Personalized Wearable Movement Profiles with Environmental Context
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Solution Overview
Problem
Existing wearable devices rely on population-level movement models that fail to accurately predict individual health issues or injuries due to lack of consideration for personal movement patterns and environmental context, leading to inaccurate health assessments.
Innovation Solution
Collecting and integrating individual movement data with environmental data to generate personalized power spectral profiles, using AI models to analyze and predict health and behavior, and aggregate data for population-level insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If population-level movement models are used for health prediction, then broad generalizations can be made, but accuracy for individual predictions deteriorates
Solution Approach 1:
The patent segments the population-level data into individual-level profiles by creating personalized movement profiles for each user. The system divides the aggregate dataset into individual components, analyzing each person's unique movement patterns separately rather than treating everyone uniformly, thereby resolving the contradiction between broad coverage and individual accuracy.
Solution Approach 2:
The patent applies local quality by customizing the analysis to each individual's specific movement characteristics. Instead of applying a uniform population model to everyone, the system tailors the movement profile analysis to capture each person's unique gait, stride, and movement mechanics, enabling accurate individual-level predictions while maintaining population-level insights.
2Ease of operation
If conventional movement metrics are tracked, then basic activity monitoring is achieved, but detailed health analysis capability deteriorates
Solution Approach 1:
The patent adds another dimension to conventional movement metrics by incorporating environmental context data (terrain, weather, location) alongside traditional movement data. This multi-dimensional approach transforms simple step counting into rich movement profiles that capture subtle biomechanical variations and environmental interactions, enabling detailed health analysis without complicating the core data collection process.
3Quantity of substance
If aggregate movement data is analyzed, then population-level patterns are identified, but individual baseline accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by establishing individual baselines before conducting population-level analysis. The system first creates personalized movement profiles for each user, capturing their unique normal patterns, and then uses these pre-established individual baselines to accurately interpret deviations. This preliminary individualization enables both rich data utilization and precise individual measurement.
Data Source
AI summary
A method for biomechanical data analysis and monitoring is provided. Movement data is collected from one or more wearable devices associated with a user. Environmental data is integrated with the movement data by correlating the environmental data with specific points of the movement data. A power spectral profile is built for the user based on the movement data and environmental data, and includes a movement profile for the user. The power spectra is correlated with environmental factors and with data reported by the user. Variations of the power spectral profile are provided based on the environmental factors.