The COVID-19 pandemic has deeply affected the respiratory health of people, leaving many sufferers with long term pulmonary problems. Artificial intelligence based physiological analysis of structured exercise program on lung function of recovered COVID 19 patient is studied. The research introduces an integrated data driven approach for assessing the improvement of respiratory through physical training. The approach is to integrate wearable sensor technology with machine learning algorithms. A controlled experimental study with three groups (recovered COVID-19 patients, smokers, healthy individuals) was used as a method. To that aim, each of the participants underwent an eight-week structured aerobic training program that included continuous monitoring through wearable devices of key physiological metrics, namely oxygen saturation, heart rate, respiratory rate and lactic acid levels. Trends were analyzed using machine learning models such as Random Forest and Long Short-Term Memory (LSTM) networks and used in the prediction of individual recovery progress. The trained recovered COVID-19 patients showed statistically significant improvement in lung function demonstrated by an average 5% increase in oxygen saturation and significant reduction in lactic acid. Further, the predictive models confirmed that participants who became more adapted to aerobic exercises prior to the respiratory virus had a higher probability of long-term respiratory recovery. This can serve as an indication of the potential of AI-driven personalized rehabilitation programs for increased efficacy of respiratory therapy. The role of artificial intelligence in rehabilitation sciences demonstrated by this research provides a new and transformative way for artificial intelligent algorithms to be tailored to individual patient’s physiological responses in real time. Future work will scale this approach to more patients and with more precise recovery prediction using advanced deep learning.
<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol
... Show MoreThis research sheds light on the physical environment role in creating the place attachment, by discussing one of the important factors in the attachment creation, it is the concept place dependence, consisting of two important dimensions: the place quality and the place expectation; they contain a number of the supporter physical environment sub-indicators for place attachment. Eight physical indicators were reached; they were found to have a close relationship to the place attachment, including: the open and green spaces existence, land use diversity, diversity of housing types, dwelling / population density, accessibility, transport network development degree, transport multiple mo
Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)
... Show MoreHypoxic training, which in turn is one of the methods adopted in sports training methods, especially in activities that depend on the aerobic system in its performance, which includes training with a lack of oxygen by reducing its molecular pressure, since this method targets functional organs and works temporary responses during training and permanent responses After training as an adaptation to these devices as a result of training in this way, the study aimed to identify the effect of hypoxic exercises using the training mask and the extent of the change in some biochemical indicators, in addition to that to identify the effect of these exercises on the indicator of energy expenditure and )VMA) and the achievement of the effectiveness of
... Show MoreDrug consultation is an important part of pharmaceutical care. mobile phone call or text message can serve as an easy, effective, and implementable alternative to improving medication adherence and clinical outcomes by providing the information needed significantly for people with chronic illnesses like diabetes and hypertension particularly during pandemics like COVID-19 pandemic.
The accuracy of the skillful performance of the front and back dimensions of badminton in volleyball, occurs through the investment of complex exercises (physical skills) in a single performance and its characteristics that give the correct movement behavior and speed to the accuracy of the performance of the strokes as well as the identification of changes in some physiological indicators of By using these compound exercises. The research problem lies: I found a weakness in the accuracy of the performance of the front and back dimensions strike and diagnosed this through the tests that it conducted on the players to identify and know the problem, and attributed this weakness to a weakness in the necessary physical and skill abilities and t
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