The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Felix E Zajac - One of the best experts on this subject based on the ideXlab platform.
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gait deviations associated with post stroke Hemiparesis improvement during treadmill walking using weight support speed support stiffness and handrail hold
Gait & Posture, 2005Co-Authors: George L Chen, Carolynn Patten, Dhara H Kothari, Felix E ZajacAbstract:By comparing treadmill walking in hemiparetic and non-disabled individuals at matched speeds, Chen et al. [Chen G, Patten C, Kothari DH, Zajac FE. Gait differences between individuals with post-stroke Hemiparesis and non-disabled controls at matched speeds. Gait Posture (2004)] identified gait deviations that were consistent with impaired swing initiation and single limb support in the paretic limb and related compensatory strategies. Treadmill training with harness support is a promising, task-oriented approach to restoring locomotor function in individuals with post-stroke Hemiparesis. To provide a rationale for the proper selection of training parameters, we assessed the potential of body weight support, treadmill speed, support stiffness, and handrail hold to improve the identified gait deviations associated with Hemiparesis during treadmill walking. In the six hemiparetic subjects studied, the adjustment of each training parameter was found to improve a specific set of the gait deviations. With increased body weight support or the addition of handrail hold, percentage single limb support time on the paretic limb increased and temporal symmetry improved. With increased treadmill speed, leg kinetic energy at toe-off in the paretic limb increased but remained low relative to values in the non-paretic limb. With increased support stiffness, the exaggerated energy cost associated with raising the trunk during pre-swing and swing of the paretic limb was improved. We conclude that the proper selection of training parameters can improve the gait pattern practiced by individuals with Hemiparesis during treadmill training and may improve treatment outcome.
Marimuthu Palaniswami - One of the best experts on this subject based on the ideXlab platform.
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Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020Co-Authors: Shreyasi Datta, Chandan K. Karmakar, Marimuthu PalaniswamiAbstract:Stroke survivors usually experience paralysis in one half of the body, i.e., Hemiparesis, and the upper limbs are severely affected. Continuous monitoring of Hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify Hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of Hemiparesis and 15 healthy controls, validates the use of short length (< 10 minutes) accelerometry data to identify Hemiparesis through leave-one-subject-out cross-validation based hierarchical discriminant analysis. The results indicate that the proposed approach can distinguish between controls, moderate and severe Hemiparesis with an average accuracy of 91%.
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Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020Co-Authors: Shreyasi Datta, Chandan K. Karmakar, Marimuthu PalaniswamiAbstract:Stroke survivors usually experience paralysis in one half of the body, i.e., Hemiparesis, and the upper limbs are severely affected. Continuous monitoring of Hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify Hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of Hemiparesis and 15 healthy controls, validates the use of short length (
George L Chen - One of the best experts on this subject based on the ideXlab platform.
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gait deviations associated with post stroke Hemiparesis improvement during treadmill walking using weight support speed support stiffness and handrail hold
Gait & Posture, 2005Co-Authors: George L Chen, Carolynn Patten, Dhara H Kothari, Felix E ZajacAbstract:By comparing treadmill walking in hemiparetic and non-disabled individuals at matched speeds, Chen et al. [Chen G, Patten C, Kothari DH, Zajac FE. Gait differences between individuals with post-stroke Hemiparesis and non-disabled controls at matched speeds. Gait Posture (2004)] identified gait deviations that were consistent with impaired swing initiation and single limb support in the paretic limb and related compensatory strategies. Treadmill training with harness support is a promising, task-oriented approach to restoring locomotor function in individuals with post-stroke Hemiparesis. To provide a rationale for the proper selection of training parameters, we assessed the potential of body weight support, treadmill speed, support stiffness, and handrail hold to improve the identified gait deviations associated with Hemiparesis during treadmill walking. In the six hemiparetic subjects studied, the adjustment of each training parameter was found to improve a specific set of the gait deviations. With increased body weight support or the addition of handrail hold, percentage single limb support time on the paretic limb increased and temporal symmetry improved. With increased treadmill speed, leg kinetic energy at toe-off in the paretic limb increased but remained low relative to values in the non-paretic limb. With increased support stiffness, the exaggerated energy cost associated with raising the trunk during pre-swing and swing of the paretic limb was improved. We conclude that the proper selection of training parameters can improve the gait pattern practiced by individuals with Hemiparesis during treadmill training and may improve treatment outcome.
Shreyasi Datta - One of the best experts on this subject based on the ideXlab platform.
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Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020Co-Authors: Shreyasi Datta, Chandan K. Karmakar, Marimuthu PalaniswamiAbstract:Stroke survivors usually experience paralysis in one half of the body, i.e., Hemiparesis, and the upper limbs are severely affected. Continuous monitoring of Hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify Hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of Hemiparesis and 15 healthy controls, validates the use of short length (< 10 minutes) accelerometry data to identify Hemiparesis through leave-one-subject-out cross-validation based hierarchical discriminant analysis. The results indicate that the proposed approach can distinguish between controls, moderate and severe Hemiparesis with an average accuracy of 91%.
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Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020Co-Authors: Shreyasi Datta, Chandan K. Karmakar, Marimuthu PalaniswamiAbstract:Stroke survivors usually experience paralysis in one half of the body, i.e., Hemiparesis, and the upper limbs are severely affected. Continuous monitoring of Hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify Hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of Hemiparesis and 15 healthy controls, validates the use of short length (
Tsutomu Nakada - One of the best experts on this subject based on the ideXlab platform.
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cortical reorganization in patients with subcortical Hemiparesis neural mechanisms of functional recovery and prognostic implication
Journal of Neurosurgery, 2003Co-Authors: Yukihiko Fujii, Tsutomu NakadaAbstract:Object. A systematic investigation on cortical reorganization in patients with Hemiparesis of a subcortical origin, with special emphasis on functional correlates, was conducted using functional magnetic resonance (fMR) imaging performed on a 3-tesla system specifically optimized for fMR imaging investigation. Methods. The study group included 46 patients with Hemiparesis (25 with right and 21 with left Hemiparesis) and 30 age-matched healthy volunteers as controls. All study participants were originally right handed. The characteristics of the lesion were putaminal hemorrhage in 19 patients, thalamic hemorrhage in 10 patients, and striatocapsular bland infarction in 17 patients. Functional recovery in subcortical Hemiparesis showed two distinct phases of the recovery process involving entirely different neural mechanisms. Phase I is characterized by the process of recovery and/or reorganization of the primary system. Successful recovery of this system is typically reached within 1 month after stroke onse...
