Systems and methods for generating anonymized acoustic fingerprints
Systems and methods for generating anonymized acoustic fingerprints. For a predetermined time interval, raw audio data associated with an environment can be captured and processed. Based on the captured raw audio data, a plurality of discrete data elements can be generated.
1. A method of generating an anonymized acoustic fingerprint for a dialysis environment, the method comprising:
capturing, using at least one acoustic sensor, raw ambient sound data associated with the dialysis environment during a predetermined time interval, the raw ambient sound data comprising audio data associated with at least one of medical personnel, a patient, or a dialysis machine in the dialysis environment;
processing the captured raw ambient sound data to convert the raw ambient sound data into a plurality of discrete data elements, wherein the plurality of discrete data elements are indicative of acoustic characteristics of the dialysis environment that cannot be converted back to identifiable ambient sounds of the raw ambient sound data associated with the at least one of the medical personnel, the patient, or the dialysis machine;
storing, in memory, the plurality of discrete data elements for the predetermined time interval without storing any audio information comprising identifiable ambient sounds;
identifying a pattern of the plurality of discrete data elements;
classifying, based on the identified pattern, the dialysis environment as a good environment or a bad environment; and
in response to classifying the dialysis environment as a bad environment, determining, based on the plurality of discrete data elements, assistance, education or training required for the dialysis environment to be converted to a good environment.
2. The method of claim 1 , wherein the acoustic characteristics comprise at least one of: an intensity value, a centroid value, a spread value, a skewness value, a kurtosis value, a decrease value, a rolloff value, a spectral slope value, or a mel-frequency cepstral coefficient.
3. The method of claim 1 , wherein the predetermined time interval is greater than 5 seconds.
4. The method of claim 1 , further comprising predicting, based on the acoustic characteristics, one or more performance outcomes associated with the dialysis environment.
5. The method of claim 4 , wherein the one or more performance outcomes comprises patient satisfaction.
6. The method of claim 1 , wherein:
the raw ambient sound data comprises sound generated by at least one of: a heart or arteriovenous fistula thrill; and
the method further comprises determining, based on the plurality of discrete data elements, a medical condition of the patient in the dialysis environment.
7. The method of claim 1 , wherein the at least one acoustic sensor comprises a microphone.
8. The method of claim 1 , wherein classifying, based on the identified pattern, the dialysis environment as a good environment or a bad environment comprises:
identifying, based on the identified pattern, at least one condition of the dialysis environment, the at least one condition comprising employees in the dialysis environment operating efficiently, persons in the dialysis environment are being treated respectfully, or there existing a low risk of safety issues; and
in response to identifying the at least one condition of the dialysis environment, classifying the dialysis environment as a good environment.
9. The method of claim 1 , further comprising predicting, based on the plurality of discrete data elements, when a patient in the dialysis environment will leave a dialysis chair in the dialysis environment.
10. The method of claim 1 , further comprising:
capturing, using at least one sensor, raw pressure data associated with the dialysis environment during a predetermined time interval;
processing the captured raw pressure data to convert the raw pressure data into a second plurality of discrete data elements; and
storing, in memory, the second plurality of discrete data elements for the predetermined time interval.
11. The method of claim 1 , wherein the audio data comprises an audible interaction between the patient and the medical personnel.
12. The method of claim 1 , wherein the audio data comprises an audible alert or an operational noise associated with the dialysis machine.
13. A system, comprising:
at least one acoustic sensor configured to capture raw sound data associated with a dialysis environment;
a computer-readable medium comprising computer-executable instructions; and
one or more processors configured to execute the computer-executable instructions,
wherein, when the one or more processors are executing the computer-executable instructions, the one or more processors are configured to carry out operations comprising:
receiving, from the at least one acoustic sensor, raw ambient sound data associated with the dialysis environment during a predetermined time interval, the raw ambient sound data comprising audio data associated with at least one of medical personnel, a patient, or a dialysis machine in the dialysis environment;
processing the received raw ambient sound data to convert the raw ambient sound data into a plurality of discrete data elements, wherein the plurality of discrete data elements are indicative of acoustic characteristics of the dialysis environment that cannot be converted back to identifiable ambient sounds of the raw ambient sound data associated with the at least one of the medical personnel, the patient, or the dialysis machine;
storing, in memory, the plurality of discrete data elements for the predetermined time interval without storing any audio information comprising identifiable ambient sounds;
identifying a pattern of the plurality of discrete data elements;
classifying, based on the identified pattern, the dialysis environment as a good environment or a bad environment; and
in response to classifying the dialysis environment as a bad environment, determining, based on the plurality of discrete data elements, assistance, education or training required for the dialysis environment to be converted to a good environment.
14. The system of claim 13 , wherein the plurality of discrete data elements comprises at least one of: an intensity value, a centroid value, a spread value, a skewness value, a kurtosis value, a decrease value, a rolloff value, a spectral slope value, or a mel-frequency cepstral coefficient.
15. The system of claim 13 , wherein the predetermined time interval is greater than 5 seconds.
16. The system of claim 13 , wherein the at least one acoustic sensor comprises a microphone.
17. The system of claim 13 , wherein, when the one or more processors are executing the computer-executable instructions, the one or more processors are further configured to carry out operations comprising predicting, based on the plurality of discrete data elements, one or more performance outcomes associated with the dialysis environment.
18. The system of claim 13 , wherein:
the raw ambient sound data comprises sound generated by at least one of: a heart or arteriovenous fistula thrill; and
the operations further comprise determining, based on the plurality of discrete data elements, a medical condition of a patient in the dialysis environment.
19. A non-transitory computer-readable storage medium having instructions executable by one or more processors to cause the processors to perform operations comprising:
capturing, using at least one acoustic sensor, raw ambient sound data associated with a dialysis environment during a predetermined time interval, the raw ambient sound data comprising audio data associated with at least one of medical personnel, a patient, or a dialysis machine in the dialysis environment;
processing the captured raw ambient sound data to convert the raw ambient sound data into a plurality of discrete data elements, wherein the plurality of discrete data elements are indicative of acoustic characteristics of the dialysis environment that cannot be converted back to identifiable ambient sounds of the raw ambient sound data associated with the at least one of the medical personnel, the patient, or the dialysis machine;
storing, in memory, the plurality of discrete data elements for the predetermined time interval without storing any audio information comprising identifiable sounds;
identifying a pattern of the plurality of discrete data elements;
classifying, based on the identified pattern, the dialysis environment as a good environment or a bad environment; and
in response to classifying the dialysis environment as a bad environment, determining, based on the plurality of discrete data elements, assistance, education or training required for the dialysis environment to be converted to a good environment.
20. The non-transitory computer-readable storage medium of claim 19 , wherein the plurality of discrete data elements comprises at least one of: an intensity value, a centroid value, a spread value, a skewness value, a kurtosis value, a decrease value, a rolloff value, a spectral slope value, or a mel-frequency cepstral coefficient.
21. The non-transitory computer-readable storage medium of claim 19 , wherein the predetermined time interval is greater than 5 seconds.
22. The non-transitory computer-readable storage medium of claim 19 , the operations further comprising predicting, based on the plurality of discrete data elements, one or more performance outcomes associated with the dialysis environment.
23. The non-transitory computer-readable storage medium claim 19 , wherein:
the raw ambient sound data comprises sound generated by at least one of: a heart or arteriovenous fistula thrill; and
the operations further comprise determining, based on the plurality of discrete data elements, a medical condition of a patient in the dialysis environment.
24. The non-transitory computer-readable storage medium of claim 19 , wherein the at least one acoustic sensor comprises a microphone.