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4 articles for “stride length”
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The Effects of Dual Task Exercise Versus Task Specific Locomotion Training to Improve Walking Ability in Stroke: A Comparative Study
Abstract: Purpose of the study: The main aim of the study is to see whether the Dual-Task exercise versus Task Specific locomotion training improve gait/locomotion in chronic stroke patients. Participants: The study included a total of 30 participants, comprising both males and females, who had chronic stroke. All participants were at least limited community ambulatory individuals with Berg Balance Scale scores ranging from 41 to 56. Interventions: All the subjects were …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 13, Issue 1, 2023 · pp. 13–28 Read article
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Quantitative and Qualitative Analysis of Gait in Right and Left Side Stroke Patients
Abstract: To determine the gait pattern in right and left sided stroke patients which will be useful, in diagnosis of gait abnormality, help in correcting the gait pattern for gait re-education and also to prevent deformity. Thirty six subjects [31 male, 5 female] who were affected by stroke were randomly assigned to one of two intervention groups. Group I consist of 18 patients with right side stroke. Group II consist of …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 1, Issue 1-3, 2011 · pp. 21–37 Read article
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Counter Terrorism Prediction and Risk Evaluation (C-TRIP)
Abstract: The global landscape in the 21st century is marked by complex and evolving security challenges, none more pressing than the threat of terrorism. Acts of terror have left a profound impact on societies, economies, and governments worldwide, underscoring the critical importance of effective counter terrorism strategies. The “Counter Terrorism Prediction and Risk Evaluation (C-TRIP)” represents a significant stride in addressing this ever-pressing challenge. In a time marked by global security …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 14–24 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article