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The growing demand for efficient and continuous patient healthcare monitoring has driven the integration of cutting-edge technologies such as Deep Learning (DL) and the Internet of Things (IoT) into modern medical systems. This paper presents an innovative framework for Automated Patient Behaviour and Monitoring using DL algorithms and IoT-enabled sensors to enhance real-time health data analysis, behavior prediction, and anomaly detection. The proposed system is designed to continuously monitor patients’ vital signs, movements, and daily activities through a network of IoT devices such as wearable sensors, smart beds, and environmental detectors. These devices collect a variety of physiological parameters, including heart rate, temperature, blood pressure, oxygen saturation, and motion patterns. Data collected in real time is transmitted securely to a cloud platform.
Keywords:
Deep Learning, Computer Vision, ESP32 Cam, Mobile Alert, YOLO Object Detection.
Cite Article:
"AUTOMATED PATIENT BEHAVIOUR AND MONITORING USING DL AND IOT", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 10, page no.a345-a355, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510033.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator