Contribution of vision and its age-related changes to postural stability in obstacle crossing during locomotion

Kunimune, Sho; Okada, Shuichi

doi:10.1016/j.gaitpost.2019.03.012

Cited by 15 publications

(16 citation statements)

References 30 publications

(60 reference statements)

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“…Previous studies have reported that instability during walking is primarily in the ML and decline in ML stability is a major risk factor of fall [26,27]. The RMSs of acceleration in ML axis is often employed as an index to evaluate the walking stability and higher RMS is generally associated with higher postural disturbance and risk of falls [28,29]. Thus, our findings reveal that patients with BPPV were not able to attenuate the ML and AP axes acceleration in a tolerable level to maintain a stable visual field and postural stability, which may explain the reason that BPPV patients still have high fall risks despite employing this more conservative strategy.…”

Section: Discussionmentioning

confidence: 99%

Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning

Zhang

Yao

et al. 2021

J NeuroEngineering Rehabil

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Background Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims to investigate the walking stability of BPPV patients in clinical settings and propose a machine-learning-based classification method for determining the severity of gait disturbances of BPPV. Methods Twenty-seven BPPV outpatients and twenty-seven healthy subjects completed level walking trials at self-preferred speed in clinical settings while wearing two accelerometers on the head and lower trunk, respectively. Temporo-spatial variables and six walking stability related variables [root mean square (RMS), harmonic ratio (HR), gait variability, step/stride regularity, and gait symmetry] derived from the acceleration signals were analyzed. A support vector machine model (SVM) based on the gait variables of BPPV patients were developed to differentiate patients from healthy controls and classify the handicapping effects of dizziness imposed by BPPV. Results The results showed that BPPV patients employed a conservative gait and significantly reduced walking stability compared to the healthy controls. Significant different mediolateral HR at the lower trunk and anteroposterior step regularity at the head were found in BPPV patients among mild, moderate, and severe DHI (dizziness handicap inventory) subgroups. SVM classification achieved promising accuracies with area under the curve (AUC) of 0.78, 0.83, 0.85 and 0.96 respectively for differentiating patients from healthy controls and classifying the three stages of DHI subgroups. Study results suggest that the proposed gait analysis that is based on the coupling of wearable accelerometers and machine learning provides an objective approach for assessing gait disturbances and handicapping effects of dizziness imposed by BPPV.

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Section: Discussionmentioning

confidence: 99%

Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning

Zhang

Yao

et al. 2021

J NeuroEngineering Rehabil

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“…Our results demonstrate that independent of age, TT, S1, and S2 increased while crossing an obstacle. An explanation of these ndings consists of the fact that stepping over obstacles increases gait challenges at every age, even in the middle-age population [21,22]; however, this motor task is still more demanding for the elderly compared to young adults [23]. Previous studies reported that the elderly used a more conservative strategy for crossing obstacles relative to young adults, including a slower crossing speed and higher foot clearance while crossing over obstacles [24,25].…”

Section: Discussionmentioning

confidence: 99%

Age-Related Changes in Postural Control: transitional task

Michalska

Kamieniarz

Sobota

et al. 2020

Preprint

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Background Aging, being a natural process, involves many functional and structural changes within the body. Identifying the onset of age-related postural changes will provide insight into the role of aging on postural control during locomotion. The aim of this study was to identify age-related postural changes during a transitional task under different conditions. Methods Sixty healthy females divided into three age groups: A (50–60 y/o), B (60–70 y/o), and C (70–80 y/o). The transitional task was measured by two force platforms. The procedure consisted of three phases: quiet standing, transfer onto a second platform, and quiet standing on the second platform. Four different conditions were applied: unperturbed transfer, obstacle crossing, step-up, and step-down. Double-support time, transit time, and stability time before and after the step task were analyzed. Results The transit time was longer by 30% for subjects over 70 y/o. The double-support time was longer by 11% among adults 60–70 y/o, while in people over 70 y/o it was longer by almost 50% compared to the 50–60 y/o subjects. The stability time before the transitional task was longer by 17% among adults over 60 y/o compared to middle-age subjects. The stability times before and after the transitional task were longer for adults in the 50–60 y/o category. Conclusion The proposed procedure is adequate for assessing age-related changes in postural control while undergoing a transitional task. An analysis of the double-support time and stability time before and after the step task enabled the detection of early signs of balance changes in middle-age adults. Independent of age, the transitional task parameters changed with the increasing difficulty of the tasks.

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“…Previous studies have reported that instability during walking is primarily in the ML and decline in ML stability is a major risk factor of fall [27,28]. The RMSs of acceleration in ML axis is often employed as an index to evaluate the walking stability and higher RMS is generally associated with higher postural disturbance and risk of falls [29,30]. Thus, our ndings reveal that patients with BPPV were not able to attenuate the ML and AP axes acceleration in a tolerable level to maintain a stable visual eld and postural stability, which may explain the reason that BPPV patients still have high fall risks despite employing this more conservative strategy.…”

Section: Discussionmentioning

confidence: 99%

Walking Stability in Patients With Benign Paroxysmal Positional Vertigo: An Objective Assessment Using Wearable Accelerometers and Machine Learning

Zhang

Yao

et al. 2020

Preprint

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Background: Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims to investigate the walking stability of BPPV patients in clinical settings and propose a machine-learning-based classification method for determining the severity of gait disturbances of BPPV. Methods: Twenty-seven BPPV outpatients and twenty-seven healthy subjects completed level walking trials at self-preferred speed in clinical settings while wearing one accelerometer on the head and one on the lower trunk. Temporo-spatial variables and six walking stability related variables (root mean square (RMS), harmonic ratio (HR), gait variability, step/stride regularity, and gait symmetry) derived from the acceleration signals were analyzed. A support vector machine model (SVM) based on the gait variables of BPPV patients were developed to classify the BPPV severity of gait disturbances. Results: The results showed that BPPV patients employed a conservative gait and significantly reduced walking stability compared to the healthy controls. Significant different mediolateral HR at the lower trunk and anteroposterior step regularity at the head were found in BPPV patients among mild, moderate, and severe DHI (dizziness handicap inventory) subgroups. SVM classification achieved promising accuracies with area under the curve (AUC) = 0.87, 0.80, and 0.95 respectively for classifying the three stages of DHI subgroups. Conclusions: Results suggested that the proposed gait analysis that is based on the coupling of wearable accelerometers and machine learning provides an objective approach for assessing gait disturbances and handicapping effects of dizziness imposed by BBPV.Trial registration: The trial was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn) on March 29, 2018. Registration number: ChiCTR1800015432 (http://www.chictr.org.cn/showproj.aspx?proj=25587).

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Contribution of vision and its age-related changes to postural stability in obstacle crossing during locomotion

Abstract: Contribution of vision and its age-related changes to postural stability in obstacle crossing during locomotion

Cited by 15 publications

References 30 publications

Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning

Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning

Age-Related Changes in Postural Control: transitional task

Walking Stability in Patients With Benign Paroxysmal Positional Vertigo: An Objective Assessment Using Wearable Accelerometers and Machine Learning

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