Wearable health measurement device and a method for determining a health status of a user

Abstract

The embodiments relate to wearable health measurement device (100), comprising means for measuring body movements close to a body mass center; means for measuring at least one biosignal; means for defining an estimated biosignal (320) for the measured biosignal (310) based on the measured body movements (330); means for comparing the measured biosignal (310) and the estimated biosignal (320); means for analysing time periods according to correlation between the measured biosignal (310) and the estimated biosignal (320); and means for delivering the analysed time periods to be displayed on an electronic apparatus. The embodiments relate to a method for the wearable health measurement device.

Description

[0001]WEARABLE HEALTH MEASUREMENT DEVICE AND A METHOD FOR DETERMINING A HEALTH STATUS OF A USER Technical Field The present solution generally relates to an apparatus for measuring health- related parameters on a user and to a method for determining a health status of the user. Background Over decades, athletes have been interested in their heart rates (HR) during exercise, whereupon devices for personal HR monitoring has rapidly evolved. Upon the development of the sensor technology, devices with more intelligent measurement and analysis capabilities have become available for any consumer, who is interested in their health and well-being. Sports-related measurement devices typically comprise a belt or a band to receive physical signals directly from user’s chest, an arm or a forehead, which signals are then transmitted wirelessly to user’s wrist-watch or other device for further analysis or reporting. So called activity watches (or smartwatches) are capable of measuring the heart rate and other body signals directly from the wrist, and making the analysis on user’s well-being and activity by themselves. More wearable health measurement devices are entering the market. One example is a ring that measures body signals from user’s finger, and transmits the measured data to an analysis device, such as a mobile phone. These devices are limited to their possibilities to make health-related analysis, whereupon broader perspective on the matter is needed. Summary The present invention improves the prior art by providing a health measurement device that is not limited to the data obtained from fingers, chest, forehead, but can provide more extensive analysis on well-being of the user. The scope of protection sought for various embodiments of the invention is set out by the independent claims. The embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the invention. Various aspects include a method, an apparatus and a computer readable medium comprising a computer program stored therein, which are characterized by what is stated in the independent claims. Various embodiments are disclosed in the dependent claims. According to a first aspect, there is provided a computer-implemented method for determining a health status of a user, the method comprising receiving data concerning body movements close to a body mass center and measured by a measurement device; receiving at least one biosignal measured by the measurement device; defining an estimated biosignal for the measured biosignal based on the measured body movements; comparing the measured biosignal and the estimated biosignal; analysing time periods according to correlation between the measured biosignal and the estimated biosignal; and delivering the analysed time periods to be displayed on a display of an electronic apparatus. According to a second aspect, there is provided a wearable health measurement device, comprising means for measuring body movements close to a body mass center; means for measuring at least one biosignal; means for defining an estimated biosignal for the measured biosignal based on the measured body movements; means for comparing the measured biosignal and the estimated biosignal; means for analysing time periods according to correlation between the measured biosignal and the estimated biosignal; and means for delivering the analysed time periods to be displayed on an electronic apparatus. According to an embodiment, the measurement device is a wearable device being attached to an edge of a waistband of a clothing. According to an embodiment, said at least one biosignal is one of following: heart rate, heart rate variability, skin impedance, humidity of the skin, humidity close to the skin, heat flux, temperature of the skin. According to an embodiment, the biosignal is measured by a photoplethysmogram (PPG) or electrocardiogram (ECG) sensor of the measurement device. According to an embodiment, the biosignal is measured by at least one temperature sensor of the measurement device. According to an embodiment, a feedback is generated concerning the analysed time periods, and the generated feedback is provided in written or audial format through a user interface of the electronic apparatus or the measurement device. Description of the Drawings In the following, various embodiments will be described in more detail with reference to the appended drawings, in which Fig.1 shows a simplified example of a wearable health measurement device according to an embodiment; Fig.2 shows an example of the measurement part of the wearable health measurement device according to an embodiment; Fig.3 shows an example of correlation between body mass center activity and HR; Fig.4a shows an example of the wearable health measurement device being attached to a clothing; Fig. 4b shows an intersection of the wearable health measurement device shown in Fig.4a; Fig.5 shows another example of the wearable health measurement device being attached to a clothing; Fig.6 shows an example of a system for the wearable health measurement device; and Fig.7 is a flowchart illustrating a method according to an embodiment. Description of Example Embodiments The following description and drawings are illustrative and are not to be construed as unnecessarily limiting. The specific details are provided for a thorough understanding of the disclosure. However, in certain instances, well- known or conventional details are not described in order to avoid obscuring the description. Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The present embodiments relate to a wearable health measurement device. Despite the term, the device can be considered as an activity measurement device, well-being device, health device, or any device that is able to measure health-related parameters or signals from a user’s body and is able to derive analysis therefrom relating to user’s well-being. Shortly, the wearable health measurement device is referred interchangeably to as “a device” or “an apparatus”. Term “health-related parameters” refers to electrical and non- electrical signals that can be measured and monitored from a user’s body. In addition, for the purposes of the present embodiments, “health-related parameters” also cover other measurable data from a user or user’s environment. Such data may comprise any one or more of the following: temperature, humidity, altitude, air condition, air quality, carbon dioxide (CO2) or carbon monoxide (CO) or other gases in the air. It is appreciated that in addition to these examples, or alternatively, other environmental data can be measured as well. Figure 1 illustrates a structure of the wearable health measurement device 100, according to an embodiment. It is to be noticed that the illustration in Figure 1 is a simplified example of a possible configuration of the device, and the design can vary from what is shown in Figure 1. The device may be consisted of two parts, wherein one of the parts is a measurement part that houses electronics and sensors. Another part, i.e., clip part, acts as fastening mechanism. The parts of the device can be mechanically connected, or the parts of the device can be integrated. Figure 1 shows the device 100 comprising a measurement part 101 and a clip part 103. The measurement part 101 comprises sensing elements (not shown in Figure 1) and purpose of the clip part 103 is to attach the device 100 to a place (e.g., a clothing, a garment) where the measurement is to be carried out from the user’s body. The measurement part comprises a first side 101a and a second side 101b, wherein the first side 101a is (at least partly) in contact with user’s skin when the device 100 is used, and the second side 101b is facing the clip part 103. The first side 101a of the device 100 comprises the sensors / electrodes for measuring data from the user. The second side 101b of the device 100 is outwards from user’s skin and body. The clip part 103 is connected to the measurement part 101 with a hinge 104 at one end of the measurement part 101. The device 100 comprises a groove part 105 between the measurement part 101 and the clip part 103 to receive the edge of the clothing or waistband. The clip part 103 has a design, where a locking has an offset distance between a closing end 106 and the hinge 104 axis. When the clip part 103 is opened, the closing end 106 opens and allows a clothing or garment to be set to the groove part 105. The design of the clip part 103 may be such that it allows a clothing to be clipped within the device. For example, the clip part 103 may be flat and curved towards the measurement part (as shown in Figure 1). The clip part 103 may have one or more holes, to allow humidity to go through the clip part. Clip part 103 with one hole may have a circular design. Alternatively, the clip part 103 may be uniform. The purpose of the wearable health measurement device is to measure signals on a user from a waist area of the user. The waist may be defined to by located about 10 cm above a waistline or a hipline, in which the waist line is defined as a smallest circumference of central body area, and the hip line is the largest circumference of central body area. On the other hand, the measurement area may be defined by an upper edge or band of the underwear or pants typically used, wherein the device may be placed between 0 to 20 centimeters to left or right from the front center line, and inside the underwear or pant’s upper band. Figure 2 illustrates an example of the measurement part 101 according to an embodiment. As said, the measurement part 101 comprises a plurality of sensors for measuring data from a user. The measurement part 101 may comprise first sensor(s) a located on (or in primary transducing contact with) the first side facing the skin, and second sensor(s) located on (or in primary transducing contact with) second side of the measurement part 101. The second sensors may benefit from the mechanical properties of the clip mechanism. The first sensors may comprise at least one skin electrode and one heart sensor, such as a photoplethysmogram (PPG) sensor, and skin temperature sensor. The PPG sensor may comprise at least one light-emitting diode (LED) and one photodetector. In addition, the first side may have one or more light emitters, and one or more light detectors for optical measurements, e.g., plethysmography. The sides (e.g., second side) not facing the skin may include, e.g., one or more instances of a contact temperature measurement point (temperature sensor), and infrared (IR) sensor, ambient light sensor, or a relative humidity measurement (RH) sensor. In the example of Figure 2, there are thermal contacts including temperature sensors 202 to electrode areas 201. In addition, in the example of Figure 2, there is an infrared (IR) temperature sensor 203, which does not have to be in contact with the skin. In addition, the IR temperature sensor or another IR temperature sensor can be arranged on the second side of the measurement part, as described above. In the example of Figure 2, there are also receivers, such as photodetectors 204 (“photosensors”), and light sources, such as light- emitting diodes (LEDs) 205 for IR light, red light and green light. The receivers and light sources may form a PPG sensor. In the example of Figure 2, there are also electrical contacts 206 to electrode areas 201. Finally, the electrode areas 201 are provided with metal or other electrical conducting material (not shown in Figure 2) on the top of the electrode area grooves. These elements 201, 202, 203, 204, 205, 206 are provided on the first side 101a of the measurement part 101. The device comprises at least one movement sensor (e.g., linear acceleration)(not shown in Figure 2) situated inside the measurement part 101. The movement sensor can be a commercially available 3-axis accelerometer providing acceleration data in three orthogonal dimensions. It is known to define the orientation of the device by calculating the direction of the Earth’s gravity direction. For measuring body movements, it is possible to use one axis acceleration signal, or combined two axes signal, or combined 3-axes acceleration signal. The acceleration axes signals can be combined in different ways, for example by summing up in squares, or calculation their combined vector signal or otherwise summed or multiplied with different coefficient for example enhancing the body’s up and down movement direction. The accelerometer component can be for example LIS2DUXS12 (by ST Microelectronics), which comprises signal processing capabilities such as activity recognition and gesture detection, or the component can be BMA 456 or BMA 400 (by Bosch Sensortec). The acceleration signal can be processed, such as filtered by a low pass or high pass filters, to count movement pulses and count pulses per time window, such as minute or hour. It is also possible to calculate cumulative smoothed movement signal over certain time window, such as minute or hour. The processed signal can be then used as measured body movement signal to be compared to other biosignal. It is also possible to use other signal processing methods, for example to analyze movement changes more accurate in the case of changes of movement such as standing up or starting activity such as walking or running. One of the analyzing methods for body movements comprises first defining the direction of the device related to earth gravity axis, then analyzing movements up-down related to the gravity axis, and left-right and forward-backward movements related to the main movement direction, which may correspond, for example, walking. The device may have electrodes or contact points for electrodes on all sides of the device. On the first side of the measurement part, they directly interface the skin impedance or biopotential measurements. On the other sides, the electrodes may constitute an impedance-based humidity / evaporation measurement, or interface the clip to external circuitry on clothing. Thus, as shown above, the measurement part comprises a plurality of sensors being configured to measure health-related data from user’s body, which health-related data is used to make analysis on user’s health and well-being. Also, the measurement part may comprise sensors being configured to measure data from the environment. In addition to, or instead of, the sensors as discussed above, the device may comprise one or more of the following sensors: - optical heart rate sensor(s) (OHRM), which can operate in a transmissive and / or reflective mode; - electrodes and electronics for electrocardiography (ECG); - electrodes and electronics for measuring electrical impedances, such as galvanic skin resistance (GSR) and / or bioimpedance analysis (BIA); - contact or contactless temperature sensor(s), such as one or more of the following: thermistor(s), infrared temperature sensor(s), thermocouple(s); - heat flux sensor(s); - humidity sensor(s); - inertial sensor(s), such as one or more of the following: linear accelerometer(s), magnetometer(s), gyroscope(s); - barometer(s); - mechanical force sensor(s), such as pressure sensor(s) and / or strain gauge(s); - acoustic sensor(s), such as contact microphone(s) and / or contactless microphone(s); - optical sensor(s), such as charge-coupled device (CCD) imaging sensor(s) and / or complementary metal-oxide-semiconductor (CMOS) imaging sensor(s). The optical heart rate sensor may be placed close to the point, where the wearable device experiences the greatest mechanical support from the user’s clothing, such as near the joint of the clip mechanism. The humidity sensor(s) may be placed such that at least one of the sensors is located away from the user’s skin, but underneath the user’s clothing, such as at one end of the wearable device. The thermal (i.e., temperature, heat flux) sensors may be located such that at least one of the sensor(s) is at skin contact, while the others can be located out of skin contact, e.g., the exterior or interior of the enclosure of the wearable device. According to an embodiment, the device can contain four or more temperature sensors located in such way, that at least two of the sensors are in skin contact, and at least two of the sensors are not in contact with the skin, in such a way that the thermal resistance between one pair of a skin-external temperature sensors, and another pair with similar setup is dissimilar. The temperature sensors may be able to measure temperature at a resolution of at least 0.01 degrees Celsius relative to one another, and 0.1 degrees Celsius absolute temperature accuracy. It is appreciated that in some embodiments, the temperature scale can be other than Celsius scale. Also, in some embodiments, the number of the temperature sensors may be less than four. The measurement data being measured by the sensors may comprise one or more of the following: PPG, HR, skin conductance / impedance, skin temperature, body temperature through heat flow sensors, electrocardiography (ECG), tissue impedance for determining tissue content (fat, muscle, bone), electromyography (EMG), infrared sensor for skin temperature, humidity, accelerometer. According to an embodiment, the heart rate is measured by optical means using PPG, for example. It is to be noticed that when such measurement is made by wrist devices, the accuracy may not always be perfect. By measuring HR using a PPG sensor located on the waist, as described by the present embodiments, a more accurate HR value can be received. This is because, hand movements and other disturbance cause unreliable measurement value. For signal and data filtering, there is a need to estimate the HR value or its range to avoid unreliable big or low values to be displayed or recorded. Known methods comprise e.g., median filtering, history data or hand movement corrections. It is well known that heart rate indicates the exercise or activity level of the user. Higher activity means higher heart rate and lower activity level typically means lower heart rate. It is also known that activity or motion level measured by a wrist device is used for correcting the measured heart rate by the same device with PPG sensor, especially at higher activity levels because the PPG measurement may become unreliable due to high motion artefacts. Heart rate value may be also higher due to other reasons than activity. Sympathetic activity due to stress or other mental or physiological reason may cause heart rate to elevate. Also diseases such as inflammatory conditions, flu for example, as well as sleep phases may cause heart rate be higher than normal compared to average activity caused heart rate level. For example during sleeping HR can vary although there is no activity or activity is very minimal. Currently it is difficult to differentiate if the increased heart rate is related wholly or partially to the body activity. Especially when the device is a wrist device, a hand movement activity can vary widely although the body activity is low. It is thus difficult to define if the measured heart rate is related to the body activity or something else. The present embodiments provide an accurate and reliable method to measure the body mass activity and heart rate with the same device from an optimal location. As discussed above, the wearable health measurement device is located close to the body mass center, whereupon the measured activity represents closely the body center mass activity. It has been observed that the center body mass activity relates closely to heart rate value in a normal health condition. This correlation can be kept as a body center mass activity – heart rate relation. The correlation formula may be very similar between users. Further, the correlation formula can be personalized by measuring both the body center mass activity and heart rate at different activity levels and repeat over hours, days or weeks. The estimated heart rate value based on the body movement signal can be presented for example in the form of: HR_est = A * acc_MAD + B, (Equation 1) in which refers to estimated heart rate, acc_MAD refers to accelerometer signal (averaged), A is a coefficient parameter between movement signal and heart rate (multiplier) and B is an offset parameter. The parameters A and B can be defined as a common parameters or they can be defined as personalized parameters measuring and monitoring body movements and heart rate of a user over certain periods, for example hours, days or weeks. Examples of biosignal estimation methods:The aim of the method is to (1) perform discrete-time biosignal measurementss[t], such as heart rate, heart rate variability, temperature, heat flux or humidity,and to (2) provide an estimate ˆs[t] for the same biosignal using data collected from motion sensors (e.g., linear accelerometers, gyroscopes or magnetometers) worn close to the user’s body center of mass. The aforementioned biosignals depend on various factors such as environmental conditions, physical and mental health status, feeding, and physical activity. The effects of physical activity can overshadow more subtle effects from other causes; therefore, for biosignal analytics purposes, it is beneficial to estimate the effect of physical activity, so that it can be removed from the observed signals. More precisely, let s[t] be a biosignal (e.g. heart rate) sampled at a given frequency (e.g. once every 10 seconds). Let m[t] be a signal representing motion observed by motion sensors. For example, in the case of linear accelerometers, m[t] can be an aggregate value m[t] = g(x, y, z), where g is some function of the measurement accelerations x, y and z, aggregated to the same sampling interval as s[t] and x, y, and z refers to acceleration signals of three different axes of the 3-axis accelerometer sensor. Based on the values of g, the signal s is estimated as ^̂^[ ^^] = ^^( ^^[ ^^]) (Equation 2) where f(·) is some function that minimizes the error between observations s[t]and estimates ^̂^[ ^^]. After calculating the estimates ^̂^[t], a discrepancy signald[t] can be calculated asd[t] = s[t] − ^̂^[t]. (Equation 3)The discrepancy signal represents the disagreement between the observed signal and the signal estimated based on motions; thus, it can be treated as the signal of interest with the effects of motion canceled. The aggregate function g(·) can be e.g., the mean absolute deviation of the acceleration signals (x, y, z) calculated over some time period, e.g.10 seconds. The aggregate value m[t] representing motion can thus be a scalar or a vector, depending on the choice of function. One suitable choice for the aggregate function is the mean absolute deviation (MAD) (Equation 4) where Rmean refers to the mean resultant acceleration over the time window of N samples ^^^^ ^^ ^^ ^^=1^^∑^^^=^1 ^^^^(Equation 5) and ri is the instantaneous acceleration at step i, (Equation 6) The function f(·) that maps the motion aggregate m to the estimated signal value ^̂^ can be a linear one (such as a linear regression model) or a nonlinear one (such as a neural network model). In the case of a linear model, the model (Equation 2) becomes ^̂^[ ^^] = ^^ ^^[ ^^] + ^^ (Equation 7) where α is the slope of the regression model, and β is the intersect term. However, it must be noted that the effects of physical activity on biosignals, such as heart rate, can also be latent in nature. For example, the heart rate of an individual may stay at elevated levels for several minutes after the physical activity stops. Thus, the model f(·) can be extended to include information about past movement as its inputs. For example, past values of the motion aggregate m[t − 1], m[t − 2], . . . can be added as the inputs for the linear regression model (Equation 7). Another option is to use e.g., recurrent neural networks or other autoregressive models. Moreover, the physiological parameters, such as age, body mass index, or sex, can contain information about how the observed signal s evolves during and after physical activity. Thus, the model f(·) can also be extended to include such variables in a numerical form. Calibrating the model Because the response of a signal s[t] on physical activity may depend on several factors, it is necessary to calibrate the model f so that s is estimatedcorrectly. One way of achieving this is to collect both s and acceleration values(x, y, z) from a user over some duration of time (e.g., 24 hours), and includescheduled bouts of physical activity within the time frame. This way, the model can be calibrated to match the actual observed signals s[t]. This can be done e.g., by adjusting the model parameters αi, i ∈ [1, ... ,M] such that the squared error between the observed and estimated values of s is minimized, resulting in an optimized model fopt: ^^^^ ^^ ^^= ^^argmin ( ^̂^ − ^^)2(Equation 8) ^^, ^^∈[1,…. ^^]The optimization process may be performed iteratively, for example by using the backpropagation algorithm. When the normal correlation formula (either general or personalized) is available, the measured body mass activity level can be translated to an estimated heart rate level using the formula. When measuring the real heart rate at the same time, the estimated HR and measured HR can be compared. By analysing the measured HR and estimated HR based on the measured body mass center activity gives a lot of information about the users health condition. If the measured HR is higher than the estimated one, the user may have higher sympathetic activity, stress or other mental or body activity than normally. If the measured HR is lower than the estimated one, the user may be less stressed, be more relaxed, be in better physical or mental condition or exercise more efficiently due to improved physical technique than earlier. The present embodiments provide a solution where body movement activity is measured by the wearable health measurement device placed on a waist, and this information is used when defining HR. It has been found out that activity of the center of body mass correlates well to the heart rate measured. Thus, the measurements concerning activity level of the center of the body mass gives a good estimate of HR of a person. During the use, the data can be collected to personalize data and calibrate the body activity more accurately to HR of the person. Further analysis and optimization can be targeted to the use and correlation during sleep (night) and daytime (daily activity) and during physical exercise (training). The body movements close to the body mass centered are measured by the wearable health measurement device. In particular, the one or more of the following sensors perform the measurement: accelerometer (e.g., a 3-axis linear accelerometer) or gyroscope or magnetometer or a pressure sensor. The body movement data is used to generate an estimated hear rate. In addition, the wearable health measurement device measures at least one of the following biosignals: heart rate, heart rate variability, skin impedance, humidity of the skin or humidity close to the skin, heat flux, or temperature of the skin. A biosignal can be measured by using a PPG sensor or an ECG sensor or any other sensor providing an aforementioned measurement. A corresponding estimated biosignal value is defined, which estimated biosignal value is defined based on the body movement level. The estimated biosignal value can be estimated using the equations as presented above or a table describing the relationship between the body mass center movement level and the biosignal. The estimated biosignal value is then compared to the measured biosignal value to see whether they correlate or not. The estimations and the comparations can be performed on a user device. It is appreciated that the these functions can, instead, be made on a cloud. It is also possible that some of the functions are performed on a user device, and some of them are performed on a cloud. According to an embodiment at least two biosignals are measured and at least two estimated biosignals are defined based on the measured body movements. When said at least two biosignals are measured, these at least two biosignals are compared to their corresponding estimated biosignals, and the comparison results are further compared between the biosignals. The time periods when the compared biosignals correlate or do not correlate are analyzed. In addition, the time periods and / or status relating to the user are displayed on a display of a smartphone or a computer, and / or interpreted in text or speech by the user interface of the wearable health measurement device or of the smartphone or the computer. The correlation of the biosignals may indicate several issues relating to the user. For example, the correlation may enable analysing energy consumption or evaluating reasons behind slow heart rate. For example, when a person is sitting with a slow heart rate, the heart rate may vary depending on what the person is doing at the same time. Reading while sitting may not increase the heart rate, however eating may cause higher heart rates. The analysis performed by the present embodiments help in solving why the heart rate increases in a situation where it should be slow (e.g., while sitting), and whether the increased heart rate has an understandable and acceptable reason (e.g., eating). This can be made by measuring the body mass center activity. The difference d between the estimated and measured biosignal (e.g., HR) can be used, for example, to detect • Physical or mental stress (measured HR or humidity higher than estimated due to increased sympathetic nervous system activity); ^ Fever or infection (measured HR, temperature, heat flux, or humidity higher than estimated due to increased basal metabolic rate); • Abnormally hot or cold environment (measured temperature, heat flux or humidity higher or lower than estimated due to environmental conditions); • Meal intake (measured heart rate, heart rate variability HF / LF ratio, temperature, heat flux, humidity higher than expected due to postprandial diet-induced thermogenesis); Upon detecting discrepancy within measured and estimated biosignals, the possible cause for the discrepancy is estimated. For example, the discrepancy between the measured and estimated biosignal(s) may be treated as a separate time series sequence defined as the pointwise difference between the measured and estimated signal(s). The endpoints of the discrepancy sequence(s) can be defined as the time interval when the error between the observed and estimated signal(s) exceeds the standard error of the estimator. The time series sequence(s) under consideration can also be limited in time such that the minimum length of the sequence is five minutes (to exclude momentary false estimations) and the maximum length of the sequence is six hours (in the case of an onset of a long condition, such as disease). The cause of the discrepancy may then be estimated by e.g., extracting the following features from the discrepancy sequence(s): • Time of day at the centerpoint(s) of the sequence(s); • Duration of the sequence(s); • Polynomial coefficients resulting from fitting a n:th degree polynomial to the sequence(s); • Fast Fourier Transform coefficients extracted from the sequence(s); • Statistical measures (mean, median, percentiles, minimum, maximum, skewness, kurtosis) extracted from the sequence(s). After extracting the features, the features can be passed on to e.g., a pre- trained classifier model, which has been trained on annotated dataset representing measurements from various conditions (stress, fever, meals etc.). The classifier model can be e.g. a neural network estimator, a decision tree, a support vector machine, or other suitable classification algorithm. After estimating the cause for the observed discrepancy, the user can be notified of the observed discrepancy (e.g., via a smartphone application), alongside a potential explanation for the discrepancy. Figure 3 illustrates an example of correlation of the body mass activity 330 and actual HR 310 of a person. The estimated HR 320 is based on the body mass activity 330. Figure 3 shows that measured HR 310 is higher than the estimated HR 320, whereupon further analysis is needed. Figure 4a illustrates an example of the wearable health measurement device being attached to a waistline 410 of a clothing 400. As show in Figure 4a, the clip part 103 of the device is located outside the clothing 400, and the measurement part 101 is located inside the clothing 400 so that the measurement part 101 is at least mostly in contact with user’s skin. Figure 4b is an exploded view of the wearable health measurement device of the intersection A-A of Figure 4a. In Figure 4b, elements the measurement part 101 with hinge 104 and the clip part 103 are shown. The first side 101a of the measurement part 101 is towards user’s skin. The waistline 410 fabric of the clothing 400 is shown to be located between the measurement part 101 and the clip part 103. In addition, Figure 4b shows a textile contact area 415 on the clothing 410 that is in contact with the second side 101b of the measurement part 101. The purpose of the clip part 103 is to attach the device to the user’s clothes such as the waistband of pants or underwear or other clothing. The clip part 103 may include a base part and a cam actor level mounted rotatably to the base part. The clip part 103 is attached to the measurement part on a side other than the skin-contacting side. Thus, the device is mechanically attached into user’s clothing in such a way that the device is in contact with the user’s skin. The clip mechanism allows attaching and detaching of the device easily without damaging the clothing. Figure 5 shows the wearable health measurement device according to another embodiment being attached to a clothing 400. In this embodiment, the device is additionally electrically connected to a piece of clothing, such as underwear, such that the piece of clothing can be used to provide e.g., external skin contact electrodes for ECG or BIA measurements. Thus, in the embodiment of Figure 5, the clothing comprises a textile conductor 520 within the waistband 410 and an additional electrode area 525, wherein the conductor 520 electrically connects the additional electrode area 525 to the device. The additional electrode area 525 may comprise silver conductive fabric and an evaporative guard (e.g., nitrite). The additional electrode area 525 may be placed on the waistband 410 of the clothing 400. In this embodiment, the clothing may be a specific clothing to be used with the wearable health measurement device according to the embodiments. The clothing 400 of this example may be sold as a device accessory, and in addition to the electrode area the clothing may have further measurement units. In addition to above elements, the wearable health measurement device according to an embodiment comprises other electronics as follows: - a microprocessor; - memory for storing data measured by the sensors; - a radio frequency communication device for relaying the data measured by the sensors; - a battery; - optionally one or more of the following: wireless charging means; a display and / or indicator lights and / or switches in the enclosure of the device; a loudspeaker; a mechanical vibrator. The physical structure and enclosure of the wearable health measurement device according to embodiments have been designed such that the device has few or no sharp edges for user comfort. In addition, the wearable health measurement device according to embodiments has a flat shape. The thickness may be less than 5mm. However, is some embodiments the thickness may be 5mm or more. Other dimensions (width and / or height and / or diameter) may be less than 50mm. However, in some embodiments, one or more of the dimensions may be 50mm or more. It is appreciated that these dimensions may have the same size or each of them may have their own size differing from the others. According to an embodiment, the measurement part has larger dimension in thickness than the clip part. This enables a contact pressure against the skin. The wearable health measurement device according to embodiments is able to perform measurements from the user and the environment continuously at predetermined intervals. Alternatively, the device can reduce, halt or continue its measurements and operation under an external influence, such as movement, in order to increase the battery’s life of the device. The device can store all of its measurement data from a time span of 24 hours. Alternatively, the device can store the measurement data over a smaller or larger time period. The measurement data from the wearable health measurement device can be transferred to another device, such as a mobile phone, for processing, data visualization and user feedback. Alternatively, or in addition, the measurement data can be delivered from the device to an external server (e.g., in a cloud network), where the processing and long-term storage of the measurement data takes place. The data can be downloaded from the cloud to another device, such as a mobile phone, for data visualization and user feedback. Also the wearable health measurement device may comprise means for giving feedback to a user. Such means may comprise a LED of piezo or vibrating element to provide optical and / or haptic feedback and / or alarms and / or indication triggered by measured data and / or time or other feedback defined by the device or communicated from a mobile device through a wireless connection to the device. Some of the embodiments of the wearable health measurement device have been discussed above. Some of the further embodiments are considered next. According to an embodiment, the measurement part 101 has a larger dimension in thickness than the clip part 103. According to an embodiment, the thickness Tmof measurement part 101 may be two times bigger that the thickness Tcof the clip part 103. The thickness Tmof measurement part may be 3-10mm and the thickness Tcof the clip part may be 1 – 4 mm measured from the thickest point. According to an embodiment, a PPG sensor may comprise at least one light source (e.g., a LED) and one receiver (e.g., a photodetector). According to another embodiment, the PPG sensor comprises at least two light sources and two receivers. The line of light sources and the receivers may be perpendicular to the axis of the hinge. According to an embodiment, the first side 101a may comprise two skin electrodes (against the skin) for measuring the impedance between the electrodes. According to an embodiment, the electrode area may have larger dimension in the perpendicular direction to the axis of the hinge. According to an embodiment, the second side of the measurement part may have at least one temperature sensor to form a temperature heat channel with one temperature sensor on the first side to form a heat flux sensor. According to an embodiment, the second side of the measurement part may have at least one electrical connecting area for contacting the second side to the external electrode area supported by the inner side (towards the skin) of the upper edge of the underwear or panties. According to an embodiment, the clip locking mechanism may tighten the upper edge of the garment between the clip and the second side of the device preventing the garment to move after locking the clip. According to an embodiment, the edge of the garment may be positioned at least over the middle point the measurement part in vertical (y- axis) dimension. According to an embodiment, open space may be formed in the part of the wearable device, between the measurement part and clip part when they are closed, and the garment is set to its designed position. According to an embodiment, one electrode of the first side can be used as first ECG electrode and the electrode connected to the connecting area of the second side can be used as the second ECG electrode. The ECG signal can be measured between the first and second ECG electrodes. According to an embodiment, one electrode area may be used for impedance or ECG measurement is connected thermally to the skin temperature sensor, so that metal of the electrode area conduits the skin temperature to the skin temperature sensor connected thermally to the electrode in one point of the electrode area. The measurements made by the wearable health measurement device generate an output, such as HR, heart rate variability (HRV), skin temperature, body temperature, perfusion index, ECG, EMG, oxygen saturation (SpO2), activity, body position (sitting, standing, laying), sleep stature, respiration. These outputs will further be formed and communicated with features such as overall vitality; sleep and recovery, activeness and strain, HRV and respiration, circadian alignment, hormonal alignment, biological rhythms and habits identification, feeding or meal detection, readiness, forward-looking recommendations, etc. The wearable health measurement device has the following advantages. For example, the measurement part is arranged in a direction that minimizes moving artefact in perpendicular to the axis of hinge. This means that the sensing areas and skin contacts are arranged using wider area of the device. The wearable health measurement device can transmit the collected data to a client application that can locate on a mobile device. Figure 6 illustrates such an example. The implementation may be done in a similar way as the data is transmitted from a smartwatch or smart ring to a mobile device and further to a (e.g., cloud) server. In the example of Figure 6, the wearable health measurement device 100 forms a data transfer network with a mobile device 610 for data delivery 1. The mobile device can be a smartphone, a tablet device, a laptop, a general computer etc. The data transfer network may be a Bluetooth connection, a Near Field Connection (NFC) or any other current or future wireless or wired data transfer network. For that respect, both the wearable health measurement device 100 and the mobile device 610 have corresponding radio interface and communication means. The mobile device 610 stores the client application 620 that may have been downloaded from a server to the mobile device 610. Alternatively, the mobile device 610 may only comprise a browser that is able to retrieve a view to the client application being located in a (e.g., cloud) server. However, despite the location of the client application, the client application is part of the computing system of the wearable health measurement device. The purpose of the client application 620 is at least to visualize the data received from the wearable health measurement device 100 and generate feedback or analysis on the well-being of the user. The mobile device 610 may be in connection with a (e.g., cloud) server 650 to deliver data from the client application 620 to the server 650. The server 650 may gather data from multiple users, to make broader analysis on a general wellbeing of plurality of users and to generate statistics. According to some embodiments, the user-related analysis may also be done at the server 650 instead of the mobile device 610, whereupon such analysis may be returned back to the mobile device 610 for visualization. The data transfer network 2 between the mobile device 610 and the server 650 may be created by any known wireless or wired communication technology. Examples of such are mobile networks of different generations (3G, 4G, 5G, etc.) and local and / or wide area networks. The mobile device 610 comprises at least one processor and at least one memory, wherein the memory stores computer program product having computer-implemented instructions according to which visualization of data, processing of data, analysis of data, etc. can be implemented. Thus, the computer program product is embodied on a non-transitory computer readable medium. The memory may also store measurement data. In addition, the mobile device 610 comprises a user interface, e.g., in the form of graphical user interface (GUI). In addition to the GUI, the mobile device 610 may contain other user interaction means, e.g. a microphone and / or loudspeakers. The measurement data received from the wearable health measurement device may be at least partly stored in the mobile device 610 and at least partly in the cloud 650. For example, the cloud 650 may store history data, whereas the mobile device 610 may store measurement data obtained within a certain shorter time period (e.g., a week, a month). It is appreciated that a client application may be interpreted to be an element belonging to the wearable health measurement device, despite it has a physical location at the mobile device. Therefore, the wearable health measurement device may utilize capabilities (e.g., processing power) of the mobile device through the client application. A computer-implemented method for determining a health status of a user according to an embodiment is shown in Figure 7. The method comprises receiving 710 data concerning body movements close to a body mass center and measured by a measurement device; receiving 720 at least one biosignal measured by the measurement device; defining 730 an estimated biosignal based on the measured body movements; comparing 740 the measured biosignal and the estimated biosignal; analysing 750 time periods according to correlation between the measured biosignal and the estimated biosignal; and delivering 760 the analysed time periods to be displayed on a display of an electronic apparatus. The steps can be implemented by a respective module of a computer system of the wearable health measurement device. If desired, the different functions discussed herein may be performed in a different order and / or concurrently with other. Furthermore, if desired, one or more of the above-described functions and embodiments may be optional or may be combined. Although various aspects of the embodiments are set out in the independent claims, other aspects comprise other combinations of features from the described embodiments and / or the dependent claims with the features of the independent claims, and not solely the combinations explicitly set out in the claims. It is also noted herein that while the above describes example embodiments, these descriptions should not be viewed in a limiting sense. Rather, there are several variations and modifications, which may be made without departing from the scope of the present disclosure as, defined in the appended claims.

Claims

Claims:

1. A computer-implemented method for determining well-being of a user, the method comprising - receiving data concerning body movements close to a body mass center and measured by a measurement device; - receiving at least one biosignal measured by the measurement device; - generating an estimate of the measured biosignal as a function of the measured body movements; - comparing the measured biosignal and the generated estimate to determine whether there is a discrepancy; - repeating the previous steps; - analysing the comparison over a period of time to detect times when the measured biosignal and the estimate correlate and / or not correlate; and - delivering the analysed comparison relating to the period of time to be displayed on a display of an electronic apparatus.

2. The method according to claim 1, wherein the measurement device is a wearable device (100) being attachable to an edge of a waistband of a clothing.

3. The method according to claim 1 or 2, wherein said at least one biosignal is one of following: heart rate, heart rate variability, skin impedance, humidity of the skin, humidity close to the skin, heat flux, temperature of the skin.

4. The method according to claim 1 or 2, wherein the biosignal is measured by a photoplethysmogram (PPG) or electrocardiogram (ECG) sensor of the measurement device.

5. The method according to claim 1 or 2, wherein the biosignal is measured by at least one temperature sensor of the measurement device.

6. A wearable measurement device for determining well-being of a user, comprising - means for measuring body movements close to a body mass center; - means for measuring at least one biosignal; - means for generating an estimate of the measured biosignal as a function of the measured body movements; - means for comparing the measured biosignal and the generated estimate to determine whether there is a discrepancy; - means for repeating the previous steps; - means for analysing the comparison over a period of time to detect times when the measured biosignal and the estimate correlate and / or not correlate; and - means for delivering the analysed comparison relating to the period of time to be displayed on a display of an electronic apparatus.

7. The wearable measurement device according to claim 6, wherein the measurement device is a wearable device (100) being attachable to an edge of a waistband of a clothing.

8. The wearable measurement device according to claim 6 or 7, wherein said means for measuring at least one biosignal are configured to measure at least one of following: heart rate, heart rate variability, skin impedance, humidity of the skin, humidity close to the skin, heat flux, temperature of the skin.

9. The wearable measurement device according to claim 6 or 7, wherein said means for measuring at least one biosignal is a photoplethysmogram (PPG) or electrocardiogram (ECG) sensor of the measurement device.

10. The wearable measurement device according to claim 6 or 7, wherein said means for measuring at least one biosignal comprises at least one temperature sensor of the measurement device.

11. The wearable measurement device according to any of preceding claims 6 to 10, further comprising means for generating a feedback concerning the analysed time periods, and providing the generated feedback in written or audial format through a user interface of the electronic apparatus or the measurement device.

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