WO2021040292A1

WO2021040292A1 - Electronic device and method for providing personalized information based on biometric information

Info

Publication number: WO2021040292A1
Application number: PCT/KR2020/010797
Authority: WO
Inventors: Konstantin Aleksandrovich PAVLOV; Alexey Viacheslavovich PERCHIK; Elena Konstantinovna VOLKOVA; Nikolay Aleksandrovich LUTSYAK; Hyejung Seo; Rasul Rushanovich KHASIANOV; Vladislav Valerevich LYCHAGOV; Minji Kim
Original assignee: Samsung Electronics Co Ltd
Current assignee: Samsung Electronics Co Ltd
Priority date: 2019-08-23
Filing date: 2020-08-13
Publication date: 2021-03-04
Anticipated expiration: 2022-02-23

Abstract

According to the disclosure, a method for operating an electronic device is provided. The method comprises obtaining first information related to a user photoplethysmogram (PPG) at a first time, obtaining second information related to at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality during a first period, providing personalized information for the user, based on the first information and the second information, obtaining fourth information related to the customized information during a second period, obtaining fifth information related to the PPG at a second time, and maintaining or altering the customized information based on the fourth information and the fifth information.

Description

ELECTRONIC DEVICE AND METHOD FOR PROVIDING PERSONALIZED INFORMATION BASED ON BIOMETRIC INFORMATION

The disclosure relates to an electronic device and method for providing personalized information based on biometric information.

The concept of a healthy lifestyle includes a whole range of components. This is not just a diet or sports, it is a lifestyle aimed at rejuvenating and healing the whole body, giving up bad habits, creating a daily regimen including good rest, productive work and physical activity.

Results of numerous studies show that human health at any age is more than 50% dependent on lifestyle. Specialists highlight many advantages, answering the question of what is useful for a healthy lifestyle. This is the achievement of active longevity, reducing risks of developing chronic diseases, getting rid of bad mood, depression and stress, gaining the desired figure.

Market needs: health status and eating patterns estimation for lifestyle quality control.

The user has a smart watch and a smartphone. He needs recommendations how to improve his health conditions, monitor compliance with the doctor's recommendations and, if necessary, adjust and periodically monitor the changes in the health status.

It is known that health status estimation can be carried out, inter alia, based on data from the accelerometer/gyroscope of the photoplethysmogram sensor (PPG) from user devices (US 20170249445; US 9402597).

One of the methods for estimating health status is to estimate the health status of the vascular system based on photoplethysmography (PPG analysis).

The state of the blood vessels determines how long and active a person will live. Most serious complications arise due to problems with the vessels, and vice versa, many diseases affect the state of the blood vessels. Photoplethysmography is a method of recording blood flow parameters using a near-infrared or visible wavelength radiation source and a photodetector. The greater the volume of blood in a tissue at a given moment, the more radiation is absorbed, and, therefore, less radiation gets on a photodetector.

Both smart watches and smartphones comprise optical photoplethysmogram sensors. A smartphone comprising an optical photoplethysmogram sensor is the main implementation of the claimed disclosure, since such a smartphone sensor allows obtaining a better signal from a finger. Concerning a smart watch, the signal is obtained from the wrist.

The heart carries out the movement of blood in the vessels. Photoplethysmography allows measuring the volumetric pulse of the blood caused by a periodic change in blood volume with each heartbeat, the heart rate and the heart rate variability. With a reduction in the ventricular myocardium, blood under pressure enters from the heart into the aorta and pulmonary artery. Rhythmic contractions of the myocardium form the rhythmic expansion of the vascular wall (pulse), which under the influence of the propagation of pressure waves from the initial part of the aorta to the arterioles and capillaries leads to the appearance of pulse waves.

The contour of a volume pulse wave (FIG. 3A) is formed as a result of the interaction between the left ventricle and the vessels of the pulmonary circulation. Finger photoplethysmogram reflects the merger of two volume pulse waves (teeth). The first tooth is formed due to a systolic, direct wave, formed by the flow of blood into the systole, transmitted directly from the left ventricle to the fingers of the upper extremities (anacrotic phase). The second tooth is formed due to the reflected wave, which occurs due to the reflection of the blood flow from the periphery to the heart), transmitted through the aorta and large major arteries to the lower extremities, and sent back to the ascending aorta and further to the fingers of the upper extremities (dicrotic phase). The propagation velocity of the pulse wave through the vessels does not depend on the speed of blood flow, but is determined by the diameter of the vessel, the wall thickness and the elasticity of the vessel, as well as the density of the blood. Therefore, photoplethysmography helps to identify features of narrowing (stenosis and sclerosis) to evaluate vascular tone and heart function.

It is believed that the frequency and duration of the pulse wave depend on the work of the heart muscle, and the magnitude and shape of the peaks of the photoplethysmogram depends on the state of the vascular wall. FIG. 3A shows a photoplethysmogram of a vessel of a healthy person and FIG. 3B shows a photoplethysmogram of a vessel of a person affected by atherosclerosis, which is characterized by the waveform of the photoplethysmogram. For a healthy person, the pulse wave has a rather steep rise and a relatively high-reflected wave. The narrowing of the arteries leads to a smoothing of the rise, lengthening of the dicrotic phase and a decrease in the height of the reflected wave. When the vascular is significantly narrow, the reflected wave may be absent at all.

Factors that increase vascular stiffness and lead to a change in the shape of the photoplethysmogram curve are the following: age, arteriosclerotic diseases, cerebral infarction, coronary artery disease, chronic kidney disease, diabetes mellitus, hypertension, metabolic syndrome and others.

The shape of the photoplethysmogram curve of a healthy person should correspond to his age. Deviations indicate a health problem. Thus, according to the waveform of the photoplethysmogram, it is possible to assess the state of the vascular system and evaluate the vascular age. It may differ from real age; and accordingly, the health status can be evaluated.

Another way is to estimate health status by detecting eating habits. The main data source in this case is the accelerometer and gyroscope, which are part of inertial measuring modules (IMUs) embedded in a smart watch, i.e. sensors that detect the acceleration of a smart watch along three axes and the speed of rotation around these axes.

The signals of the accelerometer and gyroscope are different signals when the user performs various actions, such as walking, working using a keyboard, eating, etc., said signals can be determined. At the same time, movements associated with eating are well defined if the smart watch is on the dominant hand (right-handed, left-handed) and worse defined if the smart watch is on the non-dominant hand.

Mobile applications forwellness monitoring are widely represented on the market, for example, Samsung Health, Apple Activity; they track: activity level, heart rate, PPG signal, stress level, food intake, sleep, sport activities, however, they require manual input of information from user (for example, time of food intake), while there is a low accuracy of data entry, since it depends on the user's memory and honesty. It is possible to make a conclusion that there is no solutions concerning estimation of the health status. The market fails to provide solutions for PPG analysis. Thus, there is no objective method for assessing the health status of users using electronic devices available on the market.

Disclosed in US 9,402,597 (published August 02, 2016) is a mobile vascular health evaluation processes. The method comprises the steps of: receiving, from a portable physiological measuring device configured for temporary attachment to a human body, data representing one or more physiological metrics or parameters of the body; receiving, from one or more Doppler vascular sensors configured for temporary attachment to peripheral artery locations of the body and a portable Doppler vascular signal measuring device coupled to the Doppler vascular sensors, vascular function information for the body; determining medical condition data indicating one or more medical conditions of the patient based on analysis and correlations of the vascular function information and the patient physiological metrics; generating and providing output records specifying one or more of: recommendations for actions that an individual or healthcare provider should take in response to the recommendations or parameters; or one or more reports, animations or figures.

One or more computing devices perform the method, while the doctor is involved in the evaluation. The patient must constantly enter information, i.e. the device requires manual data input. The doctor monitors the received data and constantly insert corrections; it is necessary to constantly monitoring whether there are changes. The device is oriented to clinical patients (not healthy users), it is not designed for general consumers, since it cannot simultaneously monitor eating habits and evaluate the state of health with relevant recommendations and adjusting recommendations.

Disclosed in US 20170249445 (published August 31, 2017) is a system for monitoring nutritional intake. The system comprises a wearable housing configured for releasable attachment to a user; a biosensor supported by the wearable housing for disposition adjacent to a blood vessel; the biosensor configured to collect pulse profile data; an output device; and a processing circuit connected to the biosensor and the output device. The processing circuit is configured to: receive the pulse profile data from the biosensor; generate a nutritional intake value from the received pulse profile data; and control an output device to output the nutritional intake value.

Estimation of food consumption is carried out according to photoplethysmogram. However, the device does not comprise any recommendation system. An evaluation algorithm is disclosed when a person has eaten or not. The system is configured to estimate Kcal, for example, 400 grams of food corresponds to 700 kcal.

The system does not provide simultaneous monitoring of eating habits and assessment of health status with relevant recommendations and adjustment of recommendations.

Disclosed in US 2017340219 (published November 30, 2017) are a smart watch and human-to-computer interface for monitoring food consumption. Wrist-worn device (the smart watch) serves to measure a pulse transit time non-invasively and calculate a blood pressure value using the pulse transit time. A wrist-worn device includes a wrist-worn elongate band, at least four EKG or ICG electrodes coupled to the wrist-worn device for detecting a ventricular ejection of a heart, a photoplethysmogram (PPG) sensor coupled to the wrist-worn device for detecting arrival of a blood pressure pulse at the user's wrist, and a controller configured to calculate a pulse transit time (PTT) for the blood pressure pulse. The controller calculates one or more blood pressure values for the user based on the PPG.

The system comprises specific sensors in the smart watch that are not available on the market. The description does not specify which parameters are to be measured, how to evaluate nutrition according to the sensor signals. The system does not give recommendations on physical activity, nutrition and sleep. The system is no self-correcting recommendation system and does not provide automatic nutrition monitoring. The system does not provide simultaneous monitoring of eating habits and assessment of health status with relevant recommendations and adjustment of recommendations.

Disclosed in US 7953613 (published July 03, 2008) is a health maintenance system for comprehensive health assessment, abnormality detection, health monitoring, health pattern and trend detection, health strategy development, and health history archiving. The health maintenance system comprises a subscriber segment and a system segment, communicatively coupled. The subscriber segment acquires subscriber personal and health data from at least one subscriber, analyzing the data; identifies specific health abnormalities; prescribes at least one customized subscriber health product, instructs the subscriber on the implementation of the prescribed health product, compiles and preserves the subscriber's health history data-including abnormalities and prescribed health products, and performs monitoring of subscriber health conditions. The subscriber segment acquires subscriber data from the subscriber segment, stores and maintains the data, facilitates retrieval of data by the subscriber and emergency medical personnel analyzes subscriber patterns and trends, develops new health products and modifies existing health products, and monitors the effectiveness of health products.

The system involves a doctor for user's parameters monitoring, i.e. is oriented for clinical patients. The system is more automatic than the systems mentioned above. Doctors observe, look, correct. However, there is no automatic nutrition monitoring, requires manual data input.

Disclosed in US 2014136226 (published April 15, 2014) is a system for managing cardiovascular health status, that is, a system for computing a disease risk score of a patient based on a recommendation of treatment or lifestyle changes. The system comprises an input device for receiving risk parameter values for computing the disease risk score according to a risk score algorithm, and for receiving the recommendation of treatment or lifestyle changes, an interpreter for computing at least one risk parameter value for computing the disease risk score, on the basis of the recommended treatment or lifestyle changes, a risk calculator for calculating the disease risk score on the basis of the received risk parameter values and the computed risk parameter value, and an output device for communicating the calculated disease risk score. Thanks to the interpreter, the recommended treatment or lifestyle change is "translated" into a value of a risk parameter for computing the disease risk score, thereby allowing calculating the risk score resulting from the treatment or lifestyle change.

The system has the following drawbacks: no estimation of user's vascular status; no self-correcting recommendation system; no automatic nutrition monitoring.

Disclosed in US 2014349256 (published November 27, 2014) are smart watch and human-to-computer interface for monitoring food consumption. The system for monitoring a person's food consumption comprising: a wearable sensor that automatically collects data to detect probable eating events; a voluntary human-to-computer interface that is used by the person to enter food consumption data wherein the person is prompted to enter food consumption data when an eating event is detected by the wearable sensor; and a data analysis component that analyzes food consumption data to estimate the types and amounts of ingredients, nutrients, and/or calories that are consumed by the person. In an example, the wearable sensor can be part of a smart watch or smart bracelet. In an example, the voluntary human-to-computer interface can be part of a smart phone. The integrated operation of the wearable sensor and the voluntary human-to-computer interface disclosed in this disclosure offers accurate measurement of food consumption with low intrusion into the person's privacy.

The system has the following drawbacks: the system involves medical doctor for user's parameters monitoring, i.e. is oriented for clinical patients, there is no automatic nutrition monitoring, requires manual data input.

According to an embodiment of the disclosure, there is provided an electronic device and method for providing personalized information based on biometric information.

According to an embodiment of the disclosure, there is provided an electronic device and method for generating recommendations for the user's healthy lifestyle.

According to embodiments of the disclosure, there is provided an electronic device and method for providing personalized information for generating recommendations for the user's healthy lifestyle.

The technical results achieved according to the disclosure include enhancing the accuracy of estimation of the user's health condition by real-time monitoring the user's eating habits and physical activity and may further include detecting the relevance between the user's activity and physiological changes which occur to the user upon correcting the user's behavior. The user's behavior is measured by the smart watch and signals from the smart watch. Most appropriate recommendations, e.g., recommendations personalized for the user's eating habits and physical activity, are provided based on the measurements.

The proposed system is efficient when the user wears the smartphones on both hands.

The proposed system and method makes it possible to provide personalized recommendations for changing lifestyles and controlling lifestyles with minimal user participation in order to develop user habits leading to better health conditions.

According to an embodiment of the disclosure, a method for operating an electronic device is provided. The method comprises obtaining first information related to a user photoplethysmogram (PPG) at a first time, obtaining second information related to at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality during a first period, estimating third information related to food intake based on the first information and the second information, providing personalized information for the user based on the first information, the second information and the third information, obtaining fourth information related to the customized information during a second period, obtaining fifth information related to the PPG at a second time, and maintaining or altering the customized information based on the fourth information and the third information.

According to an embodiment of the disclosure, an electronic device is provided. The electronic device comprises a display, at least one processor connected with the display, and a memory connected with the at least one processor. The memory stores instructions executed to enable the processor to obtain first information related to a user photoplethysmogram (PPG) at a first time, obtain second information related to at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality during a first period, estimate third information related to food intake based on the first information and the second information, control the display to provide personalized information for the user, based on the first information and the second information, obtain fourth information related to the customized information during a second period, obtain fifth information related to the PPG at a second time, and maintain or alter the customized information based on the fourth information and the fifth information.

The technical result is achieved by providing a personalized system for forming recommendations to user in implementation a healthy lifestyle, the system comprises:

a smartwatch comprising

an accelerometer, gyroscope and photoplethysmogram (PPG) sensor that are configured to sense and provide signals concerning the user's physical activity, heart rate, blood oxygenation level, and sleep quality; said data, together with data of the user profile and location based on geolocation, are used for estimating the time of food intakes, eating habits and patterns of physical activity, taking into account the right-handed person or left-handed person,

a smartphone comprising a PPG sensor forming a photoplethysmogram signal by touching with the user finger, and comprising a processing unit for processing signals of the PPG sensor for assessing vascular stiffness defining a vascular age, and

a unit for generating recommendations to the user on food intakes and physical activity, configured to detect a correspondence between the a user behavior and user physiological changes based on signals received from a smartwatch and/or a smartphone, and selecting the most appropriate recommendations and displaying said recommendations.

Preferably, the personalized system further comprises a smart weight scales configured to generate and transmit signals of user's weight dynamics to the unit for generating recommendations on food intakes and physical activity.

Preferably, the unit for generating recommendations to the user on food intakes and physical activity is further configured to determine different types of deviations from the user's usual behavior, in particular, eating while moving.

According to the other embodiment of the personalized system for forming recommendations to user in implementation a healthy lifestyle the system comprises:

a smartwatch comprising

an accelerometer, gyroscope and photoplethysmogram (PPG) sensor that are configured to sense and provide signals concerning the user's physical activity, heart rate, blood oxygenation level, and sleep quality; said data, together with data of the user profile and location based on geolocation, are used for estimating the time of food intakes, eating habits and patterns of physical activity, taking into account the right-handed person or left-handed person;

said PPG sensor located on the display side of the smartwatch and configured to form a photoplethysmogram signal by touching with the user finger, and

a unit for processing the signals of the PPG sensor for estimating a vascular stiffness defining vascular age, and

a unit for generating recommendations to the user on eating habit pattern, configured to detect a correspondence between the user behavior and physiological changes of the user, based on signals received from the smartwatch, and also selecting the most appropriate recommendations and displaying recommendations on the watch display.

Preferably, the personalized system further comprises a smart weight scales configured for generating and transmitting signals of user's weight dynamics to the unit for generating recommendations for the user on food intakes and physical activity.

Preferably, the personalized system comprises a ECG sensor comprising two electrodes one of which is located on the display side of the smartwatch, and the second electrode is located on the back surface of the smartwatch, the sensor is configured to generate and transmit ECG signals to the unit for generating recommendations for the user on food intakes and physical activity.

Preferably, the personalized system further comprises an invasive/non-invasive glucose sensor, configured to generate signals of the dynamics of changes in the glucose level in the user's blood and transmit said signals to the unit for generating recommendations to the user on food intakes and physical activity.

The technical result is achieved by providing a method for forming recommendations to user in implementation of a healthy lifestyle, the method comprises the steps of:

measuring photoplethysmogram on a smartphone by touching with the user's finger to estimate the vascular age, based on which the user's health status is determined;

detecting unhealthy eating habits, including the time of food intakes, duration, regularity and physical activity, including time periods and intensity, based on observations and collected data on user behavior, and forming recommendations for the user how to change eating habits and physical activity for improving the health status;

repeatedly measuring the photoplethysmogram, based on which the dynamics of changes of the health status is estimated based on the estimation of the vascular age, and if necessary, changing recommendations based on the individual characteristics of the user's habits;

conducting periodic monitoring of compliance with the recommendations and, if necessary, adjusting or changing the recommendations to achieve positive dynamics of the user's health status based on the estimation of the vascular age.

According to the other embodiment of the method for forming recommendations to user in implementation a healthy lifestyle, the method comprises the steps of:

measuring photoplethysmogram on a smart watch by touching with the user's finger to estimate the vascular age, based on which the user's health status is determined;

Preferably, in the method further the dynamics of the user's weight is taken into account when adjusting or changing the recommendations to achieve positive dynamics of the user's health status.

Preferably, in the method further the user's electrocardiogram ECG is taken into account when adjusting or changing the recommendations to achieve positive dynamics of the user's health status.

Preferably, in the method further the dynamics of changes in glucose content is taken into account when adjusting or changing the recommendations to achieve positive dynamics of the user's health status.

The technical result is realized due to the fact that in addition to the data obtained from the accelerometer and gyroscope, the data of the photoplethysmogram signal is used. As a result, a significant improvement in the relevance of recommendations to the user is achieved. In addition, in comparison with known systems, the proposed system allows not to use manual user input. Known solutions provide recommendations being common to all users, for example, to reduce a weight a user is recommended to run, despite the contraindications that a user may have.

Existing systems monitor a plurality of parameters, however a user needs to put on and constantly wear a set of specialized sensors, which causes inconvenience and leads to a refusal to use the system. The claimed disclosure does not require additional sensors and does not require modification of existing smart watches and smartphones.

Using an accelerometer/gyroscope and a photoplethysmogram sensor allows to comprehensively estimating the user's lifestyle (nutrition/activity/sleep) without interacting with the user.

The recommendations for improving the user's personal healthy lifestyle includes a standard recommendations database approved by persons skilled in a healthy lifestyle, and personalized recommendations are selected from said database by identifying the user's behavior characteristics and individual health and lifestyle characteristics.

According to the disclosure, it is possible to detect broad applications in wellness management using the personalized recommendations for the user's healthy lifestyle.

Other aspects of the disclosure will be apparent from the following description, which is given merely for illustration purposes, taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an electronic device in a network environment according to an embodiment;

FIG. 2 is a view illustrating steps of a method for generating recommendations for a user in implementation of a healthy lifestyle, according to an embodiment;

FIG. 3A is a view illustrating a pulse wave, e.g., a photoplethysmogram (PPG) for healthy human blood vessels, according to an embodiment;

FIG. 3B is a view illustrating a pulse wave, e.g., a PPG for human blood vessels suffering from atherosclerosis, according to an embodiment;

FIG. 4 is a view illustrating a personalized system for generating recommendations for a user in implementation of a healthy lifestyle according to an embodiment, in which the system includes a smart watch and a smartphone;

FIG. 5 is a view illustrating a personalized system for generating recommendations for a user in implementation of a healthy lifestyle according to an embodiment, in which the system includes a smart watch, a smartphone, and a smart scale;

FIG. 6 is a view illustrating a personalized system for generating recommendations for a user in implementation of a healthy lifestyle according to an embodiment, in which the system includes a second PPG sensor positioned on the display side of a smart watch and the smartwatch configured to form a PPG signal by touching with the user's finger;

FIG. 7 is a view illustrating a personalized system for generating recommendations for a user in implementation of a healthy lifestyle according to an embodiment, in which the system includes a second PPG sensor positioned on the display side of a smart watch, the smart watch configured to form a PPG signal by touching with the user's finger, a smart scale, an ECG sensor with two electrodes, one positioned on the display side of the smart watch and the other on the back surface of the smart watch, and an invasive/non-invasive glucose sensor;

FIG. 8 is a view illustrating the results of estimation of a vascular condition based on analysis of PPG signals from 40,000 users, which are divided into three classes of vascular conditions: poor/good/excellent, according to an embodiment;

FIG. 9 is a view illustrating the rest results of an algorithm automatically detecting meals for 50 users based on signals from an accelerometer, a gyroscope, and a smart watch PPG sensor, according to an embodiment;

FIG. 10 is a view illustrating example results of analysis of PPG signals from users aged 40 and example recommendations corresponding thereto, according to an embodiment;

FIG. 11A is a view illustrating an example PPG signal obtained by an electronic device, using a PPG sensor according to an embodiment;

FIG. 11B is a view illustrating an example signal processed using an obtained PPG signal, according to an embodiment;

FIG. 11C is a view illustrating an accelerometer/gyroscope signal obtained using an accelerometer and gyroscope;

FIG. 11D is a view illustrating an example signal processed using an obtained accelerometer/gyroscope signal;

FIG. 11E is a view illustrating information related to average oxygenation level, HRV, and vascular stiffness obtained from a processed PPG signal, according to an embodiment;

FIG. 11F is a view illustrating information obtained from a processed accelerometer/gyroscope/PPG signal, according to an embodiment;

FIG. 12 is a view illustrating an example method for estimating whether a food intake action occurs, by an electronic device, according to an embodiment;

FIG. 13 is a view illustrating an example method for estimating whether a food intake action occurs, by an electronic device, according to an embodiment; and

FIG. 14 is a view illustrating an example method for generating personalized recommendations by an electronic device according to an embodiment.

FIG. 1 is a block diagram illustrating an electronic device 101 in a network environment 100 according to various embodiments. Referring to FIG. 1, the electronic device 101 in the network environment 100 may communicate with an electronic device 102 via a first network 198 (e.g., a short-range wireless communication network), or an electronic device 104 or a server 108 via a second network 199 (e.g., a long-range wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 via the server 108. According to an embodiment, the electronic device 101 may include a processor 120, memory 130, an input device 150, a sound output device 155, a display device 160, a sensor module 176, an interface 177, a power management module 188, a battery 189, a communication module 190, a subscriber identification module (SIM) 196, or an antenna module 197. In some embodiments, at least one (e.g., the display device 160) of the components may be omitted from the electronic device 101, or one or more other components may be added in the electronic device 101. In some embodiments, some of the components may be implemented as single integrated circuitry. For example, the sensor module 176 (e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) may be implemented as embedded in the display device 160 (e.g., a display).

The processor 120 may execute, for example, software (e.g., a program 140) to control at least one other component (e.g., a hardware or software component) of the electronic device 101 coupled with the processor 120, and may perform various data processing or computation. According to one embodiment, as at least part of the data processing or computation, the processor 120 may load a command or data received from another component (e.g., the sensor module 176 or the communication module 190) in volatile memory 132, process the command or the data stored in the volatile memory 132, and store resulting data in non-volatile memory 134. According to an embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)), and an auxiliary processor 123 (e.g., a graphics processing unit (GPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor 121. Additionally or alternatively, the auxiliary processor 123 may be adapted to consume less power than the main processor 121, or to be specific to a specified function. The auxiliary processor 123 may be implemented as separate from, or as part of the main processor 121.

The auxiliary processor 123 may control at least some of functions or states related to at least one component (e.g., the display device 160, the sensor module 176, or the communication module 190) among the components of the electronic device 101, instead of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or together with the main processor 121 while the main processor 121 is in an active state (e.g., executing an application). According to an embodiment, the auxiliary processor 123 (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., the communication module 190) functionally related to the auxiliary processor 123.

The memory 130 may store various data used by at least one component (e.g., the processor 120 or the sensor module 176) of the electronic device 101. The various data may include, for example, software (e.g., the program 140) and input data or output data for a command related thereto. The memory 130 may include the volatile memory 132 or the non-volatile memory 134.

The program 140 may be stored in the memory 130 as software, and may include, for example, an operating system (OS) 142, middleware 144, or an application 146.

The input device 150 may receive a command or data to be used by other component (e.g., the processor 120) of the electronic device 101, from the outside (e.g., a user) of the electronic device 101. The input device 150 may include, for example, a microphone, a mouse, a keyboard, or a digital pen (e.g., a stylus pen).

The sound output device 155 may output sound signals to the outside of the electronic device 101. The sound output device 155 may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as playing multimedia or playing record, and the receiver may be used for an incoming calls. According to an embodiment, the receiver may be implemented as separate from, or as part of the speaker.

The display device 160 may visually provide information to the outside (e.g., a user) of the electronic device 101. The display device 160 may include, for example, a display, a hologram device, or a projector and control circuitry to control a corresponding one of the display, hologram device, and projector. According to an embodiment, the display device 160 may include touch circuitry adapted to detect a touch, or sensor circuitry (e.g., a pressure sensor) adapted to measure the intensity of force incurred by the touch.

The sensor module 176 may detect an operational state (e.g., power or temperature) of the electronic device 101 or an environmental state (e.g., a state of a user) external to the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, a photoplethysmogram (PPG) sensor 171, an acceleration meter sensor 172, a gyroscope sensor 173, an electrocardiogram (ECG) sensor 174, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more specified protocols to be used for the electronic device 101 to be coupled with the external electronic device (e.g., the electronic device 102) directly (e.g., wiredly) or wirelessly. According to an embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface.

A connecting terminal 178 may include a connector via which the electronic device 101 may be physically connected with the external electronic device (e.g., the electronic device 102). According to an embodiment, the connecting terminal 178 may include, for example, a HDMI connector, a USB connector, a SD card connector, or an audio connector (e.g., a headphone connector).

The power management module 188 may manage power supplied to the electronic device 101. According to one embodiment, the power management module 388 may be implemented as at least part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to an embodiment, the battery 189 may include, for example, a primary cell which is not rechargeable, a secondary cell which is rechargeable, or a fuel cell.

The communication module 190 may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 101 and the external electronic device (e.g., the electronic device 102, the electronic device 104, or the server 108) and performing communication via the established communication channel. The communication module 190 may include one or more communication processors that are operable independently from the processor 120 (e.g., the application processor (AP)) and supports a direct (e.g., wired) communication or a wireless communication. According to an embodiment, the communication module 190 may include a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules may communicate with the external electronic device via the first network 198 (e.g., a short-range communication network, such as Bluetooth^TM, wireless-fidelity (Wi-Fi) direct, or infrared data association (IrDA)) or the second network 199 (e.g., a long-range communication network, such as a cellular network, the Internet, or a computer network (e.g., LAN or wide area network (WAN)). These various types of communication modules may be implemented as a single component (e.g., a single chip), or may be implemented as multi components (e.g., multi chips) separate from each other. The wireless communication module 192 may identify and authenticate the electronic device 101 in a communication network, such as the first network 198 or the second network 199, using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module 196.

The antenna module 197 may transmit or receive a signal or power to or from the outside (e.g., the external electronic device). According to an embodiment, the antenna module may include one antenna including a radiator formed of a conductor or conductive pattern formed on a substrate (e.g., a printed circuit board (PCB)). According to an embodiment, the antenna module 197 may include a plurality of antennas. In this case, at least one antenna appropriate for a communication scheme used in a communication network, such as the first network 198 or the second network 199, may be selected from the plurality of antennas by, e.g., the communication module 190. The signal or the power may then be transmitted or received between the communication module 190 and the external electronic device via the selected at least one antenna. According to an embodiment, other parts (e.g., radio frequency integrated circuit (RFIC)) than the radiator may be further formed as part of the antenna module 197.

At least some of the above-described components may be coupled mutually and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)).

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 via the server 108 coupled with the second network 199. Each of the

electronic devices

102 and 104 may be a device of a same type as, or a different type, from the electronic device 101. According to an embodiment, all or some of operations to be executed at the electronic device 101 may be executed at one or more of the external

electronic devices

102, 104, or 108. For example, if the electronic device 101 should perform a function or a service automatically, or in response to a request from a user or another device, the electronic device 101, instead of, or in addition to, executing the function or the service, may request the one or more external electronic devices to perform at least part of the function or the service. The one or more external electronic devices receiving the request may perform the at least part of the function or the service requested, or an additional function or an additional service related to the request, and transfer an outcome of the performing to the electronic device 101. The electronic device 101 may provide the outcome, with or without further processing of the outcome, as at least part of a reply to the request. To that end, a cloud computing, distributed computing, or client-server computing technology may be used, for example.

The electronic device according to various embodiments may be one of various types of electronic devices. The electronic devices may include, for example, a portable communication device (e.g., a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. According to an embodiment of the disclosure, the electronic device is not limited to the above-listed embodiments.

It should be appreciated that various embodiments of the disclosure and the terms used therein are not intended to limit the technological features set forth herein to particular embodiments and include various changes, equivalents, or replacements for a corresponding embodiment. With regard to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It is to be understood that a singular form of a noun corresponding to an item may include one or more of the things, unless the relevant context clearly indicates otherwise. As used herein, each of such phrases as "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C," may include all possible combinations of the items enumerated together in a corresponding one of the phrases. As used herein, such terms as "1st" and "2nd," or "first" and "second" may be used to simply distinguish a corresponding component from another, and does not limit the components in other aspect (e.g., importance or order). It is to be understood that if an element (e.g., a first element) is referred to, with or without the term "operatively" or "communicatively", as "coupled with," "coupled to," "connected with," or "connected to" another element (e.g., a second element), it means that the element may be coupled with the other element directly (e.g., wiredly), wirelessly, or via a third element.

As used herein, the term "module" may include a unit implemented in hardware, software, or firmware, and may interchangeably be used with other terms, for example, "logic," "logic block," "part," or "circuitry". A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment, the module may be implemented in a form of an application-specific integrated circuit (ASIC).

Various embodiments as set forth herein may be implemented as software (e.g., the program 140) including one or more instructions that are stored in a storage medium (e.g., internal memory 136 or external memory 138) that is readable by a machine (e.g., the electronic device 101). For example, a processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor. This allows the machine to be operated to perform at least one function according to the at least one instruction invoked. The one or more instructions may include a code generated by a complier or a code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Wherein, the term "non-transitory" simply means that the storage medium is a tangible device, and does not include a signal (e.g., an electromagnetic wave), but this term does not differentiate between where data is semi-permanently stored in the storage medium and where the data is temporarily stored in the storage medium.

According to an embodiment, a method according to various embodiments of the disclosure may be included and provided in a computer program product. The computer program products may be traded as commodities between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or be distributed (e.g., downloaded or uploaded) online via an application store (e.g., Play Store^TM), or between two user devices (e.g., smart phones) directly. If distributed online, at least part of the computer program product may be temporarily generated or at least temporarily stored in the machine-readable storage medium, such as memory of the manufacturer's server, a server of the application store, or a relay server.

According to various embodiments, each component (e.g., a module or a program) of the above-described components may include a single entity or multiple entities. According to various embodiments, one or more of the above-described components may be omitted, or one or more other components may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In such a case, according to various embodiments, the integrated component may still perform one or more functions of each of the plurality of components in the same or similar manner as they are performed by a corresponding one of the plurality of components before the integration. According to various embodiments, operations performed by the module, the program, or another component may be carried out sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order or omitted, or one or more other operations may be added.

A personalized system 400 (FIG. 4) for forming recommendations to user in implementation of a healthy lifestyle, according to an embodiment, comprises: a smartwatch 410 comprising an accelerometer, gyroscope 411 and photoplethysmogram (PPG) sensor 412 that are configured to sense and provide signals concerning the user's physical activity, heart rate, blood oxygenation level, and sleep quality. Said data, together with data of the user's profile and location based on geolocation, are used for estimating the time of meals, eating habits and patterns of physical activity, taking into account the right-handed person or left-handed person.

System 400 comprises smartphone comprising a PPG sensor 421 providing a photoplethysmogram signal by touching with a user's finger, and comprising a processing unit for processing signals of the PPG sensor for assessing vascular stiffness defining to a vascular age.

System 400 also comprises a unit for generating recommendations to a user on food intakes and physical activity, integrated in a smartphone 420 or smart watch 410 and configured to detect a correspondence between a user behavior and user physiological changes based on signals received from a smartwatch and/or a smartphone, and selecting the most appropriate recommendations and displaying said recommendations.

In the preferred embodiment, a personalized system 500 (FIG. 5) for forming recommendations further comprises a smart weight scale 530 configured to generate and transmit signals of user's weight dynamics to the unit for generating recommendations on food intake and physical activity.

The unit for generating recommendations to the user on food intakes and physical activity is further configured to determine different types of deviations from the user's usual behavior, in particular, eating while moving.

According to the other embodiment of the claimed disclosure, wherein a personalized system 600 (FIG. 6) for forming recommendations to user in implementation a healthy lifestyle comprises smart watch 610 comprising an accelerometer, gyroscope 611 and photoplethysmogram (PPG) sensor 612 that are configured to sense and provide signals concerning the user's physical activity, heart rate, blood oxygenation level, and sleep quality; said data, together with data of the user's profile and location based on geolocation, are used for estimating the time of food intakes, eating habits and patterns of physical activity, taking into account the right-handed person or left-handed person.

According to said embodiment the smart watch 610 comprises the second (additional) photoplethysmogram (PPG) sensor 613 located on the display side of the smart watch 610 and configured to form a photoplethysmogram signal by touching with the user's finger.

A unit (not shown in FIGS.) for processing the signals of the PPG sensor for estimating a level of blood oxygenation, vascular stiffness defining vascular age is integrated into the smart watch 610.

System 600 further comprises a unit for generating recommendations to the user on eating habit pattern and physical activity, integrated into the smart watch 610; the unit is configured to detect a correspondence between the user's behavior and physiological changes of the user, based on signals received from the smart watch, and also selecting the most appropriate recommendations and displaying said recommendations on the watch display.

In the preferred embodiment, a personalized system 700 (FIG. 7) for forming recommendations to user further comprises a smart weight scale 730 configured for generating and transmitting signals of user's weight dynamics to the unit for generating recommendations for the user on food intakes and physical activity.

In an embodiment the personalized system 700 (FIG. 7) for forming recommendations to user further comprises a ECG sensor 714 comprising two electrodes one of which is located on the display side of the smartwatch, and the second electrode is located on the back surface of the smartwatch, the sensor is configured to generate and transmit ECG signals to the unit for generating recommendations for the user on food intakes and physical activity.

In an embodiment the personalized system 700 (FIG. 7) for forming recommendations to user further comprises an invasive/non-invasive glucose sensor 715, configured to generate signals of the dynamics of changes in the glucose level in the user's blood and transmit said signals to the unit for generating recommendations to the user on food intakes and physical activity.

According to the disclosure a method 200 (FIG. 2) for forming recommendations to user in implementation of a healthy lifestyle is performed in the following way.

At the first step 201 the user puts on the smart watch and conducts measuring photoplethysmogram on a smartphone by touching with the user's finger to estimate the health status (excellent/good/poor). The user's photoplethysmogram signal is analyzed (FIG. 3A, Fig. 3B) by comparing the received user's photoplethysmogram signal with a signal received for a healthy person of age equal to the age of the given user, the waveform and the coefficients calculated from the signals associated with the stiffness of the vessels are compared.

In the second step 203, after a short period of time, about a week after the first measurement, the system detects unhealthy eating habits (eating time, duration, regularity) and physical activity (periods and intensity) based on observations and collected data on user's behavior and generates recommendations for the user on how to change eating habits and physical activity to improve health.

Personalized recommendations are selected from a set of standard recommendations approved by persons skilled in a healthy lifestyle. Recommendations are selected depending on the goal of the user, in particular, the user can choose maintaining the form, i.e. maintaining a healthy lifestyle, losing or gaining weight. Recommendations are selected based on the user's current health status, for example, if the user has problems concerning vessels, the physical activity limited.

Further, over a long period, in particular, about three months, the system automatically monitors user's behavior, estimating compliance with recommendations.

Food intakes are detected based on the signals of the gyroscope, accelerometer together with the analysis of the photoplethysmogram signals, the periods and intensity of the user's physical activity are calculated based on the signals of the accelerometer and gyroscope.

In the third step 203, after a time interval about three months after the first measurement, the user performs another measurement of the photoplethysmogram signal, based on which the system estimates the dynamics of the health state in accordance with the age of the vascular system. The state of the vascular system improves as a result of compliance with the recommendations, or not changes, if the state of the vascular system was initially excellent. In case of deterioration of the state of the vascular system when implementing the recommendations, a decision is made to change said recommendations.

After that, weekly, monitoring 204 of compliance with the recommendations is carried out and, in case of improvement of the heals status, the user is motivated to follow the recommendations; in case the user does not follow the recommendations, for example, if the user has a habit of sleeping 5 hours a day, the recommendations can be replaced by less rigid ones, the recommendation to sleet 8 hours is too hard, it is necessary to provide recommendations in order to consistently increase sleep time.

Frequent measurements of the photoplethysmogram is impractical, since the state of the vascular system changes three to four months after a lifestyle change.

The algorithm for determining the vascular age from the photoplethysmogram signal is based on the determination of the vascular stiffness index that is calculated using the amplitude characteristics, the first and second derivatives, and other parameters of the photoplethysmogram.

For a more accurate determination of vascular age, a machine learning algorithm is used, for example, the Random Forest trained on data on the state of vascular age and signals of photoplethysmogram of 40,000 persons, said algorithm is used for processing the pre-prepared (filtered and normalized) signals of photoplethysmogram. The result of estimating the vascular age is shown in FIG. 8, wherein the estimation corresponds to 3 classes: poor/good/excellent condition of the vascular age.

The user's eating habits are identified by analyzing the signals of the accelerometer, gyroscope and photoplethysmogram sensor after the preprocessing stages that include filtering and normalizing by using the deep machine learning algorithm (recurrent neural networks) trained to recognize motion patterns corresponding to food intakes for a sample of 50 persons and 1100 food intakes.

The deep machine learning algorithm uses an XGboost algorithm. The method of analyzing the user's eating habits is described below in detail.

The photoplethysmogram signal is recorded in four spectral ranges: red, infrared, green and blue. The signals recorded in different channels are compared with each other. The result of the comparison is the spectral scattering coefficient that can be used for determining whether the chemical composition of the blood is changed.

The user's blood composition changes after eating, the scattering coefficients at different wavelengths change accordingly, further, the oxygen saturation of the blood is determined by the spectral scattering characteristics, which is also used as an input parameter for an algorithm based on a neural network along with spectral scattering coefficients.

One more parameter used by the algorithm based on the neural network for determining the eating process is the change in heart rate variability associated with the activity of the autonomic regulation systems of the digestive system that responds to food intakes.

FIG. 9 shows the results of the detection of food intakes. For a dominant hand, the boxplot is shown on the right. The F-measure is used (F1-score is a joint estimation of accuracy, precision and completeness, recall), it is equal 0.7, on average. The boxplot on the right is for a non-dominant hand; here the F-measure is lower than and equal to 0.63.

An example of forming recommendations for a 40-year-old user is provided in FIG. 10.

In the first case, the algorithm estimates the vascular age of the user to be 29 years, which is much less than the real age of the user, therefore the user is recommended to continue to lead a healthy lifestyle. The recommended interval for measuring the photoplethysmogram signal is 4 months; minimal user interaction is required.

In the second case, the algorithm estimates the vascular age of the user to be 45 years, which is close to the real age of the user, therefore the user is motivated to maintain and develop the habits leading to better healthy lifestyle. The recommended interval for measuring the photoplethysmogram signal is 3 months; the system informs the user about non-compliance with the recommendations, if any.

In the third case, the algorithm estimates the vascular age of the user to be 60 years, which is much higher than the real age of the user, therefore, the user is advised to undergo examination in a medical clinic.

First, according to various embodiments of the disclosure, the electronic device may estimate the user's eating habits in various manners. A method of estimating the user's eating habits by an electronic device, according to various embodiments of the disclosure, is described below with reference to FIGS. 11A to 11F.

FIG. 11A is an example illustrating an example PPG signal obtained by an electronic device, using a PPG sensor according to an embodiment.

Referring to FIG. 11A, signal 1110 denotes a PPG signal obtained using a red channel of a red wavelength sensor before the user eats food. Signal 1120 denotes a PPG signal obtained using the red channel after the user eats food. Signal 1130 denotes a PPG signal obtained using an infrared (IR) channel before the user eats food. Signal 1140 denotes a PPG signal obtained using the infrared (IR) channel after the user eats food.

The noise included in the obtained PPG signal, i.e., PPG sensor noise, may be removed by smoothing the obtained PPG signal. The smoothing operation may be performed by a low pass filter (LPF) or based on an exponential moving average scheme, but not limited thereto. The noise-removed PPG signal may be filtered by a high pass filter (HPF), so that motion artifacts may be reduced.

FIG. 11B is a view illustrating a processed signal of a PPG signal obtained by an electronic device according to an embodiment. The electronic device obtains parameters of FIG. 11e, which are described below, using the processed PPG signal. The electronic device estimates whether a food intake event occurs using a machine learning (ML) algorithm based on the obtained parameters.

FIG. 11C is a view illustrating an accelerometer/gyroscope signal obtained by an electronic device using an accelerometer/gyroscope according to an embodiment.

Referring to FIG. 11C, the electronic device may remove accelerometer/gyroscope noise by applying an exponential moving average scheme to the obtained accelerometer/gyroscope signal, thereby smoothing the accelerometer/gyroscope signal. The electronic device down-samples the smoothed accelerometer/gyroscope signal to thereby reduce the computation costs of the machine learning algorithm and normalizes the smoothed accelerometer/gyroscope signal to allow the accelerometer/gyroscope signal to have a value ranging from 0 to 1.

FIG. 11D is a view illustrating an accelerometer/gyroscope signal generated by processing an obtained accelerometer/gyroscope signal by an electronic device according to an embodiment.

Referring to FIG. 11D, the electronic device obtains the parameters of FIG. 11F, which are described below, using the processed accelerometer/gyroscope signal and estimates whether a food intake event occurs using the machine learning algorithm based on the obtained parameters.

According to an embodiment, it is assumed that accelerometer/gyroscope/PPG signals sensed for a preset time, e.g., 32,000 hours or more, and multiple (e.g., 400 or more) food intake events, which are differentiated by the accelerometer/gyroscope/PPG signals, are created into a database for, e.g., 106 people, and the database is stored as a database for the machine learning algorithm.

The signals and parameters described above in connection with FIGS. 11A to 11F may be obtained using an infrared (IR) wavelength sensor and a red wavelength sensor.

FIG. 12 is a view illustrating an example process of estimating whether a food intake event occurs based on the operation of processing a PPG signal and the processed PPG signal shown in FIGS. 11A to 11F, according to an embodiment.

Referring to FIG. 12, the electronic device obtains a PPG signal from the PPG sensor (1200). The electronic device smooths and processes the obtained PPG signal (1210). The smoothing operation may be performed using a low pass filter or based on an exponential moving average scheme. The electronic device may obtain information related to average blood oxygenation level, hear rate variability (HRV), and vascular stiffness from the processed PPG signal. The average blood oxygenation level, HRV, and vascular stiffness-related information is shown in FIG. 11E (1220). The electronic device obtains food intake-related probabilities using the machine learning algorithm based on the average blood oxygenation level, HRV, and vascular stiffness-related information (1230). The electronic device estimates whether a food intake event occurs based on the obtained food intake-related probabilities. (1240). Here, the electronic device generates and provides personalized recommendations for the user, based on the result of estimation of whether a food intake event occurs.

FIG. 13 is a view illustrating an example process of estimating whether a food intake event occurs based on the operation of processing an accelerometer/gyroscope signal and the processed accelerometer/gyroscope/PPG signals shown in FIGS. 11A to 11F.

Referring to FIG. 13, the electronic device obtains an accelerometer/gyroscope signal from the accelerometer/gyroscope sensor (1300). The electronic device smooths the obtained accelerometer/gyroscope signal to thereby remove noise. The smoothing operation may be performed using a low pass filter or based on an exponential moving average scheme. The processed accelerometer/gyroscope signal is down-sampled to reduce the computation costs of the algorithm, and normalization is performed to allow the processed accelerometer/gyroscope signal to have a value ranging from 0 to 1 (1310). The electronic device obtains the pieces of information shown in FIG. 11F from the processed accelerometer/gyroscope signal and the processed PPG signal (S1320). The electronic device obtains food intake-related probabilities using the machine learning algorithm based on the obtained information (1330). The electronic device estimates whether a food intake event occurs based on the obtained food intake-related probabilities (1340). Here, the electronic device generates and provides personalized recommendations for the user, based on the result of estimation of whether a food intake event occurs.

FIG. 14 is a view illustrating a method of operating an electronic device according to an embodiment.

Referring to FIG. 14, the electronic device measures the PPG and obtains biometric information, such as vascular stiffness, based on the measured PPG (1400). The electronic device obtains user health information (e.g., the user's physical activity, heart rate, blood oxygenation level, sleep quality) during a preset first period, e.g., about one week (1410). The electronic device generates personalized recommendations based on the obtained biometric information and user health information and provides the generated personalized recommendations (1420). The personalized recommendations may be provided to the user via, e.g., a device included in the electronic device. Thereafter, the electronic device monitors whether the personalized recommendations are followed during a preset second period, e.g., about three months (1430). The electronic device measures the PPG and obtains biometric information such as vascular stiffness, based on the PPG (1440).

The electronic device identifies whether the condition of the vascular system has improved based on whether the personalized recommendations are followed and the biometric information obtained in operation 1440 (1450). The electronic device determines, and provides, whether to maintain the provided personalized recommendations based on whether the vascular system has improved. Upon determining that the condition of the vascular system has improved, the electronic device maintains the personalized recommendations (1460). Upon determining that the condition of the vascular system has not improved, the electronic device alters the personalized recommendations. (1470).

Then, electronic device monitors whether the maintained personalized recommendations or the altered personalized recommendations are followed during a preset third period, e.g., one week, and returns to operation 1450 to identify whether the condition of the vascular system has improved (1480).

According to an embodiment of the disclosure, there is provided a personalized system forming recommendations for the user in implementation of healthy lifestyle. The personalized system comprises a smart watch including an accelerometer, a gyroscope, and a PPG sensor configured to sense and provide signals related to the user's physical activity, heart rate, blood oxygenation level, and sleep quality (where, location data which is based on the data, the user's profile, and geolocation is used to estimate food intake times, eating habits, and physical activity patterns, considering whether the user is right-handed or left-handed), a smartphone including a PPG sensor for generating a PPG signal by touching with the user's finger and a processing unit for processing signals from the PPG sensor measuring vascular stiffness which defines the vascular age, and a unit configured to generate recommendations for the user's food intake and physical activity, detect the relevance between the user's behavior and the user's physiological changes based on signals received from the smart watch and/or the smartphone, select the most appropriate recommendations, and display the recommendations.

Here, the personalized system further comprises a smart scale configured to generate the user's weight dynamics signals and transmit the signals to the unit generating the recommendations for the user's food intake and physical activity.

Here, the unit generating the recommendations for the user's food intake and physical activity is configured to determine other types of deviations, in particular eating on the move, than the user's routine behavior.

According to an embodiment of the disclosure, there is implemented a personalized system forming recommendations for the user in implementation of a healthy lifestyle. The personalized system comprises a smart watch including an accelerometer, a gyroscope, and a PPG sensor configured to sense and provide signals related to the user's physical activity, heart rate, blood oxygenation level, and sleep quality (where, location data which is based on the data, the user's profile, and geolocation is used to estimate food intake times, eating habits, and physical activity patterns, considering whether the user is right-handed or left-handed, and the PPG sensor is positioned on a display side of the smart watch and configured to form a PPG signal by touching with the user's finger), a unit for processing signals from the PPG sensor to estimate vascular stiffness which defines vascular age, and a unit configured to generate recommendations for the user's eating habit pattern, detecting the relevance between the user's behavior and the user's physiological changes based on signals received from the smart watch, select the most appropriate recommendations, and display the recommendations on the display of the smart watch.

Here, the personalized system includes an ECG sensor with two electrodes, one of which is positioned on the display side of the smart watch and the other on the back surface of the smart watch. The sensor is configured to generate ECG signals and transmit the ECG signals to the unit generating the recommendations for the user's food intake and physical activity.

Here, the personalized system further comprises an invasive/non-invasive glucose sensor configured to generate dynamics signals for variations in blood glucose level of the user and transmit the signals to the unit generating the reflective metals for the user's food intake and physical activity.

According to an embodiment of the disclosure, there is provided a method for forming recommendations for a user in implementation of a healthy lifestyle. The method comprises measuring the PPG on a smartphone by touching with the user's finger to estimate the vascular age based on the user's health condition being determined, detecting a rule including food intake times, duration, periods, and strength and unhealthy eating habits including the physical activity based on the observations for the user's behavior and gathered data and forming recommendations for the user about how to change the eating habits and physical activity to improve the health condition, repeatedly measuring the PPG based on the dynamics of health condition variations being estimated based on the estimation of the vascular age and, if necessary, altering the recommendations based on the individual characteristics of the user's habits, and periodically monitoring whether the recommendations are followed and, if necessary, adjusting or altering the recommendations to achieve positive dynamics for the user's health condition based on the estimation of the vascular age.

According to an embodiment of the disclosure, there is provided a method for forming recommendations for a user in implementation of a healthy lifestyle. The method comprises measuring the PPG on a smart watch by touching with the user's finger to estimate the vascular age based on the user's health condition being determined, detecting a rule including food intake times, duration, periods, and strength and unhealthy eating habits including the physical activity based on the observations for the user's behavior and gathered data and forming recommendations for the user about how to change the eating habits and physical activity to improve the health condition, repeatedly measuring the PPG based on the dynamics of health condition variations being estimated based on the estimation of the vascular age and, if necessary, altering the recommendations based on the individual characteristics of the user's habits, and periodically monitoring whether the recommendations are followed and, if necessary, adjusting or altering the recommendations to achieve positive dynamics for the user's health condition based on the estimation of the vascular age.

Here, the method further comprises considering the user's weight dynamics upon adjusting or altering the recommendations to achieve positive dynamics for the user's health condition.

Here, the method further comprises considering the user's ECG upon adjusting or altering the recommendations to achieve positive dynamics for the user's health condition.

Here, the method further comprises considering the dynamics for variations in glucose content upon adjusting or altering the recommendations to achieve positive dynamics for the user's health condition.

According to an embodiment of the disclosure, in the method for operating the electronic device, an artificial intelligence (AI) model using the accelerometer and gyroscope and PPG-related information may be used to infer or predict the user's physical activity, heart rate, blood oxygenation level, and eaten food information. The processor may perform a pre-treatment process on the data for conversion into a form appropriate for use as an input to the artificial intelligence model. The artificial intelligence model may be created by training. Here, "created by training" means that a predefined operation rule or artificial intelligence model configured to achieve a desired feature (or goal) is created by training a default artificial intelligence model with multiple pieces of training data and a training algorithm. The artificial intelligence model may include a plurality of neural network layers. Each of the plurality of neural network layers includes a plurality of weight values and performs neural network computation by computation between the result of computation by a previous layer and the plurality of weight values.

Reasoning prediction is a technique of determining and logically inferring and predicting information and includes, e.g., knowledge-based reasoning, optimization prediction, preference-based planning, or recommendation.

It should be understood that although some embodiments of the disclosure have been described above, the disclosure is not limited to such specific embodiments. It is rather assumed that all alternatives, modifications, or equivalents thereto may belong to the scope of the disclosure without departing from the essence and scope of the disclosure as defined in the appended claims.

Further, although amendments are made to the claims during the prosecution, all the equivalents to the claims are retained.

Each of the aforementioned components of the electronic device may include one or more parts, and a name of the part may vary with a type of the electronic device. The electronic device in accordance with various embodiments of the disclosure may include at least one of the aforementioned components, omit some of them, or include other additional component(s). According to an embodiment of the disclosure, some of the components may be combined into an entity, but the entity may perform the same functions as the components.

The embodiments disclosed herein are proposed for description and understanding of the disclosed technology and does not limit the scope of the disclosure. Accordingly, the scope of the disclosure should be interpreted as including all changes or various embodiments based on the technical spirit of the disclosure.

Claims

A method for operating an electronic device, the method comprising:

obtaining first information related to a user photoplethysmogram (PPG) at a first time;

obtaining second information related to at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality during a first period;

estimating third information related to food intake based on the first information and the second information.
The method of claim 1, further comprising:

providing personalized information for the user based on the first information, the second information, and the third information;

obtaining fourth information related to the customized information during a second period;

obtaining fifth information related to the PPG at a second time; and

maintaining or altering the customized information based on the fourth information and the fifth information.
The method of claim 1, wherein the user PPG is sensed via a PPG sensor included in the electronic device, and

wherein at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality is sensed by an accelerometer and a gyroscope included in the electronic device.
The method of claim 1, wherein at least one of the first information and the second information is received from another electronic device.
The method of claim 1, wherein the third information is obtained by estimating food intake-related information by comparing the first information and the second information with data in a database based on processed information.
The method of claim 2, wherein the fourth information includes at least one of information related to the user weight dynamics, information related to the user physical activity, information related to the user electrocardiogram (ECG), and information related to the user glucose level.
The method of claim 1, wherein the first information includes information related to vascular stiffness.
The method of claim 7, wherein the vascular stiffness-related information is obtained based on a result of comparison between the user PPG and a preset PPG.
An electronic device, comprising:

a display;

at least one processor connected with the display; and

a memory connected with the at least one processor, wherein the memory stores instructions executed to enable the processor to:

obtain first information related to a user photoplethysmogram (PPG) at a first time;

obtain second information related to at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality during a first period;

estimate third information related to food intake based on the first information and the second information.
The electronic device of claim 9, wherein instructions are further executed to enable the processer to:

control the display to provide personalized information for the user based on the first information, the second information, and the third information;

obtain additional information related to the customized information during a second period;

obtain fourth information related to the PPG at a second time; and

maintain or alter the personalized information based on the additional information and the fourth information.
The electronic device of claim 9, further comprising:

a PPG sensor configured to sense the user PPG; and

an accelerometer and gyroscope configured to sense at least one of the user physical activity, heart rate, blood oxygenation level, or sleep quality.
The electronic device of claim 9, further comprising a receiver configured to receive at least one of the first information and the second information from another electronic device.
The electronic device of claim 9, wherein the third information is obtained by estimating food intake-related information by comparing the first information and the second information with data in a database based on processed information.
The electronic device of claim 10, wherein the fourth information includes at least one of information related to the user weight dynamics, information related to the user physical activity, information related to the user electrocardiogram (ECG), and information related to the user glucose level.
The electronic device of claim 9, wherein the first information includes information related to vascular stiffness.