Micro Motion Detection for Determining at least one Vital Sign of a Subject

Abstract

A system and method for determining at least one vital sign of a subject includes a plurality of pressure sensors configured to be placed in a vicinity of the subject's body, and configured and operable to sense movements of skin of the subject's body within at least one region on the skin and generate sensing data corresponding to the at least one region. The sensing data comprises a plurality of measured signals being indicative of a common physiological event differentiated in time and intensity from one another and a control unit in data communication with each of pressure sensors of the plurality of pressure sensors. The control unit comprises an analyzer processing utility configured and operable to receive the sensing data corresponding to each of the at least one region; generate, for the pressure sensors associated with each of the at least one region, a pressure variation profile for the region; identify therein one or more predetermined signatures indicative of at least one physiological event of the subject; generate signature data thereof; extract at least one time stamp from the signature data; and generate vital sign data indicative of at least one vital sign of the subject based thereon.

Claims

1. A system for determining at least one vital sign of a subject, the system comprising: a plurality of pressure sensors configured to be placed in a vicinity of the subject's body, and configured and operable to sense movements of skin of the subject's body within at least one region on the skin and generate sensing data corresponding to said at least one region; said sensing data comprising a plurality of measured signals being indicative of a common physiological event differentiated in time and intensity from one another; a control unit in data communication with each of pressure sensors of the plurality of pressure sensors; said control unit comprising an analyzer processing utility configured and operable to: receive the sensing data corresponding to each of said at least one region; generate, for the pressure sensors associated with each of said at least one region, a pressure variation profile for said region, wherein the pressure variation profile is a curvature of an n-dimensional curve, wherein “n” is the number of pressure sensors associated with the same region; identify therein one or more predetermined signatures indicative of at least one physiological event of the subject; generate signature data thereof; and extract at least one time stamp from said signature data; and generate vital sign data indicative of at least one vital sign of the subject based thereon.

2. The system of claim 1, wherein said plurality of the pressure sensors are arranged in at least first and second arrays located in vicinities of first and second different regions of the subject's body, respectively, and providing corresponding first and second sensing data indicative of movements of the skin in the first and second region.

3. The system of claim 2, wherein the first and second sensing data are indicative of different physiological events.

4. The system of claim 3, wherein said control unit is configured and operable to analyze first and second signatures and corresponding time stamps, and determine a relation between the first and second time stamps, said relation being indicative of at least one physiological condition of the body.

5. The system of claim 4, wherein said relation comprises time difference.

6. The system of claim 1, wherein the pressure sensors associated with the same region are located in sub-regions arranged in a spaced-apart relationship within said region.

7. The system of claim 1, wherein a layer is disposed between the subject's skin and each of the plurality of pressure sensors.

8. The system of claim 7, wherein the layer is a part of a mattress or a pillow.

9. The system of claim 1, wherein some of the plurality of pressure sensors are placed in physical contact with the skin of subject.

10. The system of claim 1, wherein the pressure sensors are configured to sense micro-movements.

11. The system claim 1, wherein the sensing data comprises intensity of pressure samples of each sensor over time.

12. (canceled)

13. The system of claim 1, wherein the predetermined signature is characterized by a threshold of at least one projection of the signal curve.

14. The system of claim 1, wherein at least one region is a part of the head of the subject.

15. The system of claim 1, wherein at least one region is a part of the abdomen and at least a second region is a part of the chest of the subject.

16. The system of claim 1, wherein the pressure sensors comprise a piezoelectric component and a capacitor component.

17. The system of claim 1, further comprising an input module being in data communication with each of pressure sensors of the plurality of pressure sensors to receive the sensing data corresponding to each of said at least one region.

18. The system of claim 17, wherein the input module is configured to receive an electrical signal data of the subject; and the analyzer processing utility is configured to extract one or more time stamps of physiological events from the electrical signal data and generate vital sign data indicative of at least one vital sign of the subject based on a relation between time stamps extracted from the electrical signal data and time stamps extracted from the signatures identified in the pressure variation profile.

19. The system of claim 18, wherein the electrical signal data comprises an ECG signal.

20. The system of claim 19, comprising an ECG measurement device to provide said electrical signal data.

21. The system of claim 1, wherein the vital signs are at least one of pulse wave propagation velocity and intracranial pressure.

22-32. (canceled)

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0060] FIGS. 1A-1C are block diagrams of different embodiments of the system according to the present disclosure.

[0061] FIG. 2 is an example of data presentation received from the measurement of six pressure sensors.

[0062] FIG. 3 is a schematic illustration of an example of a sensor array for sensing different regions of a patient that lies horizontally.

[0063] FIG. 4 is a schematic illustration of the aorta that demonstrates different portions that are measured by the non-invasive system and method of the present disclosure.

[0064] FIG. 5 is a chart of an example of presentation of the pressure variation profile having a curvature variation profile that is generated based on measurement of the pressure sensors.

[0065] FIG. 6 is a schematic illustration of a sensor array in a head-pressure measuring unit.

[0066] FIGS. 7A-7B are examples of presentations of the pressure variation profile having a curvature variation profile that is generated based on sensing data of 4 sensors that sensed micro motions from the head of a subject. FIG. 7B shows a marking of the identified signatures from FIG. 7A as compared with morphology in the invasively measured intracranial pressure pulse waves.

[0067] FIG. 8 is a chart of comparative data between non-invasive measurement of the present disclosure, and conventional invasive measurement of intracranial pressure.

[0068] FIG. 9 is a flow diagram of a method for determining a vital sign of a subject according to the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

[0069] A system according to the present disclosure is exemplified in FIG. 1A, which is a schematic block diagram of the system. The system 100 for determining a vital sign of a subject includes a plurality of pressure sensors 104A, 104 B . . . configured to be placed in a vicinity of the subject's body, and configured and operable to sense movements of skin of the subject's body within at least one region on the skin. Although in the figure, for sake of illustration only, two pressure sensors 104A, 104 B are shown, the invention is not limited to such a configuration, and any desired number of pressure sensors may be used. Measurements from different pressure sensors related to the same sensing region are correlated to a common physiologic event such as propagation of blood through the aortic arch or the opening of the aortic valve. Each pressure sensor is configured to sense pressure from a different sub-region, which constitutes a portion of the sensing region. The pressure sensors 104A, 104B are configured to sense micron-sized movements of the skin. The sensors 104A, 104B may comprise a piezoelectric component. The sensors 104A, 104B may further comprise a capacitor component. The piezoelectric component is configured to sense relatively fast signals, and the capacitor component is configured to sense relatively slow signals. Each sensor has a fast sampling rate component configured as a piezoelectric transducer and a slow sampling rate component configured as a capacitor. Examples of such sensors are described in PCT applications with publication Nos. WO 2015/051770, WO 2017/220055 and WO 2017/220056.

[0070] Pressure sensors 104A and 104B generate a sensing data SD.sub.i, which is the collection of pressure measurements from all pressure sensors associated with a common sensing region. System 100 comprises a control unit 106 in communication with the pressure sensors 104A and 104B and is configured and operable for receiving and analyzing the sensing data to generate vital sign data indicative of at least one vital sign of the subject. The control unit 106 is configured generally as a computing/electronic utility including inter alia such utilities as data input and output modules/utilities 106A and 106B, memory 106D (i.e. non-volatile computer readable medium), and analyzer/data processing utility 106C. The utilities of the control unit 106 may thus be implemented by suitable circuitry and/or by software and/or hardware components including computer readable code configured for implementing the operations of method 900 shown in FIG. 9 and described below.

[0071] The features of the present invention may comprise a general-purpose or special-purpose computer system including various computer hardware components, which are discussed in greater detail below. Features within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions, computer-readable instructions, or data structures stored thereon. Such computer-readable media may be any available media, which are accessible by a general-purpose or special-purpose computer system. By way of example, without limitation, such computer-readable media can comprise physical storage media such as RAM, ROM, EPROM, flash disk, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other media which can be used to carry or store desired program code means in the form of computer-executable instructions, computer-readable instructions, or data structures and which may be accessed by a general-purpose or special-purpose computer system. Computer-readable media may include a computer program or computer application downloadable to the computer system over a network, such as a wide area network (WAN), e.g. Internet.

[0072] In this description and in the following claims, a “control unit” is defined as one or more software modules, one or more hardware modules, or combinations thereof, which work together to perform operations on electronic data. For example, the definition of a processing utility includes the hardware components of a personal computer, as well as software modules, such as the operating system of a personal computer. The physical layout of the modules is not relevant. A computer system may include one or more computers coupled via a computer network. Likewise, a computer system may include a single physical device where internal modules (such as a memory and processor) work together to perform operations on electronic data. While any computer system may be mobile, the term “mobile computer system” or the term “mobile computer device” as used herein, especially include laptop computers, netbook computers, cellular telephones, smartphones, wireless telephones, personal digital assistants, portable computers with touch sensitive screens, and the like. Control unit 106 may be comprised of a processor embedded therein running a computer program, or attached thereto. The computer program product may be embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. The specified functions of the processor can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0073] The control unit 106 or the optional data input module 106A, if any, may include a communication module for receiving the sensing signal data SD.sub.i. The sensing data SD.sub.i of each array may thus be communicated to the input module 106A or directly to the control unit 106. The analyzer processing utility 106C receives the sensing data SD.sub.i from the data input module 106A and analyzes it to generate a pressure variation profile for each region being associated with each pressure sensor. The pressure variation profile is generally a profile indicative of difference between pressures which originated from different sensors of a common region. The variation profile is indicative of internal physiological events or phenomena and their expression on the skin surface, constituting together the signal curve, which may be manifested by its Euclidian invariants by a series of functions such as Cartan's curvatures. In some embodiments, by using invariants of group theory (Euclidean, Affine) a time stamp or pattern may be extracted from the signature. The time stamp or pattern may be defined as Cartan and/or affine curvatures which correspond to the pattern of the body force process that is occurring directly inside the body, and for which a direct measurement is not possible. Memory 106D is configured for storing a learning database i.e. preselected data indicative of profiles of the pressure variation profile correlated with an internal physiological event. The database may be implemented with Microsoft Access, Cybase, Oracle, or other suitable commercial database systems. Memory 106D and may be relayed via wireless or wired connection by an external unit to a central database. The processing utility 106C may record the received sensing signal data SD.sub.i in a learning database in memory 106C and/or may query/cross-reference the received sensing signal data SD.sub.i with data in the learning database to identify signatures Sig.sub.i in the pressure variation profile. To this end, the preselected data stored in the learning database may be used to compare the signatures Sig.sub.i in the pressure variation profile with the signatures of an internal physiological event stored in the learning database. The signatures Sig.sub.i are indicative of at least one physiological event of the subject. The processing utility 106C is thus configured to identify in the pressure variation profile for each region, one or more predetermined signatures, and generate signature data thereof. The processing utility 106C or an extractor module 112 is then configured to extract time stamps corresponding to the signature data, and process them to determine at least one vital sign of the subject and generate vital sign data indicative of at least one vital sign of the subject based thereon. Time stamps of the predetermined signatures refer to a certain identifiable time-dependent profile of the signature data. The learning database includes also preselected data indicative of time stamps of the predetermined signatures correlated with at least one vital sign. This last step may be performed by the processing utility 106C or by an extractor module 106E receiving from the processing utility 106C the signatures Sig.sub.i and being capable for extracting time stamps of the signatures and determining at least one vital sign of the subject upon analysis of time stamps of the signatures. The at least one vital sign of the subject may then be outputted by the optional data output module 106B.

[0074] FIGS. 1B-1C are block diagrams of other embodiments of the system according to the present disclosure. The system 110 of FIG. 1B includes one or more pressure sensors arrays 102. For sake of simplicity, one pressure sensors array 102 is shown in the figure. However, the invention is not limited to one pressure sensor array, and any desired number of pressure sensor arrays may be used. Each pressure array 102 includes a plurality of pressure sensors. Each array 102 is associated with a sensing region, which is a location on the surface skin of the subject. The system 110 1B may be connected to an electrocardiogram (ECG) unit 114 that is configured to measure electrocardiogram signals of the subject under inspection, generate electrocardiogram data (ECG) of the subject, and communicate it to the input module 106A. The sensing data SD.sub.i may be measured simultaneously together with the ECG data. The electrocardiogram data ECG is analyzed by the analyzer processing utility 106C and predetermined electrocardiogram signatures EDS are identified.

[0075] More specifically, in this case, the learning database of memory 106D comprises preselected data indicative of electrocardiogram signatures EDS.sub.i correlated with internal physiological parameters and/or specific physiological events. The preselected data is used to compare the signatures EDS with the preselected signatures stored in the learning database and to correlate between identified signatures and specific physiological events according to their relation to the electric-cardiogram signatures EDS.sub.i. Alternatively and additionally, extractor module 106E may extract electrocardiogram time stamps therefrom to find a relation between them and the time stamps extracted from the identified signatures Sig.sub.i. By finding the relation between the time stamps of the two measurements (i.e. pressure-based and electrical based measurement), internal physiological parameter or at least one vital sign of the subject can be determined. FIG. 1C shows another embodiment of the system 120 of the present disclosure. The system 130 differs from that of FIG. 1A in that it receives measured data MD from an external measurement unit 116 such as an ECG unit, pulse measurement device etc. The external measurement unit 116 generates the measurement data MD and transmits it to the input module 106A. The external measured data MD is analyzed by the analyzer processing utility 106C and measurement data signatures MDS are identified. Time stamps thereof are then extracted from measurement data signatures MDS by the extractor module 106E so as to generate, based thereon, vital sign data VSD. The vital sign data VSD comprises data related to at least one vital sign or physiological parameter of the subject and it may be transmitted to an external notification unit 118, such as a monitor or an audio speaker, via the data output module 106B to output the vital sign data to a user.

[0076] As described above, each sensing region is sensed by an array of a plurality of sensors being configured to generate sensing data that can undergo further analysis to derive the vital sign of the subject. Each sensor of the array is configured to sense from a different sub-region that is comprised within the sensing region. FIG. 2 shows a graph illustrating an example of an array of six sensors sensing from six sub-regions of a common sensing region. The graph presents the normalized voltage of the pressure sensed by each sensor over time, wherein the voltage of the n.sup.th sensor has been shifted by 5 multiplied by n for visualization convenience. This is an example of sensing data SD.sub.i that is generated by the sensors and communicated to the control unit for further analysis.

[0077] In the figures throughout the application, like elements of different figures were given similar reference numerals shifted by the number of hundreds corresponding to the number of the figures. For example, element 304 in FIG. 3 serves the same function as element 104 in FIGS. 1A-1C.

[0078] FIG. 3 is a schematic illustration of a top view of a subject with indications of regions and sub-regions being sensed on the subject's skin. In this specific and non-limiting example, the subject 320 lies on a patient's bed that accommodates two arrays of sensors, each of the arrays having three pressure sensors. The plurality of pressure sensors is arranged in an optimized arrangement, in order to monitor various biological/physiological parameters. In some embodiments, this technique may utilize a preliminary stage of selection of the sensing signals of certain measurement sessions performed by a desired group/array of the plurality of pressure sensors and also selection of the predetermined time intervals. The processing of the measured signals from the multiple sensors may also utilize comparison between the signals measured by different sensors arranged with a known distance between them.

[0079] The pressure sensors can sense movements of a micron-size scale directly (e.g. upon physical contact with the skin of subject) or indirectly, namely via a layer (not shown) of a mattress or pillow. A layer (not shown) can thus be disposed between the subject's skin and the sensors. The layer may be a portion of a mattress or a pillow. In the example, the first array is configured to sense a portion of the chest region 322 of the subject. Each of the sensors 304A, 304B, 304C is configured to sense a corresponding sub-region 324A, 324B, 324C. The second array is configured to sense a portion of the abdomen region 326. Each of the sensors 304D, 304E, 304F is configured to sense a corresponding sub-region 328D, 328E, 328F. Therefore, sensors 304A, 304B, 304C generate a first sensing data and sensors 304D, 304E, 304F generate a second sensing data. The first sensing data, derived from the chest region 322, comprises data indicative of physiological events which occur in the chest and its vicinity. For example, the first sensing data may comprise data relating to the opening of the aortic valve and blood passage through the aortic arch. Each of the sensors 304A, 304B, 304C measures pressure on the skin surface of the subject 320, differentiated in time and intensities from the other sensors. However, it should be understood that the sensing data from each sensor is correlated to the same internal physiological events. This is exemplified in FIG. 4, which is an illustration of the aorta, including the aortic arch. These measurements constitute data for generating the pressure variation profile in regions 422 and 426. Sensors 304A, 304B, 304C of FIG. 3 generate sensing data indicative of physiological events occurring in the chest, and sensors 304D, 304E, 304F generate sensing data indicative of physiological events in the abdomen and its vicinity. For example, a physiological event in the abdomen may be bifurcation of the aorta into the common iliac arteries. In the same manner, sensors 304D, 304E, 304F measure pressure on the skin surface of the subject 320, differentiated in time and intensities from the other sensors, but correlated to the same internal physiological events.

[0080] An example of a presentation of the pressure variation profile is shown in a chart in FIG. 5. FIG. 5 shows the pressure variation profile as generated by the control unit as a function of time in milliseconds. The obtained data are synchronized with respect to the R-wave of the corresponding ECG signal that is localized at 100 ms. The bright areas represent a value of the first Cartan curvature of the pressure variation profile. The brighter the area, the greater the curvature. The dark areas represent times with low pressure variation curvature. As can be seen, there are gray and white areas that have a constant time delay with respect to the R-wave of the corresponding ECG signal (R wave is localized at 100 ms). They represent curvature changes related to heart muscle contraction and blood ejection (note that the R-wave serves as a trigger for heart contraction). There are also areas characterized by alternating and noisy brightness, which represent a pressure variation profile that is not related to the triggering R-wave and is dominated by background noise or subject motion. In this specific and non-limiting example, the left side of the chart is the pressure variation profile generated based on a first sensing data, derived from a sensing region associated with the chest of a subject, and the right side of the chart is the pressure variation profile generated based on a second sensing data, derived from a sensing region associated with the abdomen of a subject. The areas of interest are identified, 530 on the left side of the chart, and 532 on the right side, and the time of these areas of interest is determined. The time of each of the areas of interest can be determined based on an average of a plurality of measurement cycles or based on a single measurement cycle. These areas of interest are representations of internal physiological events and are indicative of a physiological condition or parameter of the subject. In this example, area 530 represents two physiological events, the opening of the aorta, which is represented by the top bright line in area 530, and the pulse propagation through the aortic arch, which is represented by the bottom bright line in area 530. Area 532 represents a reflection of bifurcation of the blood in the abdomen.

[0081] The time difference between the times of the physiological events, namely the time difference DT between the time stamps thereof, are indicative of a vital sign of the subject. In this example, the time difference between the propagation of the pulse through the aortic arch and the bifurcation of the blood in the abdomen is indicative of the pulse wave propagation velocity. By knowing the distance the blood travels between the aortic arch and the bifurcation in the abdomen, the wave propagation velocity can be calculated. The distance may be obtained according to some parameters of the subject, such as age, gender, etc., which are known from the literature.

[0082] This kind of measurement can be taken continuously while a patient lies on a patient's bed without the need to physically connect the patient to any measurement device. The sensors can be embedded within the patient's bed, e.g. in the mattress or below the mattress, and sense the micro-movements of the patient, as long as the patient lies on the bed and generally does not move.

[0083] FIG. 6 is a schematic illustration of an array of sensors which can be accommodated in a head measuring unit such as a pillow. As can be appreciated, sensors 604A and 604B are arranged at one side of the pillow 640, and sensors 604C and 604D are arranged on the other side. This configuration allows to measure sufficient data from micro-movements of the skin surface of the head to determine data indicative of the intracranial pressure of the subject.

[0084] The measurement of vital signs from micro-movements of the head is similar to the speed pulse rate detection. From the measured motion of the head, the Cartan invariants are counted, namely the pressure variation profile is generated, and the corresponding events and signatures, are found. To determine the value of intracranial pressure, the time lag/difference between these events and an ECG R-wave of QRS complex of the ECG is extracted and calculated.

[0085] Without being bounded to theory, the following is an example of finding the relation between the time lag/difference between the events detected in the head and the ECG R-wave.

[0086] If the Moens-Korteweg equation is translated into arterial pressure, the relation V˜a√{square root over (P)}, is obtained, where a is a constant, V is the pulse velocity and P is the arterial pressure.

[0087] By determining the pulse wave propagation velocity, the corresponding arterial pressure can be calculated. This is commonly used in pressure estimation by pulse wave pulse measurement. However, this relationship is also related to noninvasive measurement of intracranial pressure. In the cranial cavity, the arteries are embedded in a non-zero pressure environment. From the point of view of the artery, this means that pressure on its wall comprises arterial pressure, from which the intracranial pressure is related, according to the following equation:

CPP=MAP−ICP

[0088] where CPP is arterial wall pressure (perfusion pressure), MAP is mean arterial pressure and ICP is mean intracranial pressure. According to the above, this may lead to the assumption:

[00001]$\Delta T~\frac{1}{V}~\frac{1}{\sqrt{\mathrm{MAP}-\mathrm{ICP}}}~a\left(\mathrm{ICP}-\mathrm{MAP}\right),$

[0089] where a is a constant. In order to determine the MAP, the arterial pressure may be measured, preferably on the patient's hands.

[0090] Delay time ΔT is the time difference between the ECG R-wave and the moment of the event detected in inversions from the head being measured. If intracranial pressure increases, the time difference increases, and vice versa. In fact, there are several events in the head that essentially duplicate the morphology of the invasively measured intracranial pulse.

[0091] Intracranial pulse-induced cardiac activity usually contains 3 maximas known as P1, P2 and P3 (ICP pulse morphology).

[0092] The inventors found that the data of the invasively measured ICP wave morphology corresponds to mechanical phenomena in the head, which subsequently manifests itself as events in the corresponding invariant. This is demonstrated in FIGS. 7A-7B illustrating cardiac stroke cycles vs. time in milliseconds. FIG. 7A is a mechanical invariant, counted with 4 headrest sensors (as illustrated in FIG. 6 above) for time intervals that start with R-wave ECG and end with 600 milliseconds (time-lock). In this connection, it should be noted that the evaluation of ICP can be done with an array of two sensors placed under the patient's head. An array of four mechanical sensors can also be used as shown in FIG. 6, configured to provide complex head motions from more directions, and therefore better resolution. The individual invariants, namely the measurement cycles, are arranged next to each other so that consecutive heart strokes are presented along the X-axis, while the corresponding calculated invariant is displayed on the Y-axis. The result is a two-dimensional map on which signatures of events (maximum invariants) are displayed. In other words, a bright colored area represents a relatively high rate of curvature change, and a dark colored area represents a relatively low rate of curvature change. There are total of 11568 consecutive cardiac strokes shown in FIGS. 7A-7B.

[0093] The extracted time stamps of the identified signatures of events are shown in FIG. 7B together with the invasively measured intracranial pressure. The identified signatures are marked in the figure as the lines corresponding to P1, P2 and P3. As can be appreciated, the events represent the morphology of the invasively measured pulse wave, including their changes over time (corresponding to the maxima of P1, P2, P3).

[0094] FIG. 8 shows comparative data between the present disclosure's non-invasive measurement, and an invasive measurement of ICP of a patient. The figure presents data of consecutive 8-day measurement, in which the dark grey areas represent the present disclosure's non-invasive measurements, and the light grey areas the invasive measurements. The non-invasive method correctly follows long-term trends in intracranial pressure changes. Local mismatches are mostly driven by patient movements on the bed, which also lead to inaccuracies in invasive measurement.

[0095] FIG. 9 is a flow diagram of an example of a method for carrying out the invention of the present disclosure for determining a vital sign of a subject. The method includes receiving sensing data step 940 of pressure measurements from one or more regions of the skin surface of a subject. The sensing data is analyzed step 942 to generate a pressure variation profile step 944 of the measurements. Predetermined signatures are identified step 946 in the pressure variation profile, and the predetermined signatures are indicative of internal physiological events that are expressed on the skin surface of the subject. Time stamps of the identified predetermined signatures are extracted step 948 and, based on the extracted time stamps, data indicative of at least one vital sign is generated step 950. The data may be generated based on a single time stamp or any relation between two or more time stamps of the identified predetermined signatures.