RESUMO
BACKGROUND: Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network. METHODS: It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach. RESULTS: The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%. SIGNIFICANCE: Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.
RESUMO
Myocardial ischemic injury is a primary cause of death among various cardiovascular disorders. The condition occurs due to interrupted blood supply and vital nutrients (necessary for normal cellular activities and viability) to the myocardium, eventually leading to damage. Restoration of blood supply to ischemic tissue is noted to cause even more lethal reperfusion injury. Various strategies, including some conditioning techniques like preconditioning & postconditioning have been developed to check detrimental effects of reperfusion injury. Many endogenous substances have been proposed to act as initiator, mediators and end effectors of these conditioning techniques. Substances like adenosine, bradykinin, acetylcholine, angiotensin, norepinephrine, opioids, etc., have been reported to mediate cardioprotective activity. Among these agents, adenosine has been widely studied and suggested to have the most pronounced cardioprotective effects. The current review article highlights the role of adenosine signaling in the cardioprotective mechanism of conditioning techniques. The article also provides an insight into various clinical studies that substantiate the applicability of adenosine as a cardioprotective agent in myocardial-reperfusion injury.
RESUMO
Human gait is a periodic motion of body segments-the analysis of motion and related studies is termed gait analysis. Gait Analysis has gained much popularity because of its applications in clinical diagnosis, rehabilitation methods, gait biometrics, robotics, sports, and biomechanics. Traditionally, subjective assessment of the gait was conducted by health experts; however, with the advancement in technology, gait analysis can now be performed objectively and empirically for better and more reliable assessment. State-of-the-art semi-subjective and objective techniques for gait analysis have limitations that can be mitigated using advanced machine learning-based approaches. This paper aims to provide a narrative and a comprehensive analysis of cutting-edge gait analysis techniques and insight into clinical gait analysis. The literature of the previous surveys during the last decade is discussed. This paper presents an elaborated schema, including gait analysis history, parameters, machine learning approaches for marker-based and marker-less analysis, applications, and performance measures. This paper also explores the pose estimation techniques for clinical gait analysis that open future research directions in this area.
