Wearable Data Aggregation for Behavioral Pattern Detection
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Solution Overview
Problem
Wearable devices generate vast amounts of raw data that lack context and insights into user health and activities, failing to provide users with meaningful trends, averages, or patterns, and are often incompatible with each other.
Innovation Solution
A method and system that aggregate historical data from wearable devices into characterizations of baseline user behavior patterns and adherence or deviation from those patterns, using computational alignment techniques like dynamic time warping to generate regularity scores, which are transmitted to users for feedback and health-related actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If wearable devices collect and transmit raw data to connected devices, then the quantity of data increases, but the data lacks context and meaningful insights for users
Solution Approach 1:
The patent introduces an intermediary processing system that receives raw data from wearable devices, applies computational alignment techniques (such as dynamic time warping) to detect patterns and behaviors, and transforms the data into meaningful insights. This intermediary layer bridges the gap between raw data collection and user-comprehensible information, preventing information loss while maintaining data quantity.
Solution Approach 2:
The system performs preliminary processing of raw data by detecting user habits and behaviors through pattern recognition algorithms before presenting information to users. By pre-processing the data to identify trends, anomalies, and behavioral patterns, the system prepares meaningful insights in advance, eliminating the need for users to manually interpret raw data.
2Measurement precision
If wearable devices monitor user biometric signals and activities continuously, then measurement precision improves, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts only the essential features and patterns from continuous biometric data streams using computational alignment techniques. Instead of processing all raw data, the system identifies and extracts meaningful behavioral patterns, habit formations, and anomalies, thereby reducing processing complexity while maintaining measurement precision for critical health metrics.
3Measurement precision
If wearable devices store historical data for pattern recognition, then behavior detection accuracy improves, but data storage requirements increase
Solution Approach 1:
The patent creates compressed representations of historical data through computational alignment and pattern detection. Instead of storing all raw historical data, the system generates condensed pattern models and behavioral signatures that capture the essential information needed for accurate behavior detection, significantly reducing storage requirements while maintaining detection accuracy.
Data Source
AI summary
Biometric data or metrics of interest to a user are observed by wearable devices over a recurring time interval and aggregated into a representation of the user's baseline habits or patterns of behaviour. Present measurement of the same data or measures of interest within the recurring time interval provides a measure of the user's adherence to, or deviation from, the established habits or patterns as represented by a regularity score. Dynamic time warping barycenter averaging can account for time dependencies in the data or metrics of interest in both the baseline computation of past user habits and the characterization of the user's present behaviours. User regularity scores can be displayed to the user to both drive positive behavioural changes as well as initiate different health-related actions or recommendations for the user. Regularity scores can be computed repeatedly in line with long term changes in user habits and patterns of behavior.