Medical Device Spectral Activity Processing
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Solution Overview
Problem
Current medical devices relying on time domain representations of accelerometer data for determining activity levels in patients are less accurate, leading to suboptimal selection of stimulation parameters for electrical stimulation therapies.
Innovation Solution
Converting accelerometer information into frequency domain coefficients using techniques like FFT, applying filters to isolate relevant frequency ranges, and assigning different weights to coefficients from various axes to enhance accuracy in determining activity levels, thereby improving the selection of stimulation parameters for personalized therapy delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If time domain representation of accelerometer data is used to determine activity levels, then the device complexity is reduced, but the measurement precision of activity levels deteriorates
Solution Approach 1:
The patent transforms accelerometer data from time domain to frequency domain using Fast Fourier Transform (FFT), changing the representation parameter from temporal sequences to spectral components. This allows filtering of specific frequency ranges (e.g., 0.75-10 Hz for human activity) to improve activity level determination accuracy while managing processing complexity through efficient FFT algorithms.
Solution Approach 2:
The patent extracts relevant frequency components from the full spectrum of accelerometer data by applying bandpass filters. Specifically, it isolates frequency ranges corresponding to human activity (0.75-10 Hz) while excluding other frequencies, thereby improving measurement precision by focusing only on diagnostically relevant spectral information.
2Measurement precision
If frequency domain coefficients are used to determine activity levels, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent segments the frequency spectrum into distinct bands (e.g., 0.75-10 Hz for human activity, other ranges for different conditions) and processes each segment separately. This segmentation allows selective filtering and weighting of frequency coefficients, improving activity level determination while organizing complex processing into manageable modular stages.
Solution Approach 2:
The patent performs preliminary FFT transformation and frequency domain filtering on accelerometer data before activity level determination. By pre-processing the data to extract and weight relevant frequency components, it simplifies the subsequent activity classification task and improves overall measurement precision through advance spectral analysis.
3Quantity of substance
If all accelerometer data is processed without filtering, then the quantity of information is maximized, but the reliability of activity level determination deteriorates due to non-relevant data
Solution Approach 1:
The patent extracts only the relevant frequency components from the complete accelerometer data spectrum by applying bandpass filters. It isolates frequency ranges corresponding to human activity (0.75-10 Hz) while excluding other frequencies, thereby improving reliability by focusing on diagnostically relevant information and discarding non-relevant data components.
Solution Approach 2:
The patent applies different weighting factors to different frequency domain coefficients based on their relevance to human activity. Specifically, it weights coefficients within the 0.75-10 Hz range differently from other frequencies, giving appropriate emphasis to locally relevant spectral information while minimizing the influence of non-relevant frequency components.
Data Source
AI summary
A system for providing stimulation to a patient includes one or more processors implemented in circuitry and one or more accelerometers configured to generate one or more accelerometer signals. The one or more processors are configured to determine accelerometer information for a medical device associated with the patient based on the one or more accelerometer signals and convert the accelerometer information into frequency domain coefficients. The one or more processors are further configured to determine an activity level for the patient based on the frequency domain coefficients and determine one or more stimulation parameters based on the activity level. The one or more processors are further configured to output electrical stimulation to the patient based on the one or more stimulation parameters.