Development of a vibrotactile cueing device that implicitly increases walking speed during gait training in stroke patients: an observational case series study

Ling, Jiayi; Yasuda, Kazuhiro; Hayashi, Yuki; Imamura, Saeko; Iwata, Hiroyasu

doi:10.1080/03091902.2021.1970839

Cited by 4 publications

(1 citation statement)

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“…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%

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: Introductionmentioning

confidence: 99%

Gait Variability at Different Walking Speeds

Padulo,

Rampichini,

Borrelli

et al. 2023

JFMK

View full text Add to dashboard Cite

show abstract

Somatosensory Information in Skilled Motor Performance: A Narrative Review

Whittier

Patrick

Fling

2023

Journal of Motor Behavior

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Gait Variability at Different Walking Speeds

Padulo,

Rampichini,

Borrelli

et al. 2023

Preprint

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Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body seg-mental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, has not been clarified whether GV is associated with walking speed to exploit effective better coordination level. Fourteen male stu-dents (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, 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 Apple Watch S. 7 (AppleTM), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 minutes each one, at SS-20% / SS+20% of SS, with 5 minutes of passive recovery in-between. Heart rate (HR) was monitored beat-to beat and nor-malized as HR%MAX, while the rate of perceived exertion (RPE) (CR-10) 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 with higher walking speed (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 un-changed (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), 3.36 ± 1.09 % (high variability) at 5.94 ± 0.70 km·h-1 (P = 0.001). These results indi-cate that while the metabolic demand and kinematics variables changing 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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Development of a vibrotactile cueing device that implicitly increases walking speed during gait training in stroke patients: an observational case series study

Cited by 4 publications

References 20 publications

Gait Variability at Different Walking Speeds

Gait Variability at Different Walking Speeds

Somatosensory Information in Skilled Motor Performance: A Narrative Review

Gait Variability at Different Walking Speeds

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