Wearable Content Recommendations via Physiological Data Correlation
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Solution Overview
Problem
Existing applications for wearable devices fail to effectively recommend content that significantly improves user health and wellness by not accurately identifying and suggesting relevant content based on physiological and classifier data.
Innovation Solution
A system that collects physiological data and classifier data from wearable devices, processes this information to provide personalized content recommendations, using techniques such as machine learning and data mining to rank and order content based on its effectiveness in impacting user health and wellness, and displays this content through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system provides personalized content recommendations based on physiological data, then user health and wellness improvement is enhanced, but system complexity increases
Solution Approach 1:
The system segments the content recommendation process into distinct functional modules: a data collection module that gathers physiological data from wearable devices, a data processing module that analyzes the collected data, and a content delivery module that presents personalized recommendations. This modular segmentation reduces overall system complexity by making each component independent and manageable while maintaining effective personalized recommendations.
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between raw physiological data collection and content recommendation delivery. This intermediary module processes and interprets physiological data, transforming it into actionable insights that drive personalized content recommendations, thereby reducing the complexity burden on both data collection and delivery components.
2Loss of information
If the system collects and processes physiological data for content recommendations, then recommendation relevance improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant physiological data features needed for content recommendations rather than processing all available data. The data processing module identifies and extracts key indicators from physiological data that directly correlate with health and wellness outcomes, reducing computational power requirements while maintaining high recommendation relevance.
Solution Approach 2:
The patent implements partial processing by focusing computational resources on analyzing only the most critical physiological parameters and time periods relevant to content recommendations. Rather than continuously processing all physiological data, the system selectively processes data that has the highest impact on recommendation accuracy, optimizing the balance between information relevance and processing power consumption.
Data Source
AI summary
Methods, systems, and devices for content recommendation are described. A device may receive physiological data associated with a user from a wearable device. The device may receive classifier data indicating an activity in which the user engaged. The classifier data is provided by the user via a graphical user interface (GUI) of the device running an application. The device may correlate the received physiological data and the received classifier data associated with the activity in which the user engaged, and select content for recommending to the user. The content may correspond to a respective effectiveness for regulating the received physiological data. The device may cause the GUI of the device to display the selected content for recommending to the user.