Muscle demand and kinematic similarities between pediatric-modified motor-assisted elliptical training at fast speed and fast overground walking: Real-world implications for pediatric gait rehabilitation

Cesar, Guilherme M.; Buster, Thad W.; Gonabadi, Arash Mohammadzadeh; Burnfield, Judith M.

doi:10.1016/j.jelekin.2022.102639

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…A similar U-shaped trend in gait variability, analyzed using long-range correlation through detrended fluctuation analysis, has been observed in both running and walking gaits for female participants [14], although physiological effort measurements were not included. In both walking and running conditions, Jordan et al [14,29] found that the lowest gait variability was observed at 100-110% of the preferred walking speed and 100% of the running speed, respectively. Therefore, the PCI and the long-range correlation using detrended fluctuation analysis might be equivalent in assessing gait variability from a methodological standpoint.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous studies have focused on investigating the variability of the gait cycle paradigm (gait variability, GV) to better understand the bioenergetics and control of the human locomotion [21]. This seemingly simple activity [22][23][24][25][26][27] involves a complex task [28][29][30][31][32][33][34] requiring a precise synergy [35][36][37][38][39][40] between lower limb coordination [41][42][43][44][45][46][47] and muscle contraction [48][49][50][51][52][53][54] in response to both natural and non-natural conditions [55]. As a result, individuals need to continuously explore new strategies [56-63] and promptly adapt the motor task [64][65][66][67][68][69][70] to the immediate environment conditions, adjusting their footstep cycle to the most appropriate one.…”

Section: Introductionmentioning

confidence: 99%

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Gait Variability at Different Walking Speeds

Padulo,

Rampichini,

Borrelli

et al. 2023

JFMK

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Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body segmental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, it has not been clarified whether GV is associated with walking speed, a clarification needed to exploit effective better bilateral coordination level. For this aim, fourteen male students (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, and body height 1.78 ± 0.05 m) took part in this study. After three days of walking 1 km each day at a self-selected speed (SS) on asphalt with an Apple Watch S. 7 (AppleTM, Cupertino, CA, USA), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 min at each one, SS − 20%/SS + 20%/ SS, with 5 min of passive recovery in-between. Heart rate (HR) was monitored and normalized as %HRmax, while the rate of perceived exertion (RPE) (CR-10 scale) was asked after each trial. Kinematic analysis was performed, assessing the Contact Time (CT), Swing Time (ST), Stride Length (SL), Stride Cycle (SC), and Gait Variability as Phase Coordination Index (PCI). RPE and HR increased as the walking speed increased (p = 0.005 and p = 0.035, respectively). CT and SC decreased as the speed increased (p = 0.0001 and p = 0.013, respectively), while ST remained unchanged (p = 0.277). SL increased with higher walking speed (p = 0.0001). Conversely, PCI was 3.81 ± 0.88% (high variability) at 3.96 ± 0.47 km·h−1, 2.64 ± 0.75% (low variability) at SS (4.94 ± 0.58 km·h−1), and 3.36 ± 1.09% (high variability) at 5.94 ± 0.70 km·h−1 (p = 0.001). These results indicate that while the metabolic demand and kinematics variables change linearly with increasing speed, the most effective GV was observed at SS. Therefore, SS could be a new methodological approach to choose the individual walking speed, normalize the speed intensity, and avoid a gait pattern alteration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gait Variability at Different Walking Speeds

Padulo,

Rampichini,

Borrelli

et al. 2023

JFMK

View full text Add to dashboard Cite

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Integrating Therapy into Play: Stand-On Ride-On for a Child with Cerebral Palsy

Flanagan,

Diaz,

Cesar

et al. 2024

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities

Mohammadzadeh Gonabadi,

Buster,

Cesar

et al. 2024

Journal of Applied Biomechanics

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This study investigated how data series length and gaps in human kinematic data impact the accuracy of Lyapunov exponents (LyE) calculations with and without cubic spline interpolation. Kinematic time series were manipulated to create various data series lengths (28% and 100% of original) and gap durations (0.05–0.20 s). Longer gaps generally resulted in significantly higher LyE% error values in each plane in noninterpolated data. During cubic spline interpolation, only the 0.20-second gap in frontal plane data resulted in a significantly higher LyE% error. Data series length did not significantly affect LyE% error in noninterpolated data. During cubic spline interpolation, sagittal plane LyE% errors were significantly higher at shorter versus longer data series lengths. These findings suggest that not interpolating gaps in data could lead to erroneously high LyE values and mischaracterization of movement variability. When applying cubic spline, a long gap length (0.20 s) in the frontal plane or a short sagittal plane data series length (1000 data points) could also lead to erroneously high LyE values and mischaracterization of movement variability. These insights emphasize the necessity of detailed reporting on gap durations, data series lengths, and interpolation techniques when characterizing human movement variability using LyE values.

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Muscle demand and kinematic similarities between pediatric-modified motor-assisted elliptical training at fast speed and fast overground walking: Real-world implications for pediatric gait rehabilitation

Cited by 4 publications

References 21 publications

Gait Variability at Different Walking Speeds

Gait Variability at Different Walking Speeds

Integrating Therapy into Play: Stand-On Ride-On for a Child with Cerebral Palsy

Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities

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