p < 0.038). The AE in both phases was smaller for the hip (p <0.006). In the balance phase the IEMG was higher for the RA (p = 0.034). Conclusion: These results suggest that the transport of school backpacks with loads of more than 10%BW causes changes in the kinematic and in the muscular recruitment pattern.

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Section: Data Processingmentioning

confidence: 99%

Kinematic and electromyographic analysis of school children gait with and without load in the backpack

et al. 2018

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“…The graphs representing the displacement of these markers on the y-axis (anteriorposterior) were used, allowing the visualization of the markers in the sagittal plane, recording times in milliseconds, in which the initial contact event 1, removal of fingers, and initial contact 2 occurred in each gait cycle 27,28 . From the definition of those events, temporospatial gait parameters (i.e., speed and stride length) were calculated using Visual 3D software.…”

Section: Data Reductionmentioning

confidence: 99%

Impact of leg length and body mass on the stride length and gait speed of infants with normal motor development: A longitudinal study

Rodriguez

Chagas

Silva

et al. 2013

Braz. J. Phys. Ther.

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| Background: Gait acquisition is supported by changes in the neuromusculoskeletal system of the child. Changes in the dimensions of the body structures resulting from the growth of the child partly explain gait improvement in the first year of life. Objectives: To evaluate whether changes in body mass and leg length modulate the effect of independent gait practice (experience) on gait speed and stride length. Method: Thirty-two infants with normal development were monitored monthly from the acquisition of independent gait until six months post-acquisition. Longitudinal evaluations included measurements of the body mass and leg length of each child. Temporospatial variables of gait (speed and stride length) were documented using the Qualisys Pro-reflex ® system. The data were analyzed using multilevel regression models, with a significance level of α=0.05. Results: An effect of the practice time on speed (p<0.0001) and stride length (p<0.0001) was observed. The change in leg length had a marginal effect on the rate of gait speed change: children whose leg growth was faster showed a higher rate of speed change (p=0.07). No other effects of anthropometric parameters were observed. Conclusions: The results suggest that the practice time promotes the improvement of the gait pattern of infants in the first year of life. However, the effects of the leg length and body weight of infants on the benefit of practice time remain undefined.Keywords: movement; anthropometry; children; gait. HOW TO CITE THIS ARTICLERodriguez EB, Chagas PSC, Silva PLP, Kirkwood RN, Mancini MC. Impact of leg length and body mass on the stride length and gait speed of infants with normal motor development: A longitudinal study. Braz J PhysTher.

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“…Gait analysis is the systematic measurement and description in the study of the human locomotion [1] and is commonly used in research, medical and sports applications. The Pedar is deemed to be one of the most commonly used devices for in-shoe pressure measurement [2] for gait analysis.…”

Section: Introductionmentioning

confidence: 99%

Measurement Accuracy of the Body Weight with Smart Insoles

Tan

Weizman

Fuss

2018

The 12th Conference of the International Sports Engineering Association

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Abstract:A participant wearing the Pedar-X performed 6 activities on level ground: Slow, medium and fast walk, medium and fast run, and limping. Static BW was measured prior each activity. The dynamic and static BWs were calculated from the mean of the sum of forces of both feet over time and compared to the force measured from the force-plate. As the base pressure during the swing phase was not zero, it was treated in 3 ways: including the base pressure; subtracting the mean base pressure from the swing phase; subtraction of the base pressure from the entire signal. The calculated BWs were normalised to the actual BW of the participant. From the results, the BWs calculated had 10% error when static and 6% error when walking. To zero or subtract the baseline pressures improved the BW measurement by 1.75% and 4% respectively. Running data could not be analysed at a sampling rate of 50 Hz.

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Assessment and validation of a simple automated method for the detection of gait events and intervals

Cited by 199 publications

References 9 publications

Kinematic and electromyographic analysis of school children gait with and without load in the backpack

Kinematic and electromyographic analysis of school children gait with and without load in the backpack

Impact of leg length and body mass on the stride length and gait speed of infants with normal motor development: A longitudinal study

Measurement Accuracy of the Body Weight with Smart Insoles

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