Biofeedback System Using Relevance Assessment for Mental State Control
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Solution Overview
Problem
Conventional biofeedback systems, such as wearable devices from manufacturers like Garmin and FitBit, fail to accurately assess attention and fatigue using pulse and movement data alone, which is insufficient for sophisticated applications like semi-autonomous vehicle control systems, as they lack the ability to provide a gradient of mental state assessment and interpret physiological biometrics in the context of task performance.
Innovation Solution
A system and method utilizing multiple biometric sensors, including EEG, EMG, GSR, and ECG, to acquire and process data using machine learning algorithms, generating a continuous mental state score that controls devices such as vehicles or alerts based on relevance and significance, enabling real-time monitoring and feedback for improved performance and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple biometric sensors (EEG, EMG, GSR, ECG) and machine learning algorithms are used to accurately assess mental state, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system segments the complex biofeedback functionality into separate modular components: multiple independent biometric sensors (EEG, EMG, GSR, ECG) that can be selectively activated, and separate machine learning models for different mental state assessments. This modular architecture allows the system to achieve high measurement precision through multiple data sources while managing complexity through independent, reusable modules that can be configured based on specific application needs.
2Productivity
If biometric data is continuously monitored and processed in real-time to provide immediate feedback, then productivity and response time are improved, but use of energy increases
Solution Approach 1:
The system implements periodic sampling of biometric data rather than continuous monitoring, with adjustable sampling rates that adapt to the current task demands and mental state. Machine learning models process data in discrete time windows, performing assessments at optimized intervals rather than continuously. This periodic processing approach maintains real-time feedback capability and productivity while significantly reducing computational load and energy consumption compared to continuous processing.
3Ease of operation
If biometric data is assessed in isolation without task context, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system introduces task performance data as an intermediary that bridges the gap between simple biometric monitoring and accurate mental state assessment. The machine learning models integrate both biometric signals and task performance metrics, using the task context as a mediator to disambiguate mental states. For example, elevated heart rate can be interpreted differently depending on whether task performance is declining or maintaining, allowing the system to maintain measurement precision while preserving ease of operation through automated contextual interpretation.
Data Source
AI summary
Described is a system for biofeedback, the system including one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations including using a first biometric sensor during performance of a current task, acquiring first biometric data, and producing a first biometric value by assessing the first biometric data. The one or more processors further perform operations including determining a first relevance based on a first significance of a first correlation between the first biometric value and the current task, and controlling a device based on the first relevance and the first biometric value.