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After we obtained the distance of each axis, we measured the CCD via the vector sum of the three axes, as in Eq. Because the user can hold the smartphone in any orientation and the compression direction may be different in some conditions, we required the data from the three-axis accelerometer. To be used as the feedback device for continuous CC, the depth should not be shown instantaneously.

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The rescuers should compress deeper or shallower according to the feedback value, and showing an average of previous compression depths is preferred to ensure a smoother change in depth. As shown in Eq. Furthermore, it shows the average depth of the previous four compressions with the current compression after the fifth compression:. The smartphone used in our study features a three-axis accelerometer and storage. The LVDT directly transduces the linear distance to voltage.

When manually simulating the CC, two estimated depth data points from the smartphones and the reference depth data were simultaneously recorded. Configuration of the experimental setting for evaluation. CCD, chest compression depth. To evaluate the accuracy of CCD estimation, we defined the error with Eq. The error was the difference between the estimated depth d and the reference measurement d LVDT:.

In each experiment, CCs were performed, and the error for each compression was calculated. The mean and standard deviation SD of errors were calculated as the results. The first experiment served to evaluate the accuracy at the various depths of CC. We divided CCD into three levels: In the first experiment, the patient is thought to be laid on the flat ground, as shown in Figure 3a ; the smartphone is placed orthogonal to the ground, and the compressing direction is the same as the gravity direction.

The various grasping orientations and directions of compression. In the second, we designed four types of conditions, as shown in Figure 3. The conditions varied the grasping orientation of the smartphone Fig. Figure 3a is the condition recommended by PocketCPR. Figure 3b simulates that the smartphone is attached to the armband. Figure 3c simulates the possibility of any person partially grasping the smartphone. Figure 3d simulates compressing at an angle to the gravity direction, which simulates CPR for pregnant women or CPR in an elevator. Table 1 shows the mean SD of errors Eq.

Table 2 shows the mean SD errors of the absolute value of the difference between the reference method and the smartphone-based method for various grasping orientations and compression directions from Fig. When the compressing direction was the gravity direction, as shown in Figure 3a—c , the means SD of the absolute errors were 1. Data are mean standard deviation values. Conditions a , b , c and d are shown in the respective panels of Figure 3.


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In U-CPR, we showed the depth as an average of five compressions. In PocketCPR, the average requires more than eight to nine compressions. Result of an experiment to observe the sudden change of chest compression depth. In this study, we described the concept of a new real-time CCD estimation algorithm that was implemented on an Android smartphone. For evaluation of the algorithm, we analyzed the accuracy for various depths, grasping orientations, and compressing directions.

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As a result, the means of errors were below 2. This error is in the acceptable range to be used as a feedback device for CCD. As explained in the Introduction, CCs for pregnant women should be performed in a direction oblique to the gravity direction. In a narrow space, such as an elevator, oblique compression is also necessary to continue CC without interruption.

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According to the right CPR posture, the center of mass of the rescuer should be directly above the patient. The possibility of oblique compression is not high, and the difference in the angle from the gravity direction is not large. Thus, we prefer U-CPR. Three cases of different chest compression CC.


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  5. When the rescuer uses the feedback device, he or she must change the depth when it shows an alarm. If the device shows the instantaneous depth, the rescuer may oscillate between insufficient and excessive compression. Therefore, U-CPR displays the depth as the average of the five previous depths. A delay should occur simultaneously. A detailed examination of Figure 4 indicates that an average of eight or nine compression results in a long delay for PocketCPR.

    U-CPR suffers from some limitations. When the patient lies down on a foam mattress, U-CPR can overestimate the CCD because it also measures the compression of the mattress.

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    The mattress compression can be reduced by using a backboard. This limitation is also caused by using an accelerometer. In addition to the feedback function, many other helpful functions are necessary for CPR-related apps. To implement a completion app, we will validate the app with the mannequin for large numbers of rescuers. Future work includes the development of a useful app with a CCD feedback function to ensure its widespread use to save more people. National Center for Biotechnology Information , U. Telemedicine Journal and e-Health.

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