Biobehavioral Rhythm Modeling from Wearable Sensor Streams
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Solution Overview
Problem
Current methods for modeling biobehavioral rhythms from mobile and wearable data streams face challenges in processing massive, noisy, and incomplete longitudinal data, and require automation to extract useful knowledge across various temporal granularities, while also struggling with the interpretation of numerous rhythm models generated by different data sources.
Innovation Solution
A computational framework that processes raw sensor data from mobile and wearable devices to extract high-level features, models biobehavioral rhythms for each sensor feature and their combinations, detects periodicity, and uses machine learning to predict health and lifestyle outcomes, capable of handling various periods and sensor combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If passive sensing of physiological and behavioral signals from mobile and wearable devices is used to study human rhythms broadly, then the scope of study expands to real-world settings, but the data becomes massive, noisy, and incomplete requiring careful processing
Solution Approach 1:
The patent segments the longitudinal timeseries data into multiple temporal granularities (e.g., daily, weekly, monthly cycles) to process and analyze different aspects of the data at appropriate resolutions. This allows the system to handle the massive and noisy data by breaking it down into manageable segments that can be processed systematically while preserving useful fine-grained knowledge.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes feature extraction, rhythm detection, and normalization algorithms. These intermediaries transform the raw noisy data into processed features that highlight rhythmic patterns while filtering out noise, thereby improving data reliability while maintaining the broad scope of real-world study.
2Loss of information
If each data source is explored to identify biological and behavioral indicators, then comprehensive insights are achieved, but the process becomes exhaustive and requires automation
Solution Approach 1:
The patent implements automated rhythm detection algorithms that self-adjust to identify biological and behavioral indicators across different data sources. The system automatically detects periodicities, extracts features, and identifies rhythmic patterns without requiring manual analysis, thereby achieving comprehensive insights while reducing processing time through automation.
Solution Approach 2:
The patent changes parameters such as detection thresholds, time window lengths, and feature extraction methods to automatically adapt to different data sources and rhythmic patterns. This allows the system to comprehensively analyze multiple data sources by adjusting parameters rather than manually processing each source, reducing time while maintaining completeness.
3Adaptability or versatility
If exhaustive number of rhythm models are generated from each data source, then comprehensive modeling is achieved, but manual interpretation becomes difficult
Solution Approach 1:
The patent extracts and prioritizes the most significant rhythmic features and patterns from the exhaustive number of generated models. By identifying and extracting key indicators (such as dominant periods, amplitude, phase information), the system reduces the complexity of interpretation while maintaining comprehensive modeling coverage. This allows researchers to focus on the most meaningful findings rather than sifting through all models.
Solution Approach 2:
The patent transforms the high-dimensional space of numerous rhythm models into a lower-dimensional representation by aggregating results across data sources and temporal granularities. This dimensional reduction produces summary statistics and visualizations that maintain comprehensive modeling information while significantly improving ease of interpretation for researchers.
Data Source
AI summary
A technique for providing biobehavioral rhythm models that generate a series of characteristic features which are further used for measuring stability in biobehavioral rhythms and to predict different outcomes such as health status through a machine learning component. A computational framework is provided for modeling biobehavioral rhythms from mobile and wearable data streams that rigorously processes sensor streams, detects periodicity in data, models rhythms from that data and uses the cyclic model parameters to predict an outcome. The framework can reliably discover various periods of different length in data, extract cyclic biobehavioral characteristics through exhaustive modeling of rhythms for each sensor feature; and provide the ability to use different combination of sensors and data features to predict an outcome.