Machine Learning-Based IoT Healthcare Solution for Personalized Wellness Recommendations

Abstract

This invention discloses a Machine Learning-Based IoT Healthcare Solution designed to provide personalized wellness recommendations by leveraging real-time data from wearable IoT devices and machine learning (ML) algorithms. The system comprises a network of health sensors that monitor physiological and environmental data, including heart rate, sleep patterns, activity levels, and contextual factors such as time and weather. Data is preprocessed and analyzed using machine learning techniques, including clustering, regression, and neural networks, to detect health trends, identify anomalies, and forecast individual wellness needs. Based on these insights, a recommendation engine generates tailored wellness guidance covering areas such as physical activity, dietary adjustments, sleep improvement, and mental health. A continuous feedback mechanism enables the system to refine its recommendations over time based on user responses, leading to progressively personalized and effective wellness strategies. This invention addresses the growing demand for proactive, individualized health solutions, enhancing user engagement and supporting long-term health and well-being through data-driven, adaptive recommendations.

Specification

Description:This invention discloses a Machine Learning-Based IoT Healthcare Solution designed to provide personalized wellness recommendations by leveraging real-time data from wearable IoT devices and machine learning (ML) algorithms. The system comprises a network of health sensors that monitor physiological and environmental data, including heart rate, sleep patterns, activity levels, and contextual factors such as time and weather. Data is preprocessed and analyzed using machine learning techniques, including clustering, regression, and neural networks, to detect health trends, identify anomalies, and forecast individual wellness needs. Based on these insights, a recommendation engine generates tailored wellness guidance covering areas such as physical activity, dietary adjustments, sleep improvement, and mental health. A continuous feedback mechanism enables the system to refine its recommendations over time based on user responses, leading to progressively personalized and effective wellness strategies. This invention addresses the growing demand for proactive, individualized health solutions, enhancing user engagement and supporting long-term health and well-being through data-driven, adaptive recommendations. , C , Claims:1. A machine learning-based healthcare system comprising:
o An IoT sensor network for capturing real-time health data.
o A data processing module to normalize and prepare data for ML algorithms.
o A machine learning module configured to analyze health data, detect anomalies, and predict wellness needs.
o A recommendation engine that generates personalized wellness recommendations based on real-time data.
2. The system of claim 1 wherein the machine learning module uses a combination of clustering, regression, and neural networks.
3. The system of claim 2 wherein the recommendation engine adjusts recommendations based on user feedback using reinforcement learning techniques.
4. A method for generating wellness recommendations, comprising steps of:
o Collecting health data from IoT sensors.
o Preprocessing and normalizing the collected data.
o Analyzing the processed data using machine learning models to predict health needs.
o Generating personalized wellness recommendations based on ML outputs.
5. The method of claim 4, further comprising a feedback mechanism to adapt recommendations based on user interactions.

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