Smart Watch Sensor Network for Interoperable Health Monitoring
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Solution Overview
Problem
Existing health monitoring systems lack interoperability and efficiency in collecting and analyzing vital signs and movement data from diverse medical devices, leading to fragmented healthcare management and increased healthcare costs.
Innovation Solution
A wireless network of wearable sensors and devices that communicate via personal area networks, integrating data from various medical appliances and converting formats for interoperability, allowing seamless data sharing and analysis across different communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple diverse medical devices with different communication protocols are used to collect health data, then the comprehensiveness of health monitoring is improved, but the system complexity and difficulty of data integration increase
Solution Approach 1:
The patent employs a standardized communication interface and data format conversion layer that acts as an intermediary between diverse medical devices and the central processing system. This mediator translates various communication protocols (Bluetooth, WiFi, proprietary protocols) into a unified internal format, enabling seamless integration of blood pressure monitors, glucose meters, heart rate sensors, and other medical devices without requiring complex custom integration for each device type.
Solution Approach 2:
The system implements a universal data collection architecture that can handle multiple types of medical devices through a common interface. The standardized data structures and communication protocols allow the same system to collect, process, and analyze data from various sources (blood pressure, glucose, heart rate, activity trackers) using a single unified platform, thereby reducing overall system complexity while maintaining broad device compatibility.
2Productivity
If manual data collection and analysis methods are used, then system simplicity is maintained, but healthcare management efficiency and productivity decrease
Solution Approach 1:
The system implements automated data collection, validation, and analysis capabilities that operate without continuous human intervention. Sensors continuously monitor vital signs and automatically transmit data to the processing system, which then performs real-time analysis, generates health reports, and provides recommendations. This self-service automation eliminates manual data entry and analysis, significantly improving healthcare management efficiency while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The system incorporates automated feedback mechanisms where collected health data is immediately analyzed and used to generate actionable insights. The system provides real-time feedback to patients through mobile applications and alerts, and generates comprehensive reports for healthcare providers. This automated feedback loop eliminates the need for manual data interpretation and enables proactive health management, boosting productivity while maintaining clear system boundaries.
3Speed
If real-time health monitoring and data analysis are implemented, then health management responsiveness is improved, but energy consumption and operational costs increase
Solution Approach 1:
The system implements periodic sampling of health parameters rather than continuous monitoring at maximum resolution. Sensors collect data at optimized intervals based on the specific parameter being monitored (e.g., higher frequency for heart rate during exercise, lower frequency for baseline blood pressure). This periodic action maintains real-time monitoring capabilities while significantly reducing energy consumption compared to continuous high-rate sampling.
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sampling rate, data transmission frequency, and analysis depth based on current health conditions and user activity states. During normal conditions, the system uses lower-power modes with reduced sampling rates. When anomalies are detected or during high-activity periods, the system increases monitoring intensity. This adaptive parameter adjustment maintains responsiveness when needed while minimizing energy consumption during stable periods.
Data Source
AI summary
Systems and methods include a cellular, WiFi, and Bluetooth transceiver coupled to a processor; an accelerometer or a motion sensor coupled to the processor; and a sensor coupled to the processor to sense mood or body vital sign.