Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Impacts of Falls are risky for the elderly patients as it can lead to serious injuries or cause death in severe cases. In this work, we disclose new distinct aspect of integrated architecture for biomedical embedded control systems intended for use in Fall detection for in-patient monitoring. We designate human characteristics for postures recognition into two portions: stationary postures and active transitions; subsequently 3-axial accelerometers have used at different body locations to enhance sensitivity of detection and increase its precision. Our system can monitor different types of stationary postures: Sitting, Standard Fowler{u2019}s (30{u00B0}-60{u00B0}), Supine, Prone, and Lateral Recumbency. Falls are considered as active transitions. The real-time patients monitoring system consists of three modules: wearable accelerometers module with vital signs monitoring sensors such as heart rate, body temperature, etc., intravenous infusion monitoring module, and wireless module. Each module equipped with alarm system to alert the medical staff at emergency situations

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