Description:TITLE OF INVENTION
Smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling

FIELD OF INVENTION
The present invention generally relates to the field of disease detection, particularly to early detection of chronic diseases. More particularly, the present invention relates to a smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling.

BACKGROUND OF INVENTION

US20250099014 – “Method and system for real-time calibration of ear-EEG device” describes “The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.”

None of the above-mentioned prior arts neither teaches nor discloses about a smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling.

OBJECTS OF INVENTION
One or more of the problems of the conventional prior art may be overcome by various embodiments of the system of present invention.

It is the primary object of the present invention is a smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling.

SUMMARY OF INVENTION
It is an aspect of the present invention is a smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling.

DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING FIGURES
The present invention as herein described about a smart wearable framework that uses intelligent predictive analytics and integrates multi-sensor data to detect chronic illnesses early. The framework is made up of a small wearable gadget that has environmental and physiological sensors built into it, such as motion, temperature, galvanic skin reaction, electrocardiography, and photoplethysmography (PPG and ECG). To guarantee low latency and energy efficiency, the system continually gathers real-time biometric inputs, which are then analyzed on-device utilizing edge AI. Through communication with a smartphone application, the wearable gadget collects data, visualizes it using a health dashboard, and sends out notifications when abnormalities are found. Early warning signs of chronic illnesses including diabetes, respiratory ailments, and cardiovascular disorders are predicted using a customized machine learning model. Additionally, the mobile app facilitates safe data synchronization with a cloud-based backend that hosts improved AI models and stores long-term medical records. This embodiment promotes rapid medical intervention, individualized health tracking, and preventative healthcare delivery.
, Claims:CLAIMS:
We claim,
1. A smart wearable framework for early detection of chronic diseases using multi-sensor fusion and predictive modelling, a method claim, a small wearable gadget that has environmental and physiological sensors built into it, such as motion, temperature, galvanic skin reaction, electrocardiography, and photoplethysmography (PPG and ECG);
wherein, to guarantee low latency and energy efficiency, the system continually gathers real-time biometric inputs, which are then analyzed on-device utilizing edge AI and through communication with a smartphone application, the wearable gadget collects data, visualizes it using a health dashboard, and sends out notifications when abnormalities are found; and
wherein, an early warning signs of chronic illnesses including diabetes, respiratory ailments, and cardiovascular disorders are predicted using a customized machine learning model.