Review of Artificial Intelligence System For Correcting Exercise Movements and Health Monitoring

2021 ◽  
Vol 9 (9) ◽  
pp. 1615-1620
Author(s):  
Arya Patki
Keyword(s):  
Artificial Intelligence ◽  
Health Monitoring ◽  
Tracking System ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Breathing Techniques ◽  
Fitness Training ◽  
Made In

Abstract: Exercising/yoga/fitness training is an important aspect in everyone's life especially with the modern hectic lifestyle which is very unhealthy. The correctness of an exercise can be judged by certain parameters such as perfect angles between body points and correct breathing techniques and also number of repetitions in some forms of exercise. In yoga, health benefits are achieved only when a person stays in the position for long enough. The Artificial Intelligence system which will help in tracking the movements of user while performing any exercise or yoga asanas will be beneficial. For this system Computer Vision will be used to visualize the body points of the user. Post visualizing the body points, machine learning algorithms are used to detect the posture of the user and check if it is correct or not. Accordingly, the system will generate an output to tell the user the correction to be made in the posture. Also a heartbeat tracking system is used to monitor the heartbeat of the user so that while performing any exercise or yoga asanas if user feels any abnormality in the body it can be tracked by the user's heartbeat and the system could guide the user before any injury occurs. Keywords: CV, CNN, R-CNN, Neural Network, OpenPose, BlazePose, PPG

scholarly journals A Survey on Machine Learning Adversarial Attacks

2020 ◽  
Vol 7 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Flávio Luis de Mello
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Learning Systems ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Artificial Intelligence System ◽  
Comprehensive Overview ◽  
Security Issues ◽  
Intelligence System

It is becoming notorious several types of adversaries based on their threat model leverage vulnerabilities to compromise a machine learning system. Therefore, it is important to provide robustness to machine learning algorithms and systems against these adversaries. However, there are only a few strong countermeasures, which can be used in all types of attack scenarios to design a robust artificial intelligence system. This paper is structured and comprehensive overview of the research on attacks to machine learning systems and it tries to call the attention from developers and software houses to the security issues concerning machine learning.

330 DEVELOPMENT AND VALIDATION OF AN ARTIFICIAL INTELLIGENCE SYSTEM TO OPTIMIZE CLINICIAN REVIEW OF PATIENT RECORDS

2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Records ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Development And Validation

scholarly journals COVID-19-induced vascular angiopathy: CTPA signs in critically ill patients other than acute pulmonary embolism and high-lung opacity scores

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mohamed Hossameldin khalifa ◽  
Ahmed Samir ◽  
Ayman Ibrahim Baess ◽  
Sara Samy Hendawi
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Lung Volume ◽  
Acute Pulmonary Embolism ◽  
Pulmonary Angiography ◽  
Vascular Tree ◽  
Ct Pulmonary Angiography ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Severity Scores

Abstract Background Vascular angiopathy is suggested to be the major cause of silent hypoxia among COVID-19 patients without severe parenchymal involvement. However, pulmonologists and clinicians in intensive care units become confused when they encounter acute respiratory deterioration with neither severe parenchymal lung involvement nor acute pulmonary embolism. Other radiological vascular signs might solve this confusion. This study investigated other indirect vascular angiopathy signs on CT pulmonary angiography (CTPA) and involved a novel statistical analysis that was performed to determine the significance of associations between these signs and the CT opacity score of the pathological lung volume, which is calculated by an artificial intelligence system. Results The study was conducted retrospectively, during September and October 2020, on 73 patients with critical COVID-19 who were admitted to the ICU with progressive dyspnea and low O2 saturation on room air (PaO2 < 93%). They included 53 males and 20 females (73%:27%), and their age ranged from 18 to 88 years (mean ± SD=53.3 ± 13.5). CT-pulmonary angiography was performed for all patients, and an artificial intelligence system was utilized to quantitatively assess the diseased lung volume. The radiological data were analyzed by three expert consultant radiologists to reach consensus. A low CT opacity score (≤10) was found in 18 patients (24.7%), while a high CT opacity score (>10) was found in 55 patients (75.3%). Pulmonary embolism was found in 24 patients (32.9%); three of them had low CT opacity scores. Four other indirect vasculopathy CTPA signs were identified: (1) pulmonary vascular enlargement (57 patients—78.1%), (2) pulmonary hypertension (14 patients—19.2%), (3) vascular tree-in-bud pattern (10 patients—13.7%), and (4) pulmonary infarction (three patients—4.1%). There were no significant associations between these signs and the CT opacity score (0.3205–0.7551, all >0.05). Furthermore, both pulmonary vascular enlargement and the vascular tree-in-bud sign were found in patients without pulmonary embolism and low CT-severity scores (13/15–86.7% and 2/15–13.3%, respectively). Conclusion Pulmonary vascular enlargement or, less commonly, vascular tree-in-bud pattern are both indirect vascular angiopathy signs on CTPA that can explain the respiratory deterioration which complicates COVID-19 in the absence of severe parenchymal involvement or acute pulmonary embolism.

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