Patent Publication Number: US-2020281536-A1

Title: Personal health monitoring

Description:
TECHNICAL FIELD 
     The present invention relates generally to systems and methods for monitoring an individual&#39;s health based on sensors associated with the individual, also known as personal health monitoring. 
     BACKGROUND 
     Personal health monitoring is a growing field and finds many applications, e.g. fitness tracking and medical surveillance to name a few. In personal health monitoring, a user&#39;s health may be monitored based on readings from one or more sensors worn by the user. The sensors may be integrated in a monitoring device worn by the user, e.g. a wristband, a watch, a pedometer, a fitness tracker, etc. Monitoring devices may also support external sensors, e.g. a chest strap with heart rate sensors, a cadence sensor, etc. Simple monitoring devices are only capable of measuring a single parameter. However, many monitoring devices are capable of monitoring and reporting multiple health-related parameters, such as position, step count, heart rate, blood glucose level, skin temperature, detected food intake (eating, drinking) and activities (e.g. running, sleeping), etc. 
     WO2016/164485 discloses an activity classification server that receives raw data from one or more activity-tracking devices worn by a user. The activity-tracking devices operate at a given sampling rate to provide the raw data. The server processes the raw data to classify the user&#39;s activities into one or more identifiable states. To optimize energy usage and thereby prolong battery life of the activity-tracking devices, the server adjusts the sampling rate of the activity-tracking devices based on the state of the user at any given time. 
     There is a continued need to optimize the performance of health monitoring devices, e.g. with respect to energy consumption and user experience. 
     SUMMARY 
     It is an objective of the invention to at least partly overcome one or more limitations of the prior art. 
     Another objective is to enable resource-efficient monitoring of an individual&#39;s health based on multiple measured health-related parameters. 
     A further objective is to improve the user experience during monitoring of an individual&#39;s health. 
     One or more of these objectives, as well as further objectives that may appear from the description below, are at least partly achieved by a method, a computer-readable medium, a portable electronic device, a computing device and a system according to the independent claims, embodiments thereof being defined by the dependent claims. 
     A first aspect of the invention is a method for monitoring a user&#39;s health based on one or more sensors that are associated with the user. The method comprises: obtaining sensor data from a set of sensors among the one or more sensors, and generating, based on the sensor data from the set of sensors, measurement values of a primary parameter. The method further comprises: identifying, among a default set of secondary parameters, one or more selected secondary parameters which, for the user, are found to correlate with the primary parameter. 
     A second aspect of the invention is a computer-readable medium comprising computer instructions which, when executed by a processor, cause the processor to perform the method of the first aspect or any of its embodiments. 
     A third aspect of the invention is a portable electronic device, which is configured for connection to one or more sensors that are associated with a user. The portable electronic device is configured to: obtain sensor data from a set of sensors among the one or more sensors; generate, based on the sensor data from the set of sensors, measurement values of a primary parameter; and identify, among a default set of secondary parameters, one or more selected secondary parameters which, for the user, are found to correlate with the primary parameter. 
     A fourth aspect of the invention is a computing device configured to communicate, over a communication network, with a portable electronic device in accordance with the third aspect. The computing device is configured to: receive, from the portable electronic device, measurement values of the default set of secondary parameters and measurement values of the primary parameter, which have been generated based on sensor data from at least one of the one or more sensors associated with the user; analyze the measurement values of the default set of secondary parameters and the measurement values of the primary parameter for identification of the one or more selected secondary parameters; and transmit an indication of the one or more selected secondary parameters to the portable electronic device. 
     A fifth aspect is a system for monitoring a user&#39;s health. the system comprises: one or more sensors associated with the user; a control module configured to obtain sensor data from a set of sensors among the one or more sensors, and generate, based on the sensor data from the set of sensors, measurement values of a primary parameter; and an analysis module configured to identify, among a default set of secondary parameters, one or more selected secondary parameters which, for the user, correlate with the primary parameter. 
     Other objectives, as well as features, aspects and advantages of embodiments of the present invention will appear from the following detailed description, from the attached claims as well as from the drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Embodiments of the invention will now be described in more detail with reference to the accompanying schematic drawings. 
         FIG. 1  is a block diagram of a system environment for health monitoring in accordance with an embodiment. 
         FIG. 2  is a block diagram of a system for health monitoring in accordance with an embodiment. 
         FIGS. 3A-3B  are flow charts of methods for health monitoring in accordance with embodiments. 
         FIG. 4  is a flow chart of a method for health monitoring in accordance with a detailed embodiment. 
         FIGS. 5A-5C  are block diagrams of example implementations of the system in  FIG. 2 . 
         FIG. 6  is a block diagram of an analysis module in a system for health monitoring in accordance with an embodiment. 
         FIG. 7  is a block diagram of a local monitoring device in a system for health monitoring in accordance with an embodiment. 
         FIG. 8  is a block diagram of a machine that implements a local or remote monitoring device in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure may satisfy applicable legal requirements. Like numbers refer to like elements throughout. 
     Also, it will be understood that, where possible, any of the advantages, features, functions, devices, and/or operational aspects of any of the embodiments of the present invention described and/or contemplated herein may be included in any of the other embodiments of the present invention described and/or contemplated herein, and/or vice versa. In addition, where possible, any terms expressed in the singular form herein are meant to also include the plural form and/or vice versa, unless explicitly stated otherwise. As used herein, “at least one” shall mean “one or more” and these phrases are intended to be interchangeable. Accordingly, the terms “a” and/or “an” shall mean “at least one” or “one or more,” even though the phrase “one or more” or “at least one” is also used herein. As used herein, except where the context requires otherwise owing to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” is used in an inclusive sense, that is, to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention. 
     Well-known functions or constructions may not be described in detail for brevity and/or clarity. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. 
     Before describing embodiments of the invention in more detail, a few definitions will be given. 
     As used herein, “health monitoring” refers to monitoring of the well-being of an individual in a general sense. The monitoring may aim at detecting or predicting an undesirable condition of the individual, e.g. occurrence of a known health problem or health issue of the individual, or occurrence of a potential health problem of the individual. Such a health problem may include any type of medical condition. Another example of an undesirable condition is that the individual falls to the ground. Alternatively, the health monitoring may aim at estimating or verifying a desirable condition, e.g. a fitness level. 
     As used herein, a “sensor” refers to any device that may be associated with an individual and is configured to measure a quantity related to the individual, in a broad sense. Non-limiting examples of sensors include accelerometers, gyroscopes, altimeters, pedometers, vibration sensors, blood glucose sensors, blood pressure sensors, skin temperature sensors, ambient temperature sensors, pupil size sensors, pulse oximeters, heart rate monitors, global positioning systems (GPS), sweat sensors, moisture sensors, insulin level detectors, and bioelectric current sensors. 
     A sensor may be “associated with” an individual by being worn by the individual, e.g. attached to the individual&#39;s body or clothing or implanted into the individual&#39;s body, or by being located in proximity of the individual in a monitoring situation. In one example, the sensor is included in a wearable or portable device, such as a fitness monitor, a wristband, a chest strap, a helmet, headphone, a mobile phone, an action camera, an adhesive patch, eyeglasses, a hearing aid, etc. In another example, the sensor is installed in the same building or room as the user, in a bed or in an exercise device. 
     As used herein, a “set” of items is intended to imply a provision of one or more items. Thus, a “set of sensors” may designate a single sensor or multiple sensors. Likewise, a “set of parameters” may designate a single parameter or multiple parameters. 
     As used herein, a “primary parameter” is any mandatory parameter that has been predefined to enable detection of a specific undesirable or desirable condition of the individual. In other words, the primary parameter is linked to the main purpose of the health monitoring. To give a few non-limiting examples, the primary parameter may be blood glucose level when the monitoring is aimed at detecting or predicting hypoglycemia, heart rate when the monitoring is aimed at detecting or predicting a heart disease or cardiac arrest, body orientation when the monitoring is directed to detecting or predicting a fall of the individual, and heart rate or heart rate variability when the monitoring is aimed at determining the fitness level of the individual. Although all examples herein involve health monitoring based on a single primary parameter, it is also conceivable that the health monitoring is based on more than one primary parameter. 
     As used herein, a “secondary parameter” is any parameter other than the primary parameter that may be monitored or calculated based on the monitoring and may be of potential relevance in the specific monitoring context. Thus, secondary parameters are optional parameters that may be monitored to supplement the primary parameter, e.g. to enable or improve prediction of the undesirable or desirable condition of the individual. 
     Each of the primary and secondary parameters may be defined by raw data from one sensor, or refined data derived by processing the raw data from one or more sensors. Each of the primary and secondary parameters may represent physiological or biometric data, motion data, position data, orientation data, etc. Examples of primary and secondary parameters include, without limitation, heart rate, speed, acceleration, angular velocity, orientation, position, blood glucose level, blood pressure, breathing rate, skin temperature, moisture, sweat rate, oxygen saturation, insulin level, bioelectric current, energy consumption, step count, body motion, brain activity, muscle motion, activity index, stress index, food intake index, resting index, snoring index, etc. 
     As used herein, “multiple regression” or “multiple regression analysis” is given its ordinary meaning and refers to a process for estimating relationships among variables. The focus of multiple regression may be to determine the relationship between a response variable (also known as criterion variable) and a number of predictor variables, specifically to parameterize a regression function which relates the response variable to the predictor variables and which may be linear or non-linear. As is well-known in the art, multiple regression comprises an optimization of the regression function based on observations of the response and predictor variables, and results in regression coefficients of the regression function. As also well-known in the art, multiple regression analysis may involve computing the statistical significance of the individual regression coefficients, e.g. so-called p-values, based on a hypothesis test. 
     Some embodiments of the invention relate to a technique for monitoring the health of an individual or user based on measurement values of a primary parameter. In accordance with some embodiments, the technique is personalized for the individual to the extent that the technique provides, for the health monitoring, measurement values of a personalized set of secondary parameters, which are selected among a default set of available secondary parameters based on their correlation with the primary parameter, and thereby their relevance for the current health monitoring of the individual. The personalized set is typically a subset of the default set. This means that the personalized technique may be designed to only generate measurement values for a subset of the available secondary parameters, while ensuring that the measurement values are relevant to the health monitoring by the primary parameter, e.g. for predicting the primary parameter. In another example the personalized technique may be designed to prioritize measurements for a subset of the available secondary parameters, e.g. in scenarios when there is a need to reduce the energy consumption or data transmission at a monitoring device. In practice, the technique is executed on or more electronic devices. It is realized that the personalization will save resources on the electronic device(s), e.g. processing power, by reducing the number of secondary parameters. The personalization may also save resources whenever it excludes, from the personalized set, a secondary parameter that is costly to generate in terms of processing power, e.g. by involving many computations or by requiring the measurement values to be generated at high sampling rate. Further, to the extent that the measurement values are transmitted between devices, the personalization will also reduce the required bandwidth of the transmission channel and/or decrease the transmission time. The personalization may also facilitate prediction of the primary parameter based on the measurement values of the personalized set of secondary parameters, since the secondary parameters in the personalized set have been selected based on their correlation with the primary parameter. Thus, the functional relation between the primary parameter and the personalized set of secondary parameters may be known, or can at least be efficiently computed. 
     The personalization may also have the additional technical advantage of facilitating selection of one or more relevant secondary parameters to be visualized to the individual, e.g. on a display, to improve the individual&#39;s understanding of how to avoid an undesirable condition or achieve an desirable condition, whatever is relevant. 
     The personalization may also have the additional technical advantage of enabling personalized alarms based on one or more relevant secondary parameters, e.g. to alert the individual that an undesirable condition is approaching and allowing the individual to take countermeasures. 
     In accordance with some embodiments, the technique is personalized for the individual to the extent that the technique identifies and presents the personalized set of secondary parameters to the user, thereby allowing the user to gain an understanding about the secondary parameters that are (most) relevant for the primary parameter and thus the health monitoring. 
     In summary, the personalization in accordance with embodiments of the invention may reduce energy consumption and/or improve performance and/or improve user experience. 
       FIG. 1  illustrates an implementation example of personal health monitoring in accordance embodiments of the invention. An individual  10  is provided with a number of sensors S 1 -S 3 . In the illustrated example, sensor S 1  is worn on the upper arm, S 2  is worn at the hip, and S 3  is worn on the wrist. The sensors S 1 -S 3  provide respective sensor data. The sensor data is acquired by a portable electronic device  12 , designated PED in the following. The PED  12  may be any electronic device which is capable of being carried, held or worn by a user. For example, the PED  12  may be a handheld device, such as a mobile phone, smartphone, tablet, laptop, etc, as well as a wearable computer (“wearable”). The PED  12  may be a generic device capable of performing different tasks, e.g. by executing different application programs, or a specialized device tailored to perform a single specific task. 
     As will described in greater detail below, the PED  12  is a personal monitoring device that generates, based on the sensor data from the sensors S 1 -S 3  or a subset thereof, measurement values of a primary parameter and a personalized set of secondary parameters. As shown, the PED  12  may be further configured to report the measurement values to a computing device  14 , which is configured to perform a remote health monitoring, e.g. by storing the measurement values, by displaying the measurement values, by analyzing the measurement values for identification of trends or for prediction, by generating alarms or alerts for monitoring personnel such as caretakers, medical staff, clinical experts, etc. Alternatively or additionally, the PED  12  may be configured perform a local health monitoring, e.g. by storing the measurement values, displaying health-related data or generating an alarm when certain measurement values fulfill an alarm criterion. In the illustrated example, the PED  12  is configured to define a user interface (UI)  15  for displaying measurement values of the primary parameter, e.g. in a first UI section or window  15 A, and measurement values of one or more secondary parameters, e.g. in a second UI section or window  15 B. The measurement values may be displayed in plain text, as indicated by  16 , or graphically, as indicated by  17 . In the illustrated example, the PED  12  is also operable to selectively generate an alarm signal  18 . 
       FIG. 2  is a block diagram of a system  20  for personal health monitoring in accordance with an embodiment. The system  20  may be implemented in the context of  FIG. 1 . The system  20  comprises a number of sensors S 1 -SN, with N≥1. A control module  21  is connected to the sensors S 1 -SN and configured to selectively acquire sensor data from the sensors. The control module  21  is further configured to selectively generate measurement values of primary and secondary parameters based on the sensor data. The control module  21  is further connected to an analysis module  22 , which is operable to analyze measurement values received from the control module  21  for determination of the personalized set of secondary parameters for the specific individual that is being monitored. The control module  21  is further connected to a display module  23  and an alarm module  24 . The display module  23  may be configured by the control module  21  to display a personalized user interface (cf.  15  in  FIG. 1 ). The alarm module  24  may be operable by the control module  21  to generate an alarm signal (cf.  18  in  FIG. 1 ). 
     The system  20  of  FIG. 2  may be partitioned onto physical devices in different ways. In one extreme, all components of the system  20  are arranged in physically separated devices, such that the sensors S 1 -SN are physically separated from each other and from the modules  21 - 24 , which also are physically separated from each other. In another extreme, the sensors S 1 -SN and the modules  21 - 24  are all arranged in a single physical device, e.g. the PED  12  in  FIG. 1 . Further examples are given below with reference to  FIGS. 5A-5C . 
     Generally, the connections between components in the system  20  of  FIG. 2  may be implemented by wired or wireless data transmission, based on any suitable communication protocol known in the art. 
       FIG. 3A  illustrates a method  30 A for personal health monitoring in accordance with an embodiment. With reference to the system  20  in  FIG. 2 , the method  30 A may be executed by the control module  21 . Step  31  identifies, among a default set of secondary parameters, one or more selected secondary parameters that are found to correlate with the primary parameter, designated P in the following. The selected secondary parameter(s) define the personalized set of secondary parameters, designated [S] P  in the following. The personalized set [S] P  is typically a subset of the default set of secondary parameters, designated [S] T  in the following. The default set [S] T  may be given by a default configuration of the control module  21 , and the generation of measurement values for the secondary parameters in [S] T  is enabled by hardware circuitry and/or computation algorithms in the control module  21 . 
     In step  31 , the control module  21  may identify [S] P  by generating and transmitting measurement values of P and [S] T  to the analysis module  22 , which thereby returns an indication of [S] P  to the control module  21 . The corresponding process for determining [S] P  in the analysis module  22  will be exemplified below with reference to  FIG. 4 . By step  31 , the control module  21  acquires an individual configuration for use in a subsequent monitoring phase, which is represented by a repeating sequence of steps  32 - 35  in  FIG. 3 . In step  32 , sensor data is acquired from one or more of the sensors S 1 -SN. In step  33 , a measurement value for the primary parameter is generated based on the sensor data. In step  34 , a measurement value is generated for the respective secondary parameter in [S] P . In step  35 , the measurement values may be provided for health monitoring, which may be performed as described in relation to  FIG. 1 . 
     It should be understood that step  32  may acquire sensor data at any desired sampling rate and from a monitored set of sensors that may include any sensor or combination of sensors among sensors S 1 -SN. The monitored set of sensors is given by the secondary parameter(s) that are included in the personalized set [S] P . Further, steps  33  and  34  may be implemented to generate the measurement values of the respective parameter at an individual sampling rate. The sampling rate for the respective parameter may be either predefined or dynamically determined, e.g. as described in above-mentioned WO2016/164485. 
       FIG. 3B  illustrates a method  30 B for personal health monitoring in accordance with an embodiment. With reference to the system  20  in  FIG. 2 , the method  30 B may be executed by the control module  21 . The method  30 B involves a monitoring phase comprising a repeating sequence of steps  32  and  33 , which acquire sensor data and generate measurement values for the primary parameter P, as described hereinabove. Although not shown in  FIG. 3B , the monitoring phase may also provide the measurement values of the primary parameter P for health monitoring (cf. step  35  in  FIG. 3A ). Subsequent to the monitoring phase, the method  30 B proceeds to step  31  which, as described for  FIG. 3A , identifies the personalized set [S] P  of secondary parameters among the default set [S] T  of secondary parameters. For example, in step  31 , the control module  21  may identify [S] P  by generating and transmitting measurement values of P and [S] T  to the analysis module  22 , which thereby returns an indication of [S] P  to the control module  21 . The method  30 B then proceeds to step  36 , which presents the secondary parameters in [S] P  to the user, e.g. on the display module  23 . The method  30 B does not generate measurement values of the secondary parameter(s) in [S] P , but enables the user to gain information about secondary parameter(s) that are of relevance to the primary parameter and the aim of the health monitoring. 
       FIG. 4  illustrates a method  30 C for personal health monitoring in accordance with an embodiment. With reference to the system  20  in  FIG. 2 , the method  30 C may be executed by the control module  21  and the analysis module  22  in combination. The method  30 C involves an analysis phase  40 A, a configuration phase  40 B, and a monitoring phase  40 C. 
     The analysis phase  40 A comprises steps  40 - 44 . By steps  40 - 41 , measurement values are repeatedly generated for the primary parameter P and the secondary parameters in the default set [S] T  for a predefined time period, resulting in time-sequences of measurement values. By analogy with steps  32 - 34  in  FIG. 3A , step  40  involves acquiring sensor data and generating measurement values based on the sensor data. Step  40  may acquire the sensor data from all of the available sensors S 1 -SN, but it is conceivable that the sensor data is acquired from a subset thereof. The set of sensors that provide sensor data in step  40  may be given by a default configuration and may be denoted a “default set of sensors”. 
     In step  42 , the resulting measurement values are analyzed for identification of the one or more selected secondary parameters that define the personalized set [S] P . The analysis in step  42  may comprise a sub-step  42 A of computing a relevance score or priority for each secondary parameter in the default set [S] T . The relevance score may indicate the relative impact of the secondary parameter on the primary parameter. The relevance score may be determined by operating any suitable analysis technique or data mining technique on the measurement values from steps  40 - 41 . Many such techniques are readily available to the person skilled in the art. In one embodiment, the relevance score is indicative of a degree of correlation between the primary parameter and the respective secondary parameter. In one embodiment, step  42 A performs a multiple regression analysis of the measurement values from step  40 - 41 . The multiple regression analysis may comprise optimizing a regression function which has a response variable given by the primary parameter P and predictor variables given by the secondary parameters in the default set [S] T . The relevance scores may then be generated as a function of the regression coefficients of the optimized regression function, and possibly also as a function of the statistical significance of the respective regression coefficient. For example, the relevance score for a parameter may be set in proportion to the magnitude of its regression coefficient, provided that the regression coefficient is deemed to be statistically significant. Parameters with regression coefficients that are deemed not to be statistically significant may be given a low relevance score. 
     Generally, in all embodiments disclosed herein, the analysis to identify the one or more selected secondary parameters that correlate with the primary parameter may be based on any conceivable algorithm or algorithms for this purpose, including but not limited to multiple regression analysis, machine-learning analysis, statistical analysis, or any similar estimation method, or any combination thereof. 
     The analysis in step  42  may further comprise a sub-step  42 B that involves obtaining and analyzing a user profile for the individual  10 , e.g. by use of big data analytics. The user profile may define one or more properties of the individual  10 , such as age, gender, weight, height, BMI, medical history, country of residence, country of birth, ethnicity, etc. Sub-step  42 B may further comprise comparing the user profile  10  to aggregated data for a larger population of individuals, where the aggregated data represents measurement values of primary and secondary parameters obtained for the larger population of individuals, which are associated with a respective user profile. The aggregated data may thereby indicate, directly or indirectly, that certain sets of secondary parameters are relevant for different user profiles, or for different values of one or more properties in the user profiles. Thus, sub-step  42 B may match one or more properties of the individual, given by the user profile, to the aggregated data, so as to identify a set of secondary parameters that are likely to have a significant impact on the primary parameter, or even identify a likely order of relevance within such a set of secondary parameters. In such an implementation, sub-step  42 B may be seen to assign a second relevance score to the respective secondary parameter in [S] T . 
     The analysis in step  42  further comprises a sub-step  42 C which may determine [S] P  as a function of the output of sub-step  42 A, and optionally as a function of the output of sub-step  42 B. In one example, [S] P  may be defined to include a predefined number of the secondary parameters that have the highest relevance score or all of the secondary parameters that have a relevance score above a predefined limit. In another example, the relevance scores from sub-step  42 A may be modified, e.g. weighted, by the second relevance scores from sub-step  42 B, so as to relatively increase the relevance score of the secondary parameters that are deemed by sub-step  42 B to have a large relevance. In an alternative embodiment, sub-step  42 C may identify [S] P  only as a function of the output of sub-step  42 B. 
     In a variant, the second relevance score from sub-step  42 B may used for defining the regression function, e.g. to exclude certain secondary parameters. In such a variant, sub-step  42 C will identify [S] P  as a function of the output of sub-step  42 A and, implicitly, as a function of the output of sub-step  42 B. 
     Generally, it is conceivable that step  42  adds one or more secondary parameters to the default set [S] T  and/or removes one or more secondary parameters from the default set [S] T  as part of the analysis. For example, step  42  may combine one or more secondary parameters into a new secondary parameter, which thereby may be included in the personalized set [S] P  depending on the outcome of the analysis. 
     Although not shown in  FIG. 4 , the analysis phase  40 A may include a further step of determining a sampling rate for the primary parameter and/or one or more secondary parameters in the personalized set [S] P , e.g. as described in WO2016/164485. 
     Step  43  selects at least one display parameter, DP, as a function of [S] P  from step  42 . The DP(s) may be selected based on the relevance scores, optionally weighted by the second relevance scores. In one example, step  43  may select the secondary parameter(s) with the highest relevance score or the secondary parameters that have a relevance score above a predefined limit. In another example, at least one DP may be a new parameter that is formed based on one or more of the secondary parameters in [S] P . 
     Step  44  selects at least one alarm parameter, AP, as a function of [S] P  from step  42 . The AP(s) may be selected based on the relevance scores, optionally weighted by the second relevance scores. In one example, step  44  may select the secondary parameter(s) with the highest relevance score or the secondary parameters that have a relevance score above a predefined limit. In another example, at least one AP may be a new parameter that is formed based on one or more of the secondary parameters in [S] P . Step  44  may also determine an appropriate alarm criterion, e.g. an alarm limit for the respective AP. The alarm criterion may be determined based on the above-mentioned aggregated data and/or the measurement values from steps  40 - 41 . 
     The configuration phase  40 B comprises steps  45 - 47 . Step  45  configures a personalized measurement based on [S] P  from step  42 , so as to generate measurement values for P and [S] P , at a respective sampling rate, during forthcoming step  48 . Step  46  configures a display control function to provide a personalized UI for presentation of the measurement values of the DP(s), and optionally the measurement values of P, during forthcoming step  48 . Step  47  configures a personalized alarm control function to monitor the AP(s) with respect to an alarm criterion, which may be predefined or determined by step  44  to indicate an alarm condition. Step  47  also configures the personalized alarm control function to generate an alarm signal when the alarm criterion is met. 
     The monitoring phase  40 C comprises steps  48 - 49 . Step  48  executes the personalized measurement of P and [S] P , operates the display control function to present the measurement values of the DP(s) in the personalized UI, and operates the personalized alarm control function to monitor the measurement values of the AP(s) for an alarm condition. Step  48  may also provide the measurement values of P and [S] P  for further remote or local monitoring, e.g. as described in relation to  FIG. 1 . Step  48  is repeated until step  49  detects a reconfiguration event, which causes step  49  to proceed to step  40  and thereby initiate the analysis phase  40 A. In one example, the reconfiguration event is generated by a timer at regular time intervals, e.g. once a day, week or month. In another example, the reconfiguration event is generated on-demand by the individual, e.g. by the individual pushing in a button on the PED  12  in  FIG. 1 . In further examples, the reconfiguration event is generated if the number of alarms per unit time exceeds a limit, or based on statistical characteristics of the measurement values that are generated during step  48  for one or more parameters. For example, the reconfiguration may be generated if there is a significant and abrupt change in the measurement values. 
     In one embodiment of the system  20  in  FIG. 2 , the control module  21  is configured to perform steps  40 - 41  and steps  45 - 49 , and the analysis module  22  is configured to perform steps  42 - 44 . In such an embodiment, the control module  21  may provide the measurement values from steps  40 - 41  to the analysis module  22 , which then returns an indication of the personalized set [S] P , the AP(s) and the DP(s) to the control module  21 . The analysis module  22  may also provide the relevance scores or a corresponding rating of the secondary parameters, the AP(s) or the DP(s) to the control module  21 . 
     In another embodiment of the system  20  in  FIG. 2 , the control module  21  is configured to perform steps  40 - 41  and steps  43 - 49 , and the analysis module  22  is configured to perform step  42 . In such an embodiment, the control module  21  may provide the measurement values from steps  40 - 41  to the analysis module  22 , which then returns an indication of the personalized set [S] P  together with the relevance scores or a corresponding rating of the secondary parameters in [S] P  to allow the control module  21  to determine the AP(s) and the DP(s). 
     To further exemplify the operation and advantages of the system  20  and the method  30 , a non-limiting example will be given with reference to  FIG. 1 . In this example, the PED  12  has been produced to monitor and report status with respect to blood glucose level, which is thus the primary parameter. The blood glucose level may to a greater or lesser extent be dependent on other parameters, denoted secondary parameters herein. For example, the blood glucose value at a given time point could be a function dominated by the number of steps walked during the last hour before the given time point and the amount of food intake during the last 2 hours before the given time point. Hence, it may be possible to predict the expected future values of blood glucose level (primary parameter) by carefully measuring a selected set of secondary parameters. Further, the dependency of different secondary parameters could be different for different individuals. For example, a first individual may have a strong relationship between the primary parameter and a certain set of secondary parameters while a second individual may have a weak relationship between the primary parameter and the secondary parameters that are important for the first individual, but a strong relationship between the primary parameter and a set of other secondary parameters. Hence, there is a need to individually determine which secondary parameters the PED  12  should monitor in order to optimize the monitoring and prediction of the primary parameter. In one implementation example of  FIG. 1 , the individual  10  wears a sensor S 1  capable of measuring the blood glucose level, a pedometer S 2  capable of measuring number of walking steps, and a sensor S 3  capable of measuring at least heart rate, skin temperature, blood pressure, skin moisture, GPS position, acceleration, angular velocity and orientation. Assuming that the PED  12  comprises modules  21 - 24  of system  20  ( FIG. 2 ), the PED  12  may be configured to perform a health monitoring in accordance with  FIG. 4  for the purpose of predicting and counteracting hypoglycemia in the individual  10 . The PED  12  is configured to use the blood glucose level from sensor S 1  as primary parameter and is operable, in a default configuration, to generate the following secondary parameters, which thus define the default set [S] T : heart rate, skin temperature, blood pressure, skin moisture, number of walking steps per unit time, a food intake index (e.g. computed based on acceleration, orientation, angular velocity from sensor S 3 ), an activity index (e.g. computed based on acceleration, GPS position, orientation, angular velocity from sensor S 3  and number of walking steps from sensor S 2 ), and a stress index (e.g. computed based on heart rate, blood pressure and skin moisture from sensor S 3 ). The method executed by the PED  12  identifies, by step  42 , a personalized set [S] P  for the individual  10  with the following selected secondary parameters: heart rate, number of walking steps, activity index and food intake index, with the food intake index having the highest relevance score. The PED  12  then presents, in section  15 A, the measured values of the blood glucose level and, in section  15 B, the measured values of the food intake index. The PED  12  also monitors the food intake index and generates an alarm signal when the food intake index reaches a threshold value. For another individual which is equipped with the same system  20 , the PED  12  instead identifies a personalized set [S] P  with the following selected secondary parameters: activity index, blood pressure and food intake index, presents the activity index and the blood pressure as DPs in section  15 B and monitors the activity index as an AP. Thus, even if the health monitoring targets the same primary parameter for the two individuals, the personalization may result in differences in the secondary parameters that are monitored, presented to the individual and result in alarm generation. 
       FIGS. 5A-5C  exemplify implementations of the system  20  in  FIG. 2 . In all examples, the control module  21  uses a default configuration for measuring [S] T  and reporting the measurement values to the analysis module  22 , while the analysis module  22  conducts analytics on the reported data, e.g. big data analytics, multi-regression analytics, machine-learning analytics, statistical analytics or any similar estimation method for identifying significant secondary parameters. Once the analysis module  22  has identified the significant selection of secondary parameters for the specific user and determined an individual configuration, this individual configuration is transmitted to the control module  21 , which adapts its measuring, reporting and display presentation scheme accordingly. 
     In the example of  FIG. 5A , the sensors S 1 -SN are physically separated from the PED  12 , which comprises the control module  21 , the display  23  and possibly the alarm module  24  (not shown). The PED  12  is configured to communicate over a network  51  with a computing device  50 , e.g. a remote server such as a cloud sever, which comprises the analysis module  22 . The network  51  may comprise any combination of wide area and/or local area and/or personal area networks (WAN/LAN/PAN). The computing device  50  has access to a database  52 , which may contain the user profile of the individual and the aggregated data. With reference to  FIG. 1 , the PED  12  is a personal monitoring device associated with the individual that is being monitored. As indicated in  FIG. 5A , the PED  12  transmits, during the analysis phase  40 A ( FIG. 4 ), measurement values of the primary parameter P and the secondary parameters in the default set [S] T  to the computing device  50  via network  51 . This causes the computing device  50 , by the analysis module  22 , to determine the personalized set [S] P  and transmit an indication of thereof to the PED  12  over the network  51 . In the illustrated example, the computing device  50  also transmits an indication of the display and alarm parameters DP, AP. Although not shown in  FIG. 5A , the PED  12  may be further configured to transmit, during the subsequent monitoring phase  40 C, the measurement values of P and [S] P  over the network  51  to a computing device for remote monitoring (cf.  14  in  FIG. 1 ), which may or may not be identical to the computing device  50 . It is realized that the personalization in accordance with embodiments of the invention reduces the need for processing in the PED  12  and improves the user experience for the individual being monitored. It is also realized that the amount of data transferred to the computing device  14  will reduced by the personalization. 
     Compared to  FIG. 5A , the example in  FIG. 5B  differs by the sensors S 1 -SN being integrated in the PED  12 , and the example in  FIG. 5C  differs by the analysis module  22  being integrated in the PED  12 . Otherwise, the description of  FIG. 5A  is equally applicable to  FIGS. 5B-5C . 
       FIG. 6  is a block diagram of an analysis module  22  in accordance with an embodiment. The analysis module  22  comprises a statistical analysis module  60  which is configured to implement step  42 A ( FIG. 4 ), a profiling module  61  which is configured to implement step  42 B, a reporting definition module  62  which is configured to implement  42 C and outputs [S] P , a UI configuration module  63  which is configured to implement step  44  and outputs DP, and an alarm configuration module  64  which is configured to implement  45  and outputs AP. As understood from the description of  FIG. 4 , the profiling module  61  may retrieve user profile data from a user profile database  52 A and aggregated data from a population database  52 B. 
       FIG. 7  is a block diagram of a PED  12  in accordance with an embodiment. The PED  12  comprises a communication module  70  which is configured to establish a communication channel, by wire and/or wirelessly, with the sensors S 1 -SN and the computing device  50  ( FIGS. 5A-5B ), and optionally with the computing device  14  ( FIG. 1 ). The PED  12  further comprises a control module  21 , a display module  23  and an alarm module  24 . The control module  21  comprises a measurement controller  71  which is configured to implement steps  40 - 41  ( FIG. 4 ) by retrieving sensor data, computing measurement values of [S] T , and transmitting the measurement values to the analysis module  22 . The measurement controller  71  is also configured to implement the monitoring phase  40 C (steps  48 - 49 ). The control module  21  further comprises a reporting controller  72  which is configured to implement step  45  by configuring the measurement controller  71  to perform the monitoring phase  40 C in accordance with [S] P , a UI controller  73  which is configured to implement step  46 , and an alarm controller  74  which is configured to implement step  47 . 
       FIG. 8  is a diagrammatic representation of a machine  80  that may represent the PED  12  or the computing device  50 . The machine  80  comprises a communication module  70  defining one or more interfaces for data communication in accordance with any suitable protocol or protocols. The machine  80  further comprises one or more processors  81 , e.g. a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), a field programmable gate array (FPGA), or any combination thereof. The machine  80  further comprises system memory  82 , which may include computer memory in the form of volatile and/or non-volatile memory such as read only memory (ROM), random access memory (RAM) and flash memory. The memory  82  may store computer instructions  83  (e.g. software or program code) for causing the machine  80  to perform any one of the methodologies discussed herein. The instructions  83  may be supplied to the machine  80  on a computer-readable medium  84 , which may be a tangible (non-transitory) product (e.g. magnetic medium, optical medium, read-only memory, flash memory, digital tape, etc) or a propagating signal. When executed by the processor  81 , the instructions  83  may cause the processor(s)  81  to perform any one of the methodologies discussed herein. In this context, it is to be understood that any of the modules described in the foregoing may be implemented by the processor(s)  81  executing the instructions  83 . However, it is also conceivable that one or more of the modules are implemented solely by dedicated hardware in the machine  80 .