Patient Monitoring and Disease Analysis Based on IoT Wearable Sensors and Cloud Computing

The number of patients to be treated in healthcare facilities is increasing over time due to the growing awareness and importance of formal healthcare. Most healthcare centers lacked modern automation systems, such as continuous patient monitoring, which of schedule the doctor or nurse's visits with the patient. This research is designed to implement a new method of patient monitoring system in a treatment room, using wearable sensors enabled by the Internet of Things (IoT) technology and patient data analysis in cloud computing. The proposed system consists of several sensors to retrieve patient information, such as body temperature, heart rate, blood pressure, Electrocardiogram (ECG), and motion sensor. Those parameters are used to analyze patient disease and healthcare during treatment with real-time monitoring to ensure medical professionals obtain the latest update on patient health. The system is designed in an embedded module that is applicable for mobile phones and connected through a Wireless Fidelity (Wi- Fi) system in healthcare facilities. All the patient data retrieved by IoT sensors is delivered to cloud computing to store the data and then analyzed using Long Short-Term Memory (LSTM) Algorithm to examine data related to the patient health and illness. Results show the performance of the IoT sensing system working well and are able to detect and send the data in real-time to healthcare centers globally through a mobile device. Based on real case scenario testing performance, the system accuracy ability to send data is more than 95% while any abnormality is readily detected. Overall, the system has enormous potential for further development and widespread use in the healthcare industry for efficient operations.