Wound Assessment Using Camera and Heat Sensor
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Solution Overview
Problem
Existing wound monitoring solutions fail to adequately consider wound temperature and its effect on optimal wound healing trajectories, and they lack a mechanism to shorten the healing process.
Innovation Solution
A method and system that utilize a camera to identify wounds, heat sensors to detect wound temperature, and machine learning to process wound features and temperature data, determining an optimal healing trajectory and predicting future wound conditions, while also triggering responses such as warming the wound if it is outside a therapeutic temperature zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated wound documentation solutions are implemented, then labor time for documenting wound characteristics is reduced, but wound temperature monitoring and its effect on healing trajectories is not adequately considered
Solution Approach 1:
The system combines multiple monitoring functions into a single automated platform that simultaneously captures wound visual characteristics via camera, measures temperature via heat sensor, and tracks humidity via humidity sensor. This multi-functional approach ensures comprehensive wound assessment while maintaining automation efficiency, resolving the contradiction between time savings and assessment completeness.
2Productivity
If traditional wound monitoring methods are used, then implementation is simple, but the ability to shorten optimal wound healing trajectory is limited
Solution Approach 1:
The system continuously monitors wound temperature and humidity, compares readings against optimal healing ranges, and provides real-time feedback through alerts and notifications. This feedback mechanism enables dynamic adjustment of wound care interventions to maintain optimal healing conditions, thereby accelerating healing while managing system complexity through automated decision support.
Solution Approach 2:
The system performs self-monitoring and self-assessment of wound conditions, automatically capturing data and generating reports without requiring constant clinical intervention. This self-service capability reduces the complexity burden on healthcare providers while enabling continuous monitoring that can accelerate healing trajectories.
3Measurement precision
If manual wound assessment is performed, then equipment requirements are minimal, but measurement precision and objectivity are insufficient
Solution Approach 1:
The system replaces manual visual assessment with automated image analysis using computer vision algorithms that objectively measure wound characteristics. Temperature measurements are replaced with digital heat sensor readings, and humidity assessment is substituted with electronic sensor data. This substitution of mechanical/manual methods with automated sensing and analysis systems enhances measurement precision and objectivity while managing complexity through integrated processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more objective and efficient tracking and assessment of wounds, allowing for quicker decision-making and potentially shorter healing times by maintaining optimal wound temperature.
Implementation Method 1
detecting a temperature of the wound from at least one heat sensor
Implementation Method 2
detecting a temperature of the wound from at least one heat sensor
Data Source
AI summary
A method for tracking and assessing a wound includes identifying a wound from an image output by a camera, identifying a set of wound features based on the image, detecting a temperature of the wound from at least one heat sensor, triggering at least one response if the wound temperature is outside of a therapeutic temperature zone, processing the set of wound features and wound temperature using machine learning, determining an optimal wound healing trajectory based on the set of wound features and the wound temperature using machine learning, predicting at least one future wound condition based on the optimal wound healing trajectory using machine learning, and displaying the optimal wound healing trajectory and the at least one future wound condition on a display.