Cloud Physiological Data Modeling for Real-Time Health Feedback
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Solution Overview
Problem
Conventional health care systems rely on limited physiological data from wearable sensors, lacking comprehensive and real-time analysis capabilities, especially without professional physician intervention, making accurate preventive determinations difficult.
Innovation Solution
An intelligent system utilizing cloud computing and artificial intelligence to process physiological data from multiple sources, including user-end devices and medical institutions, through machine-learning to establish a physiological prediction model, enabling comprehensive and accurate determination of physiological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional physiological sensors are used to monitor care recipients, then basic physiological data can be collected, but the data is limited and insufficient for accurate preventive determination
Solution Approach 1:
The patent combines multiple physiological sensors (heart rate sensor, temperature sensor, sleep sensor, breathing sensor, fall detection sensor) into an integrated monitoring system that collects diverse physiological data simultaneously, transforming limited single-parameter monitoring into comprehensive multi-parameter health assessment
Solution Approach 2:
The monitoring system is designed to perform multiple functions including heart rate monitoring, temperature measurement, sleep analysis, breathing detection, and fall detection, replacing the need for separate specialized devices and enabling comprehensive physiological state determination from a single system
2Measurement precision
If comprehensive physiological data is collected from multiple sources, then accurate physiological state determination can be achieved, but the system complexity increases
Solution Approach 1:
The patent implements a nested data processing architecture where raw physiological data from multiple sensors is first processed locally by the monitoring device, then aggregated and further analyzed by the health management system, with results fed back to provide personalized recommendations, creating a layered processing structure that manages complexity
3Speed
If real-time physiological monitoring is implemented, then timely health assessment can be provided, but the system requires continuous operation consuming energy
Solution Approach 1:
The monitoring system implements periodic data collection and analysis cycles, where physiological parameters are monitored continuously but comprehensive analysis is performed at scheduled intervals or when threshold values are exceeded, allowing the system to balance real-time responsiveness with energy conservation by entering low-power states between analysis cycles
Data Source
AI summary
An intelligent method and an intelligent system for processing physiological data are provided. The method is operated in a cloud system for implementing a health cloud. A database of the intelligent system stores personalized physiological data collected from various user-end devices, and physiological data transmitted from various medical data sources. A big data is therefore formed. The intelligent system receives continuous physiological data and non-continuous physiological data from each of the user-end devices by a communication circuit. The intelligent system performs big-data analysis to establish a physiological prediction model for predicting various physiological states, and therefore determines a physiological status according to the continuous physiological data or the non-continuous physiological data transmitted from each of the user-end devices. A feedback message is accordingly created and sent to a user in real time.