Heart Rate Variability Analysis for Objective Stress Detection
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Solution Overview
Problem
Current methods for monitoring stress levels are limited, particularly in detecting early signs of stress, as they often rely on subjective self-assessment tools that fail to differentiate between varying levels of stress and require professional intervention, lacking objective and daily monitoring capabilities.
Innovation Solution
A computer-implemented method and system that utilizes heart rate data analysis, incorporating a knowledge base with expert-evaluated data and machine-learning computational models to assess mental states, distinguishing between normal, moderate, and severe stress levels by processing heartbeat records and activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-assessment questionnaires are used to monitor stress levels, then individuals can self-administer the assessment, but the method cannot objectively differentiate between different levels of stress and requires professional intervention
Solution Approach 1:
The patent replaces subjective self-assessment questionnaires with objective physiological measurements using heart rate monitors and activity trackers. The system automatically collects and analyzes heart rate variability, sleep patterns, and activity data to compute stress metrics, eliminating the need for manual questionnaire completion while providing precise, differentiated stress level measurements through computational algorithms.
Solution Approach 2:
The patent introduces a computational model and knowledge base as intermediaries between raw physiological data and stress assessment. The system processes heart rate and activity data through algorithms that reference expert-evaluated training data, automatically generating objective stress level classifications without requiring professional intervention for interpretation.
2Measurement precision
If professional health care assessments are used to identify stress symptoms, then accurate detection of stress levels is achieved, but early signs of stress remain undetected until psychological and physical health impacts become significant
Solution Approach 1:
The patent continuously monitors physiological parameters in the background and computes stress metrics automatically, enabling early detection of stress patterns before they manifest as significant health problems. The system analyzes trends in heart rate variability, sleep quality, and activity levels to identify emerging stress issues proactively, rather than waiting for symptoms to become apparent.
Solution Approach 2:
The patent implements continuous, automated monitoring of physiological data through wearable devices that collect heart rate and activity information throughout the day. This continuous data stream enables ongoing stress assessment without interruption, allowing the system to detect early signs of stress and track changes over time, providing timely alerts before health impacts occur.
3Measurement precision
If regular appointments with health care professionals are scheduled for stress monitoring, then comprehensive assessment is provided, but the frequency and cost of such appointments create barriers to ongoing stress management
Solution Approach 1:
The patent enables individuals to automatically monitor and manage their own stress levels through a system that collects physiological data, computes stress metrics, and provides personalized feedback without requiring professional intervention. The knowledge base contains expert-evaluated training data that empowers the system to autonomously assess stress levels and provide actionable recommendations, making comprehensive stress monitoring accessible and affordable for everyday use.
Data Source
AI summary
A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric. The indication of stress condition is provided (310) as output.