Identifying false positive data within a set of blast exposure data

1. A method of identifying false positive data from a set of blast exposure data using at least one processor of a user device, the method comprising:

receiving, at the user device, the set of blast exposure data comprising raw pressure data over time collected by one or more blast sensors configured to detect blast pressure data associated with a blast exposure;

determining a bias associated with the raw pressure data;

debiasing the raw pressure data according to the bias to produce debiased pressure data;

identifying, by the at least one processor, one or more features of a plurality of predefined features within the debiased pressure data,

wherein the one or more features of the plurality of predefined features are associated with one or more predefined false positive classes; and

flagging at least a portion of the set of blast exposure data based on the one or more features of the plurality of predefined features.

2. The method of claim 1 , further comprising filtering the debiased pressure data to produce filtered pressure data.

3. The method of claim 2 , further comprising:

integrating the debiased pressure data over time to produce impulse data; and

integrating the filtered pressure data over time to produce filtered impulse data.

4. The method of claim 1 , wherein the one or more predefined false positive classes comprises a noise class.

5. The method of claim 1 , wherein the one or more predefined false positive classes comprises a sensor error class.

6. The method of claim 1 , wherein the one or more predefined false positive classes comprises a physical impossibility class.

7. The method of claim 6 , further comprising:

identifying, within the set of blast exposure data, a negative impulse associated with the physical impossibility class; and

removing at least an additional portion of the set of blast exposure data relating to the negative impulse.

8. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by at least one processor, perform a method of identifying false positive data from a set of blast exposure data, the method comprising:

receiving the set of blast exposure data comprising raw pressure data over time collected by one or more blast sensors configured to detect blast pressure data associated with a blast exposure;

determining a bias associated with the raw pressure data;

debiasing the raw pressure data according to the bias to produce debiased pressure data;

filtering the debiased pressure data to produce filtered pressure data;

identifying one or more features of a plurality of predefined features within any of the raw pressure data, the debiased pressure data, pressure change data, the filtered pressure data, and filtered pressure change data,

wherein the one or more features of the plurality of predefined features are associated with one or more predefined false positive classes; and

flagging at least a portion of the set of blast exposure data based on the one or more features of the plurality of predefined features.

9. The one or more non-transitory computer-readable media of claim 8 , the method further comprising:

determining a false positive score associated with the set of blast exposure data based on the one or more features of the plurality of predefined features.

10. The one or more non-transitory computer-readable media of claim 9 , the method further comprising:

automatically removing at least an additional portion of the set of blast exposure data based on the false positive score.

11. The one or more non-transitory computer-readable media of claim 8 , wherein the plurality of predefined features comprises:

a maximum impulse value feature;

a head to tail difference value feature;

a maximum pressure value feature; and

a baseline-shift feature.

12. The one or more non-transitory computer-readable media of claim 8 , wherein the plurality of predefined features comprises:

a sawtooth signal feature;

a plateau signal feature; and

a sine wave signal feature.

13. The one or more non-transitory computer-readable media of claim 8 , wherein the plurality of predefined features comprises a feature associated with a section of substantially flat pressure within the set of blast exposure data.

14. A system comprising:

one or more body-mounted blast sensors operable to detect blast pressure data associated with a blast exposure; and

at least one processor programmed to perform a method of identifying false positive data from a set of blast exposure data, the method comprising:

receiving the set of blast exposure data comprising raw pressure data over time from the one or more body-mounted blast sensors;

determining a bias associated with the raw pressure data;

debiasing the raw pressure data according to the bias to produce debiased pressure data;

identifying one or more features within the debiased pressure data,

wherein the one or more features are associated with one or more predefined false positive classes;

determining a false positive score associated with the set of blast exposure data based on the one or more features; and

if the false positive score exceeds a predetermined threshold value, flagging the set of blast exposure data.

15. The system of claim 14 , the method further comprising filtering the debiased pressure data to produce filtered pressure data.

16. The system of claim 15 , wherein the debiased pressure data is filtered using a Savitzky-Golay filter.

17. The system of claim 14 , wherein the one or more body-mounted blast sensors include:

a head-mounted blast sensor;

a shoulder-mounted blast sensor; and

a chest-mounted blast sensor.

18. The system of claim 14 , further comprising:

a machine learning model trained to identify false positive data within the set of blast exposure data.

19. The system of claim 14 , the method further comprising:

identifying a peak overpressure within the set of blast exposure data.

20. The system of claim 19 , the method further comprising:

normalizing the raw pressure data based on the peak overpressure.