Nocturia Detection Using Sensor Fusion and Sleep Validation
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Solution Overview
Problem
Existing methods for nocturia detection are inaccurate due to reliance on subjective questionnaires and assumptions about bathroom visits, and lack of consideration for sleep patterns, leading to inefficiencies and potential misdiagnosis.
Innovation Solution
A system utilizing a combination of statistical analysis, rule-based analysis, and machine learning (SVM classifier) to analyze sensor data from multiple locations within a facility, creating a featured engineered sensor dataset to detect nocturia patterns without requiring supervised learning, and correlating these with sleep patterns to validate nocturia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If questionnaires are used for ambulatory assessment of nocturia, then treatment definition and therapeutic success measurement can be established, but compliance issues, embarrassment, and subjective bias occur
Solution Approach 1:
The patent replaces the mechanical/questionnaire-based assessment system with an automated sensor-based detection system. Motion sensors, weight sensors, and audio sensors objectively detect bathroom visits, bed exits, and urination sounds, eliminating subjective bias and compliance issues while maintaining measurement precision for nocturia assessment
Solution Approach 2:
The system enables self-service detection where the sensors automatically monitor and record nocturia events without requiring active patient participation or questionnaire completion. The system autonomously detects bathroom visits, correlates them with sleep patterns, and generates assessments, freeing patients from compliance burdens
2Productivity
If assumptions are made that when the person is not in bedroom during nights, they must have gone to bathroom, then bathroom visits can be predicted through label data set, but accuracy decreases due to lack of sleep pattern consideration
Solution Approach 1:
The patent merges multiple detection approaches by combining motion sensor data, weight sensor data, audio sensor data, and sleep pattern analysis. This integrated system correlates bathroom visit detections with actual sleep state transitions, significantly improving measurement precision while maintaining high detection efficiency through automated multi-parameter monitoring
Solution Approach 2:
The system implements feedback mechanisms where detected bathroom visits are correlated with sleep pattern data and sensor readings. The system continuously refines its detection accuracy by comparing predicted events with actual sensor-confirmed events, using this feedback to improve future detections and reduce false positives
Data Source
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AI summary
Nocturia has been defined as the need for an individual to wake up one or more times during the night to void. Further, Nocturia detection also requires analysis of sleeping pattern of the person. In such cases a lot of assumptions are made when the person is not in bedroom during nights. A method and system for detection and validation of Nocturia in the person has been provided. The system is utilizing a statistical based analysis, a rule based analysis, a machine learning based analysis and analysis of sleeping pattern of the person to detect and validate Nocturia. The system ensures that the person is not disturbed in his/her daily activities. Further, the processes deployed in the system are completely un-supervisory in nature meaning it does not have the dependency of needing to have trained machine learning dataset.