Output Control Device for Autonomic Nerve Activity Management
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Solution Overview
Problem
Current technologies for controlling internal states, such as biofeedback and neurofeedback, are insufficient in enabling users to easily manage their autonomic nerve activity, as they often rely on predefined optimal physiological index values that can vary individually and change over time, making it difficult to define a preferred internal state and perform effective feedback.
Innovation Solution
An output control device and method that uses a biosensor to detect physiological index values, calculating a state point in a physiological index space, and controlling output information based on changes in this space to help users control their internal states more effectively, incorporating mental stretch training, unsupervised and supervised individualization learning, and mental switch training to adaptively manage autonomic nerve activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined optimal physiological index values are used for feedback, then the feedback system can be simplified, but the system fails to account for individual variations and temporal changes in preferred internal states
Solution Approach 1:
The system automatically collects physiological data over time and uses machine learning to determine each user's preferred internal states without requiring manual input or predefined parameters. The system serves itself by autonomously adapting to individual users through unsupervised learning of their physiological patterns and supervised learning from their feedback.
Solution Approach 2:
The system transitions from using fixed predefined physiological index values to dynamically determined parameter ranges that adapt to each user's individual characteristics and changing preferences over time, allowing the feedback targets to evolve based on learned patterns.
2Measurement precision
If multiple physiological parameters are monitored to capture individual preferences, then personalization accuracy improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system employs dynamic machine learning models that continuously adapt to users' changing physiological patterns and preferences over time, rather than relying on static thresholds or fixed parameters, enabling accurate personalization that evolves with the user.
Solution Approach 2:
The system implements multi-level feedback mechanisms where users provide supervisory feedback on their preferred states, which is then used to refine the machine learning models, creating a continuous improvement loop that enhances personalization accuracy while managing complexity through intelligent data utilization.
3Speed
If real-time physiological monitoring is implemented, then immediate feedback can be provided, but the difficulty of detecting and measuring physiological states increases
Solution Approach 1:
The system uses machine learning models as intermediaries that translate complex, noisy physiological sensor data into meaningful interpreted states and preferences, simplifying the detection and measurement process while maintaining real-time responsiveness through automated pattern recognition.
Data Source
AI summary
There is provided an output control device, an output control method, and a program to provide technologies for enabling users to control their internal states more easily. The output control device including: an output control unit configured to control output of output information in accordance with a change in a state point in a physiological index space corresponding to a physiological index value based on biosensors. The output control unit controls the output of the output information in accordance with distribution density of previous state points for which a current state point serves as a standard in the physiological index space.