Health tracking device

• US 10,251,597 B2
• Filed: 04/21/2016
• Issued: 04/09/2019
• Est. Priority Date: 04/21/2016
• Status: Active Grant

• Current Assignee: Viavi Solutions, Inc. (Lumentum Holdings Inc.)
• Original Assignee: Viavi Solutions, Inc. (Lumentum Holdings Inc.)
• Inventors: O'Brien, Nada A., Hruska, Curtis R., Von Gunten, Marc K., Pederson, Christopher G., Hsiung, Changmeng
• Primary Examiner(s): Holmes, Rex R
• Application Number:
  US15/135,110
• Publication Number:
  US 20170303846A1
• Time in Patent Office: 1,083 Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4266 sweat secretion
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: G01N 21/27 using photo-electric detection G01N21/31 takes precedence; circuits for computing concentration logarithmic circuits G06G7/24; photometric circuits in general G01J
• CPC Class Codes: G01N 33/02 Food
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

Claims

1. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to;

receive a first spectroscopic classification model that is associated with identifying a health condition based on a chemometric signature and that is generated based on a calibration performed utilizing a spectrometer on a group of subjects;

obtain a set of properties regarding a user,

the set of properties including first sensor data regarding the user;

generate a second spectroscopic classification model based on the first spectroscopic classification model and the set of properties regarding the user;

utilize the second spectroscopic classification model to determine a nutritional content of a food item consumed by the user; and

periodically update the second spectroscopic classification model based on second sensor data regarding the user.

2. The non-transitory computer-readable medium of claim 1, where the one or more instructions, that cause the one or more processors to generate the second spectroscopic classification model, cause the one or more processors to:

perform a support vector machine classifier optimization technique or a support vector regression classifier optimization technique on the first spectroscopic classification model to generate the second spectroscopic classification model; and

where the one or more instructions, that cause the one or more processors to periodically update the second spectroscopic classification model, are to;

perform the support vector machine classifier optimization technique or the support vector regression classifier optimization technique on the second spectroscopic classification model to update the second spectroscopic classification model.

3. The non-transitory computer-readable medium of claim 1, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

obtain the chemometric signature,

the second sensor data including information identifying the chemometric signature, and

the chemometric signature being determined based on sensor data from another spectrometer;

perform a classification of the chemometric signature based on the second spectroscopic classification model,

the classification being associated with identifying the health condition; and

provide information identifying the health condition based on performing the classification of the chemometric signature.

4. The non-transitory computer-readable medium of claim 1, where the one or more instructions, that cause the one or more processors to obtain the set of properties regarding the user, cause the one or more processors to:

obtain the first sensor data from a plurality of sensors,

the plurality of sensors including at least two of;

another spectrometer,

an accelerometer,

a heart rate sensor,

a blood pressure sensor,

a blood sugar sensor,

a perspiration sensor,

a skin conductivity sensor, or

an imaging sensor.

5. The non-transitory computer-readable medium of claim 1, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

periodically transmit information relating to the second spectroscopic classification model or the second sensor data to cause an update to the first spectroscopic classification model.

6. The non-transitory computer-readable medium of claim 1, where the one or more instructions, when executed by the one or more processors, cause the one or more processors to:

receive the chemometric signature from another spectrometer,

the other spectrometer being different from the spectrometer;

determine the health condition based on the second spectroscopic classification model,

the health condition being a blood glucose level of the user; and

provide information identifying the blood glucose level of the user.

7. The non-transitory computer-readable medium of claim 6, where the one or more instructions, when executed by the one or more processors, are further to:

receive another chemometric signature of the user from the spectrometer;

detect, based on the second spectroscopic classification model, a threshold difference between the health condition and another health condition relating to the other chemometric signature; and

provide an alert based on detecting the threshold difference.

8. A device, comprising:

a memory; and

one or more processors to;

receive a first spectroscopic classification model that is associated with identifying a health condition based on a chemometric signature and that is generated based on a calibration performed utilizing a spectrometer on a group of subjects;

obtain a set of properties regarding a user,

the set of properties including first sensor data regarding the user;

generate a second spectroscopic classification model based on the first spectroscopic classification model and the set of properties regarding the user;

utilize the second spectroscopic classification model to determine a nutritional content of a food item consumed by the user; and

periodically update the second spectroscopic classification model based on second sensor data regarding the user.

9. The device of claim 8, where the one or more processors, when generating the second spectroscopic classification model are to:

perform a support vector machine classifier optimization technique or a support vector regression classifier optimization technique on the first spectroscopic classification model to generate the second spectroscopic classification model; and

where the one or more processors, when periodically updating the second spectroscopic classification model are to;

perform the support vector machine classifier optimization technique or the support vector regression classifier optimization technique on the second spectroscopic classification model to update the second spectroscopic classification model.

10. The device of claim 8, where the one or more processors are further to:

obtain the chemometric signature,

the second sensor data including information identifying the chemometric signature, and

the chemometric signature being determined based on sensor data from another spectrometer;

perform a classification of the chemometric signature based on the second spectroscopic classification model,

the classification being associated with identifying the health condition; and

provide information identifying the health condition based on performing the classification of the chemometric signature.

11. The device of claim 8, where the one or more processors, when obtaining the set of properties regarding the user, are to:

obtain the first sensor data from a plurality of sensors,

the plurality of sensors including at least two of;

another spectrometer,

an accelerometer,

a heart rate sensor,

a blood pressure sensor,

a blood sugar sensor,

a perspiration sensor,

a skin conductivity sensor, or

an imaging sensor.

12. The device of claim 8, where the one or more processors are further to:

periodically transmit information relating to the second spectroscopic classification model or the second sensor data to cause an update to the first spectroscopic classification model.

13. The device of claim 8, where the one or more processors are further to:

receive the chemometric signature from another spectrometer,

the other spectrometer being different from the spectrometer;

determine the health condition based on the second spectroscopic classification model,

the health condition being a blood glucose level of the user; and

provide information identifying the blood glucose level of the user.

14. The device of claim 13, where the one or more processors are further to:

receive another chemometric signature of the user from the spectrometer;

detect, based on the second spectroscopic classification model, a threshold difference between the health condition and another health condition relating to the other chemometric signature; and

provide an alert based on detecting the threshold difference.

15. A method, comprising:

receiving, by a device, a first spectroscopic classification model that is associated with identifying a health condition based on a chemometric signature and that is generated based on a calibration performed utilizing a spectrometer on a group of subjects;

obtaining, by the device, a set of properties regarding a user,

the set of properties including first sensor data regarding the user;

generating, by the device, a second spectroscopic classification model based on the first spectroscopic classification model and the set of properties regarding the user;

utilizing, by the device, the second spectroscopic classification model to determine a nutritional content of a food item consumed by the user; and

periodically updating, by the device, the second spectroscopic classification model based on second sensor data regarding the user.

16. The method of claim 15, where generating the second spectroscopic classification model comprises:

performing a support vector machine classifier optimization technique or a support vector regression classifier optimization technique on the first spectroscopic classification model to generate the second spectroscopic classification model; and

where periodically updating the second spectroscopic classification model comprises;

performing the support vector machine classifier optimization technique or the support vector regression classifier optimization technique on the second spectroscopic classification model to update the second spectroscopic classification model.

17. The method of claim 15, further comprising:

obtaining the chemometric signature,

the second sensor data including information identifying the chemometric signature, and

the chemometric signature being determined based on sensor data from another spectrometer;

performing a classification of the chemometric signature based on the second spectroscopic classification model,

the classification being associated with identifying the health condition; and

providing information identifying the health condition based on performing the classification of the chemometric signature.

18. The method of claim 15, where obtaining the set of properties regarding the user comprises:

obtaining the first sensor data from a plurality of sensors,

the plurality of sensors including at least two of;

another spectrometer,

an accelerometer,

a heart rate sensor,

a blood pressure sensor,

a blood sugar sensor,

a perspiration sensor,

a skin conductivity sensor, or

an imaging sensor.

19. The method of claim 15, further comprising:

periodically transmitting information relating to the second spectroscopic classification model or the second sensor data to cause an update to the first spectroscopic classification model.

20. The method of claim 15, further comprising:

receiving the chemometric signature from another spectrometer,

the other spectrometer being different from the spectrometer;

determining the health condition based on the second spectroscopic classification model,

the health condition being a blood glucose level of the user; and

providing information identifying the blood glucose level of the user.