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Technological development in biomedical procedures has given an upper understanding of the ease of evaluating and handling critical scenarios and diseases. A sustainable model design is required for the post-medical procedures to maintain the consistency of medical treatment. In this article, a telerobotic-based stroke rehabilitation optimization and recommendation technique cum framework is proposed and evaluated. Selecting optimal features for training deep neural networks can help in optimizing the training time and also improve the performance of the model. To achieve this, we have used Whale Optimization Algorithm (WOA) due to its higher convergence accuracy, better stability, stronger global search ability, and faster convergence speed to streamline the dependency matrix of each attribute associated with post-stroke rehabilitation. Deep Neural Networking assures the selection of datasets from training and testing validation. The proposed framework is developed on providing decision support with a recommendation of activities and task flow, these recommendations are independent and have higher feasibility with the scenario of evaluation. The proposed model achieved a precision of 99.6%, recall of 99.5 %, F1-score of 99.7%, and accuracy of 99.9%, which outperform the other considered optimization algorithms such as antlion and gravitational search algorithms. The proposed technique has provided an efficient recommendation model compared to the trivial SVM-based models and techniques. [ABSTRACT FROM AUTHOR]

Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end instrumentations. In this article, a detailed evaluation is processed with a Hybrid Optimization Algorithm (HOA). In the proposed technique, data are preprocessed using a label encoder and the missing values of the dataset are filled. Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA) extract inter-connected patterns and learning features using a dedicated Deep Neural Networking (DNN) support. The proposed Hybrid Optimization Algorithm extracts features and the resultant values demonstrate a high accuracy range of 97.34%. [ABSTRACT FROM AUTHOR]

Objectives: To identify the risk factors for cerebral palsy in hail region to help in designing preventive measures as a primary trial in management of cerebral palsy as it's a major cause of disability and to determine the clinical profile and major associated d isabilities. Method: Screening all the medical files for children with cerebral palsy in the main rehabilitation center in hail region (Prince Salman center) to identify and analyze possible risk factors and to determine the clinical profile of children with cerebral pal sy. Results: The most common risk factor found was low birth weight with percentage of 86.4% of children had birth weight of less than 2500g followed by mothers age more than 35years(80%) and then hypoxia(48%). Conclusion: Avoiding pregnancy with high mothers' age and continued fetal monitoring can decrease risk of cerebral palsy. [ABSTRACT FROM AUTHOR]

Background and Purpose. Equinovarus foot is a common sign after stroke. The aim of this study is to investigate the effect of task specific exercises, gait training, and visual biofeedback on correcting equinovarus gait among individuals with stroke. Subjects and Methods. Sixteen subjects with ischemic stroke were randomly assigned to two equal groups (G1 and G2). All the patients were at stage 4 of motor recovery of foot according to Chedoke-McMaster Stroke Assessment without any cognitive dysfunction. E-med pedography was used to measure contact time, as well as force underneath hind and forefoot during walking. Outcome measures were collected before randomization, one week after the last session, and four weeks later. Participants in G1 received task specific exercises, gait training, and visual biofeedback and a traditional physical therapy program was applied for participants in G2 for 8 weeks. Results. Significant improvement was observed among G1 patients (P ≤ 0.05) which lasts one month after therapy termination. On the other hand, there were no significant differences between measurements of the participants in G2. Between groups comparison also revealed a significant improvement in G1 with long lasting effect. Conclusion. The results of this study showed a positive long lasting effect of the task specific exercises, gait training, and visual biofeedback on equinovarus gait pattern among individuals with stroke. [ABSTRACT FROM AUTHOR]