WO2017009880A1

WO2017009880A1 - Customizable monitoring system for correlated vital signs

Info

Publication number: WO2017009880A1
Application number: PCT/IT2016/000168
Authority: WO
Inventors: Franco BOLDI
Original assignee: Xeos.It Srl
Priority date: 2015-07-10
Filing date: 2016-07-06
Publication date: 2017-01-19
Also published as: ITUB20152047A1

Abstract

Method of monitoring of vital parameters of an individual comprising the steps of: - · Matching to the individual body, of sensors and electrodes suitable for the detection of vital parameter concerning the cardiovascular and respiratory activity, temperature and physiological data such as activity, movement, and placement. • Processing of presented data in order to detect interpretable information on vital parameter and physiological information and sings obtained through the correlation of different parameter. • System for customized analyses on the collected data or on the correlation between multiple data. Analysis based on the comparison with threshold values or intelligent algorithms. Threshold values are automatically detected according to personal informations, medical history, level of activity, individual evidences, or manually by specialized personnel. Customizable system in which indices of activity and critical issues are the result of the combination of the most, vital parameters detected and clinical information /and registry individual data.

Description

Customizable monitoring system for correlated vital signs

Following the invention presentation, in such a way that the technical solution and advantageous effects of the invention are comprehensible.

The present invention defines a method and a system of monitoring the vital signs of an individual, in order to identify, for example, the presence of heart pathologies or, in alternative, to monitor the health conditions of an individual. A few systems for the survey of vital parameters of an individual, both in the healthcare and in the sports field, are currently known. In the sports field, such systems are principally related to fitness, and offers functionalities which are only restricted to the simple visualization of data; a different approach is used in the medical field, where data analysis systems for diagnosis purpose seem to be necessary. In this situation, it is however missing a system capable of living information in advance about the onset of pathologies and of obtaining in real time information about the condition of a supposedly hospitalized patient. The only duty left for a specialized operator is therefore the formulation of a diagnosis based on the surveyed conditions. Moreover, the analysis made directly by an operator, is very dispersive in terms of time. Also, the data analysis is often belated and does not allow a real time identification of pathologies. In the case of heart diseases, the software used for the readout of vital traces (holter, loop recorder...) offer systems for the identification of atypical beats, or of abnormal conditions, by in terms of similarity.

The level of reliability of such algorhythms is however insufficient in terms of guaranteeing a fully reliable system; more than 70% of the detected events seem to be, in fact, false positives. Moreover, the current software shuts out any way of data profiling. All the information needs, in fact, to be compared and analyzed considering the specific person. Therefore, in absence of a dedicated software, such operations have to be performed manually. Finally, the present systems, do not offer any strategy that allows the correlation of different data. These are most frequently managed and analyzed one by one, whereas often it would be better to analyze such data in combination one another, preferably according to activities performer and physical conditions described by the overall clinical history of a person. What could produce an alert state for a person in specific conditions, can be absolutely normal for another patient and therefore irrelevant for diagnostic analysis purpose .

The aim of the present invention is that of offering a method based on an individual vital signs monitoring system, capable of identifying the onset of critical events before their manifestation, using strategies of personalization, specific for the user and of correlating different data typologies. Such strategies include instruments capable of showing only the data portions that are relevant for a diagnostic purpose, without considering the invalid records (artifacts) and using such information to determine malfunctioning in the use of the system itself.

The purpose of such methodology is the simplification of the management of critical situation for a professional, who, from now on, would not have to examine a huge number of data, therefore focusing his attention only on the situation considered truly significant. An example of such system can be the surveillance of families of pathologies (such as nocturnal apneas) in which the number of accesses is bigger than the available resources dedicated to the management of the pathology. The system will therefore have, in this specific realization, the function of screening and selecting the patients in advance, in order to allow a better management of resources and time. This can be applied in the screening activities for those pathologies in which the symptoms are extremely widespread and for which the analysis of all the suspects would saturate the available resources of specialized centers.

These and other aims of this invention are pursued with a monitoring method, according to claim 1, and with a monitoring system according to claim 16. The depending claims describe the preferable forms of realization of the invention. According to claim 1, a monitoring method of vital sign, which is personalized for each individual, includes the phases of:

Association of sensors (named electrodes 101), dedicated to the collection of electric impulses, generated by the heart electric activity, giving information about the biometric data of a person (HR, RR, BR, ECG . . . ) , to a wearable device 103.

Connection of such electrodes to an electronic device capable of elaborating impulses in order to achieve data that can be interpreted by a machine or an operator.

Collection of data related to body accelerations and rotations and temperature, through sensors mounted within the electronic device.

Collection of the information user-specific, such as anagraphic data and/or medical history 401.

• Definition of a set of rules and analysis that can be applied by the user, according to a specific user- profile 402.

Combination and correlation of different typologies of data in order to identify new data from those dependent ones, or also conditions that can occur varying input data.

· Elaboration and application of algorhythms FIG 5 on the collected data, compared to the selected user- specific analysis, and in specific conditions. The analysis and application of algorhythms will therefore be dependent on a specific user. Also the parameters used within the algorhythmic 403 301 processes will be user-specific, in order to achieve a profiled and customized system.

· Activation of second level analysis on the data correlation, following the evidences retrieved with a previous analysis 403.

The system therefore allows, following analysis, to give alerts personalized for each user. The personalization is available starting from choices made by qualified operators or algorhythms that, automatically, are capable of evaluating a situation as risky for the normal state of health of the user.

In a first phase of analysis, the collected values can be compared with threshold values 404. Such threshold is user-specific and preferably based on the medical history and the physical and anagraphic characteristics of the user. Normally, in case these values are exceeded, the system is capable of automatically generating an alarm. Such alarm can be customized according to the level of criticality. The criticality level will be indicated according to the gravity of a situation, identified by the variance of a data in the admissibility range, intended for the specific user. After the alert signal, the management of the criticality can be customized, for example how will the user be informed of this event (phone call, acoustic signals, direct signaling to operators) . The correlation between data shows several advantages in the field of the identification of criticality, offering more accurate analysis, not only on a single value, but also on a plurality of values. In fact, a few pathologies are often caused by several factors, such as pulmonary, heart, pressure or brain data. As an example, the identification of nocturne apnoeas is verified through the analysis of pulmonary and heart rate, chest and eye movements, pulmonary fluxes, oxygen saturation and symptomatic evidences in the patient.

Generally, in the main analysis, correlation and comparison based on the specific level of activity are performed 405. In a preferable form of realization, the level of physical activity in an individual is identified through a combination of two or more vital parameters, detected by the sensors. In the realization of the system, the same level of activity is personalized according to the condition of the user and of the environment. For example, an elderly, as a patient in post-traumatic rehabilitation, will show coefficients that are different than those shown by an athlete in shape. Such coefficients will contribute to a different evaluation of the level of physical activity identified by the system.

Preferably, the level of physical activity of the individual is obtained through the combination of at least one of the heart rate, pulmonary rate and acceleration detected values, and also of customizations obtained by the knowledge of the clinical history of the patient, of his age, sex, race, up to specific characteristics that can be inserted by the user himself or by specialized operators, such as clinical information and milestones. Optionally, the determination of the threshold values related to physical activity is automatic or continuous, in set intervals of time. The determination of the level of physical activity is can be retrieved not only by exceeding the data-specific threshold values, but also by complex logics based on technologies capable of associating a data degree of belonging to a specific category .

In a beneficial form of realization, the determination of threshold values for the esteem of the level of activity occurs when we perform a phase of training in which the vital parameters are detected for a certain amount of time; on the base of the collected evidences and on automatic learning algorhythms, it is capable of automatic configuring.

Beneficially, the data of the vital parameters that generated an alert signal, are sent from the elaboration device to a server, in order for the signal to be analyzed by specialized personnel.

Amongst the critical condition that can be identified through software, there are alerts dependant on single parameters and on combinations of parameters.

Concerning alarms derivable from the ECG and derived data (heart rate, RR variance) :

• Tachycardia or bradycardia events

• Heart arrhythmias, Bradyarrhythmias , Tachyarrhythmias

• Pathologies derived by heart arrhythmias, atrial and ventricular fibrillation, bundle-branch blocks, ventricular PVCs .

Concerning the pulmonary wave values:

• Events of hyperventilation and hypoventilation;

• Volumes of oxygen and/or inspired air insufficiency Concerning the temperature values

• Fevers, anomalies of temperatures according to the surrounding environment.

Concerning the values of acceleration and rotation:

• Survey of fall

· Number of steps

Concerning the data about position:

• Exit from pre-determined areas.

As previously explained, the system is moreover capable of combining different data one another, in order to detect alerts concerning different physical conditions connected to medical and sports activity. This way, it is possible to deduce information such as stress index and injury time. Through the correlation of more than two different data, it is possible to detect events such as :

• Nocturnal apnoeas: such condition is identified considering both the data about heart rate, pulmonary rate, position and body acceleration. Specifically, an alert of this kind must be detected in the condition in which the system classifies the activity as "sleep". In such activity, the number of apnoea episodes is evaluated through the evaluation the pulmonary rate and the pulmonary wave. Therefore, only the episodes of lack of breathing longer than 10 seconds are numbered. In case of a number that is less than 5, the system does not classify episodes of nocturnal apneas.

In case the number is included within 5 and 15, the event is classified as light, within 15 and 30, moderate and above 30, severe. The presence of arrhythmias in the heart rhythm, observed through the ECG, allows to specify further the degree of identified alert. Also, the system includes in the evaluation, the case in which the user had previously shown events that indicate the presence of apneas, strokes, ventricular insufficiency and other pathologies .

• Level of stress and sudden-onset sleep: in the identification of this condition, data about RR variability and level of activity are evaluated at the same time. Through the combined analysis of the ways in which heart rate varies, related to the activity state of a person, and evaluating the behaviours of the sympathetic and parasympathetic nervous systems, it is in fact possible to establish when a user is about to reach physical and mental exhaustion. This must be correlated to the level of activity, in order to identify a critical situation only when this represent a true danger, for example, not during the normal sleep.

To such examples of detection, preferably following the identification of critical situation shown by the analysis of the single data, it is possible to make a second kind of analysis, more focused on the identification of pathologies in the ECG wave FIG 5. These analysis evaluates mainly the variation of PQ and ST traits, as shown in FIG 6, anomalies in the P, Q, R, S, T waves of the ECG and finally possible absences. It is also important to consider the definition of different strategies of data analysis. For example, in case of particular pathologies, specific analysis focused on improving the quality of the analysis itself, is performed.

Other possible forms of parameter correlations occur in:

• Infections: increase of temperature and heart rate

• Faintness: decrease of the heart and pulmonary rate, peak of acceleration indicating falls.

The same value of activity, used as additional data for the determination of critical thresholds, in different cases is the result of an analysis of correlated data. In general, the level of activity is identified through the use of accelerometers that give information about how the user is moving. Preferably, the system described in the present finding, uses the accelerometer data as an indication, related to the force to which the user is subject, a pedometer system, to evaluate how much the user is moving, correlated to a system of position evaluation. Above such data, to evaluate the level of activity, heart and pulmonary rate values are used. In detail, if the pulmonary rate detects more than 30 acts per minute, there is an index of high activity, from 20 to 30, moderate, from 10 to 20 light. The same occurs concerning heart rate: the activity is indicated as light up to 100 bpm, moderate from 100 to 140 bpm and high above 140 bpm. Of course, all the data are strongly connected to the individual, in particular to the values of age and sex and weight.

For example, the detection of vital parameters for a 30 years old not trained man who performs a moderately intense physical activity (10 km/h on tapis roulant) , shows modifications in the ECG path in terms of acceleration of heart beats and depth of the pulmonary wave (breathlessness ) . The variations are represented in the ECG as a dilatation (narrowing) of the synusal wave, with a subsequent narrowing of all the included fields. The values of heart rate during an activity that can be called elevate for the user, are set at about 40 acts per minute. In alternative, the same training conditions can be defined moderate for a professional athlete of the same age. In this case, the modification of the ECG and the pulmonary wave paths are noticeably mitigated, and the values for the user set between 150 bpm and 30 pulmonary acts per minute. On the other hand, for a patient affected by heart problems of the same age and sex, such rate values result troublesome and can cause the detection of a problem, therefore generating an alert. As described in the example, there is a variation of parameters based on the value of physical activity. Such variations are even more stressed when it comes to variations connected to parameters such as age, sex, race and physical condition. In conditions similar to those of the aforementioned exercise, a non trained 40 (instead of 30) years old man, should be showing a higher heart rate. Similar considerations can be made for several conditions such as in a resting state.

• High level of activity: heart rate higher than 110- 120 bpm, (plus factors connected to age and body) , pulmonary wave >25 breath per minute, a medium to high acceleration (depending on the type of activity) , temperature which is 2 degrees higher than the standard value .

• Normal level of activity: heart rate included between 65 and 110 bpm, pulmonary rate included between 12 and 24 breaths per minute, low acceleration, normal basal temperature (36-37 degrees) .

• Low level of activity (resting state) : heart rate lower than 80 bpm, pulmonary rate lower than 16 breaths per minute, no acceleration, temperature that is lower than the basal value (factor of 0,2-0,5) . In a resting situation, can be also useful to consider the position of the body, in order to establish the level of activity .

In one of the realization forms, the system is capable of setting, through the readout of data, a degree of belonging for all of the levels of activity. It is particularly relevant in the context of realizations of intelligent analysis based on non boolean logics, such as 3 values metrics, fuzzy logics, probability theory, by which means a certain value can be considered as belonging to more than one different categories at the same time.

The values used as threshold, for which it is possible to perform analysis, can be also subjects to modifications derived from the measurements of the sensor itself. The number of data collection can in fact indicate a different severity in the patient's biometry, for example in the case he showed more episodes belonging to a specific pathology.

In general, the monitoring system includes:

• A number of sensors dedicated to detect vital parameters such as the electric activity of the heart, the heart rate, the pulmonary rate, the temperatures, the acceleration values and eventual additional data such as saturations, galvanic reactions of the skin, pressure or alternative data detectable by external devices .

β Instruments of elaborations, capable of receiving electric signals concerning vital parameters coming from the sensors, and of performing elaborations of said signals in order to achieve representative data for vital parameters . " Instruments of data analysis, capable of comparing the data obtained from the elaboration instruments, with threshold values, set according to the characteristics of the individual such as age and sex, his health state, the level of physical activity he is performing and, in case such data exceed the threshold values, of gene rating an alert signal.

In addition, it is necessary to perform a customization that keeps record not only of the said physiologic parameters, but also of the anagraphic data. The clinical iter of a patient determines, in fact, a variation of the standard parameters. A user who underwent heart surgery, will presumably show different threshold values compared to a healthy user. Furthermore, data such as profession, lifestyle, and location can be useful to determine variations in the analysis algorhythms. Basically, a person who is used to specific heights, will show differences in the pulmonary trait, people with a stressful profession will show values typical of higher rate and reduced RR variability. The same examinations performed in the medical field, keep record of the physical characteristics and the medical history of an individual. Other example, a patient with pacemaker will show particular traits concerning his parameters, different from those of a person without pacemaker.

In the context of the present invention, with the term "elaboration" of signals we generally mean the sum of the computational operations performed on the electric signals detected by the sensors, and on the values detected by the electronic components, for example the application of filters 501 502 and transformations 503 in order to delete noise from such signals and/or extract significant parts, in order to achieve representative data of vital parameters, useful for a following analysis 302. Such phase is propaedeutic to the phase of data analysis, for this must be necessarily performer on data that are non-conditioned by external noise, or generally identifiable as artifacts. In the context of the present invention, with the term "analysis" of data, we mean the application of algorhythms, such as those applied on the physical condition of the user, based on the comparison of such data, eventually combined one another, as described later, with threshold values, or, in alternative, with instruments of non-boolean logics, with algorhythms of correlated evaluation of data, capable of setting the presence of a possible criticality In a preferable realization form, the sensors are integrated in a garment, or an accessory, wearable by the individual who is the subject of monitoring.

In one of the preferable forms of realization, the elaboration instruments include a dedicated electronic board, incorporated within the wearable garment or accessory.

In a preferable realization form, the customized data analysis software is installed directly on the electronic board. In different realizations, on the electronic board can be installed only a few specific analysis software functionalities, while others can be placed on gateway, server, or alternative devices.

In a preferable realization form, the monitoring system include also a mobile device with gateway function, for example a smartphone, a tablet or a smartwatch 202 203, or in alternative a device capable of interfacing with the protocols of the electronic device.

In order to allow the real time active communication of data, the electronic system is provided with components that allow the exchange of information via wireless FIG 2. Such communication systems can in alternative be based on bluetooth technology (classic or low usage versions), such as IFI technology, data connection 3g/4g. The presence of such modules, allowing real time traffic data, gives the possibility of distributing the analysis algorhythms both on applications for mobile and on remote servers, allowing, and this way, to optimize the performances of the system and to distribute the calculation effort that would otherwise be demanded only to the electronic device.

In one preferable realization form, the system includes also a server 204 that is operatively connected to the dedicated electronic board, or to the mobile device, if present, or to both.

In such realization form, the analysis instrument can be also intended on the server and/or only on the server. The server is capable of recording and/or visualizing the data obtained by the analysis instruments and to allow a control of such data performed by specialized personnel. Preferably, the device communicates with the dedicated board, and has mainly functions of analysis and/or visualizations of the results of the data analysis, and of interaction with the server.

Summing up, the instrument of analysis are peaceable in three different devices, in a non-mutually exclusive way: in the electronic board, in the gateway (smartphone, tablet, smartwatch or similar dedicated devices), in the server .

The instrument of analysis can be realized with hardware, software or rather a combination of both, devices. In a preferable form of realization, the instruments of analysis are implemented in software/firmware way, distributed on the different components of the system. The transmission of data to the server can happen in two cases: in continuous streaming or following the detection of an anomaly. When an anomaly is detected, the system is capable of intercepting such situation and starting the entering of data.

Furthermore, in one realization form, there is a data saving system that can be placed on the server or on the mobile device.

The method and the detection system here described allow a continuous remote monitoring of the vital parameters on the users, being them healthy or with heart problems

(heart irregularities), or chronic problems (for example people affected by Alzheimer's) . Especially in this latter case, our purpose is to offer an assistential monitoring of the patient, capable of recognizing which are the critical and dangerous cases for the patient's or third parties health 406. In such occurrence, the system is capable of instantaneously reacting, indicating the type and level of criticality, and of giving, to specialized operators, a system dedicated to the visualization and control of the detected values, offering therefore the possibility of managing anomalies and potentially risky situations.

Other than this main function, the system offers also instruments for long, medium and short time analysis of the detected data, and for integrations with traditional systems of medical records management 401.

In a preferable form of realization, the system and the detection method here proposed, use a number of sensors associated to the body of the monitored user, an electronic board dedicated to the elaboration of signals detected by the sensors, a mobile device connected to the dedicated electronic board and a server, for example a web portal. The mobile application is represented by a software that, through the use of a wireless data transmission protocol, for example bluetooth 3.0 or bluetooth LE or data connection or IFI connection, is capable of receiving data coming from any kind of configured sensor that functions according to such protocol. The choice of using multiple technologies for the data communication, offers the possibility of optimizing the energetic usage, or the quality of data, according to the developed verticalization, with the aim of keeping the system performances stable. The mobile application can be installed on any kind of mobile device with gateway function, for example a smartphone, tablet, smartwatch provided with compatible operative system, for example Android or IOS, or on a dedicated microprocessor, included, for example in a wearable garment or accessory.

In one of the realization forms, the system includes both the dedicated electronic board, with main functions of data reception, elaboration and analysis, and a smartphone or tablet 201 202 in communication with such board, and having mainly function of data analysis visualization and interaction with the server.

In one realization form, the mobile device also performs data analysis, signals possible critical situations to the users, manages such criticalit ies , memorizes data in order to guarantee persistence and to present a history of such events and also, possibly, to send data and criticalit ies to a server (for example a web portal) where these can be managed and analyzed by specialized personnel 204.

Hereafter we show more specifically some of the functionalities available for the individual who uses the application. All of these functions are completely configurable by a system administrator or by the user himself, through appropriate interfaces.

The system allows the user to register to a web portal through the application 303. This way, an instance of the user himself will be created on the dedicated server 408 304. The inserted data will complete what will become the user ID. For user ID we mean anagraphic and general information for each user registered in the system.

Also, the system allows to perform automatic integration to the ID, based on the collected evidences. Following data collection, some reports will be added to the user ID, giving therefore more specificity to the profiling. The system will be customized on the same previous user detections. Such functions are part of the automatic learning of the system and of the user customization. A patient or a doctor can use to visualize the data related to the vital parameters from more than one different source (for example one sensor for heart rate and another one for pulmonary rate) . There is, therefore, the possibility to associate to one owns application a variable number of sensors 305. This possibility is realized through an appropriate panel that illustrates which are the available sensor along with information about each.

In order to obtain significant analysis for each single user, it is necessary to configure the specific parameters on which the possible criticalities can be evaluated. The configuration of such parameters can be performed in the following ways: • Manually by the patient: the patient through the setting panel can insert his standard health values. Such solution is not recommended, considering that a user is not supposed to know the values related to his biometry, and could therefore compromise the correct system functioning;

• Manually by the system administrator (professional operator or doctor) : a doctor knows the normal vital parameters of the patient he is in charge of. He is therefore capable of defining intervals of normal state of health and thresholds of parameters that, if exceeded, can cause alerts. Such function is made possible through the server, and the data are retrieved at the moment of the access in the system;

° Automatically by the system 409: the system is able to learn automatically the correct threshold values for an evaluation of criticalities . As described more specifically later on, some of these parameters are determined by the detection of the user's physical activity, through the combination of two or more vital parameters retrieved by the sensors. According to the level of physical activity of the user, the system can determine threshold values to be attributed to path parameters characterizing the ECG path of the heart electric activity of the user during a physical effort. Other parameters can be determined after a training phase. The patient can therefore perform a phase of training for using the system, through which the system itself will manage to set the parameters acceptable values, capable of determining potentially dangerous situations

Once associated all the sensors, a user can see in real time the vital parameters made available by the configured sensors. He can moreover configure, amongst the set of available parameters, only those he wants to visualize on the mobile device.

If the analysis algorhythms detect a dangerous situation for the user, the mobile device can show a graphic alarm, in order to inform the user of the critical event. Beneficially, at the same time an alert signal can be sent out to the server. In one of the realization forms, moreover, once detected a critical situation, the system is capable of reacting to this, performing operations such as message sending or automatic calls to public entities such as emergency, or also, information dispatch to numbers previously configured by the user himself. It is therefore possible to configure a series of actions that the mobile device can automatically perform, in occurrence of a critical situation. The user, through an appropriate configuration panel, can program the management of the alarms by the mobile device (telephone numbers, e-mail address or different communication channels to be informed in case of danger) . All of the data detected by the sensors are saved in suitable database, internal to the mobile device, and dispatched to the server, if there is a data net connection or a WiFi connection. The system also allows the visualization of the history of collected data, filtering them for data type and temporal windows. A patient or a doctor can therefore witness the causes of a disturb in the vital condition, with the aim of a deep research or analysis. The use of data history can be a very useful instrument to speed up the phase of pre- hospitalization/reception, simplifying the work or retrieval of an individual parameters.

The system allows, through the use of a suitable software the possibility of a complete management of what is identifiable as user ID. Such ID is characterized not only by the anagraphic data of the patient, but also by a series of behaviours or events that are useful for the overall knowledge of the system user. Such information can be added manually by a user, or preferably, by a specialized operator, or, in alternative, automatically after interfacing with the electronic record. It allows therefore to keep trace of the entire clinical history of a patient and to use the collected evidences in order to improve the analysis system. Aside the usefulness in the analysis field, the system allows to collect in a univocal way all the data related to a patient, in the context of an import-export functionality.

Such function represents a useful instrument that allows not only to have records, anytime, of the real conditions of a user, but also, at the same time, to use the anagraphic and healthcare data in order to profile a user in specific categories 401. Such customization allows to perform more accurate analysis. From the medical history can be for example deduced if a patient underwent surgery or not. In such case, the user can be classified as belonging to a class of users showing specific parameters, on which specific analysis will be performed. Subsequently, the values determining an alert will vary. For a cardiac patient, for example, the heart rate should not exceed specific values, as levels of activity.

As previously explained, the vital parameters of an individual vary not only according to his health state or to the information related to his age and sex, but also according to the physical activity performed by the monitored person. Through the use of sensors associated to the body through the wearable device, it is possible to establish if the person is resting or in activity, through heart and pulmonary data and also through data about acceleration and rotation. Thanks to this index it is possible to customize the analysis according to the current activity

As an example, for a user aged 25 years old, it will be probably detected a heart rate included between 60 and 100 bpm, in condition of resting, between 100 to 140 bpm in case of moderate activity, above 150 in case of intense activity. The expressed values, strongly vary according to factors such as the anagraphic, the physical conditions user-specific. For a person aged 40 years old, a heart rate of 100 bpm can be normal. All of the data are strongly dependent on the physical conditions of the user (here the need for an entirely customizable system) .

Another important example regards the data collection during sleep, period in which the values noticeably vary if compared to the rest of the time. This way, the analysis will be specific, not only on the patient, but also on the intensity of the effort that the patient is performing. A specific and customized system allows to perform punctual analysis, restraining the number of false positives (for example, in case of physical activity, it is normal for the heart rate to exceed thresholds of values that, in different conditions, would identify an abnormal situation) . All of these parameters related to different states of activity are configurable exactly as the general parameters customization, previously illustrated.

Considering that it is possible that a mobile device does not have the possibility of connecting to the net, the system has been designed in order to function on duplex modality, both offline and online. In case of absent connection, and therefore of inability to connect to a server to dispatch the data, these are saved within the electronic device. When the connection is available, the data dispatch becomes operative again. According to specific realizations, data can be recorded within the mobile device also once dispatched to the server, instantaneously deleted or deleted after a set period of time. In such condition, the customization is based on the typologies of analysis to perform.

For each user there is therefore the possibility of choosing which analysis are to be performed in offline mode and which in online mode. For example, in case of a runner, it will be preferable to activate a set of analysis which is specific for that kind of activity, for example for the recovery time. Preferably, it is supposed that the analysis performed on such data happens offline, without contacting directly a server and without being dependent on the presence of a connection. In such situation, data can be visualized at the end of the activity. Later, analysis algorhythms for monitoring the performances directly offline and for identifying which working areas to intensify can be applied. This way it is furthermore possible to distribute the analysis server side and client side, in order to optimize the effort demanded by the system.

Through a suitable section of alarms, an operator can constantly and preventively informed of all the users whose parameters identify a criticality. To the operator will appear a list of those individual that are in possible danger. In the list it will be indicated a level of criticality, based on the typology of alarms generated and on the typology of alarms (heart, pulmonary or in alternative other typologies). Preferably, to each type of alarm, is associated a level of criticality automatically associated based on the collected evidences. Also according to the level of criticality, it is possible to apply also customizations based on the correlation of different data. For example, a patient affected by faintness who shows symptoms of loss of consciousness, will show a level of criticality higher than that of a person who is not affected by such pathology. In alternative, for a user, the pulmonary rate data can cause an increase of the level of criticality for a pathology, but can be completely unimportant and negligible for another.

The selection of a user allows the operator to manage such data; he will be able to visualize all the alarm activated, ordered for example in terms of severity and date. He can furthermore visualize the vital parameters related to the user examined in real time, in order to offer a continuous monitoring. According to the launched alerts and to the real time conditions, the operator will be able to manage the criticality; he can for example call the user to make sure of his conditions, inform the emergency or competent authorities 407.

Considering the extreme diversity of parameters, according to the considered user, the system needs customization functionalities. The threshold parameters, namely the levels causing the alerts, vary according to the person. A cardiac patient, will show different values compared to an athlete. In order to offer a system that is as effective as possible, through the web portal it is possible to manually modify the parameters on which the analysis are performed. The doctor in charge of the patient will be able to set, according to his real needs, all of the correct values. Furthermore, in order to offer a better visualization service, it will be possible to perform selections and filters to better analyze the detected values. Above this customization, it is possible to perform also automatic customizations for the user, based on the detected survey.

Other than the user's vital parameters customization, it is possible to set a degree of customization also on the electronic devices themselves. Through such customizations it is possible to set acceptable intervals of values for the detected data, in order to establish if the sensor is correctly functioning in a specific context. The customization of the sensors employed, allows furthermore to set the parameters necessary for a specific realization. For example, in the realization for the identification of nocturnal apneas, the GPS localization is not necessary for analysis purpose. Such data would therefore cause only a waste in terms of dimensions and energy. The customization allows to optimize the performances, according to a specific realization. Moreover, customizations in terms of sampling frequency and of data dispatching are possible. In case of a continuous in time activity, it is superfluous to sample and dispatch data with high frequency, while it is more relevant to keep track of modifications, in case of variable activity in a short amount of time (for example running) .

The vital parameters of an individual vary according to the modifications of the activities performed. According to such activities, it is possible to perform different analysis. Considering that the activities are variable, it is possible to create through the web portal, different kinds of activities and to associate them to an intensity degree (for example high, medium, low) . According to this intensity level, the system will automatically set a number of standard parameters for each user, eventually modifiable by a specialized operator. In order to help the post-surgical training phase, a doctor can associate to a patient, some activities to perform for a set period of time. This way, he can remotely monitor the user, indicating which the possible mistakes in the duty performance are. The continuous remote monitoring function of the activities, is also useless in the field of remote personal training, namely, in case a user would like to be helped by a personal trainer in charge of remotely following a user, for sports purposes. According to the vital parameters, a trainer can give suggestions about performing exercise or correcting mistakes.

All of the detections performer are not only visualized but also memorized. Such functionality is extremely useful for the following reasons: * Data research: a huge amount of data can allow to perform researches aimed to identify, in case of illnesses, the various causes for the pathologies. In case more people show similar behaviours, it is possible to observe which are the possible common reasons, in order to establish causes and symptoms in specific conditions 406.

• Analysis of criticalities : in order to manage in the best way a criticality, it is necessary to keep track of the conditions of a patient before the onset of the event itself. In the sports field, for example, the ECG shows typical alterations which, if present in a non- trained user, can represent criticalities to keep under surveillance .

· Post criticalities analysis: it is anyway necessary to keep track of the conditions of a user, after a critical managed event, to control the onset of complications or relapses. In the customization it is also essential to take in consideration the clinical situation of a person, mainly if the person is in both sports of clinical rehab phase. In order to guarantee such functionality, the system can keep track of all the parameters detected by the sensors, according to two possible modalities:

· Massive information saving 410: all the data are sent to the server and memorized in time windows. Whenever a sensor is properly connected, the data begin to be dispatched (opening of the time window) and at the deactivation such window is closed. The system keeps also track of the moments in which the system goes offline and online.

• Saving of the criticalities only 411: the system opens new time windows only in case of detection of critical situations. Thus, only the vital parameters collected one second before the beginning of the criticality, and those collected one second after, will be saved.

Through the analysis of the history, it is possible not only to visualize data, but also to filter them. It is therefore possible to set basal filters according to the data typology, to visualize only certain kinds of data and to select them in order to observe only those belonging to various time values or intensity (for example to visualize only the heart rate above the 120 bpm) . The analysis of the history offers mainly functions of analysis that would otherwise be unavailable in real time. There are in fact many pathologies that, to be identify, require the evaluation of long periods of time and of a number or occurrences for a certain event. For example, the atrial fibrillation, which is the number one cause of strokes in the world, if identified in time can be cured with antiplatelet agents. Another example is that of the nocturnal apneas, for which a real time analysis can identify the single event, while the analysis of an entire night can identify the pathology that is affecting a person. A further information that can be registered by the system is the position of the monitored user, for example using GPS, WIFI, data net or other technologies. This, in order to establish, in case of indispositions, where is a user and therefore to assist him. It is possible to see directly on the map, the real time conditions of the vital parameters for each user of the system.

Though in a restricted way, the system described in the present finding can offer customization connected also to the environment surrounding the user, detected through the use of GPS mode, integrated in combination with temperature. For example, at lower temperatures, there is a substantial slowdown of the heart rate, related to an increase of the number of pulmonary acts. Also height is a factor that causes variations in the biometry of a patient, causing an increase of the stress- related conditions; for example helicopter pilots, who, exceeding a specific threshold, must behave in a proper way (oxygen etc.).

Hereby, we will describe the devices that are part of the monitoring system.

According to the specific realization of the system, the instruments of signals acquisition, elaboration, analysis, dispatch and memorization of data, can be centralized or distributed on electronic devices, "general purpose" devices ( smartphones , tablets, smartwatches ) , dedicated devices, owner or in cloud servers or a combination of such solutions. We hereby propose different solutions for the main realizations of the system.

• Acquisition of biometric data: the acquisition of data is performed, in all the system versions, by using electronic devices 201. Preferably, the software system is preconfigured for the data detection, in the format developed by the HW realizations, described in the explanation about the BIOX electronic device FIG 1, 2 , but it can be configured for the detection of external impulses, in order to be integrated with the instruments used in hospitals.

• Elaboration of data. It is beneficial a realization in which the biometric data elaboration is realized within the electronic device. The electronics is in fact in charge of deciphering, through the use of suitable components and modules, the electric impulses and the modifications of physical behaviours, codified by the modules themselves, in order to relaunch as outputs a valid and evaluable data.

· Data analysis: in one preferable realization form, the system of data analysis is not centralized in a unique component of the system itself, but rather, it is distributed. The distribution of the resources, not only brings important advantages in the computational and usage context, but also in the functional context. In terms of computation, it is in fact computationally heavy to perform complex analysis involving the microprocessor, especially if such analysis require a high amount of data. Furthermore, the distribution allows to establish which procedures are preferably to perform in terms of FW and which in terms of SW. It is therefore preferable the device to be capable, mainly for the use of the system in standalone mode, of performing first level analysis such as the calculation of related values and the identification of alerts on a single data. It is preferable for a "general purpose" or "dedicated" device, such as a smartphone, tablet or smartwatch 202 203, thus provided of good calculation power, to be in charge of performing analysis based on the correlation of different data. It is preferable for a suitable server to be in charge of the execution of the more computationally complex algorhythms, or of algorhythms requiring the higher number of data to be elaborated for analysis purpose. This is mainly due to a non exceeding restriction of physical memory on which analysis are performed. The case of species can be deduced by the identification of atrial fibrillation in strokes prevention. In such case, in fact, a long observational periodi s required. This would compromise the performances of a smartphone, obstructing its memory.

• Dispatch and memorization of data: the dispatch and memorization can be managed in different modalities according to the represented vertical. According to the memorization of data, similarly to analysis, the memorization is binded to the capacity of the device. Specifically, an electronic device will be able to memorize a number of data in a restricted period of time. As a remedy for such limitation, there are the data communication systems. Such systems allow to dispatch the detected data, via wireless protocol (WIFI or BT or data net or other) directly to a server or an intermediate device dedicated to perform further analysis and eventually dispatch data to the server via similar wireless protocols.

The choice of using large spread instruments, such as smartphones, is beneficial, in particular when it comes to the de-hospitalization of the patient, the post- surgery training phase, or the normal standard activities monitoring.

In one preferable form of realization, the sensors 101a, 101b, 101c, lOld associated to the user's body are capable of detecting information about the electric activity of the heart (heart rate, ECG, level of activity) , about the general conditions of the individual (temperature, blood pressure, glycaemia, saturation, position, etc.), and about the position, speed, acceleration. Such sensors are integrated within garments or accessories wearable by the user, for example bands, chest bands, t-shirts 105 (of various sizes and kind), trousers, socks or bracelets.

Later on we will describe some example of preferable realizations of the sensors and garments in which the sensors are integrated FIG 1. Now we describe the algorhythms that allow the monitoring system to identify precociously the main heart pathologies.

As said, the precocious identification of heart pathologies is a process generated by the retrieval of vital parameters of a user and it becomes an accurate analysis of each one of these values, performing therefore customizations based on the typologies of data and user, according to race, sex, age and performed activity. The main analysis performer on data, at first, are related to the values of:

Heart and pulmonary rate, temperature, acceleration and tri-axis rotation, position, placement (outside from a specific area) . Following such analysis, other analysis can be performer, related to more complex data, mainly ECG, pulmonary wave and values derived by their decodification . In such phase the parameters can be related one another to achieve information such as energy usage and stress levels.

Later on, user and activity-specific analysis are performed. Finally, the long analysis system is performed .

• Analysis of ECG waves and complexes, in order to reach a diagnosis of a certain pathologies. Such analysis is mainly characterized by the evaluation of different ECG channels, correlated to the activity data, which is the number one cause of heart rate and clinical history modification .

The ECG FIG 6 is the graphic registration of the electric activity of the heart, represented by a sinusal wave, made of waves and specific segments.

The study of the ECG is useful to analyze:

• Rhythm alterations (arrhythmias)

· Alterations in the conduction and propagation of the electric impulse, which causes the depolarization of the heart muscular fibers

• The state of the heart and the heart alteration caused by coronary diseases (ischemia), by other diseases that strike the heart or that involve the heart (blood hypertension, heart insufficiency, and pericarditis) .

• Alteration of sleep (following correlations of other data typologies)

' Analysis of the depression states.

0 Analysis of performance, recovery time, training capacity

The ECG is therefore the registration of the differences of potential that are generated between different measurement points, time related. The ECG shows normally 12 different leads, defined as a perspective visual of the heart muscle observation, each one of which represents the angle where the heart electric activity is registered. Six leads are called limb leads, namely three unipolar and three bipolar. The other six leads are called augmented limb leads, namely three chest unipolar leads and three precordial leads. In order to make a diagnosis for specific heart pathologies such as arrhythmias, it is not necessary to detect specific leads. However, the choice of using standard leads allows to have a complete and valid visualization for such channel. Thanks to these leads it is possible to examine each aspect of the heart muscle in terms of electric activity. Some pathologies can be identified only through a multichannel visualization.

As shown in figure FIG 6 a normal ECG shows a series of positive and negative waves, indicated with the letters P 601, Q 602, R 603, S 604, T 605 to which U 606 and Delta can be added. The distance between two waves is called trait or segment and it represents a period in which there is no difference of potential.

In particular:

• P Wave: Atrial depolarization.

· QRS Complex: Ventricular depolarization.

• T Wave: Ventricular repolarization.

• P-R Segment: End of P wave, beginning of the WRS complex (the atria are entirely depolarized) .

S-T Segment: End of S wave, beginning of T wave (ventricles are entirely depolarized) .

• P-R interval: Time of atrial-ventricular conduction.

• Q-T interval: Time of ventricular depolarization and repolarization .

In order to identify different pathologies or abnormal behaviors or the user biometric, it is necessary to achieve the length of the indicated traits and the width of the waves. Of great importance is the evaluation of the QRS trait and of the RR distance, meaning the time between a beat and the following one. An analysis based on such values allows in fact to identify episodes of arrhythmias that are the basis for many heart pathologies .

Hereafter, we will show some examples of possible strategies for the detection of segments and waves in the ECG path FIG 5.

As previously explained, the identification of criticalities is often connected to the retrieval of width and length of the waves and segments within the ECG path. To clearly identify such values, several algorhythms can be used.

The first phase to identify all the necessary components is the individuation of the QRS complex, which can occur using the following algorhythms.

1. Readout of data characterizing the ECG path.

2. Use of filters generally at 25Hz to delete the noise.

3. Use of threshold values to identify the QRS complex (generally set at 70% width of the maximum and minimum value of the time window in analysis) .

In term of noise elimination, to be able to use a path and underline the events about the QRS complex, a series of filters and transformations are applied to the original values of the ECG path.

For example, on the ECG path can be applied:

Low pass filter .

e Band pas s filter .

Transformation of the first grade derivation. • Transformation of the square of values.

• Calculation of the integral.

Concerning the use of threshold values, in one of the realization forms, in the identification of the QRS complexes, two thresholds are identified:

"signalPeakThreshold" and "noisePeakThreshold". The first one identifies the R wave in the QRS complexes (a peak) ) ; the second one identify the possible peaks that are part of the noise. The system is open to customizations also within the calculation algorhythms; these customizations are however restricted to automatisms, unavailable for the user, dedicated to improve the functioning of the information collection devices .

The used thresholds are calculated and update iteratively, to keep up with the changes.

For each peak three possible situations can occur:

Peak >= signalPeakThreshold: the peak is recognized as belonging to the QRS;

· Peak < signalPeakThreshold AND Peak >= noisePeakThreshold : the peak does not belong to the QRS complex but can belong to another complex (P or T wave)

• Peak < noisePeakThreshold: the peak is ignored. Using such algorhythms, it is therefore possible to identify the moment in which the QRS complex shows up. It is therefore possible to calculate its length and intensity in millivolt. It is furthermore possible to calculate the RR interval, namely the time passing between the two QRS complexes.

The calculation procedure for the P and T waves begins with the elimination from the ECG path of the QRS complex. The first detected wave will be a type T, followed by a P and then again a QRS.

It is then calculated the gradient in each instant, in order to increase the signal of T wave. The gradient is an essential criteria for the identification, in fact the gradient of the signal is way higher in T wave than in P wave .

It is used a SVM (Support Vector Machine) that slides on set length windows on the ECG path and according to a set of morphologies included within the CSE ECG database This way it is possible to identify the presence or absence of a T wave and set its length and width. Then, the T wave is deleted from the ECG path, in order to obtain an ECG path without QRS and T wave. The procedure for the identification of the T wave is repeated to detect the last missing wave, through a Support Vector Machine sliding on the filtered path, comparing time windows with the P wave morphology included within the CSE ECG database. This way it is possible to identify all of the fundamental components to analyze on the ECG path .

The heart rate is a parameter that is directly detected by the module for the ECG signal elaboration through algorhythms specifically inserted within the firmware side. The sensor directly detects its value, expressed as an integer. It is therefore possible to proceed directly to a customized analysis. In a specific realization form, the heart rate can be obtained observing the QRS complex of the ECG path through the use of the following algorhythms FIG 5:

• Application of a median filter (also known as "in-filter") 501;

β Calculation of the baseline 504;

- Subtraction from the baseline of the result of the filtering with the in-filter 505;

• Calculation of absolute minimum and maximum from the result of the subtraction 506;

• Calculation of a threshold cut value according to the baseline and the maximum calculated 507;

· Subtraction of the baseline;

• Transformation of the entire signal in positive values 503;

Cut of the signal below the threshold value previously calculated 502;

· Calculation of the threshold peak detection value. The detection threshold is generally a percentage calculated on the minimum and the maximum values of the detected signal, within a time window used for the analysis 508;

· First derivation symmetric to the curve obtained after the cuts;

• Individuation of the peaks through the calculated threshold value; • Calculation of the R peaks 509;

β Calculation of the R-R interval, namely the QRS variability 510;

• Calculation of the heart rate calculated through decodification of the R-R variability 511.

According to the variations of the heart rate of a patient, it is possible to identify different anomalies in the correct sinusal rhythm, showing up during episodes of tachycardia or bradycardia. The possibility of identifying such episodes is given by the presence of algorhythms that evaluate the values of heart rate, user- specifically .

In literature, the values associated to a correct resting heart rate belong to a range between the 65 and 85 bpm, with an admissibility interval included between 50 and 110 bpm. Above said interval, tachycardia episodes occur, below such interval, bradycardia episodes occur. Further analysis on vital parameters can be performed considering the R-R intervals 607. Such data represent the evaluation of the distance between one beat and the following. The tidal variation of such value represent arrhythmic problems of the heart, namely the absence of a stable rhythm. Such variation is due to a reaction of different states such as pulmonary rhythm or eventual emotive conditions. Basically, a healthy individual shows a good degree of adaptability and therefore a good variability. The evaluation of such data is useful in considering breath and activity as related data. The RR variability is represented by a graph. On the graph is performer a Resampling, followed by a Fourier transform. Later on, the spectrum of power of the tachogram is calculated. The spectrum of power represents the components of the frequency of the tachogram, and it involves the essential information to finally reach an esteem of the balancing between sympathetic and parasympathetic nervous system. The Spectrum of power (in the frequencies dominion) represents the power of the frequencies included between 0.01 and 0.4 Hz. Thus, sub-bands of frequencies are identified: VLF (Very Low Frequency) frequencies included between 0.01 and 0.04 Hz, LF (Low Frequency) frequencies included between 0.05 and 0.15 Hz, HF (High Frequency) frequencies included between 0.16 and 0.4 Hz. Later on, the ranges of normal values of all these parameters are set:

Heart rate, standard deviation tachogram (SD) , Total Power, Power VLF, Power LF, Power HF.

The data is deciphered with the diagram shown in figure, where the sympathetic and parasympathetic system are correlated .

Further analysis on vital parameter can be performed according to the respiratory wave FIG 7. The figure shows the different morphologies of the respiratory wave according to the inspiration and expiration phase. A simple analysis that can be performed on these value is related to the absence of breath activity during the sleep. The evaluation of the graph can shows pauses in the normal rhythm. The analysis only based on the respiratory graph cannot be a valid evidence if not compared with different other parameter. The system is able to compare the ECG values as R-R variability with respiratory information. If the system will reveal a slowdown in the beat per minutes and an absence of signal, related with a low activity (from the inertial sensors) and a low temperature the system can identify the apnea situation and memorize the critical situation in order to be managed after a significant number of event .

Further variable to take in consideration in the customized analysis recalls the modalities through which the anagraphic information influence the data of a specific user during a specific activity. Furthermore there is an important variation of the suitable set of parameters according to the variation of the relative data, activity-wise. Hereafter, a list of a few examples showing how activity and anagraphic factors can influence the collection and analysis performer on the data .

The validation and analysis of data about training frequencies based on the interpretation of the heart rate is particularly interesting. Such values are extremely variable according to sex, age, activity. Concerning sex and activity, the maximum frequence (HRmax) that must not be exceeded is given by this rule: HRrttax (male) = 220-age. HRmax (female) = 227-age.

Such values may vary a lot, according to the type of activity. Based on the following formulation, it is possible, for example, to evaluate the optimal frequency that the user should provide during the training (training heart rate) : Training heart rate = (180b/min - age) + 5 beats for each ten years of age starting from the third decade of life. Finally, significant variation of the frequency occur according the intensity of a physical activity that the monitored user is performing, as shown in the following table:

This example shows a specific customization for the heart rate data.

In a beneficial form of realization, the calculation of the pulmonary rate is performer using a duplex algorhythms: through the heart rate obtained by the ECG, and through the pulmonary graph. With the term pulmonary graph we mean the graph obtained through biometric impedance analysis. The graph shows time on the axis of abscissae and voltage on the axis of the ordinates. The registration of the pulmonary activity offers a representation characterized by a sinusoid wave. To the pulmonary data identified by the raw data is applied the Fourier transform, on which are applied low-pass filters. A threshold value calculated according the minimum and maximum and medium values available, is defined. Once set the threshold filter, a detection of the peaks is performed, considering only the values exceeding said threshold. Then, the peaks and the distance between them are calculated. From this, the number of breaths per minute can be calculated.

Therefore, we can have both the data obtained via biometric impedance analysis, often influenced by movement, and that deduced from the heart rate. Comparing the two data, we can obtain a valid value, noise resistant, identifying artifacts and malfunctioning generated by the correlation of the two data. The correlation offers a system of major stability and signal-noise quality.

In order to offer an efficient instrument, the parameters that allow to perform analysis and through which an alert or a pathology must necessarily be customized. All of the values ranges, which, if not satisfied, cause an alert occurrence, must be different according to the type of user and activity performed.

The values can vary according to race, sex, and age and activity type.

In this occurrence, the system behaves as a virtual trainer, capable of offering suggestions based on the typologies of training of a person. Age QRS(sec)Complex PR R S HR

segmetn(sec) wave(mm) wave(mm) (frequency)

1-2 years 0.03-0.08 0.08-0.16 2-18 0.5-21 90-165

3-4 years 0.04-0.08 0.09-0.17 1-18 0.5-21 70-140

5-7 years 0.04-0.08 0.09-0.17 0.5-14 0.5-24 65-140

8-11 0.04-0.09 0.09-0.17 0-14 0.5-25 60-130 years

12-15 0.04-0.09 0.09-0.18 0-14 0.5-21 65-130 years

>16 years 0.05-0.10 0.12-0.20 0-14 0.5-23 50-120

In the following table there are a few examples of variation in the ECG complex, according to sex and activity performed.

Above such parameters, all the values can sustain important variations according to the clinical history of a user. Who underwent surgery or is affected by specific heart pathologies will show variations of the normal ECG path that must be considered for an accurate diagnosis. Hereafter, a few examples of customization based on the type of activity the user is performing. Personalization based on running

During the performance of an activity more or less intense, the frequencies of the complex in the ECG path change. Amongst these, for example, a variation of the QT trait. The analysis of such change allows to identify extremely critical events, such as long QT syndrome, which, mainly during physical activity, can generate episodes of sudden death. For a normal activity in which the intensity grows in line, this occurs:

Beyond such values, it is possible to detect a decrease of length of the R-R intervals, an increase of width of P wave and a reduction of the QRS complex.

Biking

During biking, an increase of the sympathetic tone occurs. This can be identified after shortening of length of the QRS complex up to 4,9ms. Furthermore, there is a reduction of the distance of QRS complex from the following R peak up to 2,4 ms and QT trait, shortened 1,3 ms . The medium length of the QRS trait, following this type of activity, is decreased up to 7,1 ms . Similarly, results vary in case of a female biker.

Analysis of sleep

An important situation of variations in the ECG path can be observed during sleep. Such variation can occur in the following three modalities: • Sudden change of width of R and S waves;

• Gradual change of width of R and S waves;

• Temporary change of width of the R and S waves. In general, the width of R wave suffers from a decrease from 1, 4 mV to 0,9 mV. To the decrease of R wave corresponds an increase of the S wave. The decrease is measured as 27pV/minute while the increase 4pV/minute. A specialized technician can apply modifications, adaptations and substitution of elements with other equivalents to the forms of realization of the method and monitoring system, without overcoming the limitations given by the following claims. Each of the characteristics described as belonging to a possible form of realization can be accomplished.

Claims

1. Method of monitoring of vital parameters of an individual, comprising the steps of: a. Attaching of electrodes, in different embodiments (fabric, nanomaterials , nanoplates) to the body of the individual, suitable to detect body electrical pulses related to heart activity, respiration and body temperature. b. Acquisition of electrical signals and the signals detected by sensors mounted on the electronic board for the relevation of data concerning movement and position in the space. c. Processing of the detected signals into interpretable data. Processing of such data in order to provide additional information and correlation between the data in order to derive indices of specific conditions. d. Analysis of the collected data, recorded or derived from previous analyzes. Comparison of single data or related data with threshold values depending at least on the characteristics of an individual such as age, sex, race, state of health, activity level, medical history, signs and evidences.

2. Method according to claim 1, wherein at least some of said data is combined together before being compared with said threshold values.

3. Method according to claim 1 or 2, wherein the level of physical activity of the individual is identified through a combination of two or more of the vital parameters detected by the sensors.

4. Method according to claim 1 or 2 wherein the physical activity level of an individual is identified through information related to the clinical history of the individual.

5. Method according to any one of claims 1 to 4, wherein the sensors comprise an accelerometer suitable to provide information to the processing device related to the posture, movement, speed, and/or acceleration of the individual and/or the energy consumption of the individual. The data are used for the personalized analysis.

6. Method according to any of claims 1-4 wherein the sensors include a localization device to identify the position, usable for the correlation of vital parameters according to a specific position.

7. Method according to claim 4 or 5, wherein the 10 level of physical activity of the individual is obtained through a combination of at least the values of heart rate, respiratory rate and acceleration detected .

8. Method according to any one of the preceding claims, wherein the step of comparing data obtained from the processing of said signals with threshold values comprises : a. Comparing the data of heart rate, respiratory rate, temperature and/or acceleration with respective threshold values. b. If at least one of said threshold values is exceeded, processing the ECG signal and comparing the data obtained from such processing with the respective threshold values .

9. Method according to any of the preceding claims, wherein the determination of threshold values as a function of physical activity, , registry or medical history takes place automatically and continuously or at predetermined time intervals.

10. Method according to any of the preceding claims, 5 wherein the physical activity of the user is ranked according to at different levels of activity, such as sleeping, normal activity and sports activity, and wherein to each level are associated respective predetermined threshold values.

11. Method according to any one of the preceding claims, wherein the analysis in order to identify critical situations, are defined on the basis of levels of critical condition.

12. Method according to any of the preceding claims, wherein the determination of at least some of the threshold values takes place by carrying out a training phase in which the vital parameters of the individual who will subsequently be monitored are detected for a period of time.

13. Method according to any of the preceding claims, comprising:

- storing, for each user, threshold values of vital parameters at least on the basis of age and gender;

- detecting the physical activity of the user;

- modifying said threshold values based on the physical activity detected.

14. Method according to any of the preceding claims, wherein, if at least one threshold value is exceeded, the data of the vital parameters detected is sent to a server to be analyzed by specialised personnel, sorted according to priority.

15. The monitoring method according to the preceding claim in which the priority is established through machine learning rules or rules set by specialists taking into account the personal data of an individual, age, sex, race, business, medical history .

16. Monitoring system for monitoring vital parameters of an individual, comprising: a. A plurality of sensors suitable to detect vital 5 parameters, b. Signal processing means, suitable to receive signals related to the vital parameters coming from the sensors and processing said signals so as to obtain data 10 representative of the vital parameters; c. Data analysis means, suitable to compare the data obtained from the processing means with threshold values established as a function of at least the characteristics of the individual, such as age registry information, level of physical activity, clinical history.

17. System according to the preceding claim, wherein the processing and/or analysis means are configured to identify the level of physical activity of the individual through a combination of two or more of the vital parameters detected by the sensors .

18. System according to claim 16 or 17, wherein the sensors comprise an accelerometer suitable to provide information to the processing device related to the movement, speed or acceleration of the individual or the posture or energy consumption of the individual .

19. System according to any of claims 16-18, wherein the processing and/or analysis means are configured to determine threshold values related to physical activity automatically and continuously or at predetermined time intervals.

20. System according to any of claims 16 to 19, wherein the processing and/or analysis means are configured for : a. Comparing the data of heart rate, respiratory rate, temperature and/or acceleration with respective threshold values. b. If at least one of said threshold values is exceeded, processing the ECG signal and comparing the data obtained from such processing with the respective threshold values .

21. System according to any of claims 16 to 20, wherein the processing and/or analysis means are configured for selecting threshold values from at least three sets of threshold values, each set of threshold values being associated with a respective level of physical activity of the user.

22. System according to any of the preceding claims, comprising for storing tools in which are stored, for each user, the threshold values of vital parameters to the registry information related to the user's medical history designed to customize the analysis and the assignment of the threshold values.

23. System according to any of claims 16 to 22, wherein the sensors are integrated into a garment or an accessory wearable by the individual being monitored.

24. Systems according to any preceding claim in which the processing and analysis tools are distributed between a wearable electronic systems and / or on a mobile device with gateway function and / or to a server where the data are usable by specialized personnel .

25. System according to any previous claim in which the storage systems are distributed partially or totally between the wearable devices and / or on the mobile device with gateway function and / or to a server where the data are usable by specialized personnel .