Biomarker Tracking System Voice Deviation Analysis
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Solution Overview
Problem
Current methods for tracking degenerative diseases like Parkinson's are limited by subjective evaluation guidelines, infrequent monitoring, and inability to account for individual symptom development, leading to inadequate tracking of patients' conditions outside of doctor's visits.
Innovation Solution
A biomarker tracking system using an RGB-D or RGB camera and microphone to capture voice data, comparing it to a baseline model to generate a speech data deviation model, and producing a disease state report, which can be automatically updated and reported, allowing for more frequent and objective monitoring of patients' conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a doctor performs examinations during office visits using established rating scales, then the evaluation follows standardized guidelines, but the monitoring frequency is limited to 1-2 hours per year
Solution Approach 1:
The system enables patients to perform self-monitoring at home using automated speech analysis technology. The microphone captures speech samples and the processor automatically analyzes them against baseline models, eliminating the need for frequent doctor visits while maintaining continuous monitoring capability.
Solution Approach 2:
The manual examination process by doctors is replaced with an automated electronic system. The processor substitutes for the doctor's subjective evaluation by objectively analyzing speech characteristics such as volume, frequency, and patterns, converting qualitative assessment into quantitative measurements.
2Ease of operation
If a doctor uses subjective evaluation guidelines to assess vocal characteristics, then the examination can be performed without specialized equipment, but the measurement precision is reduced due to interpretation variability
Solution Approach 1:
The subjective human evaluation process is replaced with automated electronic speech analysis. The processor objectively measures vocal characteristics including volume in decibels, frequency patterns, and speech stability, eliminating interpreter variability while maintaining ease of operation through simple microphone-based capture.
Solution Approach 2:
The system transforms qualitative vocal assessments into quantitative parameters. Speech volume is measured in decibels, frequency is analyzed in Hertz, and speech patterns are converted into measurable data points, enabling precise objective comparison against baseline models and clinical thresholds.
3Ease of manufacture
If fixed rating scales are used for Parkinson's disease evaluation, then the examination process is standardized, but the system cannot account for individual symptom development rates
Solution Approach 1:
The system performs preliminary action by establishing an individualized baseline speech profile for each patient during initial evaluations. This baseline serves as a personalized reference point that adapts to each patient's unique symptom presentation and progression rate, allowing future comparisons to be made against their specific trajectory rather than fixed population-based scales.
Solution Approach 2:
The system transitions from static fixed rating scales to dynamic individualized tracking. The baseline model is continuously updated as new speech data is collected, allowing the evaluation system to adapt to each patient's unique progression rate and symptom development pattern while maintaining standardized analysis methods.
4Device complexity
If doctors evaluate patients only during office visits, then the examination cost and complexity are minimized, but 8,765 hours per year of condition monitoring are lost
Solution Approach 1:
The system enables continuous self-monitoring by patients in their own environments. A simple microphone captures speech samples throughout the day, and automated processing analyzes the data without requiring doctor involvement, filling the 8,765 unmonitored hours with passive continuous assessment while keeping device complexity low.
Solution Approach 2:
The system implements periodic speech sampling throughout the day rather than requiring continuous active monitoring. The microphone periodically captures speech during natural conversations or reading tasks, and the processor batches analyzes the data, providing continuous monitoring coverage with minimal patient burden and system complexity.
Data Source
AI summary
A system for tracking biomarkers in subjects. In one embodiment, the biomarker tracking system has a sensory array including an RGB-D camera or RGB camera, a memory, and an electronic processor. The microphone captures voice data, including but not limited to tremor detection data, speech volume and pronunciation data, speech strength data, changes in tonality, hesitance in voice, and changes in speed or verbiage. A stored baseline biomarker model may comprise a voice data profile which may be pre-stored in the memory of a server and include a plurality of benchmarks. This electronic processor is configured to use this pre-stored voice data and compare it to the voice data captured with the microphone. The electronic processor is further configured to determine a set of attributes for the voice data, and generates a speech data deviation model based, at least in part, on the comparison of the speech data to the stored baseline biomarker model.