CPR Education in the Modern Age

Cardio-Pulmonary Resuscitation (CPR) is a life-saving procedure, when applied properly. Since its inception in 1967, the education of the public in the proper application of this vital technique has been hampered by a lack of qualitative tools to measure the compression, decompression and frequency of the procedure as it is applied. Today, new tools are available to provide this qualitative analysis to help average citizens receive the appropriate training in CPR and to convert data collected during the training to qualitative data. This data can be stored for later review and research or transmitted through the web for comparison and company evaluation of training accuracy and effectiveness. These new tools will move CPR education closer to the goal of the American Heart Association, as stated in the American Heart Association Consensus statement of 2013, to “…develop industry standards for interoperable raw data downloads and reporting electronic data collected during resuscitation for both quality improvement and research.”

CPR Education in the Modern Age


  • Arthur Jackson, BS Electrical Engineering, Juris Doctorate in Law, CEO of Arthur Jackson CTC Inc
  • Ella Tang, MS Computer Science, CEO Ella Tang Enterprises, and Boston SEO management;
  • Ankur Pandya, B.E. Computer, Mobile Application and Games developer
  • Kashyap Jani, BE Electronics & Communication Engineering

Problem Statement

Standardizing the teaching of CPR has been a long a tortuous road, extending from the 1700s to the modern day. Out-of-hospital cardiac arrest (OCHA) survival rates continue to be dismal.1 In many instances, despite advances in procedures and technology, teaching to the general public lacks the rigor and necessary feedback to provide clarity in how to apply the technique or allow for data to be recorded, stored and shared on how these principles are being taught and applied. Despite the application of “high performance CPR”, a more rigorous approach to teaching CPR used in many regions of today’s globe, survival rates remain at 50+ percent.

Yet, in some regions, survival from out-of-hospital cardiac arrest (OHCA) has improved dramatically, impacting many lives. 2,3 So, what’s the difference between some jurisdictions and others when it comes to OHCA survival? Are there common themes that others can learn from in improving their own survival rates?

One of the common themes we see in areas with higher survival rates is a focus on providing high-quality CPR to patients in OHCA. Compression rate, depth, shock pause duration, release velocity and the use of CPR feedback have all been associated with improved outcomes and are highlighted repeatedly in the (Resuscitation) as crucial factors to improving outcomes from OHCA.4 How do we produce reliable data so all regions can benefit from reliably recorded data to improve teaching methods? The teaching of CPR has fallen victim to a lack of quality data, and repeatable data, which results in a poor understanding of how and why force is applied. This results in a lack of preparation of the rescuer to meet the demands of CPR compressions; the force needed to do a Guideline Correct Chest Compression.5 It is time to correct that flaw.

The failure to provide repeatable and qualitative data for research, record keeping and sharing with other individuals, groups, or agencies has been a fundamental flaw in the CPR education process. The first person to apply aid to an injured person is usually the public. Yet, there has been little focus on collecting data from individual classes to document what they were taught and how the CPR process was applied. Collection of real time data will provide a critical source of information to move CPR training into the modern era. In the age of the world wide web, CPR data should be at least as accessible as social data, and available to just as many or more persons and groups.
Today, we need not only a standardized CPR system, but a system that supplies qualitative and repeatable data from classrooms around the globe to create vital data for researchers and clinicians on what was happening to an injured individual before medical help arrived.


Cardiopulmonary Resuscitation (CPR) evolved out of procedures used to revive drowning victims. The technique of using mouth-to-mouth resuscitation dates to the 17th and 18th centuries. In 1740, the Paris Academy of Sciences recommended the use of mouth-to-mouth for drowning victims. Throughout the 18th and 19th centuries, chest compressions were commonly used in hospital emergency rooms to aid cardiac emergency victims. Chest compressions were found to often help reduce the loss of life in cardiac emergencies; these required a person to press on a cardiac arrest victim’s chest and could be administered with or without mouth-to-mouth resuscitation. It would not be until1960s that the two techniques were combined to create CPR.

This chest compression technique combined with resuscitation breaths was developed to establish closed chest methods to stimulate the heart during cardiac arrest. The American Heart Association (AHA) started a program to acquaint physicians with close-chest cardiac resuscitation and became the forerunner of CPR training for the general public. In 1966, the National Research Council of the National Academy of Sciences convened an ad hoc conference on cardiopulmonary resuscitation. The conference was the direct result of requests from the American Heart Association, American Red Cross and other agencies to establish standardized training and performance standards for CPR. Although an ad hoc standard was agreed to, the teaching of CPR to the general public was wildly diverse. As the American Heart Association pursued public education, it established the American Heart Association’s CPR Committee in 1963.In the same year, the American Heart Association formally endorsed CPR. However, there was no standard procedure for educating the general public on its use. Some instructors applied less than thirty compression per cycle of CPR. The procedure established by the U.S. Army required 15 compressions per cycle. This became the default definition of a CPR cycle for many in the general public until the 1990s.

In 1992, The International Liaison Committee on Resuscitation was established by the American Heart Association, the Canadian Heart and Stroke Foundation and several other organizations. This time the goal was to create an international standard by collecting research data and material from countries around the globe. This new organization would review research yearly and hold consensus meetings every five years to create the global consensus standards. (The

reason a consensus standard was developedrather than a compulsory standard can be found in U.S. Case law. See Printz v. U.S. and Mack v. U.S.)

This collection of international research exposed a host of problems in the teaching of CPR:

1. Survival rates were low.

2. Most people trained in CPR still did not know what a compression was, or how to perform compressions accurately.

3. The default standard adopted from military training (15 compressions) was still in use and needed to be replaced with today’s standard CPR (30 compressions and 2 rescue breaths) based on research current existing data.

4. Standard CPR (30 compressions and 2 rescue breaths) was in conflict/competition with other types of CPR such as compressions only CPR.

5. Existing models used to teach CPR were based on spring loaded systems that gave misleading impressions of CPR compression force required.

These were the major problems, but additional issues were revealed once global research was pooled and made available to all researchers. This prompted a flurry of research into these areas. 6,7,8,9
In 2012, High Performance CPR emerged. This is not new training, but an improved method of teaching the current CPR system accurately. Simply teaching students what to do dramatically improved the survival rates to over fifty percent. However, this was still below what was and should be expected. CPR was still not reaching full potential.
The future of cardiac arrest research is full of new concepts and research into new strategies focused on improving OHCA survival. Whether we speak about ACD+ITD CPR, ECMO, double sequential external defibrillation, or alternative drug therapies for patients in cardiac arrest of a variety of etiologies, what remains clear is that none is effective without high-quality CPR.14
The previously demonstrated interaction between interventions and high-quality CPR informs us that, without improved CPR quality, even the greatest of ideas will fall short. As a resuscitation community, we must increase our focus on improving the quality of CPR around the world. The adage of, “some CPR is better than no CPR at all” must be replaced by, “high-quality CPR is the only form of CPR.”10


In 2017, my company, Arthur Jackson CTC INC., with the assistance a small team of engineers, began work on meeting and exceeding the requirements of American Heart Association for January 2015 11 and January 2019:
“Classes should have the use of an instrumented directive feedback device in all courses that teach adult CPR skills, effective January 31, 2019. The devices should provide, real-time, audio or visual corrective feedback for evaluation and instruction on chest compression rate, depth, chest recoil and proper hand placement during CPR training.”
We started with information to define the force necessary to define a compression. The American Heart Association defined a Guidelines Compliant Compression as the application of one hundred to one hundred and fifty pounds of force to the sternum at a ninety-degree angle. This will be sufficient to produce displacement of the sternum 7 mm (approximately 2 inches) or more, to compress the heart and facilitate blood movement.

Figure 1 below is a copy of the table presented to the American Heart Association by Trenkamp and Perez in 2013 showing the force of one hundred and twenty-five pounds as sufficient to meet this requirement in fifty percent of global populations.

Guidelines Compliant Chest Compressions (GC3)

Equipment was constructed to train to the requirement of GC3. The result was the equipment shown in Figure 2. This system used a Smartweigh resistive scale, with detached digital display, with a block of high-density foam mounted on it (Fig 2(A)).
The whole assembly was placed in an Armstrong medical Actar-D unit, (Fig 2 (B and C)). The final assembly is shown in Fig 2(D).

Baseline equipment

This equipment was used to evaluate over 200 students throughout 2017. The students ranged in age from mid-twenties to sixty, in occupations from office worker to construction worker, with a gender ratio of eighty percent male to twenty percent female. Testing revealed very positive results from students. They responded favorably to being able to see how much compression force they were applying, but it failed in several important areas. While there were positive responses and the device provided good accuracy, it did not allow repeatability, data collection, or provide students with a visual representation of their effort as they did compressions. (The digital indicator only showed a number.) This would not meet or exceed the American Heart Association’s 2015 guidelines, nor the 2019 requirements.13,14

The existing equipment was modified in 2018 by adding a thin film sensor and, in 2019, a different platform that was developed to replace the Actar-D unit.

Sensor mounting

The sensor type that best suited our application was an ultra-thin and flexible printed circuit. With its thin construction, flexibility and force measurement ability, the sensor could measure force between almost any two surfaces and was durable enough to stand up to most environments. After repeated testing, we selected the Tekscan A502 for its better force-sensing properties, linearity, hysteresis, drift, and temperature sensitivity. The “active sensing area” of the sensor was as shown below in Figure 3. The sensors were constructed of two layers of substrate composed of polyester film. Thesensors are terminated with a solderable male square pin.

The need to reduce shear in this type of sensor is important and can be solved, in many cases, by placing a puck adhered to the sensing area to reduce the effect of shear on the sensor. (Shear force is present when there is any type of loading that isn’t completely ninety degrees, or normal to the sensor.) Fortunately, when the proper steps are taken, users can avoid any unintended sensitivity loss due to shear. Likewise, material deformation as a factor was controlled by using non-pliant material in the sensor mounting as suggested by Tekscan engineers. Deformation is a consideration if the sensor is placed under a compliant material with a significant friction factor. This deformation can be quantified through Poisson’s Ratio. Poisson’s Ratio is the ratio of transverse strain to axial strain when pliant material is used and forces stretch/compress it. (P= – (transverse strain/axial strain). The sliding of the deforming material across the sensor face can impart shear. (from Tekscan best practices in mechanical integration)15

The selection of a solid-state sensor and a non-spring-loaded mechanical loading system also created an awareness of such additional factors as:

  • Repeatability:The ability of the sensor to respond in the same way to a repeatedly applied force.
  • Linearity: The sensor’s response (digital output) to the applied load, over the range of the sensor.
  • Hysteresis: The difference in the sensor output response during loading and unloading, at the same force.
  • Drift: The change in sensor output when a constant force is applied over a period.
  • Temperature Sensitivity: The sensor’s change in output due to temperature. (In general, results will vary if you combine high loads on the sensor with high temperatures.)
  • These factors would be managed in the construction of a software application to transfer this information through an interface to Android devices.

    Constructing a Single Sensor Software Application (CPR Mirror App)

    To make our Android application communicate with an interface device would require months of testing. Once testing was completed, equations were constructed to provide outputs of the approximate amount of force applied (Weight, Pressure, Light, Acceleration, etc..). While actual sensor values taken directly from the sensor as raw data looked linear initially, further testing revealed an inverse logarithmic form was the correct match.

    Readings from a sensor vs. Actual Weight applied on sensor and the basic Linear and Inverse Log equations derived from those equations are shown below. The measured readings in X and Y are plotted against values generated for one such equation y = ((log (a-x) -b) *c) where a, b and c are constant values, x is the reading received from sensor and y is the actual weight pressure applied on sensor. (Values of a, b, and c were developed to stabilize the equations.)

    Construction of such equations completed our CPR Mirror App to transfer information from the sensor to an Android device. At this point, one last hurdle was found. Manufacturing differences for sensor production runs meant sensors from different manufacturing runs could vary widely when used with the same interface and App. (See Fig 6 below)

    Final result

    To correct this problem, a manual offset input was added to the CPR Mirror App to correct for this variance. The offset moves the output of our equation up or down to correct for the manufacturing variance from run to run. The CPR Mirror App now would provide a stable, repeatable, accurate output that would provide qualitative data. The App output is a real-time graph of compression pressure and rescue breath pressure. The upper graph measures air pressure in millibars or kilopascals, while the lower graph measures compression force in pounds or kilograms, shows frequency compliance, and decompression. Figure 6 below shows the output for sixteen (16) individuals in a class measured for approximately ten (10) seconds per person. This sample was taken after a completed class to gaugestudentretention.

    This is a real-time system to train and collect data to evaluate that training for research and storage of data for later evaluation and review. The final version of the CPR Mirror App provides a repeatable accuracy of plus or minus five percent along a range of zero to one hundred and twenty pounds.


    The completed interface and App make it possible to provide real-time feedback to students as they are trained in CPR. The CPR Mirror App and associated interface are designed to provide the most advanced, real-time feedback to students and professionals. CPR Mirror works through an external sensor and interfaces through blue tooth connection to an Android phone or tablet. Mirror provides the following information and features:

    • 1. The compression force in pounds or kilograms for each compression in an easy-to-read, on-screen graph
    • 2. The number of compressions per second in a graph measured in seconds
    • 3. Shows if the decompression returns to zero with each cycle of compression
    • 4. The rate of rescue breaths measured in seconds in a separate onscreen graph
    • 5. The input force of each breath measured in millibars or KPa
    • 6. Recording of each person’s CPR compression/decompression and rescue breathing for later review or archiving
    • 7. Remote viewing of the screen (with third-party software)
    • 8. Designed for Android mobile systems for ease of use
    • 9. App automatically seeks and will pair with an Android mobile device on which the App is loaded when the interface is activated
    • 10. Can be connected wirelessly (using a third-party software, Air Droid) to Smart TV or Projector for classroom instruction
    • 11. Recorded material can be shared with anyone through the internet in comma separated values or video MP4 format
    • 12. Can read up to two sensor inputs at a time and display on screen (Pressure, Temperature, and Air Pressure)
    • 13. Using Android tablet lets users not only record CPR and respiration, but tablet can be used to record video of training session for later review.
    • 14. Using Android tablet allows users to develop new scenarios that can be loaded on the tablet and used seamlessly during training
    • 15. Designed to emphasize team approach (each training station using mirror can accommodate a group of four individuals).
    • 16. Allows users a selection of measurements choices:
    • a. Acceleration (shown in units of meters per second square)
    • b. Temperature (shown in units of Celsius, or Fahrenheit, or Kelvin)
    • c. Pressure at Altitude (shown in units of meters per feet)
    • d. Weight (shown in units of pounds or kilograms; user is provided with a manual offset feature for the initial calibration of the sensor)
    • e. Tactile Pressure (Shown in units of pounds or kilograms)
    • f. Barometric Air Pressure (Shown in units of kilo-pascals or millibars)
    • g. Light (shown in units of Lux)
    • CPR Mirror and interface meet and exceed the requirements for the American Heart Association and the International Liaison Committee on Resuscitation guidelines for training and brings CPR training into the modern age. Collection of data in classes we conduct continues to be compiled. This information will, we hope, be the initial data of a database on training methods in CPR. The CPR Mirror App is being made available as open source to promote the development and retention of training data for CPR.


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      • 15. Tekscan. “Best Practices in Mechanical Integration of the FlexiForce Sensor.” n.d. PDF document. 14 August 2019

      References for Internal Libraries used and consulted:

      • Native Android SDK:, To develop native mobile applications
      • Sweet Blue:, Version: 2.0; To manage BLE
        devices for Android connectivity to perform read, write and status operations.
      • Pocket Lab native mobile app:
      • develop the IDs of different sensors such as service Id, data Id, Config Id, period duration, calibration amount, range and frequencies and to develop the equations of sensors to convert output values to proper readings.

      • Whistle Punk:
        OpenScienceJournal/whistlepunk library By; Version: 2.0; Rendering UI elements, data collection service, and sensor code.
      • MP Android Chart : ;Version 3.1.0: For rendering charts from the data
      • Lottie for Android:; Version: 2.7.0: To render some animations.

      Additional Mathematical References: