Patent Publication Number: US-2021177287-A1

Title: Apparatus and method for estimating bio-information

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims priority from Korean Patent Application No. 10-2019-0166985, filed on Dec. 13, 2019, in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes. 
     BACKGROUND 
     1. Field 
     Example embodiments relate to non-invasively estimating bio-information. 
     2. Description of Related Art 
     Recently, with the aging population, soaring medical costs, and shortage of medical personnel for specialized medical services, research is being actively conducted on information technology (IT)-medical convergence technologies, in which IT technology and medical technology are combined. Particularly, monitoring of the health condition of the human body is not limited to medical institutions, but is expanding to mobile healthcare fields that may monitor a user&#39;s health condition anywhere and anytime in daily life at home or office. Typical examples of bio-signals, which indicate the health condition of individuals, include an electrocardiography (ECG) signal, a photoplethysmogram (PPG) signal, an electromyography (EMG) signal, and the like, and various bio-signal sensors have been developed to measure these signals in daily life. Particularly, a PPG sensor may estimate blood pressure of a human body by analyzing a shape of pulse waves which reflect a cardiovascular status and the like. 
     SUMMARY 
     According to an aspect of an example embodiment, there is provided an apparatus for estimating bio-information, the apparatus including: a pulse wave sensor configured to measure a pulse wave signal from a user; and a processor configured to measure a current time interval between a plurality of element waveforms of the pulse wave signal, determine whether a current measurement posture of the user corresponds to a reference posture based on the current time interval of the plurality of element waveforms, and estimate the bio-information based on a determination of whether the current measurement posture corresponds to the reference posture. 
     The processor may be further configured to determine a reference time interval between a plurality of reference element waveforms of a reference pulse wave signal that is measured in the reference posture, and determine whether the current measurement posture of the user corresponds to the reference posture, based on a difference between the reference time interval and the current time interval. 
     The processor may be further configured to compare the difference between the reference time interval and the current time interval with a predetermined threshold value, and determine that the current measurement posture does not correspond to the reference posture based on the difference between the reference time interval and the current time interval being greater than the predetermined threshold value. 
     The processor may be further configured to determine whether the current measurement posture corresponds to the reference posture further based on a difference between a current heart rate measured in the current measurement posture and a reference heart rate measured in the reference posture. 
     The processor may be further configured to compare a first difference between the reference time interval and the current time interval with a first threshold value, compare a second difference corresponding to the difference between the current heart rate and the reference heart rate with a second threshold value, and determine whether the current measurement posture corresponds to the reference posture, based on comparisons between the first difference and the first threshold value, and between the second difference and the second threshold value. 
     The plurality of element waveforms may include at least two or more of a first element waveform related to a propagation wave, and a second element waveform and a third element waveform related to a reflection wave. 
     The processor may be further configured to obtain a differential signal of the measured pulse wave signal, and obtain times of the plurality of element waveforms by analyzing a local minimum point or a local maximum point of a waveform of the obtained differential signal. 
     The processor may be further configured to extract one or more features from the measured pulse wave signal, and estimate the bio-information based on the one or more extracted features. 
     The processor may be further configured to obtain an error correction value based on a difference between the current measurement posture and the reference posture, and correct the estimated bio-information based on the error correction value. 
     The processor may be further configured to obtain the error correction value based on at least one of a first difference between the current time interval and a reference time interval, and a second difference between a current heart rate measured in the current measurement posture and a reference heart rate measured in the reference posture. The reference time interval may be an interval between a plurality of reference element waveforms of a reference pulse wave signal that is measured in the reference posture. 
     Based on the error correction value exceeding a threshold value, the processor may be further configured to determine the threshold value to be the error correction value. 
     The bio-information may include one or more of blood pressure, vascular age, arterial stiffness, aortic pressure waveform, vascular compliance, stress index, and fatigue level. 
     The apparatus may further include a display, wherein the processor may be further configured to provide, on the display, guide information for guiding the user to change the current posture to the reference posture, based on the current measurement posture not corresponding to the reference posture. 
     The apparatus may further include a speaker, wherein the processor may be further configured to provide, through the speaker, guide information for guiding the user to change the current posture to the reference posture, based on the current measurement posture not corresponding to the reference posture 
     According to an aspect of an example embodiment, there is provided a method of estimating bio-information, the method including: obtaining a pulse wave signal from a user; measuring a current time interval between a plurality of element waveforms of the pulse wave signal; determining whether a current measurement posture of the user corresponds to a reference posture based on the current time interval of the plurality of element waveforms; and estimating the bio-information based on a determination of whether the current measurement posture corresponds to the reference posture. 
     The determining whether the current measurement posture corresponds to the reference posture may include: determining a reference time interval between a plurality of reference element waveforms of a reference pulse wave signal that is measured in the reference posture; and determining whether the current measurement posture of the user corresponds to the reference posture, based on a difference between the reference time interval and the current time interval. 
     The determining whether the current measurement posture corresponds to the reference posture may include: comparing the difference between the reference time interval and the current time interval with a predetermined threshold value; and determining that the current measurement posture does not correspond to the reference posture based on the difference between the reference time interval and the current time interval being greater than the predetermined threshold value. 
     The determining whether the current measurement posture corresponds to the reference posture may include: determining whether the current measurement posture corresponds to the reference posture further based on a difference between a current heart rate measured in the current measurement posture and a reference heart rate measured in the reference posture. 
     The determining whether the current measurement posture corresponds to the reference posture may include: comparing a first difference between the reference time interval and the current time interval with a first threshold value; compare a second difference corresponding to the difference between the current heart rate and the reference heart rate, with a second threshold value; and determining whether the current measurement posture corresponds to the reference posture, based on comparisons between the first difference and the first threshold value, and between the second difference and the second threshold value. 
     The estimating the bio-information may include: extracting one or more features from the measured pulse wave signal; and estimating the bio-information based on the extracted one or more features. 
     The estimating the bio-information may further include: obtaining an error correction value based on a difference between the current measurement posture and the reference posture; and correcting the estimated bio-information based on the error correction value. 
     The obtaining the error correction value may include: obtaining the error correction value based on at least one of a first difference between the current time interval and a reference time interval and a second difference between a current heart rate measured in the current measurement posture and a reference heart rate measured in the reference posture. The reference time interval is an interval between a plurality of reference element waveforms of a reference pulse wave signal that is measured in the reference posture. 
     The obtaining the error correction value may include, based on the error correction value exceeding a threshold value, determining the threshold value to be the error correction value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and/or other aspects will be more apparent from the following description of example embodiments, with reference to the accompanying drawings, in which: 
         FIG. 1  is a block diagram illustrating an apparatus for estimating bio-information according to an example embodiment; 
         FIG. 2  is a block diagram illustrating an apparatus for estimating bio-information according to another example embodiment; 
         FIGS. 3A to 3D  are diagrams explaining element waveforms of a pulse wave signal; 
         FIG. 4  is a block diagram illustrating a configuration of a processor according to an example embodiment; 
         FIG. 5  is a block diagram illustrating a configuration of a processor according to another example embodiment; 
         FIGS. 6A and 6B  are diagrams illustrating examples of guiding a measurement posture; 
         FIG. 7  is a flowchart illustrating a method of estimating bio-information according to an example embodiment; 
         FIG. 8  is a flowchart illustrating a method of estimating bio-information according to another example embodiment; 
         FIG. 9  is a diagram illustrating a wearable device according to an example embodiment; and 
         FIG. 10  is a diagram illustrating a smart device according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Example embodiments are described in greater detail below with reference to the accompanying drawings. 
     In the following description, like drawing reference numerals are used for like elements, even in different drawings. The matters defined in the description, such as detailed construction and elements, are provided to assist in a comprehensive understanding of the example embodiments. However, it is apparent that the example embodiments can be practiced without those specifically defined matters. Also, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail. 
     It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. Any references to singular may include plural unless expressly stated otherwise. In addition, unless explicitly described to the contrary, an expression such as “comprising” or “including” will be understood to imply the inclusion of stated elements but not the exclusion of any other elements. Also, the terms, such as ‘unit’ or ‘module’, etc., should be understood as a unit for processing at least one function or operation and that may be embodied as hardware, software, or a combination thereof. 
     Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. For example, the expression, “at least one of a, b, and c,” should be understood as including only a, only b, only c, both a and b, both a and c, both b and c, all of a, b, and c, or any variations of the aforementioned examples. 
     Hereinafter, example embodiments of an apparatus and method for estimating bio-information will be described in detail with reference to the accompanying drawings. The apparatus for estimating bio-information according to the example embodiments may be embedded in a terminal, such as a smartphone, a tablet PC, a desktop computer, a laptop computer, and the like, or may be manufactured as an independent hardware device. In this case, the independent hardware device may be a wearable device worn on an object OBJ, and examples of the wearable device may include a wristwatch-type wearable device, a bracelet-type wearable device, a wristband-type wearable device, a ring-type wearable device, a glasses-type wearable device, a headband-type wearable device, or the like, but the wearable device is not limited thereto. 
       FIG. 1  is a block diagram illustrating an apparatus  100  for estimating bio-information according to an example embodiment.  FIG. 2  is a block diagram illustrating an apparatus  200  for estimating bio-information according to another example embodiment. 
     Referring to  FIGS. 1 and 2 , the apparatuses  100  and  200  for estimating bio-information include a pulse wave sensor  110  and a processor  120 . 
     The pulse wave sensor  110  may measure a pulse wave signal, including a photoplethysmography (PPG) signal, from an object. The pulse wave sensor  110  may include a light source which emits light onto the object to detect an optical signal from the object; and a detector which detects scattered or reflected light when light emitted by the light source is scattered or reflected from body tissue such as a skin surface or blood vessels of the object. The light source may include a light emitting diode (LED), a laser diode (LD), a phosphor, and the like, but examples of the light source are not limited thereto. The detector may include a photo diode, a photo transistor (PTr), an image sensor (e.g., CMOS image sensor), and the like, but examples of the detector not limited thereto. The pulse wave sensor  110  may have various structures, such as a structure including a plurality of light sources and one detector, or a structure including an array of pairs of light sources and detectors, and the like, without specific limitation. 
     In particular, the object may be a body part which comes into contact with or is adjacent to the pulse wave sensor  110 , and may be a body part where the pulse wave signals may be easily measured. For example, the object may be a skin area of a wrist which is adjacent to a radial artery, or a skin area of the body where veins or capillaries are located. However, examples of the object are not limited thereto, and may include a distal portion of the body, such as fingers, toes, and the like, where blood vessels are densely located. 
     The processor  120  may be electrically connected to the pulse wave sensor  110 . In response to a request for estimating bio-information, the processor  120  may control the pulse wave sensor  110 , and may receive a pulse wave signal from the pulse wave sensor  110 . The request for estimating bio-information may be input from a user, or may be generated at predetermined intervals. Upon receiving an electrical pulse wave signal from the pulse wave sensor  110 , the processor  120  may perform preprocessing, such as filtering the pulse wave signal for removing noise, amplifying the pulse wave signal, converting the signal into a digital signal, smoothing the signal, and the like. 
     Upon receiving the pulse wave signal, the processor  120  may obtain cardiovascular features from the pulse wave signal, and may estimate bio-information by using the obtained features. In particular, the bio-information may include cardiovascular information such as blood pressure, vascular age, arterial stiffness, aortic pressure waveform, vascular compliance, stress index, fatigue level, and the like. For convenience of explanation, the following description will be given using blood pressure as an example if necessary. 
     Generally, a variation in Mean Arterial Pressure (MAP) is proportional to cardiac output (CO) and total peripheral resistance (TPR), as represented by the following Equation 1. 
       Δ MAP=CO×TPR   [Equation 1]
 
     Herein, ΔMAP denotes a difference in MAP between the left ventricle and the right atrium, in which MAP of the right atrium is generally in a range of 3 mmHg to 5 mmHg, such that the MAP in the right atrium is similar to MAP in the left ventricle or MAP of the upper arm. If absolute actual CO and TPR values are known, MAP may be obtained from the aorta or the upper arm. However, it may be difficult to estimate absolute CO and TPR values based on a bio-signal at a high accuracy, according to conventional bio-information estimation methods. 
     The processor  120  may obtain a feature associated with cardiac output (CO) and a feature associated with total peripheral resistance (TPR) from a pulse wave signal. Here, the feature associated with cardiac output (CO) may be a feature value which shows an increasing or decreasing trend in proportion to an actual CO value which relatively increases or decreases when an actual TPR value does not change significantly compared to a resting state. Further, the feature associated with total peripheral resistance (TPR) may be a feature value which shows an increasing or decreasing trend in proportion to an actual TPR value which relatively increases or decreases when an actual CO value does not change significantly compared to a resting state. 
     The processor  120  may obtain cardiovascular features by analyzing a waveform of the measured pulse wave signal. For example, by analyzing the waveform of the pulse wave signal, the processor  120  may obtain an area under the curve or waveform of the pulse wave signal, including heart rate information, as the feature associated with cardiac output (CO). Further, the processor  120  may obtain a ratio between an amplitude of a propagation wave and an amplitude of a first reflection wave as the feature associated with total peripheral resistance (TPR). However, the features are not limited thereto, and the processor  120  may obtain cardiovascular features further based on a shape of the waveform of the pulse wave signal, time and amplitude values of a maximum point of the pulse wave signal, time and amplitude values of a minimum point of the pulse wave signal, a duration of the pulse wave signal, components of individual element waveforms which constitute the waveform of the pulse wave signal (e.g., time and amplitude values of the element waveforms), information related to an internally dividing point between the obtained values, and the like. 
     Referring back to  FIG. 2 , the apparatus  200  for estimating bio-information may further include a communication interface  210 , an output interface  220 , and a storage  230 . 
     The communication interface  210  may communicate with an external device  250  by using wired or wireless communication techniques under the control of the processor  120 , and may transmit and receive various data to and from the external device  250 . For example, the communication interface  210  may transmit a bio-information estimation result to the external device  250 , and may receive, from the external device  250 , a variety of reference information required for estimating bio-information. For example, the communication interface  210  may receive reference blood pressure measured by a cuff manometer, a bio-information estimation model, and the like from the external device  250 . In this case, the external device  250  may include a cuff manometer, and an information processing device such as a smartphone, a tablet PC, a desktop computer, a laptop computer, and the like. 
     In this case, examples of the communication techniques may include Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, Infrared Data Association (IrDA) communication, Wi-Fi Direct (WFD) communication, Ultra-Wideband (UWB) communication, Ant+ communication, WIFI communication, Radio Frequency Identification (RFID) communication, 3G, 4G, and 5G telecommunications, and the like. However, this is merely exemplary and is not intended to be limiting. 
     The output interface  220  may output results processed by the pulse wave sensor  110  and the processor  120 . For example, the output interface  220  may visually output an estimated bio-information value using a display. Alternatively, the output interface  220  may output the estimated bio-information value in a non-visual manner by voice, vibrations, tactile sensation, and the like, using a speaker, a haptic module, or the like. By dividing a display area into two or more areas according to a setting, the output interface  220  may output a pulse wave signal graph used for estimating bio-information, a bio-information estimation result, and the like, in a first area; and may output a bio-information estimation history in the form of graphs in a second area. In this case, if an estimated bio-information value falls outside a predetermined normal range, the output interface  220  may output warning information in various manners, such as highlighting an abnormal value in red and the like, displaying the abnormal value along with a normal range, outputting a voice warning message, adjusting a vibration intensity, and the like. 
     The storage  230  may store processing results of the pulse wave sensor  110  and the processor  120 . Further, the storage  230  may store a variety of reference information required for estimating bio-information. For example, the reference information may include reference blood pressure, a bio-information estimation model, an equation for calculating an error correction value, a bio-information estimation interval, as well as user characteristics including a user&#39;s age, sex, health condition, and the like, but is not limited thereto. 
     In particular, the storage  230  may include at least one storage medium of a flash memory type memory, a hard disk type memory, a multimedia card micro type memory, a card type memory (e.g., an SD memory, an XD memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, and an optical disk, and the like, but is not limited thereto. 
       FIGS. 3A to 3D  are diagrams explaining individual element waveforms of a pulse wave signal. 
     Referring to  FIGS. 3A and 3B , a pulse wave signal  30  is generally formed by superposition of a propagation wave P 1 , which moves from the heart to the distal end of the body or branching points in the blood vessels by blood ejection from the left ventricle, and reflection waves P 2  and P 3  which return from the distal end of the body or the branching points of the blood vessels. In this case, the propagation wave P 1  is related to heart characteristics and the reflection waves P 2  and P 3  are related to vascular characteristics. As illustrated in  FIGS. 3A and 3B , the waveform of the pulse wave signal  30  is composed of individual component waveforms, such as, for example, the propagation wave P 1  which is generated by blood ejection from the left ventricle, a first reflection wave P 2  which is mainly reflected from the renal arteries, a second reflection wave P 3  which is mainly reflected from the iliac arteries, and the like. Information related to blood pressure may be included in a time interval and/or an amplitude ratio between the propagation wave and the reflection wave which are included in the waveform of the pulse wave signal  30 . 
     The processor  120  may extract components of the element waveforms related to the propagation wave and the reflection wave (e.g., time and amplitude values of a first element waveform, time and/or amplitude values of a second element waveform, and the like), and may obtain features for estimating blood pressure based on each of the extracted components of the element waveforms. In this manner, the processor  120  may estimate bio-information based on the features obtained from the pulse wave signal  30 . 
     The waveform of the pulse wave signal  30  may generally change with a blood pressure measurement posture, such that when blood pressure is estimated based on the waveform of the pulse wave signal  30 , there may be an error in the estimated blood pressure value. For example, compared to a blood pressure value in a middle part of the aorta which is measured at a height of the heart, blood pressure values in each part of the aorta, measured in a standing position or a sitting position, are increased every time the height of hydrostatic pressure is lowered. As described above, when pulse waves are transmitted within the blood vessels, pressure in the blood vessels in the transmission path is changed with posture, thus affecting the pulse wave velocity. That is, when an individual&#39;s posture is changed from a sitting position or a standing position to a lying position, blood pressure is reduced even in the renal and iliac arteries where reflections of the pulse waves usually take place, and the pulse wave velocity may also be reduced. 
     For example,  FIG. 3C  illustrates an example of time intervals between element waveforms in a normal sitting position, and  FIG. 3D  illustrates an example of time intervals between element waveforms in a lying position. In the example embodiment, as illustrated in  FIGS. 3C and 3D , by using changes in time intervals T 3 −T 1 , T 2 −T 1 , and T 3 −T 2  between a peak time T 1  of the propagation wave and peak times T 2  and T 3  of the first and second reflection waves, the processor  120  may detect a change in a user&#39;s measurement posture. The processor  120  may obtain an estimated blood pressure value based on the detected change in posture, thereby allowing continuous measurements regardless of a user&#39;s measurement posture, and improving estimation accuracy. 
       FIG. 4  is a block diagram illustrating a configuration of a processor according to an example embodiment. 
     Referring to  FIG. 4 , the processor  400  according to an example embodiment includes a feature extractor  410 , an estimator  420 , a posture change detector  430 , an error correction value calculator  440 , and a corrector  450 . 
     The feature extractor  410  may extract features for estimating bio-information based on a pulse wave signal  30 . As described above, by analyzing the waveform of the pulse wave signal  30 , the feature extractor  410  may obtain components of individual element waveforms which constitute the waveform of the pulse wave signal  30 , and may extract a feature associated with cardiac output and a feature associated with total peripheral resistance by using the obtained components of individual element waveforms. 
     For example, the feature extractor  410  may obtain a differential signal (e.g., a second-order differential signal) of the pulse wave signal  30 , and may obtain the components of individual element waveforms, related to the propagation wave and the reflection waves, by detecting a local maximum point/local minimum point of the waveform of the second-order differential signal. In this case, the order of differentiation is not specifically limited. 
     For example, the feature extractor  410  may extract a time at a first local minimum point of the second-order differential signal as a time component of a first element waveform related to the propagation wave. Further, the feature extractor  410  may obtain times at a second local minimum point and a third local minimum point as time components of a second element waveform and a third element waveform related to the reflection waves. As described above, upon obtaining the time component of the individual element waveforms, the feature extractor  410  may obtain an amplitude at a point, corresponding to the time of each element waveform in the waveform of the pulse wave signal  30 , as an amplitude component of each element waveform. 
     However, the features are not limited to the aforementioned examples, and the feature extractor  410  may obtain features based further on an area of a predetermined interval of the waveform of the pulse wave signal  30 , time and/or amplitude values at a point where an amplitude is maximum in the waveform of the pulse wave signal  30 , time and/or amplitude values located at the right and left points of the maximum amplitude point and having a predetermined ratio to the maximum amplitude value, a heart rate, and the like. 
     The estimator  420  may estimate bio-information by applying a bio-information estimation model based on the features extracted by the feature extractor  410 . For example, the bio-information estimation model may be defined as a linear function as represented by the following Equation 2, but is not limited thereto, and may be defined by using various techniques such as non-linear regression analysis, neural network, and the like. 
         HP=SF×F+OFF   [Equation 2]
 
     Herein, BP denotes an estimated blood pressure value, and F is associated with the feature value extracted by the feature extractor  410 , and may be, for example, the feature value itself or a variation in bio-information at a bio-information estimation time compared to a calibration time. In this case, the variation in bio-information may be a variation in a feature value at the bio-information estimation time compared to a feature value at the calibration time, or a normalized value obtained by dividing the variation by the feature value at the calibration time. Further, SF denotes a value pre-defined by preprocessing, and may be a scaling element for scaling a value associated with the feature values. OFF may denote a measured bio-information value for calibration, which is measured at the calibration time by using an external apparatus for measuring bio-information. 
     Once the components of the individual element waveforms are extracted from the pulse wave signal  30 , the posture change detector  430  may estimate a change in a current measurement posture at a measurement time of the pulse wave signal  30  compared to a reference posture at the calibration time, based on time intervals between the extracted individual element waveforms. The reference posture may be set for each user, and may be, for example, a sitting position, a standing position, and the like. However, the reference posture is not limited thereto, and may be set to a supine position, a side-lying position, a seated-reclining position, and the like by considering a user&#39;s various measurement environments, health condition, and the like. 
     The posture change detector  430  may compare reference time intervals between the element waveforms of the pulse wave signal  30 , which are measured in the reference posture at the calibration time, with current time intervals between element waveforms of the pulse wave signal  30  obtained in the current measurement posture at the bio-information estimation time. If differences between the reference time intervals and the corresponding current intervals are greater than a predetermined reference value, the posture change detector  430  may determine that there is a change in posture. For example, compared to the first time intervals between the element waveforms of the pulse wave signal  30  in the reference posture, if a variation in the second time intervals between the element waveforms of the pulse wave signal  30  in the current measurement posture is greater than a predetermined threshold value, the posture change detector  430  may detect that the current measurement posture is changed to a posture, which causes a drop in blood pressure, compared to the reference posture. 
     Referring back to  FIGS. 3C and 3D , the posture change detector  430  may compare a first reference time interval (i.e., T 2 −T 1 ) between the peak time T 1  of the propagation wave and the peak time T 2  of the first reflection wave of the pulse wave signal  30 , which is measured when the user is in the sitting position, with a first current time interval (i.e., T 2 ′−T 1 ′) between the peak time T 1 ′ of the propagation wave and the peak time T 2 ′ of the first reflection wave of the pulse wave signal  30 , which is measured when the user is in the lying position. The posture change detector  430  may compare a second reference time interval (i.e., T 3 −T 2 ) between the peak time T 2  of the first reflection wave and the peak time T 3  of the second reflection wave of the pulse wave signal  30 , which is measured when the user is in the sitting position, with a second current time interval (i.e., T 3 ′−T 2 ′) between the peak time T 2 ′ of the first reflection wave and the peak time T 3 ′ of the second reflection wave of the pulse wave signal  30 , which is measured when the user is in the lying position. The posture change detector  430  may compare a third reference time interval (i.e., T 3 −T 1 ) between the peak time T 1  of the propagation wave and the peak time T 3  of the second reflection wave of the pulse wave signal  30 , which is measured when the user is in the sitting position, with a third current time interval (i.e., T 3 ′−T 1 ′) between the peak time T 1 ′ of the propagation wave and the peak time T 3 ′ of the second reflection wave of the pulse wave signal  30 , which is measured when the user is in the lying position. The posture change detector  430  may determine a first difference value between the first reference time interval (i.e., T 2 −T 1 ) and the first current time interval (i.e., T 2 ′−T 1 ′), a second difference value between the second reference time interval (i.e., T 3 −T 2 ) and the second current time interval (i.e., T 3 ′−T 2 ′), and a third difference value between the third reference time interval (i.e., T 3 −T 1 ) and the third current time interval (i.e., T 3 ′−T 1 ′). The posture change detector  430  may compare the first difference value, the second difference value, and the third difference value with a predetermined first threshold value, a predetermined second threshold value, and a predetermined third threshold value, respectively. The posture change detector  430  may determine the current posture (e.g., lying position) of the user is different from the reference posture (e.g., sitting position) when at least one of the first difference value, the second difference value, and the third difference value is greater than a corresponding one of the predetermined first threshold value, the predetermined second threshold value, and the predetermined third threshold value. 
     Further, in addition to the comparison of the time intervals between the element waveforms of the pulse wave signal  30 , the posture change detector  430  may estimate a posture change based further on heart rate information. In this case, the heart rate information may include a heart rate in the reference posture at the calibration time, a heart rate in the current measurement posture at the bio-information estimation time, a variation in the heart rate at the bio-information estimation time compared to the heart rate at the calibration time, or a value obtained by normalizing the variation in the heart rate based on the heart rate at the calibration time, e.g., a value obtained by dividing the variation in the heart rate by the heart rate at the calibration time. 
     For example, in the current measurement posture compared to the reference posture, if a variation Δ(T 2 −T 1 ) in a time interval T 2 −T 1  between a first element waveform and a second element waveform is greater than a first threshold value (e.g., 4), if a variation Δ(T 3 −T 1 ) in a time interval T 3 −T 1  between a first element waveform and a third element waveform is greater than a second threshold value (e.g., 15), if a variation Δ(T 3 −T 2 ) in a time interval T 3 −T 2  between the second element waveform and the third element waveform is greater than a third threshold value (e.g., 10), and if a value ΔHR norm , obtained by normalizing a variation in heart rate, is less than a fourth threshold value (e.g., 1.02), the posture change detector  430  may detect that the current measurement posture is changed to a posture, which causes a drop in blood pressure, compared to the reference posture. 
     Once the posture change detector  430  detects that the current measurement posture is changed due to mechanism of rise/fall of blood pressure compared to the reference posture, the error correction value calculator  440  may calculate an error correction value corresponding to the posture change. 
     For example, when the reference posture is a sitting posture or a standing posture, if the posture change detector  430  detects that a current measurement posture is changed to a lying posture which causes a fall in blood pressure, the error correction value calculator  440  may calculate an error correction value based on the heart rate information (e.g., a value obtained by normalizing the variation in the heart rate in the current measurement posture compared to the heart rate in the reference posture), as represented by the following Equation 3. However, Equation 3 is a non-limiting example, the error correction value is not specifically limited, and a time interval between the element waveforms may be further reflected. 
         EV =min( e 1× HR   norm   +e 2, e 3)  [Equation 3]
 
     Herein, EV denotes the error correction value; HR norm  denotes a value obtained by normalizing the variation in heart rate based on the heart rate measured in the reference posture at the calibration time; e1, e2, and e3 are values pre-defined by preprocessing; and min denotes a function for calculating a minimum value. 
     If the posture change detector  430  does not detect a posture change, the error correction value calculator  440  may calculate an error correction value to be 0. 
     Once the error correction value is calculated, the corrector  450  may correct the estimated bio-information value which is estimated by the estimator  420 . The following Equation 4 is an example of an error correction equation, in which an error correction value, calculated when the measurement posture is changed to a posture which causes a fall in blood pressure, is applied to the blood pressure estimation model of the above Equation 2. However, the error correction equation is not limited thereto. 
         BP=SF×F+OFF−EV   [Equation 4]
 
     Herein, EV denotes the error correction value calculated by the error correction value calculator  440 , and BP denotes a final estimated blood pressure value, in which the error correction value is reflected. 
       FIG. 5  is a block diagram illustrating a configuration of a processor according to another example embodiment.  FIGS. 6A and 6B  are diagrams illustrating examples of guiding a measurement posture. 
     Referring to  FIG. 5 , the processor  500  includes a feature extractor  510 , an estimator  520 , a posture change detector  530 , and a posture guide  540 . 
     Once the pulse wave sensor  110  acquires a pulse wave signal  30 , the feature extractor  510  may extract features by analyzing a waveform of the pulse wave signal  30 . For example, the feature extractor  510  may extract cardiovascular features based on components of individual element waveforms which constitute the waveform of the pulse wave signal  30 , a total or partial area under the waveform of the pulse wave signal  30 , a maximum amplitude value of the pulse wave signal  30 , time and/or amplitude values located at the right and left points of the maximum amplitude point and having a predetermined ratio to the maximum amplitude value, and the like. 
     The posture change detector  530  may detect a change in a current measurement posture at a bio-information estimation time compared to a reference posture at a calibration time, based on time intervals between the individual element waveforms of the pulse wave signal  30  and/or heart rate information. In this case, the reference posture may be set for each user. The heart rate information may include a heart rate in the reference posture at the calibration time, a heart rate in the current measurement posture at the bio-information estimation time, a variation in the heart rate at the bio-information estimation time compared to the heart rate at the calibration time, or a value obtained by normalizing the variation in the heart rate based on the heart rate at the calibration time. 
     Upon determining that the current measurement posture is changed to a posture, which affects an estimated bio-information value compared to the reference posture, the posture change detector  530  may transmit a request for posture guide information to the posture guide  540 , and may control the pulse wave sensor to re-measure the pulse wave signal  30 . 
     The posture guide  540  may generate information for guiding a change in posture based on information on the posture change detected by the posture change detector  530 . For example, when the posture change detector  530  detects that the measurement posture is changed compared to the reference posture, the guide information may include information, such as a visual image and/or a voice signal, for guiding a user to change the posture to the reference posture. Further, when the posture change detector  530  detects that the measurement posture is not changed, the guide information may include at least one of a visual image and a voice signal for guiding a user to maintain the measurement posture. The output interface  220  of  FIG. 2  may output the guide information generated by the posture guide  540 . 
     For example, referring to  FIG. 6A , if a user&#39;s reference posture is a sitting posture, and the user&#39;s current measurement posture is a lying posture, the output interface  220  may output a text  63 , such as “please measure blood pressure in a sitting posture,” in a predetermined area of a display  62  of a mobile device  60   a . Further, along with the text  63 , the output interface  220  may simultaneously or separately output visual images  64  and  65  for inducing the user to change from the “lying posture” to the “sitting posture.” 
     Referring to  FIG. 6B , if a user&#39;s reference posture is a sitting posture, and the posture change detector  530  detects that that the user&#39;s current measurement posture matches the reference posture, the output interface  220  may output a text  66 , such as “please maintain the current posture,” in a predetermined area of a display  62  of a mobile device  60   b . Further, along with the text  66 , the output interface  220  may simultaneously or separately output a visual image  67  of the “sitting posture” during estimation of blood pressure. 
     Once the posture change detector  530  detects that the user&#39;s measurement posture matches the reference posture, the estimator  520  may estimate bio-information by using a pre-defined bio-information estimation model, as represented by the above Equation 2, based on the features extracted by the feature extractor  510  from the pulse wave signal  30 . 
       FIG. 7  is a flowchart illustrating a method of estimating bio-information according to an example embodiment. The method of  FIG. 7  is an example of a method of estimating bio-information which is performed by the apparatuses  100  and  200  for estimating bio-information according to the example embodiments of  FIGS. 1 and 2 . Various example embodiments thereof are described above in detail, and thus will be briefly described below. 
     Upon receiving a request for estimating bio-information, the apparatuses  100  and  200  for estimating bio-information may obtain a pulse wave signal  30  from an object in operation  710 . The apparatuses  100  and  200  may provide an interface for a user, and may receive the request for estimating bio-information input by the user through the interface. Alternatively, the apparatuses  100  and  200  may communicate with an external device, to receive the request for estimating bio-information from the external device. 
     Then, by analyzing a waveform of the obtained pulse wave signal  30 , the apparatuses  100  and  200  may extract components of a plurality of element waveforms which constitute the waveform of the pulse wave signal  30  in operation  720 . The waveform of the pulse wave signal  30  is generally formed by superposition of a propagation wave, which moves from the heart to the distal end of the body or branching points in the blood vessels by blood ejection from the left ventricle, and reflection waves which return from the distal end of the body or the branching points of the blood vessels. By analyzing the waveform of the pulse wave signal  30 , the apparatuses  100  and  200  may extract components of individual element waveforms related to the propagation wave and/or the reflection waves, e.g., time and amplitude information of the propagation wave and/or the reflection waves. 
     Subsequently, the apparatuses  100  and  200  may obtain cardiovascular features by analyzing the waveform of the pulse wave signal  30  in operation  730 . In particular, the cardiovascular features may include a feature associated with cardiac output (CO) and a feature associated with total peripheral resistance (TPR). For example, the apparatuses  100  and  200  may extract, as features for estimating bio-information, the components of the element waveforms which are extracted in operation  720 , a total or partial area under the waveform of the pulse wave signal  30 , a maximum amplitude value of the waveform of the pulse wave signal  30 , time and/or amplitude values located at the right and left points of the maximum amplitude point and having a predetermined ratio to the maximum amplitude value, a heart rate, or a combination of the extracted information items. 
     Next, the apparatuses  100  and  200  may estimate bio-information based on the extracted cardiovascular features in operation  740 . Upon extracting various cardiovascular features in operation  730 , the apparatuses  100  and  200  may estimate bio-information by using a pre-defined bio-information estimation model. 
     Then, based on the plurality of element waveforms extracted in operation  720 , the apparatuses  100  and  200  may detect whether a measurement posture is changed compared to a reference posture in operation  750 . For example, the reference posture may be set for each user at a calibration time. 
     Subsequently, upon detecting a user&#39;s measurement posture in operation  750 , the apparatuses  100  and  200  may determine whether the measurement posture is changed to a posture, which affects an estimated bio-information value, compared to the reference posture in operation  760 . For example, if a time interval between the element waveforms obtained in operation  720  exceeds a predetermined threshold value, the apparatuses  100  and  200  may determine that the user&#39;s measurement posture is changed to a posture which causes a fall in blood pressure due to mechanism of fall of blood pressure. Alternatively, by further considering heart rate information, if a time interval between the element waveforms exceeds a first threshold value, and if a variation in heart rate measured in the current measurement posture, compared to a heart rate measured in the reference posture, is less than a second threshold value, or a value obtained by normalizing the variation in the heart rate using the heart rate in the reference posture is less than a second threshold value, the apparatuses  100  and  200  may determine that the user&#39;s measurement posture is changed to a posture which causes a fall in blood pressure. 
     Next, upon determining in operation  760  that the measurement posture is changed, the apparatuses  100  and  200  may calculate an error correction value according to the measurement posture in operation  770 . In particular, the apparatuses  100  and  200  may calculate the error correction value based on time intervals between the element waveforms and/or the variation in the heart rate in the measurement posture compared to the heart rate in the reference posture, or a value obtained by normalizing the variation in the heart rate. 
     Then, based on the error correction value calculated in operation  770 , the apparatuses  100  and  200  may obtain a final estimated bio-information value in operation  780  by correcting the bio-information value estimated in operation  740 . Upon determining in operation  760  that the measurement posture is not changed, the apparatuses  100  and  200  may obtain the estimated bio-information value, which is estimated in operation  740 , as a final estimated bio-information value without error correction. 
     Subsequently, the apparatuses  100  and  200  for estimating bio-information may output a bio-information estimation result in operation  790 . The apparatuses  100  and  200  for estimating bio-information may provide a user with the bio-information estimation result, the user&#39;s health information analyzed using the estimation result, and the like, by using various output modules such as a display, a haptic device, a speaker, and the like. 
       FIG. 8  is a flowchart illustrating a method of estimating bio-information according to another example embodiment. The method of  FIG. 8  is an example of a method of estimating bio-information which is performed by the apparatuses  100  and  200  for estimating bio-information according to the example embodiments of  FIGS. 1 and 2 . Various example embodiments thereof are described above in detail, and thus will be briefly described below. 
     Upon receiving a request for estimating bio-information, the apparatuses  100  and  200  for estimating bio-information may obtain a pulse wave signal  30  from an object in operation  810 . 
     Then, by analyzing a waveform of the obtained pulse wave signal  30 , the apparatuses  100  and  200  may extract components of a plurality of element waveforms which constitute the waveform of the pulse wave signal  30  in operation  820 . By analyzing the waveform of the pulse wave signal  30 , the apparatuses  100  and  200  may extract time and amplitude information of individual element waveforms related to the propagation wave and/or the reflection waves. 
     Subsequently, based on the plurality of element waveforms which are extracted in operation  820 , the apparatuses  100  and  200  may detect whether a measurement posture is changed compared to a reference posture in operation  830 . 
     Next, if a user&#39;s measurement posture, detected in operation  830 , is changed to a posture which affects an estimated bio-information value compared to a reference posture in operation  840 , the apparatuses  100  and  200  may guide a user to change the measurement posture in operation  850 . In this case, the apparatuses  100  and  200  may provide the user with guide information on the reference posture by using a voice signal or a visual image. 
     Then, upon determining in operation  840  that a posture, in which an initial pulse wave signal  30  is measured, is not changed, or upon determining in operation  840  that the measurement posture is not changed by guiding the user to change the measurement posture in operation  850  and by re-obtaining the pulse wave signal  30  in operation  810  while the user is in the reference posture, the apparatuses  100  and  200  may extract cardiovascular features for estimating bio-information based on the pulse wave signal  30  in operation  860 . 
     Subsequently, the apparatuses  100  and  200  may estimate bio-information by using the extracted cardiovascular features and a pre-defined bio-information estimation model in operation  870 , and may output the bio-information estimation result in operation  880 . 
       FIG. 9  is a diagram illustrating a wearable device according to an example embodiment. The aforementioned various example embodiments of the apparatus for estimating bio-information may be mounted in a smart watch worn on a wrist as illustrated in  FIG. 9  or a smart band-type wearable device. 
     Referring to  FIG. 9 , the wearable device  900  includes a main body  910  and a strap  930 . 
     The main body  910  may be formed to have various shapes, and may include modules which are mounted inside or outside of the main body  910  to perform the aforementioned function of estimating bio-information as well as various other functions. A battery may be embedded in the main body  910  or the strap  930  to supply power to various modules of the wearable device  900 . 
     The strap  930  may be connected to the main body  910 . The strap  930  may be flexible so as to be bent around a user&#39;s wrist. The strap  930  may be bent in a manner that allows the strap  930  to be detached from the user&#39;s wrist or may be formed as a band that is not detachable. Air may be injected into the strap  930  or an airbag may be included in the strap  930 , so that the strap  930  may have elasticity according to a change in pressure applied to the wrist, and may transmit the change in pressure of the wrist to the main body  910 . 
     The main body  910  may include a pulse wave sensor  920  for measuring a bio-signal. The pulse wave sensor  920  may be mounted on a rear surface of the main body  910 , which comes into contact with the upper portion of a user&#39;s wrist, and may include a light source for emitting light onto the skin of the wrist and a detector for detecting light scattered or reflected from the object. 
     A processor  120 ,  500  may be mounted in the main body  910 . The processor  120 ,  500  may be electrically connected to various modules, mounted in the wearable device  900 , to control operations thereof. 
     Further, the processor  120 ,  500  may estimate bio-information by using a pulse wave signal  30  measured by the pulse wave sensor  920 . The processor  120 ,  500  may obtain cardiovascular features by analyzing a waveform of the pulse wave signal  30 , and may estimate bio-information by using the obtained features. For example, the processor  120 ,  500  may obtain the cardiovascular features by using various values, such as time and amplitude values of element waveforms which constitute the waveform of the pulse wave signal  30 , a heart rate, a maximum amplitude value, an area of the waveform of the pulse wave signal  30 , and the like, and by properly combining the obtained values. 
     In addition, while being worn on the user&#39;s wrist at all times, the wearable device  900  may measure bio-information in various postures of the user. For example, in addition to a case where the user is seated in an upright posture, when the user is seated while leaning back in a chair or lies down while sleeping, the wearable device  900  may also estimate bio-information. However, when calibration is performed while the user is in a seated posture at a calibration time, an error may occur in an estimated bio-information value if the measurement posture is changed. 
     Accordingly, the processor  120 ,  500  may detect a posture change at a measurement time compared to a reference posture at the calibration time, and may correct an error in an estimated bio-information value properly according to a change in measurement posture, thereby improving accuracy in estimating bio-information. In this case, by considering a change in a time interval between element waveforms of the pulse wave signal  30  and/or a variation in heart rate according to the change in measurement posture, the processor  120 ,  500  may estimate a posture change and may calculate an error correction value. 
     The main body  910  may further include a contact pressure sensor for measuring contact pressure between an object and the pulse wave sensor  920  while the pulse wave signal  30  is measured when the object is in contact with the pulse wave sensor  920 . The processor  120 ,  500  may monitor a contact state of the object based on the contact pressure measured by the contact pressure sensor, and may provide guide information on a contact position and/or a contact state for a user through a display. 
     Further, the main body  910  may include a storage which stores processing results of the processor  120 ,  500  and a variety of information. In this case, the variety of information may include reference information for estimating bio-information, as well as information associated with functions of the wearable device  900 . 
     In addition, the main body  910  may also include a manipulator  940  which receives a user&#39;s control command and transmits the received control command to the processor. The manipulator  940  may include a power button to input a command to turn on/off the wearable device  900 . 
     A display may be mounted on a front surface of the main body  910 , and may include a touch panel for receiving a touch input. The display may receive a touch input from a user, may transmit the received touch input to the processor, and may display a processing result of the processor. 
     For example, the display may display a bio-information estimation result. In this case, along with the estimation result, the display may display additional information such as a bio-information estimation date, a health condition, and the like. In this case, when a user requests detailed information by operating the manipulator  940  or by performing touch input on the display, the display may display detailed information in various manners. 
     Moreover, a communication interface, provided for communication with an external device such as a user&#39;s mobile terminal, may be mounted in the main body  910 . The communication interface may transmit a bio-information estimation result to an external device, e.g., a user&#39;s smartphone, to display the estimation result to the user. However, the communication interface is not limited thereto, may transmit and receive a variety of necessary information. 
       FIG. 10  is a diagram illustrating a smart device, to which example embodiments of an apparatus for estimating bio-information are applied. In this case, the smart device may be a smartphone, a tablet PC, and the like. 
     Referring to  FIG. 10 , the smart device  1000  includes a main body  1010  and a pulse wave sensor  1030  mounted on one surface of the main body  1010 . In this case, the pulse wave sensor  1030  may include one or more light sources  1031  and a detector  1032 . As illustrated in  FIG. 10 , the pulse wave sensor  1030  may be mounted on a rear surface of the main body  1010 , but is not limited thereto, and may be configured in combination with a fingerprint sensor or a touch panel mounted on a front surface of the main body  1010 . 
     In addition, a display may be mounted on a front surface of the main body  1010 . The display may visually display a bio-information estimation result and the like. The display may include a touch panel, and may receive a variety of information input through the touch panel and transmit the received information to the processor. 
     Moreover, an image sensor  1020  may be mounted in the main body  1010 . When a user&#39;s finger approaches the pulse wave sensor  1030  to measure a pulse wave signal  30 , the image sensor  1020  may capture an image of the finger and may transmit the captured image to the processor. In this case, based on the image of the finger, the processor  120 ,  500  may identify a relative position of the finger with respect to an actual position of the pulse wave sensor  1030 , and may provide the relative position of the finger to the user through the display, so as to guide measurement of pulse wave signals with improved accuracy. 
     By analyzing a waveform of the pulse wave signal  30  measured by the pulse wave sensor  1030 , the processor  120 ,  500  may extract components of individual element waveforms, related to a propagation wave and reflection waves, and/or heart rate information, and by using the extracted components of the element waveforms and/or heart rate information, the processor  120 ,  500  may detect a change in a user&#39;s measurement posture, may correct an error caused by the posture change, may extract features, and the like, and may estimate bio-information based on the obtained information. 
     The main body  1010  of the smart device  1000  may include a storage which stores reference information and the like for operation of the smart device  1000 , including other information input from a user, information obtained by various sensors, information processed by the processor, and other reference information required for estimating bio-information. 
     Further, the main body  1010  of the smart device  1000  may include a communication interface for communication with various external devices, e.g., a wearable device, a desktop computer, a laptop computer, a tablet PC, a cuff manometer, a smart device of another user, and the like. The processor  120 ,  500  may control the communication interface to transmit and receive a bio-information estimation result, a variety of reference information, and the like to and from another external device. 
     While not restricted thereto, an example embodiment can be embodied as computer-readable code on a computer-readable recording medium. The computer-readable recording medium is any data storage device that can store data that can be thereafter read by a computer system. Examples of the computer-readable recording medium include read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices. The computer-readable recording medium can also be distributed over network-coupled computer systems so that the computer-readable code is stored and executed in a distributed fashion. Also, an example embodiment may be written as a computer program transmitted over a computer-readable transmission medium, such as a carrier wave, and received and implemented in general-use or special-purpose digital computers that execute the programs. Moreover, it is understood that in example embodiments, one or more units of the above-described apparatuses and devices can include circuitry, a processor, a microprocessor, etc., and may execute a computer program stored in a computer-readable medium. 
     The foregoing embodiments are merely examples and are not to be construed as limiting. The present teaching can be readily applied to other types of apparatuses. Also, the description of the exemplary embodiments is intended to be illustrative, and not to limit the scope of the claims, and many alternatives, modifications, and variations will be apparent to those skilled in the art.