OPPORTUNISTIC MEASUREMENTS AND PROCESSING OF USER'S CONTEXT

• US 20160157782A1
• Filed: 12/08/2014
• Published: 06/09/2016
• Est. Priority Date: 12/08/2014
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Kumar, Arvind, Ahmad, Marisa
• Granted Patent: US 10,201,312 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2560/0462 Apparatus with built-in sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/6897 Computer input devices, e.g. mice or keyboards
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Embodiments of the present disclosure provide techniques and configurations for an apparatus for opportunistic measurements and processing of user'"'"'s context. In one instance, the apparatus may include a sensor module with sensors disposed on a work surface to maintain direct or indirect contact with portions of user'"'"'s limbs for a time period when the portions of user'"'"'s limbs are disposed on the work surface, to obtain readings of first and second parameters of user'"'"'s context over the time period of the direct or indirect contact; and a processing module to process the readings of the first and second parameters, including to identify a first feature of the first parameter and a second feature of the second parameter that is temporally correlated with the first feature, and determine a third parameter of the user'"'"'s context based on the identified first and second features. Other embodiments may be described and/or claimed.

Claims

1. An apparatus, comprising:

a sensor module that includes one or more sensors disposed on a work surface of the apparatus, the one or more sensors to maintain direct or indirect contact with at least portions of a user'"'"'s limbs for at least a determined time period when the portions of the user'"'"'s limbs are disposed on the work surface in order to obtain readings of first and second parameters of user'"'"'s context over the determined time period of the direct or indirect contact; and

a processing module coupled with the sensor module to process the readings of the first and second parameters, wherein to process includes to identify at least a first feature of the first parameter and a second feature of the second parameter that is temporally correlated with the first feature, and determine a third parameter of the user'"'"'s context based at least in part on the identified first and second features.

2. The apparatus of claim 1, wherein the processing module is to determine temporal correlation between the first and second features.

3. The apparatus of claim 1, wherein the processing module to process the readings of the first and second parameters further includes to identify a first plurality of features of the first parameter and a second plurality of features of the second parameter, wherein the first plurality of features includes the first feature and the second plurality of features includes the second feature.

4. The apparatus of claim 3, wherein the processing module to determine a third parameter of the user'"'"'s context includes to apply a first machine-learning algorithm to the first and second pluralities of features to determine empirical correlation between the first and second pluralities of features, wherein the derivation of the third parameter is based at least in part on the empirical correlation between the first and second pluralities of features.

5. The apparatus of claim 4, wherein the first machine-learning algorithm comprises a linear regression algorithm.

6. The apparatus of claim 5, wherein the user'"'"'s context comprises user'"'"'s physiological context, wherein the first parameter comprises electrocardiographic (ECG) data, the second parameter comprises photoplethysmographic (PPG) data, and the third parameter comprises blood pressure (BP) data.

7. The apparatus of claim 6, wherein the first feature comprises an R peak of a QRS complex of the ECG data, the second feature comprises a lowest point in a PPG curve of the PPG data, and the temporal correlation between the first and second features comprises a pulse transit time (PTT), wherein the PTT is a period of time that elapses between the R peak of the QRS complex of the ECG data and the lowest point in the PPG curve.

8. The apparatus of claim 7, wherein the first plurality of features includes a first set of values associated with the QRS complex and the second plurality of features includes a second set of values associated with an amplitude characteristic of the PPG curve and upward and downward slope characteristics of the PPG curve.

9. The apparatus of claim 6, wherein the BP data further comprises systolic blood pressure (SBP) data and diastolic blood pressure (DBP) data, wherein the processing module is to derive SBP data and DBP data from the application of the first machine-learning algorithm to the first and second pluralities of features.

10. The apparatus of claim 9, wherein the user'"'"'s context further comprises user'"'"'s physiological state, wherein the sensor module is further to obtain information indicating the user'"'"'s physiological state, wherein the information includes jitter data associated with the direct or indirect contact between the portions of user'"'"'s limbs and the work surface, and motion data associated with the direct or indirect contact between the portions of user'"'"'s limbs and the work surface.

11. The apparatus of claim 10, wherein the processing module is further to apply a second machine-learning algorithm to the jitter data, motion data, SBP data, and DBP data, to determine boundary values of the third parameter for the user, based at least in part on the user'"'"'s physiological state.

12. The apparatus of claim 11, wherein the one or more sensors comprise at least a selected one of:

a gyroscope or accelerometer.

13. The apparatus of claim 12, wherein the one or more sensors comprise at least two or more electrodes to provide readings of the ECG data and at least one light sensor to provide readings of the PPG data.

14. The apparatus of claim 1, wherein the work surface comprises at least a selected one of:

a keyboard of the apparatus, a bezel of the apparatus, or a back side of the apparatus, wherein the apparatus is a computing device, wherein the portions of user'"'"'s limbs include at least one of;

wrists, palms, or hands that are disposed on the work surface to interact with the apparatus.

15. An apparatus, comprising:

a casing, having at least one work surface that includes one or more sensors disposed on the work surface to maintain direct or indirect contact with portions of user'"'"'s limbs for at least a determined time period when the portions of user'"'"'s limbs are disposed on the work surface, to obtain readings of first and second parameters of user'"'"'s context over the determined time period of the direct or indirect contact; and

a processing module coupled with the one or more sensors to process the readings of the first and second parameters, wherein to process includes to identify at least a first feature of the first parameter and a second feature of the second parameter that is temporally correlated with the first feature, and determine a third parameter of the user'"'"'s context based at least in part on the identified first and second features.

16. The apparatus of claim 15, wherein the processing module to process the readings of the first and second parameters further includes to:

identify a first plurality of features of the first parameter and a second plurality of features of the second parameter, wherein the first plurality of features includes the first feature and the second plurality of features includes the second feature;

determine empirical correlation between the first and second pluralities of features; and

derive the third parameter based at least in part on the empirical correlation between the first and second pluralities of features.

17. The apparatus of claim 16, wherein the user'"'"'s context comprises user'"'"'s physiological context and user'"'"'s physiological state, wherein the first, second, and third parameters are related to the user'"'"'s physiological state, wherein the one or more sensors is further to obtain readings indicating the user'"'"'s physiological state during the maintenance of the direct or indirect contact between the portions of user'"'"'s limbs and the work surface, and wherein the processing module is further to determine boundary values of the third parameter for the user, based at least in part on the user'"'"'s physiological state, wherein the portions of limbs are disposed on the work surface to interact with the apparatus.

18. The apparatus of claim 17, further comprising a sensor aggregator module coupled with the one or more sensors, to power on the one or more sensors and to pre-process the readings provided by the one or more sensors.

19. The apparatus of claim 18, wherein the sensor aggregator module and the processing module are integrated on a system on chip (SOC), wherein the casing comprises at least a portion of a keyboard of the apparatus, a bezel of the apparatus, or a back side of the apparatus, wherein the apparatus comprises one of:

a laptop computer, a desktop computer, a tablet computer, or a smart phone.

20. A computer-implemented method, comprising:

obtaining, by a computing device, from one or more sensors disposed on a work surface of the computing device, readings of first and second parameters of a user'"'"'s context over a determined time period of a direct or indirect contact between portions of a user'"'"'s limbs and the one or more sensors;

identifying, by the computing device, from the obtained readings of the first and second parameters, at least a first feature of the first parameter and a second feature of the second parameter that is temporally correlated with the first feature; and

determining, by the computing device, a third parameter of the user'"'"'s context based at least in part on the identified first and second features.

21. The computer-implemented method of claim 20, further comprising:

identifying, by the computing device, a first plurality of features of the first parameter and a second plurality of features of the second parameter, wherein the first plurality of features includes the first feature and the second plurality of features includes the second feature;

determining, by the computing device, empirical correlation between the first and second pluralities of features; and

deriving, by the computing device, the third parameter based at least in part on the empirical correlation between the first and second pluralities of features.

22. The computer-implemented method of claim 21, wherein the user'"'"'s context comprises user'"'"'s physiological context and user'"'"'s physiological state, wherein the first, second, and third parameters are related to the user'"'"'s physiological context, and wherein the method further comprises:

obtaining, by the computing device, readings indicating the user'"'"'s physiological state, during the maintenance of the direct or indirect contact between the portions of user'"'"'s limbs and the one or more sensors disposed on the work surface; and

determining, by the computing device, boundary values of the third parameter for the user, based at least in part on the user'"'"'s physiological state, wherein the portions of limbs include hands, palms, or wrists.

23. One or more non-transitory computing device-readable media having executable instructions stored thereon that, in response to execution, cause a computing device to:

obtain, from one or more sensors disposed on a work surface of the computing device, readings of first and second parameters of user'"'"'s context over a determined time period of a direct or indirect contact between portions of user'"'"'s limbs and the one or more sensors;

identify, from the obtained readings of the first and second parameters, at least a first feature of the first parameter and a second feature of the second parameter that is temporally correlated with the first feature; and

determine a third parameter of the user'"'"'s context based at least in part on the identified first and second features.

24. The non-transitory computing device-readable media of claim 23, wherein the instructions further cause the computing device to:

identify a first plurality of features of the first parameter and a second plurality of features of the second parameter, wherein the first plurality of features includes the first feature and the second plurality of features includes the second feature;

determine empirical correlation between the first and second pluralities of features; and

derive the third parameter based at least in part on the empirical correlation between the first and second pluralities of features.

25. The non-transitory computing device-readable media of claim 24, wherein the instructions further cause the computing device to:

obtain readings indicating user'"'"'s physiological state, during the maintenance of the direct or indirect contact between the portions of user'"'"'s limbs and the one or more sensors disposed on the work surface; and

determine boundary values of the third parameter for the user, based at least in part on the user'"'"'s physiological state,

wherein the user'"'"'s context comprises user'"'"'s physiological context and the user'"'"'s physiological state, wherein the first, second, and third parameters are related to the user'"'"'s physiological context.