Wearable Readiness Scoring Using Physiological and Subjective Inputs
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Solution Overview
Problem
Current wearable computing devices lack the ability to dynamically and accurately determine an individual's readiness for daily activities, providing generic and less insightful readiness scores that do not account for both physiological and subjective factors, leading to potential over-exertion and burn-out.
Innovation Solution
A wearable device that dynamically monitors physiological signals and integrates subjective feedback to determine a readiness score based on physical and mental energy components, including heart-related metrics, accelerometer measurements, and user input, adjusting the score in real-time to provide personalized guidance for daily activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic readiness scoring methods are used, then device complexity is reduced, but measurement precision and personalization are insufficient
Solution Approach 1:
The readiness score is segmented into multiple distinct components: physical energy (sub-divided into physical energy reserve, physical energy consumption, and physical recovery), mental energy (sub-divided into mental energy reserve, mental energy consumption, and mental recovery), and health status. Each component is measured and evaluated separately using specific physiological parameters, allowing for precise measurement while organizing complexity into manageable segments.
Solution Approach 2:
The system transitions from traditional single-dimensional readiness assessment to a multi-dimensional evaluation framework. Readiness is assessed across multiple dimensions including physical energy reserve, physical energy consumption, physical recovery, mental energy reserve, mental energy consumption, mental recovery, and health status, each contributing to the overall readiness score through weighted integration.
2Measurement precision
If real-time dynamic monitoring of multiple physiological parameters is implemented, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system implements periodic monitoring of physiological parameters rather than continuous monitoring. Readiness components are evaluated at scheduled intervals (e.g., upon waking, before activities, during transitions), allowing the device to enter low-power states between measurements while still providing timely readiness assessments for daily activities.
Solution Approach 2:
The monitoring frequency and intensity are dynamically adjusted based on user state and context. The system adapts measurement patterns according to detected activity levels, time of day, and previous readiness scores, intensifying monitoring when changes are detected and reducing it during stable periods to conserve energy.
3Measurement precision
If multiple readiness components are measured and integrated, then measurement precision improves, but device complexity increases
Solution Approach 1:
The readiness assessment is segmented into distinct measurable components (physical energy reserve, physical energy consumption, physical recovery, mental energy reserve, mental energy consumption, mental recovery, and health status), each processed through dedicated algorithms that evaluate specific physiological parameters and combine them into component scores before integration into the overall readiness score.
Solution Approach 2:
The system incorporates feedback loops where preliminary readiness component assessments inform subsequent measurement priorities and parameter selection. User responses to readiness questions and actual physiological measurements feed back into adjusting the weighting and focus of subsequent evaluations, optimizing processing efficiency while maintaining precision.
Data Source
AI summary
A method of dynamically determining a readiness score of a user using a wearable device. The method includes detecting, by the wearable device when worn by the user, one or more sensor or physiological signals associated with the user, and determining, by a processor, an estimation of two or more readiness components based on the one or more sensor or physiological signals. The two or more readiness components include a physical energy of the user associated with physical energy consumption of the user, physical energy recovery of the user, or both. The two or more readiness components also include a mental energy of the user associated with mental energy consumption of the user, mental energy recovery of the user, or both. The method further includes determining the readiness score based on the estimation for each of the two or more readiness components.