Mobile defibrillator

1. A method for performing a self-rescue with a mobile defibrillator (AED) unit comprising:

receiving, via an application on a user device, personal risk factors as an input via a user interface, the personal risk factors comprising at least one of demographic or health information about a subject;

storing the personal risk factors in the user device, wherein the personal risk factors are accessible by the application;

detecting, via the application, a connection of the mobile AED unit to the user device;

detecting, via the application, that pads have been attached to a subject, wherein the pads comprise at least one accelerometer;

recording, via the application, EKG measurements of the subject made by the pads;

receiving, via the application, accelerometer data from the at least one accelerometer on the pads, the accelerometer data associated with breathing movements of a chest of the subject;

analyzing, via the application, the accelerometer data to determine a breathing pattern of the subject;

determining, based on the recorded EKG measurements and the personal risk factors and based on the determined breathing pattern, that the subject requires an electrical shock; and

administering the electrical shock to the subject via the mobile AED based on the determining.

2. The method of claim 1 , further comprising at least one of:

continuing to record EKG measurements; and

initiating a CPR protocol.

3. The method of claim 2 , wherein administering the electrical shock to the subject comprises:

determining, based on health data and the personal risk factors, an electrical shock pattern for administering to the subject, wherein the electrical shock pattern comprises a plurality of electrical shocks, each electrical shock comprising a duration and an energy level, the electrical shock pattern further comprising a determined duration between electrical shocks.

4. The method of claim 2 comprising:

sending, via the device to a server over a network, performance and health data associated with AED performance.

5. The method of claim 4 , wherein the server is configured to receive performance and health data from a plurality of user devices and a plurality of mobile AEDS and at least one of retrain or update a machine learning model for analyzing EKG measurements and a machine learning model for determining pad placement based on the received performance and health data.

6. The method of claim 2 , wherein determining that the subject requires an electrical shock comprises:

in response to detecting that the pads have been attached to the subject, displaying, via the application, notifications at a pre-defined frequency to the subject via the device, each message indicating a time-period for reply;

determining that the subject has not responded to at least one message within the time-period; and

in response to determining that the subject has not responded, determining that the action is administering an electrical shock pattern to the subject.

7. The method of claim 1 , wherein the personal risk factors comprise at least one of a height, a weight, an age, a blood pressure, one or more previous EKG ratings, or at least one pre-existing medical condition.

8. The method of claim 2 , wherein determining that the subject requires an electrical shock comprises analyzing the EKG measurements and detecting a dangerous rhythm.

9. The method of claim 8 , wherein detecting a dangerous rhythm comprises detecting at least one of rapid ventricular tachycardia or ventricular fibrillation.