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Human motion monitoring for falling detection using artificial intelligence
(Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2023) Al-Hassani, Raghad Tariq Abdulrazzaq; Atilla, Doğu Çağdaş
Hospitals must continually monitor their patients' actions to lower the chance of accidents such as patient falls and slides. Human behaviour is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behaviour is challenging since it is hard to forecast how individuals will think or act in response to a certain event. Deep learning and data mining enable turbulence prediction models. Industry progressed greatly. This study anticipated four motion turbulence types using auto encoding and two other methods. To improve accuracy. The AEC's forecasts average 0.01 inaccuracy. Consequently, the 4SFNN method divides the data into four areas for each classifier. The system now predicts 98.6025 percent. PSO optimization boosts system performance, according to our research. Bayesian Regularization training enhanced the recommended system's performance to 98.63 percent accuracy and 0.015 percent MAE.