Methods and systems for adaptable presentation of sensor data
A method of producing subject-specific metric statistics includes collecting physiological data and meta data from a subject via a sensor system. The sensor system includes at least one sensor element, at least one signal processor, and memory in communication with the at least one signal processor. The collected data is processed via the at least one signal processor to determine a plurality of metric features from the collected data. The plurality of metric features are processed using one or more data clustering techniques via the at least one signal processor to generate at least one subject-specific metric statistic and at least one sensor metric. The at least one subject-specific metric statistic and the at least one sensor metric may be displayed via a display associated with a client device.
1. A method of producing subject-specific metric statistics, the method comprising:
collecting physiological data and meta data from a subject via a sensor system, wherein the sensor system comprises at least one sensor element, at least one signal processor, and memory in communication with the at least one signal processor;
processing the collected physiological data and/or the collected meta data via the at least one signal processor to determine a plurality of metric features from the collected physiological data and/or the collected meta data, wherein each of the plurality of metric features is a feature of one or more of the collected physiological data and/or the collected meta data that is functionally related to generation of at least one sensor metric from the collected physiological data;
processing the plurality of metric features from the collected physiological data and/or the collected meta data via the at least one signal processor to generate at least one subject-specific metric statistic and the at least one sensor metric, wherein the subject-specific metric statistic comprises information related to a performance, accuracy, sensitivity, and/or selectivity of the at least one sensor metric; and
communicating the at least one subject-specific metric statistic and the at least one sensor metric to a client device for display via a display associated with the client device.
2. The method of claim 1 , wherein the meta data from the subject comprises one or more of the following: subject age, subject weight, subject height, subject gender, subject ethnicity.
3. The method of claim 1 , wherein collecting the meta data from the subject comprises receiving the meta data as input from the subject or a third party.
4. The method of claim 1 , wherein collecting the meta data from the subject comprises determining the meta data from the physiological data.
5. The method of claim 1 , wherein the at least one sensor element is a photoplethysmography (PPG) sensor, and wherein the physiological data comprises PPG data.
6. The method of claim 5 , wherein the at least one sensor metric comprises blood pressure.
7. The method of claim 1 , wherein processing the plurality of metric features via the at least one signal processor to generate at least one subject-specific metric statistic comprises utilizing one or more data clustering techniques to generate a plurality of metric feature clusters.
8. The method of claim 7 , wherein the one or more data clustering techniques comprise one or more of the following: k-means and Gaussian mixture models.
9. The method of claim 1 , wherein processing the plurality of metric features to generate at least one subject-specific metric statistic comprises processing the plurality of metric features based on a desired metric statistic.
10. The method of claim 1 , wherein the client device is a mobile communication device.
11. The method of claim 1 , wherein the plurality of metric features comprise one or more of the following: an RR-interval, a rising slope, a falling slope, an integral of a waveform associated with the at least one sensor metric, spectral features of the waveform associated with the at least one sensor metric, a feature generated by a mathematical transform of the waveform associated with the at least one sensor metric, an amplitude of the waveform associated with the at least one sensor metric, a skew of the waveform associated with the at least one sensor metric, or an auto-correlational feature.
12. The method of claim 11 , wherein the mathematical transform of the waveform associated with the at least one sensor metric comprises one or more of the following: a wavelet transform, a Fourier transform, a Teager-Kaiser energy operator, a chirplet transform, or a noiselet transform.
13. A method of producing subject-specific metric statistics, the method comprising:
collecting physiological data and meta data from a subject via a sensor system, wherein the sensor system comprises at least one sensor element, at least one signal processor, and memory in communication with the at least one signal processor;
processing the collected meta data via the at least one signal processor to determine a plurality of meta data metric features from the collected meta data, wherein each of the plurality of meta data metric features is a feature of the collected meta data that is functionally related to generation of at least one sensor metric from the collected physiological data;
processing the plurality of meta data metric features from the collected meta data via the at least one signal processor to generate at least one subject-specific metric statistic associated with the at least one sensor metric, wherein the subject-specific metric statistic comprises information related to a performance, accuracy, sensitivity, and/or selectivity of the at least one sensor metric; and
communicating the at least one subject-specific metric statistic and the at least one sensor metric to a client device for display via a display associated with the client device.
14. The method of claim 13 , wherein the meta data from the subject comprises one or more of the following: subject age, subject weight, subject height, subject gender, subject ethnicity.
15. The method of claim 13 , wherein collecting the meta data from the subject comprises receiving the meta data as input from the subject or a third party.
16. The method of claim 13 , wherein collecting the meta data from the subject comprises determining the meta data from the physiological data.
17. The method of claim 13 , wherein the at least one sensor element is a photoplethysmography (PPG) sensor, and wherein the physiological data comprises PPG data.
18. The method of claim 13 , wherein processing the plurality of meta data metric features via the at least one signal processor to generate at least one subject-specific metric statistic comprises utilizing one or more data clustering techniques to generate a plurality of metric feature clusters.
19. The method of claim 18 , wherein the one or more data clustering techniques comprise one or more of the following: k-means and Gaussian mixture models.
20. A system comprising:
at least one sensor configured to sense physiological data from a subject and receive subject meta data; and
at least one signal processor configured to:
collect the physiological data and the meta data;
process the collected physiological data and/or the collected meta data to determine a plurality of metric features from the collected physiological data and/or the collected meta data, wherein each of the plurality of metric features is a feature of one or more of the collected physiological data and/or the collected meta data that is functionally related to generation of at least one sensor metric from the collected physiological data;
process the plurality of metric features from the collected physiological data and/or the collected meta data to generate at least one subject-specific metric statistic and the at least one sensor metric, wherein the subject-specific metric statistic comprises information related to a performance, accuracy, sensitivity, and/or selectivity of the at least one sensor metric; and
display the at least one subject-specific metric statistic and the at least one sensor metric via a display.
21. The system of claim 20 , wherein the at least one signal processor is further configured to utilize one or more data clustering techniques to generate a plurality of metric feature clusters.
22. The system of claim 21 , wherein the one or more data clustering techniques comprise one or more of the following: k-means and Gaussian mixture models.
23. The system of claim 20 , wherein the at least one sensor is a photoplethysmography (PPG) sensor, wherein the physiological data comprises PPG data, and wherein the at least one sensor metric comprises blood pressure.
24. A method of producing subject-specific metric statistics, the method comprising:
collecting physiological data and meta data from a subject via a sensor system;
processing the collected meta data, via at least one signal processor, to determine a plurality of metric features from the collected meta data, wherein each of the plurality of metric features is a feature of one or more of the collected meta data that is functionally related to generation of at least one sensor metric from the collected physiological data; and
processing the plurality of metric features from the collected meta data, via at least one signal processor, to generate at least one subject-specific metric statistic and the at least one sensor metric, wherein the subject-specific metric statistic comprises information related to a performance, accuracy, sensitivity, and/or selectivity of the at least one sensor metric.