Patient Activity Peak Analysis for Accurate Health Change Detection
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Solution Overview
Problem
Existing medical systems face challenges in accurately detecting changes in patient health due to variations in daily activity patterns, leading to issues like false positives and false negatives, and require significant resource utilization.
Innovation Solution
Medical systems that analyze peak and non-peak time periods in patient activity data to determine daily activity metrics, identifying unique patterns for each individual, thereby improving detection accuracy and reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical systems monitor patient activity data without distinguishing peak and non-peak periods, then they can detect health changes, but they produce false positives and false negatives due to variations in daily activity patterns
Solution Approach 1:
The patent segments the daily activity period into peak and non-peak periods based on the patient's individual activity patterns. By dividing the monitoring day into these distinct segments and analyzing activity metrics separately for each segment, the system captures the temporal variations in patient behavior. This segmentation allows the system to compare activity levels at similar times across different days, significantly reducing false positives and false negatives that arise from comparing disparate time periods.
Solution Approach 2:
The system dynamically identifies peak and non-peak periods for each patient based on their individual activity patterns rather than using fixed time windows. The processing circuitry analyzes historical activity data to determine when each patient is most and least active, and these dynamic time periods are continuously adapted as new data becomes available. This dynamic approach accommodates variations in patient behavior and schedules, improving detection accuracy.
2Measurement precision
If medical systems use comprehensive activity monitoring to detect health changes, then detection capability is improved, but resource utilization increases significantly
Solution Approach 1:
The patent extracts and focuses analysis only on the most informative portions of the activity data - specifically the peak and non-peak periods. Instead of processing and analyzing the entire 24-hour activity dataset, the system identifies and extracts the key time segments that provide the most diagnostic value. This extraction approach maintains high detection precision while significantly reducing the computational resources, processing power, and energy required for analysis.
Solution Approach 2:
The system performs partial monitoring by focusing only on critical peak and non-peak periods rather than continuous comprehensive monitoring. By applying monitoring intensity selectively to these specific time windows and reducing or suspending analysis during less informative periods, the system achieves effective health change detection with lower resource consumption and reduced operational requirements.
3Measurement precision
If medical systems implement personalized peak and non-peak period analysis, then detection accuracy improves for individual patients, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically identifying each patient's peak and non-peak periods based on their own historical activity data, without requiring manual input, physician configuration, or external calibration. The processing circuitry autonomously analyzes the patient's activity patterns, determines their individual temporal characteristics, and configures the monitoring parameters automatically. This self-configuration approach enables personalized accurate monitoring while keeping the device interface simple and reducing operational complexity.
Data Source
AI summary
This disclosure is directed to systems and techniques for detecting change in patient health based upon peak and non-peak patient activity data. In some examples, the peak and non-peak patient activity data correspond to one or more peak (time) periods and the one or more non-peak periods, respectively, where at least one peak period and at least one non-peak period corresponds a highest activity level and a lowest activity level, respectively, for a single day. If a change in patient health is detected, the techniques described herein may direct a medical device to generate for display output indicating the detection of the change in patient health.