Wearable Physiological Sensing for Objective Quality of Life Scoring
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Solution Overview
Problem
Existing methods for assessing health-related quality of life rely heavily on subjective human feedback, which introduces variability and bias, and lack automated, objective monitoring systems.
Innovation Solution
A wearable medical monitoring system that includes sensors to collect data on physical activity, sleep metrics, and vital signs, processing this data to determine objective quality of life scores, and providing actionable insights to users or healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective human feedback is used to assess health-related quality of life, then the assessment can be performed with simple methods, but the results introduce variability and bias
Solution Approach 1:
The patent replaces subjective human feedback mechanisms with automated electronic sensing and processing systems. Sensors continuously collect objective physiological data (acceleration, heart rate, temperature, etc.) and a processor automatically analyzes this data to generate quality of life scores, eliminating human subjectivity and bias while improving measurement precision.
Solution Approach 2:
The monitoring system performs self-assessment by automatically collecting, processing, and analyzing the user's physiological data without requiring external human evaluation. The processor autonomously generates quality of life assessments based on sensor data, enabling the system to serve itself in the assessment process.
2Reliability
If automated sensor-based monitoring is implemented, then objective and consistent quality of life scores can be obtained, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a multi-functional integrated system where a single processor handles multiple sensor inputs (acceleration, heart rate, temperature, etc.) and performs various functions including data collection, analysis, quality of life scoring, and alert generation. This universal approach consolidates complexity into a single coordinating component rather than requiring separate systems for each function.
Solution Approach 2:
The monitoring system uses a hierarchical structure where the processor nests multiple levels of data processing within itself - from raw sensor data collection to intermediate analysis to final quality of life score generation. This nested organization manages complexity by structuring processing tasks in concentric layers within a single integrated unit.
Data Source
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AI summary
In an example method, a computer system receives, from a wearable medical sensor, acceleration data indicating physical activity of a subject, activity classification data, sleep data, and one or more vital sign metrics. The system determines a physical activity score, a sleep score, and a vital signs score based on the acceleration data, the activity classification data, the sleep data, and the one or more vital sign metrics. The system determines a quality of life score for the subject based on the physical activity score, the sleep score, and the vital signs score; and the system causes the quality of life score to be presented to the subject using a display screen.