Understanding gait control in post-stroke: Implications for management

Verma, Rajesh; Arya, Kapil; Sharma, Pawan Kumar; Garg, RavindraK

doi:10.1016/j.jbmt.2010.12.005

Cited by 68 publications

(75 citation statements)

References 59 publications

(63 reference statements)

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“…Patient B showed a rigid flexion and extension mass pattern that can be assumed to be influenced by spasticity. This event happened in both the stance and swing phases with a synergic mass pattern, as described by other authors15, 17, 18 ) .…”

Section: Discussionsupporting

confidence: 72%

“…However, it is known that the angle during initiation of the stance phase is between 25° and 35° for the hip joint11, 15, 16 ) . The control subject also had a greater extension angle in the stance phase (16.4°), which was close to the toe-off angle, as expected in comparison with other authors11, 15 ) . Patient B showed a rigid flexion and extension mass pattern that can be assumed to be influenced by spasticity.…”

Section: Discussionmentioning

confidence: 99%

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Kinematic Gait Analysis Using Inertial Sensors with Subjects after Stroke in Two Different Arteries

et al. 2014

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[Purpose] The aim of the present study was described the kinematic characteristics of gait in stroke patients with two different arteries involved. [Subjects and Methods] Two patients who had suffered a basilar (A) or middle (B) cerebral artery ischemic stroke were compared with a control (C). Seventeen inertial sensors were used with acquisition rate of 120 Hz. The participants walked 3 times on a 10 meter walkway. From the raw data, the three gait cycles from the middle of each trial were chosen and analyzed. [Results] During the stance phase, patients A and B had a lower hip angle at initial contact and maximum flexion angle during load response than the control. Patient A and the control subject had similar knee angle values at initial contact, and patient B presented a flexed position in the initial phase of the gait cycle. The maximum flexion angles during loading response were also higher for patient B. The sagittal plane excursion for the ankle joint was lower for patient B in comparison with the other subjects. [Conclusion] Differences during walking between patients who had stroke in different arteries may be related to an alternative compensatory strategy. Patient A and the control subject had similar gait cycle curves at all joints, while patient B showed a rigid synergic pattern.

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Kinematic Gait Analysis Using Inertial Sensors with Subjects after Stroke in Two Different Arteries

et al. 2014

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“…Although biomechanical characteristics of stroke walking have been widely explored and described (Chen, et al, 2005;Goldie, Matyas, & Evans, 2001;Kim & Eng, 2004;Lamontagne, Malouin, Richards, & Dumas, 2002;Lamontagne, Richards, & Malouin, 2000;Lamontagne, Stephenson, & Fung, 2007;Lin, Yang, Cheng, & Wang, 2006;Olney, Griffin, & McBride, 1994;Olney & Richards, 1996;Shiavi, Bugle, & Limbird, 1987;Verma, Arya, Sharma, & Garg, 2012;Woolley, 2001), only recently has the interlimb coordination been a subject for study.…”

Section: The Impact Of Decreased Interlimb Coordination On Gait Efficmentioning

confidence: 99%

Interlimb Coordination During Step-to-Step Transition and Gait Performance

Sousa

Tavares

2015

Journal of Motor Behavior

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Most energy spent in walking is due to step-to-step transitions. During this phase, the interlimb coordination assumes a crucial role to meet the demands of postural and movement control. This work reviews studies that have been carried out regarding the interlimb coordination during gait as well as the basic biomechanical and neurophysiological principles of the interlimb coordination. The knowledge gathered from these studies is useful for understanding step-to-step transition during gait from a motor control perspective and for interpreting walking impairments and inefficiency related to pathologies, like stroke. This review shows that unimpaired walking is characterised by a consistent and reciprocal interlimb influence that is supported by biomechanical models, and spinal and supraspinal mechanisms.This interlimb coordination is perturbed in subjects with stroke.

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“…Due to this asymmetry and lack of balance, about 75% of stroke patients need assistance for walking independently during the first three months after stroke onset (Verma et al, 2012). However, there are no evidence-based criteria for choosing the device to help the patient (Verma et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…However, there are no evidence-based criteria for choosing the device to help the patient (Verma et al, 2012). Tyson and Rogerson (2009) evaluated the use of cane and foot-ankle orthosis, which provided confidence and safety to the patients (20 stroke patients; mean age: 65.6 ± 10.4 years; mean time since stroke: 6.5 ± 5.7 weeks), improving their functional mobility.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of a smart walker for stroke individuals: a study on sEMG and accelerometer signals

et al. 2017

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Introduction: Stroke is a leading cause of neuromuscular system damages, and researchers have been studying and developing robotic devices to assist affected people. Depending on the damage extension, the gait of these people can be impaired, making devices, such as smart walkers, useful for rehabilitation. The goal of this work is to analyze changes in muscle patterns on the paretic limb during free and walker-assisted gaits in stroke individuals, through accelerometry and surface electromyography (sEMG). Methods: The analyzed muscles were vastus medialis, biceps femoris, tibialis anterior and gastrocnemius medialis. The volunteers walked three times on a straight path in free gait and, further, three times again, but now using the smart walker, to help them with the movements. Then, the data from gait pattern and muscle signals collected by sEMG and accelerometers were analyzed and statistical analyses were applied. Results: The accelerometry allowed gait phase identification (stance and swing), and sEMG provided information about muscle pattern variations, which were detected in vastus medialis (onset and offset; p = 0.022) and biceps femoris (offset; p = 0.025). Additionally, comparisons between free and walker-assisted gaits showed significant reduction in speed (from 0.45 to 0.30 m/s; p = 0.021) and longer stance phase (from 54.75 to 60.34%; p = 0.008). Conclusions: Variations in muscle patterns were detected in vastus medialis and biceps femoris during the experiments, besides user speed reduction and longer stance phase when the walker-assisted gait is compared with the free gait.

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Understanding gait control in post-stroke: Implications for management

Cited by 68 publications

References 59 publications

Kinematic Gait Analysis Using Inertial Sensors with Subjects after Stroke in Two Different Arteries

Kinematic Gait Analysis Using Inertial Sensors with Subjects after Stroke in Two Different Arteries

Interlimb Coordination During Step-to-Step Transition and Gait Performance

Adaptation of a smart walker for stroke individuals: a study on sEMG and accelerometer signals

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