Heart Rate Pattern Analysis for Cognitive Task Scheduling
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Solution Overview
Problem
Existing methods do not effectively determine and utilize cognitive states to improve task performance and efficiency.
Innovation Solution
An apparatus and method that analyze heart beat measurements to determine a cognitive activation score by comparing physiological patterns to a baseline, using peak detection and machine learning to assess emotional or cognitive states, and provide indications or triggers for task scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cognitive states are determined using existing methods, then task performance may be maintained, but cognitive states are not effectively determined and utilized to improve task performance and efficiency
Solution Approach 1:
The patent transforms physiological measurements (heart rate, skin conductance, temperature) into cognitive state indicators by analyzing changes in these parameters over time. The system monitors multiple physiological parameters simultaneously and uses their temporal patterns to determine cognitive states, thereby improving measurement precision for cognitive assessment and enabling better task performance optimization.
2Productivity
If physiological measurements are continuously monitored to determine cognitive states, then task performance can be optimized, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing physiological measurements in the background before cognitive tasks are performed. Baseline physiological patterns are established in advance through repeated measurements during non-task periods. When a cognitive task is initiated, the system compares real-time measurements against these pre-established baselines, enabling rapid cognitive state determination without complex real-time analysis, thus optimizing task performance while managing system complexity.
Solution Approach 2:
The system uses the user's own physiological data to create personalized baseline patterns that serve as reference standards for future cognitive state assessments. Each user's unique physiological responses are captured and stored, allowing the system to self-calibrate and provide accurate cognitive state determination tailored to individual characteristics, reducing the need for external calibration and simplifying ongoing operation.
3Measurement precision
If baseline physiological patterns are obtained through user inputs during stimulus exposure, then cognitive activation scores can be determined, but time and user burden increase
Solution Approach 1:
The system maintains continuous collection of physiological measurements during baseline acquisition, ensuring that data collection is an ongoing process rather than a discrete, time-consuming event. By continuously monitoring physiological parameters and automatically identifying patterns during stimulus exposure, the system accumulates baseline data efficiently without requiring separate dedicated calibration sessions, thereby improving measurement precision while minimizing time loss and user burden.
Data Source
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AI summary
Apparatus, methods, and systems for receiving physiological measurements relating to a user, wherein the physiological measurements comprise heart beat measurements; determining, based on intervals between peaks of the physiological measurements, a first physiological pattern; determining a cognitive activation score based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.