Emotional State Analysis for Personalized Digital Wellness Content
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Solution Overview
Problem
The rise of screen-based activities has led to increased anxiety and stress among North American workers, with existing technologies failing to effectively address these negative effects.
Innovation Solution
The development of systems and methods that proactively manage mental health by delivering customized digital wellness content based on a user's emotional state, utilizing user input, mood parameters, and historical interaction data to recommend tailored content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital content delivery systems provide personalized wellness content based on emotional state, then user mental health outcomes improve, but system complexity increases
Solution Approach 1:
The system segments the complex task of emotional state assessment into multiple independent components: physiological signal acquisition (heart rate, skin conductance, temperature), textual input analysis, and digital behavior pattern recognition. Each component processes specific data types independently before integration, reducing overall system complexity while maintaining comprehensive emotional assessment capability.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw multi-modal data inputs and wellness content recommendations. This intermediary layer processes and integrates diverse data sources (physiological signals, text, behavior patterns) into a unified emotional state representation, simplifying the connection between complex inputs and personalized content delivery.
2Measurement precision
If the system collects and processes multiple data sources to determine emotional state, then personalization accuracy improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary processing of physiological signals and behavioral data at the edge device before transmission to the server. Basic filtering, normalization, and feature extraction are completed locally, reducing the volume and complexity of data requiring intensive processing in the cloud, thereby lowering overall energy consumption while maintaining detection accuracy.
Solution Approach 2:
The system implements adaptive data collection that adjusts the level of processing based on user needs and context. Not all data sources are processed with equal depth at all times - the system applies partial processing to less critical data streams while maintaining full processing for key emotional indicators, optimizing the balance between accuracy and energy consumption.
Data Source
AI summary
Methods and devices are described for proactively managing an individual's mental health through the delivery of customized digital wellness content to a user based on their emotional state. In various examples, the present disclosure describes a method at a device. A user input is obtained and mapped to a mood parameter representative of the user's emotional state. Recommended digital wellness content is displayed on a user interface enabling the user to engage with the recommended digital wellness content, based on the mood parameter, and optionally based on the user's historical interaction with digital wellness content. In examples, engaging with the recommended digital wellness content may assist the user in reaching a state of emotional balance and promote wellness.