Pose-Conditioned Pain Detection for Patient Positioning
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Solution Overview
Problem
Existing systems fail to objectively measure and quantify pain experienced by patients in relation to their pose, leading to repeated exposure to pain and stress during patient positioning for medical imaging and diagnosis.
Innovation Solution
A system comprising a pose estimation unit, pain detection unit, and processing unit to determine and display the correlation between patient pain and pose, using camera and audio analysis with machine learning algorithms to objectively quantify pain and guide optimal patient positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pain-limited mobility is assessed during patient's first clinical assessment and then rediscovered during patient preparation for medical imaging, then pain and stress are exposed to the patient repeatedly, but manual assessment methods lack objective measurement capability
Solution Approach 1:
The patent replaces manual mechanical assessment of pain-limited mobility with an automated vision-based system using pose estimation algorithms and machine learning models to objectively measure patient pose and correlate with pain signals, eliminating subjective manual evaluation and repeated patient handling
Solution Approach 2:
The system enables automatic pain detection and pose estimation without requiring repeated manual intervention by clinicians, using automated computer vision algorithms to continuously monitor and assess patient condition, reducing both patient exposure to stress and clinician workload
2Loss of information
If manual assessment of pain-limited mobility is performed repeatedly, then patient positioning can be adjusted, but the process is time-consuming and subjective
Solution Approach 1:
The system implements continuous automated monitoring of patient pose and pain correlation throughout the assessment process, eliminating interruptions and repeated manual assessments, allowing continuous data collection and real-time feedback for optimal positioning
Solution Approach 2:
The patent introduces an intermediary automated vision system that mediates between patient and clinician, using pose estimation algorithms and machine learning models to translate complex pain-limited mobility assessment into objective, actionable data, improving both accuracy and efficiency
3Measurement precision
If automated pose estimation and pain detection systems are implemented, then objective pain measurement is achieved, but system complexity increases
Solution Approach 1:
The system uses a multi-functional integrated approach where a single vision-based platform performs both pose estimation and pain detection through machine learning models, eliminating the need for multiple separate devices and reducing overall system complexity while maintaining high measurement precision
Solution Approach 2:
The patent uses computer vision to create digital copies and representations of patient pose and pain signals, allowing objective measurement and analysis without physical contact or complex medical devices, simplifying the system while improving measurement accuracy
Data Source
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AI summary
The present invention relates to a system and a method for detecting pain experienced by a patient in relation to a pose of the patient. The system comprises a pose estimation unit configured to estimate the pose of the patient and a pain detection unit configured to detect pain experienced by the patient. The system further comprises a processing unit configured to determine a correlation between the detected pain experienced by the patient and the estimated pose of the patient. The determined correlation can be displayed on a display unit to a physician.