CPR Simulation Framework

• CPR Simulation Framework is a structured system integrating mechanical compression and algorithmic control to enhance both training and emergency response.
• The framework utilizes dual mechanisms—cardiac and thoracic pumps—with closed-loop feedback and fuzzy logic to maintain optimal compression depth and rate.
• Simulation tools, CAD analysis, and adaptive HMI design enable cost-effective prototyping and standardization across diverse patient demographics and clinical scenarios.

A cardiopulmonary resuscitation (CPR) simulation framework refers to a structured system that models, emulates, assists, assesses, or guides CPR processes—either mechanically, electronically, or algorithmically—in diverse settings such as emergency response, clinical training, wearable feedback, or algorithmic analysis. These frameworks encompass computational models, physical device control architectures, machine learning–based assessment tools, agent-based simulators, biomechanical simulations, and real-time guidance systems. They commonly integrate closed-loop feedback, adaptive control, rich sensing, and simulation-driven optimization to improve CPR outcomes and standardize best practices across broad operational conditions.

1. Foundational Principles and Mechanisms

A CPR simulation framework typically seeks to replicate or support the mechanical, physiological, and procedural aspects of chest compressions and associated life support activities. The design is frequently grounded in dual concepts:

• Cardiac Pump Mechanism: Direct compression of the sternum delivers force to the heart, expelling blood during systole. This mechanism is central to chest compression devices and simulation models emulating direct cardiac output augmentation.
  • The relevant perfusion target is commonly formalized as:

  $$\mathrm{CPP} = P_{\text{diastole}} - P_{\text{right atrium}}$$

  with effective resuscitation generally requiring CPP ≈ 15–20 mmHg (Jadoon, 16 Feb 2025).

• Thoracic Pump Mechanism: Increases in intrathoracic pressure from global chest compression (aided by elastic recoil of ribs/chest wall) drive blood into systemic circulation. Device designs often combine both effects via simultaneous sterno-thoracic compression using pistons and belt assemblies.
• Closed-Loop Feedback: Feedback systems leveraging real-time sensor data (compression rate, depth, patient-specific parameters) and adaptive controllers (e.g., fuzzy logic control with parameters such as age or body habitus) maintain American Heart Association–specified ranges (typically 100–120 compressions/min, 1–2 inches compression depth) (Jadoon, 16 Feb 2025).
• Human–Machine Interface (HMI): Real-time, user-accessible control allows the operator to adapt device behavior for different patient demographics (children, adults, seniors), with fuzzy or rule-based logic translating subjective categories into concrete compression profiles for actuation (Jadoon, 16 Feb 2025).

2. Device Engineering and Simulation Components

Mechanical CPR simulation frameworks are typically composed of:

• Actuation Assembly: Utilization of a piston assembly, constricting belts, and geared rolling mechanisms to transfer rotational motion into controlled linear compressive force.
  • Rotary-to-linear force conversion can be conceptually modeled as:

  $F = \frac{\tau}{r}$

  where $F$ is compressive force, $\tau$ is motor-generated torque, and $r$ is effective cam or lever arm radius.

• Adaptive Control Hardware/Software: Controllers reading compression sensors close the feedback loop, triggering actuator adjustments in real time to match the HMI-selected target parameters. Fuzzy inference (implemented with MATLAB simulations) uses age and prescribed ranges to select crisp setpoints for the actuators, employing defuzzification (often via centroid method) for continuous adaptation.
• Simulation in Design: Computer-aided design (CAD), finite element modeling, and virtual prototyping allow stress analysis, moment of inertia calculation, and optimization of force transmission to ensure robustness and compliance with clinical force/depth requirements before fabrication.

Table 1. Key Components in a Low-Cost CPR Device

System Aspect	Implementation Detail	Functionality
Actuation	Piston + constricting belt w/roller system	Dual cardiac/thoracic compression
Control	Fuzzy logic controller (age-dependent rules)	Real-time compression adjustment
Feedback	Compression depth/rate sensors; closed-loop control	Safety and protocol conformance
HMI	User interface (age settings for children/adults/elderly)	Operator interaction
Simulation	CAD/Stress analysis (e.g., SolidWorks), MATLAB fuzzy	Design validation; parameter tuning

3. Feedback and Adaptivity

Feedback mechanisms are essential for simulation-control integration:

• Sensor Integration: The control system acquires real-time chest displacement, applied force, and compression rate, automatically adjusting stroke length and speed to align with setpoints determined by user-selected patient class.
• Closed-loop Control Logic: Sensory data is interpreted by control algorithms (fuzzy logic leveraging multivariate input membership functions), which provides robustness to inter-patient variability and environmental disturbances.
• Real-Time Alerts: If the device deviates from required clinical parameters (e.g., depth insufficient, rate above/below AHA guideline), corrective signals modulate actuator output accordingly.

4. Simulation and Virtual Prototyping

A central facet is simulation-driven design and training:

• Structural Simulation: Prior to device manufacturing or integration, all mechanical components are simulated using computational tools (e.g., stress analysis in SolidWorks) to ensure safe operation under expected loads.
• Control System Simulation: Fuzzy logic controllers are modeled and tuned in MATLAB, translating patient profile membership functions (age-based) into compression parameters and ensuring crisp, actionable control signals.
• Scenario Testing: Multiscale simulations (virtual patients with variable anatomical features, agent-based human operator models) validate adaptability and reliability before interface with live systems or human use.

5. Cost, Accessibility, and Usability

The framework emphasizes affordability, scalability, and operator access:

• Materials and Manufacturing: Selection of lightweight and durable materials (aluminum, iron, polyester) minimizes cost while maintaining mechanical integrity. This is intended to ensure feasible adoption in resource-constrained healthcare settings (Jadoon, 16 Feb 2025).
• Adaptability: Through HMI and closed-loop adaptation, device operation can be tailored across a spectrum of user skill levels and patient demographics.
• Standardization and Compliance: The approach enables broader alignment with established clinical guidelines, facilitating inclusion in both training curricula and field deployment tools.

6. Applications in Medical Training and Field Operations

Practical deployment of CPR simulation frameworks includes:

• Clinical Training Integration: Trainees interact with the feedback-rich device or virtual framework in simulated emergencies, gaining aptitude in delivering guideline-conformant compressions under various patient-specific scenarios.
• Emergency Response: In field settings, the device acts as an adjunct to manual CPR, providing consistent compressions to mitigate human fatigue and error, especially in high-demand or prolonged resuscitation efforts (Jadoon, 16 Feb 2025).
• System-Level Integration: Simulation codebases and virtual device modules can be embedded within larger medical simulation ecosystems (e.g., integrating with agent-based team communication or physiological response models) to create comprehensive virtual patients and training scenarios.

7. Impact, Limitations, and Future Directions

The outlined CPR simulation framework exemplifies a systems-engineered approach that fuses mechanical innovation (dual cardiac/thoracic compressions), adaptive closed-loop control (real-time, fuzzy logic), robust simulation (structural and control parameters), and cost-effective production. Such a framework:

• Expands access to high-fidelity resuscitation training and backup mechanical aid in low-resource and pre-hospital settings.
• Promotes standardization and adaptability across patient populations and emergency contexts.
• Enables rapid prototyping and iterative design through simulation-driven approaches, minimizing risk and cost prior to deployment.
• Remains limited by its reliance on linear models (elasticity, pressure, force transmission), generic age-based adaptation, and the absence of direct feedback on other physiological endpoints (e.g., organ perfusion, metabolic state), which may be targets for future enhancement. Advanced sensor integration, multi-modal signal analysis, and individualized biomechanical modeling represent emerging directions.

In sum, the CPR simulation framework detailed here operationalizes dual mechanical compression principles, closed-loop feedback, and simulation-based control logic to deliver adaptable, guideline-conformant, and resource-sensitive CPR delivery for both training and real-world implementation (Jadoon, 16 Feb 2025).