HEALTH TRACKING DEVICE

• US 20170303846A1
• Filed: 04/21/2016
• Published: 10/26/2017
• 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
• Granted Patent: US 10,251,597 B2
• Time in Patent Office: 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 device, comprising:

one or more processors to;

receive, from a plurality of sensors, sensor data relating to a user,

the plurality of sensors including 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;

process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user; and

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.

2. The device of claim 1, where the one or more processors, when processing the sensor data, are to:

determine, based on a set of images of a food item obtained via the imaging sensor, a volume of the food item;

determine, based on a chemometric signature of the food item obtained via the spectrometer and based on the volume of the food item, a nutritional content of the food item; and

where the one or more processors, when providing the information identifying the health condition, are to;

providing information identifying the nutritional content of the food item.

3. The device of claim 1, where the one or more processors, when processing the sensor data, are further to:

determine an activity level of the user based on the sensor data from the plurality of sensors,

the sensor data including accelerometer sensor data and heart rate sensor data; and

where the one or more processors, when providing information identifying the health condition of the user, are to;

provide information identifying the activity level.

4. The device of claim 1, where the one or more processors are to:

receive a diet or exercise plan relating to the user; and

where the one or more processors, when processing the sensor data, are to;

determine a compliance with the diet or exercise plan by the user based on the sensor data; and

where the one or more processors, when providing information identifying the health condition of the user, are to;

provide information identifying the compliance with the diet or exercise plan by the user.

5. The device of claim 1, where the device is a wearable device.

6. The device of claim 5, where the one or more processors, when processing the sensor data, are to:

identify a correlation between the user mood and sensor data associated with another type of sensor of the plurality of types of sensors; and

where the one or more processors, when providing information identifying the health condition, are to;

provide information identifying the correlation between the user mood and the sensor data associated with the other type of sensor.

7. The device of claim 1, where the one or more processors are further to:

generate a recommendation, relating to the health condition of the user, for the user based on processing the sensor data to determine the health condition of the user,

the recommendation relating to altering a user activity level or a user diet to cause a corresponding alteration to sensor data associated with the user activity level or the user diet; and

provide information identifying the recommendation.

8. The device of claim 1, where the one or more processors are further to:

determine that the health condition of the user satisfies a threshold severity; and

transmit an alert to dispatch emergency management personnel to a location of the user based on determining that the health condition of the user satisfies the threshold severity.

9. The device of claim 1, where the one or more processors, are further to:

obtain a first classification model relating to performing spectroscopic analysis; and

where the one or more processors, when processing the sensor data, are to;

determine a blood glucose level of the user based on the sensor data and the first classification model;

calibrate the first classification model to generate a second classification model or quantitative model based on the first classification model and the blood glucose level,

calibrating the first classification model including utilizing an optimization technique; and

utilizing the second classification model or quantitative model to determine another blood glucose level based on other sensor data.

10. 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,

the first spectroscopic classification model being associated with identifying a health condition based on a chemometric signature,

the first spectroscopic classification model being 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,

the second spectroscopic classification model permitting a determination of a characteristic of the user or a food item; and

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

11. The non-transitory computer-readable medium of claim 10, 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.

12. The non-transitory computer-readable medium of claim 10, 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,

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.

13. The non-transitory computer-readable medium of claim 10, 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.

14. The non-transitory computer-readable medium of claim 10, 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.

15. The non-transitory computer-readable medium of claim 10, 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.

16. The non-transitory computer-readable medium of claim 15, 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.

17. A method, comprising:

determining, by a device, an activity level of a user based on first sensor data relating to the activity level of the user from a first set of sensors;

determining, by the device, a nutritional content of a set of food items for consumption by the user based on second sensor data relating to the nutritional content of the set of food items from a second set of sensors,

the second sensor data being obtained from a spectrometer;

obtaining, by the device, a stored diet and exercise plan for the user,

the stored diet and exercise plan including a goal relating to the activity level of the user and the nutritional content of the set of food items for consumption by the user;

determining, by the device, a user compliance with the stored diet and exercise plan based on the activity level of the user and the nutritional content of the set of food items; and

providing, by the device, a recommendation relating to user compliance with the stored diet and exercise plan based on determining the user compliance with the stored diet and exercise plan.

18. The method of claim 17, further comprising:

obtaining a generic classification model or quantitative model for spectroscopic analysis;

generating a local classification model or quantitative model for spectroscopic analysis based on the generic classification model or quantitative model and third sensor data,

generating the local classification model or quantitative model including utilizing an optimization technique; and

where determining the nutritional content of the set of food items comprises;

obtaining a first portion of the second sensor data from an imaging sensor of the second set of sensors,

the first portion of the second sensor data including a plurality of images;

determining a volume of the set of food items based on the plurality of images;

obtaining a second portion of the second sensor data from the spectrometer,

the second portion of the second sensor data including a chemometric signature;

performing a classification of the set of food items based on the chemometric signature and the local classification model or quantitative model; and

identifying the nutritional content of the set of food items based on the volume of the set of food items and the classification of the set of food items.

19. The method of claim 17, where determining the activity level further comprises:

determining a calorie expenditure by the user based on the first sensor data; and

where determining the user compliance with the stored diet and exercise plan comprises;

determining that a calorie intake by the user associated with consumption of the set of food items exceeds the calorie expenditure by a threshold amount,

the calorie intake being related to the nutritional content of the set of food items.

20. The method of claim 19, further comprising:

determining an amount of exercise that corresponds to the threshold amount; and

where providing the recommendation comprises;

providing information identifying the amount of exercise that corresponds to the threshold amount.