Pain Assessment via Sensor Activity Classification
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Solution Overview
Problem
Current pain assessment methods rely on subjective self-reported scales, which are limited by variations in pain tolerance, psychological factors, and communication biases, making them insufficient for accurately estimating pain intensity and quality of life.
Innovation Solution
A computer-implemented method using sensor signals from wearable or implanted devices to objectively assess pain by classifying physical activity into different classes based on signal intensity, enabling the calculation of a behavior assessment score indicative of pain experienced by the patient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-reported scales (NRS, ODI) are used for pain assessment, then patient feedback can be obtained, but accuracy is limited due to variation in pain tolerance, psychological factors, and communication bias
Solution Approach 1:
The patent replaces the subjective self-reporting mechanism with an objective sensor-based measurement system. Accelerometers and gyroscopes detect physical activity patterns, posture changes, and movement characteristics, which are then processed to generate activity-derived metrics that correlate with pain levels, eliminating the need for patient self-assessment
Solution Approach 2:
The patent introduces an intermediary processing system that translates raw sensor data into meaningful pain assessment metrics. The system uses algorithms to analyze activity patterns and generate behavior assessment scores that indirectly indicate pain intensity, serving as a mediator between physical measurements and pain evaluation
2Measurement precision
If posture tracking is used to detect discomfort, then objective measurement can be obtained, but the method requires tracking posture patterns over a relatively long period of time as baseline, making it complex and less practical
Solution Approach 1:
The patent performs preliminary classification of sensor signals into activity categories (e.g., walking, standing, sitting, sleeping) before analyzing posture patterns. This pre-processing step enables the system to interpret posture changes in context, reducing the need for extensive baseline tracking since the system can immediately recognize activity-specific posture variations
Solution Approach 2:
The patent segments the continuous sensor signal into discrete epochs and further classifies each epoch into specific activity categories. This segmentation allows the system to analyze posture patterns within contextually relevant time windows rather than requiring long-term continuous baseline tracking across all activities
3Reliability
If frequent posture changes are tracked to identify discomfort, then discomfort detection can be achieved, but the method requires complex baseline establishment for each patient's posture-changing habits
Solution Approach 1:
The patent creates a universal classification framework that categorizes sensor signals into standard activity types (walking, standing, sitting, sleeping) applicable to all patients. This universal system eliminates the need for patient-specific baseline establishment, as the same activity categories and analysis methods can be applied universally across different individuals
Data Source
AI summary
A method for assessing pain experienced by a patient (3) comprises: receiving a sensor signal (8) which has been generated in a sequence of epochs (10) by a sensor (2) configured for measuring a physical activity of the patient (3), wherein each epoch (10) comprises a sequence of measurement periods (11), wherein the sensor signal (8) has been generated in each measurement period (11); determining a signal intensity value (12) for each measurement period (11) from the sensor signal (8); classifying the signal intensity values (12) of each epoch (10) with different physical activity classes (13a, 13b, 13c, 13d) to obtain a classification result (15) for the epoch (10); and determining a behavior assessment score (16) indicative of the pain experienced by the patient (3) from the classification results (15) of different epochs (10).