Nervous System State Estimation via Physiological Sensors
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Solution Overview
Problem
Current wearable technologies are unable to provide deeper insights into the states of physiological systems, particularly the nervous system, beyond measuring physiological conditions, making it challenging to observe or determine the internal states of a person's brain or nervous system effectively.
Innovation Solution
A system comprising sensors, a low-performance processing device, and a high-performance computing device that estimates nervous system states based on sensed data from skin conductance or cortisol levels, using algorithms such as forward filter, backward smoothing, and neural networks to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wearable technology measures physiological conditions, then physiological data is obtained, but deeper information about nervous system states cannot be provided
Solution Approach 1:
The patent uses algorithms as intermediary components that translate raw physiological sensor data into meaningful nervous system state estimates. The processing device and computing device act as mediators between the physical sensor measurements and the informational output about autonomic nervous system activation, enabling information extraction without direct neural measurement.
Solution Approach 2:
The patent replaces direct mechanical or electrical measurement of neural activity with computational estimation methods. Instead of measuring nervous system states directly through complex neural sensors, the system substitutes computational algorithms that infer nervous system state from more easily measurable physiological parameters like skin conductance.
2Measurement precision
If a high-performance computing device is used to determine updates, then estimation accuracy is improved, but power consumption and device complexity increase
Solution Approach 1:
The patent divides the computational workload into two segments: a first algorithm running on a low-power processing device for real-time estimation, and a second computationally intensive algorithm running on a high-performance computing device for periodic updates. This segmentation allows the system to maintain accuracy while managing power consumption by distributing computational tasks across different processing capabilities.
Solution Approach 2:
The system implements periodic action by having the high-performance computing device determine updates at specific intervals rather than continuously. The second algorithm runs periodically to refine estimation parameters, while the first algorithm continuously operates on the processing device. This periodic execution of intensive computations reduces overall power consumption while maintaining estimation accuracy.
3Speed
If real-time estimation is performed, then immediate feedback is provided, but computational resources on wearable devices are limited
Solution Approach 1:
The patent segments computational tasks by placing the first algorithm (for real-time estimation) on a processing device with limited resources, while the second algorithm (for periodic updates) runs on a high-performance computing device. This segmentation enables real-time processing to occur on resource-constrained wearable hardware without requiring the full computational power that would be needed for continuous high-accuracy estimation.
Solution Approach 2:
The processing device acts as an intermediary that performs real-time estimation using the first algorithm, providing immediate feedback within resource constraints. The high-performance computing device serves as a secondary intermediary that periodically refines the estimation parameters. This two-level intermediary structure enables real-time operation on wearable devices while leveraging external computational resources for improved accuracy.
Data Source
AI summary
A system for estimating a state of the nervous system includes at least one sensor configured to sense a continuously variable non-neural physiological condition as sensed data, a relatively low performance processing device configured to receive the sensed data and estimate a state of a nervous system based on the sensed data, and a relatively high performance computing device configured to provide updates to the processing device to improve the estimate of the state of the nervous system. A method for estimating a state of the nervous system includes obtaining sensed data indicative of a continuously variable non-neural physiological condition, estimating a state of a nervous system based on the sensed data, outputting the estimated state of the nervous system, and receiving updates to improve the estimating.