ML Pain Detection via Facial Expression Analysis
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Solution Overview
Problem
Current methods for pain assessment in patients, particularly those with military-related injuries or traumatic brain injuries, face challenges in accurately and objectively determining pain levels due to subjective reporting and varying individual pain tolerance, leading to underreported pain and interference with duties.
Innovation Solution
The development of machine learning (ML) and artificial intelligence (AI) models trained on large datasets, including media and health data, to detect and assess pain levels through image analysis and vital sign inputs, providing a more accurate and objective pain assessment using neural networks and image classification techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective pain reporting methods are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces subjective mechanical reporting methods with automated machine learning-based image analysis systems. The ML model objectively analyzes facial expressions, body language, and physiological indicators from images to determine pain levels, eliminating reliance on patient self-reporting while maintaining ease of operation through automated processing.
2Measurement precision
If multiple data sources are integrated for pain assessment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources including facial expression images, body language images, vital sign data, and patient history into a unified machine learning model. This integration allows the system to comprehensively assess pain levels by synthesizing information from diverse inputs, improving measurement precision while managing complexity through consolidated processing.
3Measurement precision
If machine learning models are trained on large datasets, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on extensive datasets of facial expressions, body language, and physiological data before deployment. This advance training enables the models to rapidly and accurately assess pain levels during actual use, sacrificing initial training time to achieve high measurement precision in operational settings.
Data Source
AI summary
The technical solutions identify presence and the level of pain of a patient using ML modeling trained on media data and data on health of persons. A processor coupled with memory can receive, from an application, a media data and data on health of the person. The processor can identify, using ML models, a presence of pain and a level of pain being expressed in the media of the person responsive to providing the media and the data on health of the person as inputs to the ML models. The ML models can be trained using a plurality of media and a plurality of data on health of persons expressing a plurality of levels of pain. The processor can generate, for the application, a notification identifying the presence of pain and the level of pain expressed by the person.