Video-Based CPR Feedback for Non-Mannequin Training Assessment
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Solution Overview
Problem
Current online CPR training courses lack comprehensive, real-time, and cost-effective assessment of CPR performance, relying on expensive mannequins and virtual trainers, limiting accessibility and scalability, especially in resource-limited settings.
Innovation Solution
A system using video analysis and machine learning to evaluate CPR performance on non-mannequin training objects, such as partially filled bottles, through pose estimation and machine learning models to provide detailed feedback on compression depth, rate, hand positioning, and body posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive mannequins and virtual trainers are used for CPR assessment, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical mannequins with a computer vision-based system using standard cameras and machine learning algorithms. The system captures video of CPR performance and uses pose estimation to detect body movements, hand positions, and compression techniques, substituting mechanical sensing with optical detection and computational analysis.
Solution Approach 2:
The patent creates a virtual model of the trainee's body movements by analyzing video footage and generating pose estimates. This digital copy of the physical CPR performance allows for detailed assessment without requiring physical sensors or complex mannequins, enabling accurate measurement through computational representation.
2Measurement precision
If comprehensive real-time feedback systems are implemented, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system enables self-assessment and self-training by providing automated feedback without requiring instructors or complex evaluation equipment. The machine learning model independently analyzes video footage, detects CPR techniques, and generates performance feedback, allowing trainees to evaluate themselves using only a standard camera and software.
Solution Approach 2:
The patent replaces manual assessment by trainers with automated computer vision analysis. The system uses machine learning models to detect body movements, hand positions, and compression techniques from video, substituting human evaluation with computational analysis that provides consistent, scalable feedback.
3Measurement precision
If traditional mannequin-based training is used, then measurement precision is improved, but accessibility and scalability worsen
Solution Approach 1:
The patent creates a universal training system that works across multiple platforms and settings using standard cameras and software. The system can be deployed on smartphones, tablets, or computers without requiring specialized equipment, making CPR training accessible in diverse environments including remote areas, homes, and resource-limited settings.
Solution Approach 2:
The patent replaces expensive, durable mannequins with a software-based solution that runs on inexpensive, widely available devices. The system uses standard cameras and processors that are already present in most smartphones and computers, eliminating the need for costly specialized training equipment while maintaining assessment accuracy.
4Productivity
If automated video analysis is implemented, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system performs preliminary processing of video footage by detecting key body landmarks and movements before detailed CPR assessment. The pose estimation model pre-identifies hand positions, body orientation, and compression motions, preparing structured data that facilitates accurate subsequent analysis and reduces computational complexity during real-time evaluation.
Data Source
AI summary
A system and method for training, assessing, and providing feedback on cardiopulmonary resuscitation (CPR) performance based on at least one video of a CPR training session performed by a trainee on a non-mannequin training object. A preprocessing module is configured to process the at least one video to generate a standardized video. A marking module is configured to use pose estimation to mark points for body movements during the CPR training session based on the standardized video, and a computing module configured to compute body movement parameters for CPR based on the marked points. A classification module implements a machine learning model that classifies CPR compressions on the non-mannequin training object based on the computed body movement parameters, thereby generating compression classifications, wherein the machine learning model is trained to extract CPR-specific features. An editor module maps metrics over the standardized video based on the compression classifications and generates a feedback video based on the mapped metrics. An analysis module identifies deviations from CPR guidelines based on the feedback video and generates analysis results based on the deviations. A feedback module provides performance feedback to the trainee based on the analysis results.