System and method for detecting presence in an enclosed environment to be monitored

Abstract

Embodiments of this disclosure relate to systems and methods for detecting the presence of an object in a monitored enclosed environment. One system for detecting the presence of an object in a monitored environment includes a static charge change sensor, a vibration sensor, and an ambient pressure sensor. A processing unit is configured to acquire a static charge change signal from the static charge change sensor and detect a first signal feature in the static charge change signal indicating the presence of an object in the monitored environment. The processing unit also uses vibration and pressure signals provided by other sensors to verify the detection of the object's presence.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a system and method for detecting presence in an environment to be monitored, for example for anti-theft or anti-intrusion purposes. BACKGROUND

[0002] Electric field sensors are used as an alternative or in addition to accelerometer sensors for determining the activity of a user, or to help interpret the signals generated by other sensor devices.

[0003] In conductors, the charges have a certain degree of freedom of movement, and therefore they tend to position themselves so as to keep as far apart as possible from each other, thus distributing themselves over the entire surface of the conductor.

[0004] In the presence of an external electric field, the electrons move until they reach a state of stability; the electric field inside the conductor is zero, while the electric field directly outside the conductor is perpendicular to the conductor. The charges on the surface are concentrated in positions with a smaller radius of curvature (point effect). The charges can be transferred from one conductor to another by contact. In addition, charges can be generated on a conductor by induction.

[0005] Alternatively, in insulators the atomic structure does not allow the charges to move, on the contrary, it tends to keep them where they are generated: the charges will therefore be localized. Insulators can be electrified by friction (triboelectric effect). In the presence of an external electric field, no charge can move freely, but the dielectric molecules "deform" due to the repulsion between charges with the same sign and the creation of dipoles (bias), which makes the dielectric macroscopically charged.

[0006] There are many technologies and products that involve anti-intrusion applications and presence detection. Here is a list of the most common methods for detecting intrusions: taking thermal images of the object by infrared sensors; passive infrared reacting to temperature changes (PIR); active infrared, where the light rays from the emission point and the reception point are interrupted; microwave emission reflected by the object, which can also measure the speed of the object; ultrasound; using a light beam photodetector; using a microphone; using a camera.

[0007] All the above methods have advantages and disadvantages in detecting unwanted intrusions. This is why the most robust and precise systems combine several technologies together. For example, passive infrared sensors are sensitive to the ambient temperature, while microwave intrusion detection systems cannot detect objects behind metals. In addition, fluorescent lights or slight movements can trigger alarms. For this reason, dual technology based on the combination of PIR and microwave is very common. By cross-referencing both the information and the alarms, anti-intrusion systems become more reliable against false positives and unwanted alarms, and gain further advantages, such as immunity to pets. Here are some examples of the state of the art.

[0008] Patent document EP2533219 describes an intrusion prevention system comprising at least one microwave detection device for detecting the unauthorized entry of an object into a monitored area; the detection device comprises a transmitting antenna for transmitting microwaves and a receiving antenna for receiving reflected signals.

[0009] Patent document US6188318 describes a microwave plus PIR dual technology intrusion detector for immunizing pets.

[0010] Patent document EP1587041 describes an intrusion detection system comprising passive infrared optics and a microwave transceiver.

[0011] Devices for detecting changes in the electric field generated during the movement of a person by a person or using capacitive detection are also known. Techniques using the latter type of detection include, for example: touch screens, systems for detecting the position of occupants in a car and devices for determining the position, orientation and mass of an object, such as described in patent document US5,844,415, regarding an electric field detection device for determining the position, mass distribution and orientation of an object within a defined space, a plurality of electrodes being arranged within the defined space. This technical solution can also be used to identify gestures of a user, the position and orientation of a hand, for example for use in interaction with a processing system, in place of a mouse or joystick.

[0012] Patent document KR20110061750 refers to the use of electrostatic sensors associated with infrared sensors to detect the presence of an individual. The specific application relates to the automatic opening / closing of a door.

[0013] Patent document EP2980609 relates to the use of electrostatic field sensors in addition to magnetic sensors to detect the presence of a person in an environment.

[0014] The scientific document "Development of n-Contact Measurement System of Human Stepping" by K. Kurita, SICE Annual Conference 2008, Japan, shows a system and method for calculating the number of steps taken by an object using non-contact technology. The technology is used to detect the electrostatic induction current, which is a direct result of the movement of the object in the environment, detected by electrodes placed at a distance of 1.5 m from the object. However, the experiments shown in this document were carried out under ideal conditions, only demonstrating the applicability of this technology in step counting. SUMMARY

[0015] Some drawbacks of the prior art have been highlighted in the above background section. Moreover, none of the above documents teaches a system and / or method for detecting presence in an environment to be monitored, in particular for anti-intrusion or anti-theft purposes, aimed at minimizing the number of sensors cooperating with each other while ensuring high reliability.

[0016] Therefore, there is a need to remedy the drawbacks of the prior art by providing a system and method for detecting presence in an environment to be monitored.

[0017] According to the present disclosure, a system and method for detecting presence in an environment to be monitored are provided.

[0018] In at least one embodiment, a system for detecting presence in an environment to be monitored is provided, the system comprising a processor and an electrostatic charge variation sensor coupled to the processor and configured to detect a variation of electrostatic charge in the environment and to generate an electrostatic charge variation signal. The system further comprises one of a vibration sensor or an ambient pressure sensor, wherein the vibration sensor is operatively coupled to the environment to be monitored, the vibration sensor being configured to detect an ambient vibration in the environment to be monitored and to generate a vibration signal, the ambient pressure sensor being operatively coupled to the environment to be monitored, the ambient pressure sensor being configured to detect an ambient pressure vibration signal in the environment to be monitored and to generate a pressure signal. The processor is configured to: acquire the electrostatic charge variation signal from the electrostatic charge variation sensor; detect, in the electrostatic charge variation signal, a first signal feature indicative of presence of an object in the environment to be monitored; acquire the vibration signal or the pressure signal, respectively, from the one of the vibration sensor or the ambient pressure sensor; detect, in the acquired vibration signal or pressure signal, a respective second signal feature indicative of presence of an object in the environment to be monitored; and generate a warning signal if both the first signal feature and the second signal feature have been detected.

[0019] In at least one embodiment, a method for detecting the presence of an object in an environment to be monitored is provided, comprising: detecting a change in static charge in the environment by means of a static charge change sensor and generating a static charge change signal; detecting environmental vibration in the environment to be monitored by means of one of a vibration sensor or an environmental sensor operatively coupled to the environment to be monitored and generating a vibration signal or an environmental pressure and generating a pressure signal; acquiring the static charge change signal from the static charge change sensor by means of a processor; detecting a first signal feature in the static charge change signal indicating the presence of an object in the environment to be monitored by means of a processor; acquiring the vibration signal or the pressure signal from one of the vibration sensor or the environmental pressure sensor by means of a processor; detecting a corresponding second signal feature in the acquired vibration signal or pressure signal indicating the presence of an object in the environment to be monitored by means of a processor; and generating a warning signal by means of a processor if both the first signal feature and the second signal feature have been detected. Attached Figure Description

[0020] To better understand this disclosure, embodiments thereof are now described only by way of non-limiting examples and with reference to the accompanying drawings, in which:

[0021] Figure 1 A system for detecting presence according to an embodiment of the present disclosure is illustrated schematically, comprising an ambient charge sensor, a pressure sensor, and a vibration sensor (particularly a multi-axis accelerometer) operatively coupled to a processing unit.

[0022] Figure 2 An embodiment of an environmental static charge change sensor is shown.

[0023] Figure 3A It shows the result of Figure 1 The pressure signal S generated by the pressure sensor P Examples.

[0024] Figure 3B It shows the result of Figure 1 An example of a static charge change signal generated by a static charge change sensor.

[0025] Figure 3C It shows the result of Figure 1 The accelerometer generates and is Figure 1 An example of a processing unit part that processes a vibration signal to generate the modulus of the sensed axial component.

[0026] Figure 4A This shows the removal of the baseline or one of its background components. Figure 3A Pressure signals.

[0027] Figure 4B It shows the results after removing the relative baseline. Figure 3B The static charge change signal.

[0028] Figure 4C The first derivative of the electrostatic charge variation signal is shown. Figure 4B

[0029] Figure 4D The envelope or alternating current (AC) component of the vibration signal is shown. Figure 3C

[0030] Figure 5A The amplified portion of the electrostatic charge variation signal and the first derivative of the electrostatic charge variation signal of 5B are shown, respectively; Figure 4B Figure 4C

[0031] Figure 6 The method for detecting the presence of a human being implemented by the system of Figure 1 is shown by a flowchart, referring only to the electrostatic charge variation signal.

[0032] Figure 7 The steps of the method for analyzing the pressure signal of Figure 3C or Figure 4A are shown by a block diagram, in order to extract or identify important features for detecting the presence of a human being.

[0033] Figures 8A-8C The steps of processing the pressure signal according to the method of Figure 7 are shown diagrammatically.

[0034] Figure 9 The steps of the method for analyzing the vibration signal of Figure 3C are shown by a block diagram, in order to extract or identify important features for detecting the presence of a human being.

[0035] Figures 10A-10C The steps of processing the vibration signal according to the method of Figure 9 are shown diagrammatically.

[0036] Figure 11 and Figure 12 The respective methods for removing the baseline are shown by a block diagram, which are applicable in the context of the present disclosure to generate the signals of Figure 4A and Figure 4B .

[0037] Figure 13 The steps of the method for detecting peaks are shown by a block diagram, which can be used in the context of the present disclosure to identify positive and negative peaks, applicable in the context of the methods of Figure 6 and Figure 7 .

[0038] Figure 14 The first derivative of the electrostatic charge variation signal of Figure 4B is calculated to obtain​​​​Figure 4C The method of signaling.

[0039] Figure 15 The block diagram illustrates a method for extracting the envelope or AC component, which can be used from... Figure 3C Vibration signal generation Figure 4D The vibration signal. Detailed Implementation

[0040] Figure 1 A presence detection system or intrusion prevention system 1 is schematically illustrated. The presence detection system 1 is specifically designed to detect the presence of humans in an environment and includes: a processing unit 2, a pressure sensor 4 coupled to the processing unit 2, a static charge change sensor 6 coupled to the processing unit 2, and a vibration sensor 7, particularly an accelerometer, which is also coupled to the processing unit 2 (the accelerometer will be explicitly referred to below without loss of generality). The pressure sensor 4, static charge change sensor 6, and vibration sensor 7 are arranged in the environment to be monitored. The processing unit 2 (which may be referred to herein as a processor, and which may be or include any electrical features, circuitry, etc., suitable for performing the functions described herein with respect to the processing unit, e.g., a computer including a microcontroller) may also be arranged in the environment to be monitored, another adjacent environment, or a remote type of environment, or may be arranged at a distance from the environment to be monitored. The connection between the processing unit 2 and the aforementioned pressure sensor 4, static charge sensor 6, and accelerometer 7 can be implemented via wired or wireless technology according to any available technology.

[0041] Processing unit 2 is configured to receive (and receive during use): signal S Q Signal S Q The signal S originates from the electrostatic charge change sensor 6 and is correlated with changes in ambient charge in the monitored environment. A This indicates vibrations detected in the environment monitored by accelerometer 7; and signal S P This indicates the pressure (or pressure change) detected in the environment monitored by the accelerometer 7.

[0042] The pressure sensor 4 is arranged in, or operatively coupled to, an environment in which the presence of a human being is required to be detected, to detect a variation of the environmental pressure, for example caused by the opening of a door or window, or indicative of the entry of a foreign object into this environment. Thus, in this case, the environment to be monitored is a closed environment, for example a room in an apartment or home. It should be remembered, in fact, that the system 1 according to the present disclosure has the purpose of identifying an unwanted entry within the environment to be protected, in particular for anti-burglary purposes. When the system 1 is operating, the pressure detected is the environmental pressure present therein, which generally varies relatively slowly between day and night, due to the heating of the air or to the variation of the weather / climatic conditions. Any significant disturbance of this pressure can be indicative of the presence of an intrusion.

[0043] Similarly, the accelerometer 7 is also arranged in the environment in which the presence of a human being is required to be detected, to detect any vibrations possibly related to the steps of an intruder, in particular caused by a human being entering such an environment.

[0044] Similarly, the electrostatic charge variation sensor 6 is also arranged in the environment in which the presence of a human being is required to be detected, or operatively coupled to this environment, to detect an environmental electrostatic charge variation caused by the entry of a foreign object into this environment.

[0045] The analysis of the signals generated by the above-mentioned sensors and of their suitable combinations allows to detect the entry of an object or intruder into the environment to be monitored, thus discriminating false positives.

[0046] Figure 2 An exemplary and non-limiting embodiment of the electrostatic charge variation sensor 6 is shown. The electrostatic charge variation sensor 6 comprises a pair of input terminals 8a, 8b coupled to input electrodes E1, E2, respectively.

[0047] The two electrodes can be connected to a differential input (i.e. to a positive / negative "+" / "-" input pair of an amplifier stage or of an ADC converter). A particular case of this general configuration (without the need to change the electrical scheme) can use an electrode (e.g. E1) having a prevailing size with respect to the other electrode (e.g. E2), to the purpose of manufacturing this second electrode (E2), and from which the environmental charge variation detected can be completely negligible; in other cases, the second electrode (E2) can be removed. Figure 2

[0048] ​In this embodiment, one of the electrodes E1, E2 (for example E2) is coupled to a reference potential having a constant value (for example, the common-mode voltage or VCM, typically half of the device supply voltage), while the other electrode E1, E2 (for example E1 ) is made, for example, of an electrically conductive material and is coated with an insulating layer. The geometry of the electrode E1 determines the sensitivity, which, as a first approximation, is proportional to the surface of the electrode itself. In an exemplary embodiment, the electrode E1 sensitive to the environmental charge is square, with sides of about 2-10 cm, for example 5 cm. Other examples include electrodes made using a wire coated with an insulator, having a length equal to a few centimeters or tens of centimeters, for example 10-20 cm.

[0049] In particular, the input electrodes E1, E2 are arranged in an environment in which the presence of a human being is required to be detected, while the rest of the electrostatic charge variation sensor 6 (for example, the amplification stage described below) can also be arranged outside the environment to be monitored, or can be inside it.

[0050] The pair of input terminals 8a, 8b receives an input voltage Vd (differential signal) which is supplied to the instrumentation amplifier 12. In a manner known per se, the presence of a human being generates a variation of the environmental electrostatic charge which, in turn, generates the input voltage Vd after having been detected by the electrode E1.

[0051] In an exemplary embodiment, the instrumentation amplifier 12 comprises two operational amplifiers OP1 and OP2 and a bias stage (buffer) OP3 having the function of biasing the instrumentation amplifier 12 to the common-mode voltage VCM. CM

[0052] The inverting terminal of the amplifier OP1 is connected in a manner passing through a resistor R2 having, at its ends, a voltage equal to the input voltage Vd; therefore, a current equal to I2= Vd / R2 will flow through this resistor R2. This current I2 does not come from the input of the operational amplifiers OP1, OP2 and, therefore, flows through two resistors R1 connected in series between the outputs of the operational amplifiers OP1, OP2, so that the current I2 flows through the series of the three resistors R1 -R2-R1, generating an output voltage Vd' given by Vd' = (2R1 +R2)I2 = (2R1 +R2)Vd / R2. Therefore, Figure 2 The overall gain of the circuit is Ad = Vd' / Vd = (2R1 +R2) / R2 = 1 +2R1 / R2. The differential gain depends on the value of the resistor R2 and, therefore, can be modified by acting on the resistor R2.

[0053] ​The differential output Vd' proportional to the potential Vd between the input electrodes 8a, 8b is thus provided at the input of an analog-to-digital converter 14, the output of which provides the charge variation signal S for the processing unit 2 Q . The charge variation signal S Q is, for example, a high-resolution digital stream (16 bits or 24 bits). The analog-to-digital converter 14 is optional, since the processing unit 2 can be configured to directly process the analog signal Vd' or itself comprise an analog-to-digital converter for converting the signal Vd'.

[0054] Alternatively, in the presence of the analog-to-digital converter 14, the instrumentation amplifier 12 can be omitted, so that the analog-to-digital converter 14 receives the differential voltage Vd between the electrodes E1, E2 and directly samples the signal Vd.

[0055] The pressure sensor 4 is, for example, a pressure sensor manufactured using MEMS technology. Examples of pressure sensors usable in the context of the present disclosure include pressure sensors having a measurement range of 200 mbar - 2000 mbar and having an accuracy (absolute accuracy) of a few mbar units. However, operating around an ambient pressure of about 1000 mbar and observing the relative values around it, the relevant parameter is the ability to detect changes around the working point, i.e. high resolution and low intrinsic noise over time and amplitude. An example of a sensor for this purpose includes a sensor having a resolution of 1 Pascal (1 / 100 of a mbar), a data rate equal to 200 Hz, an RMS noise level equal to 0.5 Pascal (without applied filter).

[0056] However, in corresponding embodiments, other pressure sensors are also usable (in addition to MEMS sensors).

[0057] As mentioned above and in one embodiment, the vibration sensor 7 is an accelerometer, for example of the three- or six-axis type manufactured using MEMS technology, or a sensor comprising a combination of an accelerometer and a gyroscope.

[0058] Figure 3A An example of the pressure signal S P ( raw signal) generated by the pressure sensor 4 is shown. The abscissa axis is time and the ordinate axis is the absolute pressure value, in millibar. As Figure 3A shown, there is background noise and, at time t = 21 s, a peak 15 significantly different from this background noise, caused by a change in the ambient pressure, for example due to the opening of a door.

[0059] Figure 3B An example of the electrostatic charge variation signal S QAn example of the charge variation signal S Figure 3A . The abscissa axis is the time axis (in seconds, using the same time scale as and

[0060] ). The ordinate is the amplitude of the signal, in LSBs (“Least Significant Bit”), which is the smallest digital value at the output of the analog-to-digital converter, proportional to the voltage detected at the input electrode E1. Typically, 1 LSB corresponds to a value comprised between a few μV and tens of μV. The scaling constant (or sensitivity) depends on the gain of the amplifier, on the resolution of the analog-to-digital converter and on any digital processing (e.g. oversampling, decimation, etc.). The representation in LSBs is common in the art and disregards the quantification of physical units, since its purpose is typically to detect relative variations with respect to a stable or base state. The time of onset of the measurement is represented on the abscissa axis of the charge variation signal S Q . As sampling frequency, in the example shown, equal to 200 Hz, 200 samples correspond to each second reported on the abscissa. Figure 3B It can be seen that the electrostatic charge variation sensor 6 detects the presence of the subject in the environment with a certain delay (here, of about 2 seconds) with respect to the pressure sensor 4. The delay is due to the fact that, in the example shown, the step in the environment to be monitored does not immediately follow the opening of the door; if this were the case, the delay would thus be reduced to zero if the opening of the door coincided with the execution of the step in the environment to be monitored. The charge variation signal S Q shows a series of positive and negative peaks, which follow each other and identify the type of movement performed by the subject in the environment (here, in particular, a step). In particular, five steps can be identified, identified by a positive peak and by the immediately following negative peak, delimited by the respective rectangles 17 in dashed line in Figure 3B .

[0061] Figure 3C An example of the vibration signal S A generated by the accelerometer 7 and partially processed to generate the modulus of the axial sensing component is shown. The abscissa axis is the time axis (in seconds, using the same time scale as Figure 3A and 3B ), while the ordinate axis is the amplitude of the vibration signal S A in LSBs. In this example, an accelerometer with three detection axes is used, configured to detect three signals S Ax , S Ay , S Az along the X, Y, Z axes of a Cartesian triaxial reference system, respectively. Since the orientation of the accelerometer 7 in the environment to be monitored cannot be predicted a priori, according to the present disclosure, the signal S A is generated by combining the three components S Ax , S Ay , SAz The following calculation is performed based on the modulus of acceleration:

[0062]

[0063] from Figure 3C As can be seen, the accelerometer 7 detects vibrations distinct from the background noise in the form of multiple close positive and negative peaks, identifying the corresponding footsteps of the object in the environment. Specifically, five footsteps can be identified, essentially simultaneously with the footsteps identified by the electrostatic charge change sensor 6, by… Figure 3C The corresponding rectangle 18 is defined by the dashed line in the diagram.

[0064] In order to be able to process Figures 3A-3C To identify and extract relevant features of a signal to identify the presence of objects in an environment, one aspect of this disclosure provides the removal of a background component, similar to the average value (DC) of a signal, also known as a "baseline". Algorithms of known types can be used to remove the baseline or background signal, such as calculating and subtracting the average value from the original signal; alternatively, algorithms or methods specifically provided for this purpose can be used, for example, as referred to below. Figure 11 and 12 illustrate.

[0065] Figure 4A , 4B The 4D and 4D models respectively illustrate the results after the corresponding processing aimed at removing the background component or baseline. Figure 3A , 3B And 3C signals. Throughout this specification, since the teachings of this disclosure apply indiscriminately to both raw and processed signals, the same reference numeral S... P S Q and S A will be used Figures 3A-3C The original signal and Figure 4A , 4B And 4D signals that have undergone processing.

[0066] Specifically: Figure 4A The baseline removal is shown. Figure 3A Pressure signal S P ; Figure 4B It shows the results after removing the relative baseline. Figure 3B The static charge change signal S Q ; Figure 4C It shows Figure 4B The static charge change signal S Q First derivative S Q ′; Figure 4D It shows Figure 3C The change of the signal relative to the average value of the signal (i.e., Figure 3C Vibration signal SA (AC component).

[0067] Now for reference Figure 5A and 5B Describe a method for identifying electrostatic charge change signals S Q and its first derivative S Q Significant changes in the signal, that is, changes in the signal that may be associated with or related to the presence of humans in the environment to be monitored, more specifically, are used to verify whether the signals generated by the sensors resemble the steps performed by human objects. Figure 5A signal S Q Part of it is Figure 4B signal S Q A portion of the amplified portion, especially Figure 4B The portion 18a defined by the dashed line. Figure 5B signal S Q The part of ' is Figure 4B signal S Q A portion of the amplified portion, especially Figure 4B The portion 18b is defined by the dashed line.

[0068] Figure 5A and 5B The signal portion has multiple positive and negative peaks that follow each other periodically. For the purposes of this disclosure, the positive and negative peaks are identified. This operation can be performed using known types of peak-finding algorithms, such as those based on comparisons with predetermined or adaptive thresholds, or other algorithms specifically provided for this purpose, such as those referencing... Figure 13 As described.

[0069] refer to Figure 5A and Figure 5B The following peaks are identified, and they follow each other in chronological order. Figure 5A and 5B (All are represented relative to the same time axis on the horizontal axis). The time indication values ​​and peak amplitude values ​​are merely exemplary and do not limit this disclosure.

[0070] P1: This is the first peak in time, a positive peak, appearing at approximately 24.3 seconds after the first derivative signal S. Q 'Above, and has an amplitude value equal to approximately +30000 LSB.

[0071] P2: The second peak in time, this is a positive peak, appearing at approximately 24.4s of signal S. Q It has an amplitude value of approximately +42000 LSB.

[0072] P3: This is the third peak in time, and it is a negative peak. It appears at approximately 24.55s on the first derivative signal S.Q 'Above, and has an amplitude value equal to approximately -38000 LSB.

[0073] P4: This is the fourth peak in time, and it is a negative peak. It appears at approximately 24.65s of signal S. Q It has an amplitude value of approximately -65000 LSB.

[0074] P5: This is the peak in time, a positive peak, appearing at approximately 24.75s in the first derivative signal S. Q 'Above, and has an amplitude value equal to approximately +18000 LSB.

[0075] It will be apparent to those skilled in the art that the signal S with the first derivative Q The positive peak P1 identifier signal S appears on the top Q The rising edge of the signal S Q The signal reaches its peak at P2. Similarly, the signal with the first derivative S... Q The negative peak P3 identifier signal S appears on the top. Q The falling edge of the signal S Q The negative peak P4 is the highest point. Then, signal S... Q It begins to grow again, and this new rise is driven by the signal S of the first derivative. Q The positive peak P5 appears on the signal S. Therefore, the above applies to signal S. Q and S Q The assessment of the continuity of positive and negative peaks has the function of identifying or detecting specific trends in the signal generated by the electrostatic charge change sensor 6, which the applicant has identified as the presence of human objects (particularly the execution of footsteps) in a specific or important environment to be monitored.

[0076] In summary, considering the electrostatic charge change signal S Q and static charge change signal S Q First derivative S Q Over time, the following positive and negative peak time series were observed:

[0077] 1. Zhengfeng S Q '

[0078] 2. Zhengfeng S Q

[0079] 3. Negative peak S Q '

[0080] 4. Negative peak S Q

[0081] 5. Zhengfeng S Q '

[0082] However, the Applicant has noticed that, for a different arrangement of the electrodes E1, E2, the above time sequence (order) can be reversed, i.e. the following time sequence is observed:

[0083] 1. positive peak S Q

[0084] 2. positive peak S Q

[0085] 3. negative peak S Q

[0086] 4. negative peak S Q

[0087] 5. positive peak S Q

[0088] The configuration of the electrodes can actually generate an impact on the detection of the electrostatic charge variation signal. While the geometry (first of all the surface) and the material of the electrodes determine the sensitivity of the electrodes, their arrangement and distance in space can affect the directionality or the ability to cancel certain unwanted signal sources. Regarding the last point, it is noted that the two electrodes E1, E2 are coupled to the differential input of a differential amplifier (also called instrument amplifier) or an analog-to-digital converter (A / D or ADC); this stage performs the difference of the signals found at the "+" and "-" inputs of the amplifier. Therefore, by properly determining the size and positioning of the electrodes, it is possible to cancel (or attenuate) common mode signals, i.e. those that have the same intensity at the two inputs. Based on this, the embodiments of the present disclosure include configurations with a single electrode, with two electrodes equal but spaced apart from each other, with two electrodes of different geometry, etc. If the most stressed input is the input "+", the signal as shown in the figure is found; the opposite is true in the case of a more stressed input "-". In this case, the most stressed electrode is the one that detects a more intense potential variation (due to a charge variation in the environment) with respect to the other electrode. This can occur due to different geometries and / or different mounting points of the two electrodes.

[0089] The Applicant has verified that, when observing one of the above time sequences, it is possible to conclude that the signal portion 18a (and the first derivative 18b of the signal portion 18a) of S Figure 4B is generated by the steps of a human subject in the environment to be monitored. Figure 4C

[0090] In order to identify whether the variations of the signals S Q and S Q ' are one of the sought peaks, respective threshold values (positive or negative) A1 TH -A5 TH are provided to compare with the signals S Q and S​​​Q the trend of S

[0091] threshold A1 TH -A5 TH has a predefined / default value, determined according to the observation experience of the trend of the signal S Q and S Q , for example defined as follows:

[0092] threshold A1 TH : is selected as a fraction (for example, between 1 / 2 and 1 / 6) of the maximum value reachable by the first peak P1 (or of the maximum value known according to the experiment); for example, in this embodiment described it has a value selected in the range 8000-12000 LSB (value expressed in modulus), in particular 10000 LSB.

[0093] threshold A2 TH : is selected as a fraction (for example, between 1 / 2 and 1 / 6) of the maximum value reachable by the second peak P2 (or of the maximum value hypothesized according to the experiment); for example, in this embodiment described it has a value selected in the range 8000-12000 LSB (value expressed in modulus), in particular 10000 LSB.

[0094] threshold A3 TH : is selected as a fraction (for example, between 1 / 2 and 1 / 6) of the maximum value reachable by the third peak P3 (or of the maximum value hypothesized according to the experiment); for example, in this embodiment described it has a value selected in the range 6000-8500 LSB (value expressed in modulus), in particular 7500 LSB here.

[0095] threshold A4 TH : is selected as a fraction (for example, between 1 / 2 and 1 / 9) of the maximum value reachable by the fourth peak P4 (or of the maximum value hypothesized according to the experiment); for example, in this embodiment described it has a value selected in the range 6000-8500 LSB (value expressed in modulus), in particular 7500 LSB here.

[0096] threshold A5 TH : is selected as a fraction (for example, between 1 / 2 and 1 / 5) of the maximum value reachable by the fifth peak P5 (or of the maximum value hypothesized according to the experiment); for example, in this embodiment described it has a value selected in the range 6000-8500 LSB (value expressed in modulus), in particular 7500 LSB.

[0097] in the example of Figure 5A and 5B the thresholds have the following values: threshold A1 TH : +10000 LSB; threshold A2TH : +10000 LSB; threshold A3 TH : -7500 LSB; threshold A4 TH : -7500 LSB; threshold A5 TH : +7500 LSB.

[0098] As already described as an alternative, the threshold A1 TH - A5 TH values can be selected according to the background noise of the respective signal S Q and S Q ', for example equal to 8-12 times (for example, 10 times) the average value of the noise.

[0099] The following comparisons are then performed for each threshold:

[0100] the amplitude A1 of the peak P1, in LSB, exceeds the threshold A1 of positive value TH , P1 is identified as "positive peak";

[0101] the amplitude A2 of the peak P2, in LSB, exceeds the threshold A2 of positive value TH , P2 is identified as "positive peak";

[0102] the amplitude A3 of the peak P3, in LSB, exceeds the threshold A3 of negative value TH , P3 is identified as "negative peak";

[0103] the amplitude A4 of the peak P4, in LSB, exceeds the threshold A4 of negative value TH , P4 is identified as "negative peak";

[0104] the amplitude A5 of the peak P5, in LSB, exceeds the threshold A5 of positive value TH , P5 is identified as "positive peak".

[0105] To improve the robustness of the method proposed herein, by improving the distinction between actual steps and environmental noise or other disturbances, with reference again to Figure 5A and Figure 5B the following additional parameters can be defined and monitored:

[0106] T1 : time interval between the positive peak P2 and the negative peak P4 of the electrostatic charge variation signal S Q .

[0107] T2: time interval between the positive peak P2 of the electrostatic charge variation signal S Q and the positive peak P1 of the first derivative signal S Q '.

[0108] T3: time interval between the positive peak P2 of the electrostatic charge variation signal S Qthe positive peak P2 of the first derivative signal S Q the time interval between the negative peak P3 of the first derivative signal S

[0109] T4: the electrostatic charge variation signal S Q the negative peak P4 of the first derivative signal S Q the time interval between the negative peak P3 of the first derivative signal S

[0110] T5: the electrostatic charge variation signal S Q the negative peak P4 of the first derivative signal S Q the time interval between the positive peak P5 of the first derivative signal S

[0111] T6: the first derivative signal S Q the time interval between the positive peak P1 and the negative peak P3 of the first derivative signal S

[0112] T7: the first derivative signal S Q the time interval between the negative peak P3 and the positive peak P5 of the first derivative signal S

[0113] T8: the first derivative signal S Q the time interval between the positive peak P1 and the positive peak P5 of the first derivative signal S

[0114] verify the existence of the following relationships:

[0115] T1 = T3 + T4

[0116] T6 = T2 + T3

[0117] T7 = T4 + T5

[0118] T8 = T6 + T7

[0119] Additionally or alternatively, verify the existence of the following relationships to verify whether the duration of the time interval T2-T5 is consistent with the expected duration of a signal shape that can be associated with a step of the object:

[0120] T2 TH_L <T2 < T2 TH_H where T2 TH_L and T2 TH_H represent the boundaries of the range of time values that need to include T2 (e.g., T2 TH_L = 30-70 ms and T2 TH_H = 150-250 ms);

[0121] T3 TH_L <T3 < T3 TH_H where T3 TH_L and T3 TH_H represent the boundaries of the range of time values that need to include T3 (e.g., T3 TH_L = 30-70 ms and T3TH_H =150-250ms);

[0122] T4 TH_L <T4<T4 TH_H T4 TH_L and T4 TH_H This indicates that the boundaries of the time value range for T4 need to be included (e.g., T4). TH_L =30-70ms and T4 TH_H =150-250ms);

[0123] T5 TH_L <T5<T5 TH_H T5 TH_L and T5 TH_H This indicates that the boundary of the time value range T5 needs to be included (e.g., T5). TH_L =30-70ms and T5 TH_H =150-250ms).

[0124] In one embodiment, T1 TH_L -T5 TH_L The values ​​are all equal and equal to 50ms; T1 TH_H -T5 TH_H The values ​​are all equal and equal to 200ms.

[0125] T1 TH_H -T5 TH_H The value of can vary depending on the content described in this article and can be set based on experience after experimental observation.

[0126] Figure 6 The flowchart illustrates the process... Figure 1 The method for detecting human presence implemented by System 1, which, according to the previously described content, refers only to the electrostatic charge change signal S Q .

[0127] In step 60, processing unit 2 acquires the raw signal S from electrostatic charge change sensor 6. Q .

[0128] As mentioned earlier, in step 61, the original signal S Q It is processed to remove baseline or background signals.

[0129] In step 62, the process of searching for the electrostatic charge change signal S is performed. Q Methods for identifying positive / negative peaks, thereby characterizing, for example... Figure 5A The time series of peaks P2 and P4.

[0130] In step 63, the static charge change signal S is calculated. Q The first derivative signal SQ '.

[0131] Then, in step 64, the process for searching the first derivative signal S is performed. Q The method of identifying positive / negative peaks, thereby identifying, for example... Figure 5B The time series of peaks P1, P3 and P5.

[0132] The above conditions are then evaluated on the amplitudes A1-A5 and time intervals T2-T5 of the detected peaks. The proposed method is performed in real time, i.e., by acquiring the raw signal S. Q The samples were then used to evaluate the previously described conditions, as these samples were generated by the electrostatic charge change sensor 6.

[0133] Counter P COUNT (For example, initialized to zero) Store the number of peaks identified (five peaks P1-P5 can be used, and in some embodiments, it may be necessary to confirm the identification of the steps in that embodiment). At the initial moment before any peaks are detected, P... COUNT =0.

[0134] refer to Figure 6 Boxes 65-69, evaluate counter P COUNT The value of P. COUNT The increase in value determined access to the corresponding calculation boxes 65-69 to verify the corresponding conditions of peaks P1-P5, as previously mentioned, peaks P1-P5 differ from each other in terms of amplitude threshold and time reference.

[0135] In box 65, the amplitude value A1 is compared with the corresponding threshold A1. TH Comparison to evaluate the first derivative signal S Q 'Existence of peak P1. If the existence of peak P1 is determined by comparison with the threshold, then update the counter P.' COUNT (P COUNT =1), and from the original signal S Q Get new data. Otherwise, counter P COUNT It was reset to zero and from the original signal S Q Get new data.

[0136] Then repeat steps 60-64.

[0137] If the existence of peak P1 has been confirmed, then the counter P... COUNT The evaluation of the value is determined from step 64 to step 66, where the amplitude value A2 is compared with the corresponding threshold A2. TH To evaluate the electrostatic charge change signal S Q Existence of peak P2. If the existence of peak P2 is determined by comparison with a threshold, and the time condition for the value of interval T2 is satisfied, such that T2...TH_L < T2 < T2 TH_H then the counter P is updated (P = 2) and new data is acquired from the original signal S. Otherwise, the counter P is reset to the zero value and new data is acquired from the original signal S. COUNT COUNT Q COUNT Q

[0138] Steps 60-64 are then executed again.

[0139] If the presence of the peak P2 has been confirmed, the evaluation of the value of the counter P determines the passage from step 64 to step 67, in which the presence of the peak P3 in the first derivative signal S'is evaluated by comparing the amplitude value A3 with the corresponding threshold A3 COUNT TH Q TH_L < T3 < T3 TH_H then the counter P is updated (P = 3) and new data is acquired from the original signal S. Otherwise, the counter P is reset to the zero value and new data is acquired from the original signal S. COUNT COUNT Q COUNT Q

[0140] Steps 60-64 are then executed again.

[0141] If the presence of the peak P3 has been confirmed, the evaluation of the value of the counter P determines the passage from step 64 to step 68, in which the presence of the peak P4 in the static charge variation signal SQ is evaluated by comparing the amplitude value A4 with the corresponding threshold A4 COUNT TH TH_L < T4 < T4 TH_H then the counter P is updated (P = 4) and new data is acquired from the original signal S. Otherwise, the counter P is reset to the zero value and new data is acquired from the original signal S. COUNT COUNT Q COUNT Q

[0142] Steps 60-64 are then executed again.

[0143] If the presence of the peak P4 has been confirmed, the evaluation of the value of the counter P determines the passage from step 64 to step 69, in which the presence of the peak P5 in the static charge variation signal SQ is evaluated by comparing the amplitude value A5 with the corresponding threshold A5 COUNT ​​​​​​​​​​​​​​​​​​​​evaluation of the value of the interval T5determines the passage from step 64 to step 69, in which the presence of the peak P5in the first derivative signal S TH is evaluated by comparing the amplitude value A5with a corresponding threshold value A5 Q If the comparison with the threshold value determines the presence of the peak P5and the temporal condition of the value of the interval T5is satisfied, such that T5 TH_L <T5<T5 TH_H , the analysis of the relative portions 18a, 18b of the signals S Q and S Q ' is ended and a warning or trigger signal can be generated, which confirms the identification of a footstep in the signal generated by the static charge variation sensor 6.

[0144] The counter P COUNT is reset and new data are acquired from the original signal S Q to identify the presence of a consecutive footstep.

[0145] According to one aspect of the present disclosure, the presence of a person in the environment is confirmed after the identification of a plurality of footsteps, for example five footsteps. However, it is clear that, in order to speed up the detection, the presence of the object can be reported even after the identification of a single footstep.

[0146] As previously mentioned, in order to generate an actual alarm or a final confirmation of the presence of a human being, the present disclosure provides a joint analysis of the signals S P , S A generated by the pressure sensor 4 and the vibration sensor 7.

[0147] Figure 7 The steps of the method for analyzing the pressure signal S P are illustrated by means of a block diagram.

[0148] In one embodiment, Figure 7 the algorithm operates in real time, similarly to the method of Figure 6 , i.e. processing the data during the same acquisition step. Assuming that the pressure signal has been converted into digital, therefore, in the following the term "data" indicates the digital values of the pressure signal S P (for example, pressure values, in mbar).

[0149] In each iteration, after acquiring the pressure signal S P (step 70), the i-th pressure data Pi(amplitude value) is removed from its baseline (step 71) and stored in the buffer P BUFF (step 72); at the same time, or at a previous or subsequent time, it does not matter, the pressure signal S Psearch for a possible peak (step 73). If a peak is detected (step 74, output yes), a value PK25 is calculated (step 75) equal to 25% of the amplitude of the detected peak (this percentage value is indicative and can vary, for example, in the range 10-50%). Iteratively, each pressure datum (the i-th datum PK BUFF ) contained in the buffer P i is subtracted (step 76) from this value PK25 (operation PK i -PK25). If the value resulting from this subtraction is positive (step 77, output yes), this value is added to the variable P AREA (indicative of the area of the flat portion, between the peak and its value 25%) to perform the calculation in digital form of the integral of the signal around the detected peak (step 79). The integral can be calculated as the area subtended by the signal related to the peak (variable A in step 79), i.e. by adding the digital form of the amplitude value P i -PK25, only if the difference is greater than 0. In step 76, in fact, each sample P i is subtracted from the value PK25; if the result of this operation of step 76 is positive, this result is added to the previous area value A (where A is initialized to 0 at the start of the method); if the result of step 76 is negative, this result is ignored. This addition operation is performed at most N times; the count of these N iterations is performed by increasing the index j, regardless of the value of the result of the operation of step 76 (the increase of j allows to pass through the entire buffer 72).

[0150] Steps 76, 77, 78, 79 have the function of quantifying only the portion of area subtended by the curve in the presence of a peak, so as to be able to perform the operation of the subsequent step 80, i.e. to evaluate the peak itself.

[0151] Finally, the ratio R PK between P AREA and PK i -PK25 is calculated (step 80) (resulting in a value greater than 1), which is indicative of the "steepness" of the peak: the smaller the value of the ratio R PK , the greater the steepness, and vice versa. The value of the ratio R PK is compared with a threshold value RP THRES (step 81): if R PK <R PTHRES , the peak is steep enough to resemble the one generated by the opening of the door and a signal or trigger is generated (step 82) indicative of this event; otherwise, the method returns to step 70 by resetting the variables j and A. The choice of the threshold value RP THRES includes, for example, values comprised between 10 and 30; the smaller this value, the steeper and more time-limited the detected peak.

[0152] To explain more clearly Figure 7 The operation of the method, Figure 8A The threshold PK25 and peak PK are shown graphically on it. i Pressure signal S P Here, Peak PK i It has an amplitude value of 0.215 mbar, therefore the threshold PK25 = 0.054 mbar.

[0153] Figure 8B The following is shown after step 76 Figure 8A The signal, where execution is performed from signal S P The value PK25 is subtracted from each data point. After this operation, the peak Pk... i The value is equal to 0.16 mbar, and the ratio R PK The value is 9.44, and the area P AREA The value is equal to 1.51, and the threshold RP THRES The value is set to 15. Therefore, the evaluation in step 81 gives a positive result, i.e., R. PK <RP THRES .

[0154] Figure 8C This indicates that the door opening event was not acknowledged / identified because R PK >RP THRES In this example, after the calculation in step 76, the peak PK... i The amplitude is 0.22, the value of PK25 is 0.072, and the area P AREA The value is 8.56, and the ratio R PK It equals 38.9, and the threshold RP THRES Set it to 15.

[0155] Figure 9 A block diagram illustrates the method used to analyze vibration signal S. A The steps of the method.

[0156] In one embodiment, Figure 9 The algorithm is computed in real time, similar to Figure 6 or Figure 7 The method involves processing data during the same acquisition step. It is assumed that the vibration signal has been converted to digital form; therefore, in the following text, the term "data" refers to the vibration signal S. A The digital value (e.g., signal amplitude in LSBs).

[0157] In each iteration, after processing unit 2 has acquired the detection axis (S) of the accelerometer... Ax S Ay S Az) related to the vibration signal (step 90), the modulus of the acceleration XLM (i.e. the signal S A ) discussed above, is calculated on the basis of the signals acquired from the three axes of the accelerometer (it is assumed here that a three-axis accelerometer is used).

[0158] Then the AC component (i.e. the quantity related to the variation of the signal with respect to the average value of the signal, whose ith value is indicated as XLPKi) is obtained (step 92), which is stored in the buffer XLAC BUFFER ; at the same time a search for possible signal peaks is performed on these data (step 93). If a peak is detected (step 93, output "yes"), the value XLPK25 (step 95) is calculated as equal to 25% of the peak amplitude (a different percentage value can be chosen, for example in the range 10%-50%). Otherwise, the method returns to step 90.

[0159] Iteratively, all the values XLPKi contained in the buffer XLAC BUFFER are subtracted from the value XLPK25 (step 96). If for each sample the value resulting from the subtraction is positive, (step 97, output "yes") the value is added to the variable XLA (indicative of the area of the flat portion, between the peak and its value of 25%) implementing an operation to calculate the integral in digital format (step 98).

[0160] If the result of the above step is negative, the result is ignored. This addition and updating operation of the variable XLA is performed for a maximum of N iterations; the count of these N iterations is performed by increasing the index k, regardless of the result of the evaluation of step 97 (the increase of k allows to pass through the entire buffer XLAC BUFFER ).

[0161] Then the ratio R XL between the area A XLPK and XLPKi-XLPK25 is calculated (step 100) (greater than 1), which is indicative of the steepness of the peak; for each ith data, the smaller the value of this ratio, the greater the steepness of the rise of the signal, and vice versa.

[0162] R XLPK is compared with a threshold value RXLPK THRES (step 101): if R XLPK <RXLPK THRES , the peak identified is steep and similar to the peak generated by the footstep of the subject (step 103) and a suitable signal or trigger is generated, which confirms the presence of the subject in the environment under consideration.

[0163] To increase the reliability of the proposed method, making the vibration signal verified as generated by the steps of the subject, it is optionally possible to verify (step 102) the repetition over time of a certain number of peaks (for example, by setting a comparison threshold CountTHRES, for example equal to 2), provided that more than a predefined value T THRES does not elapse between a single step and a consecutive step (for the selection of this value, similar considerations to those previously made for the pressure signal S P are valid).

[0164] To improve the clarity of the operation of the method of Figure 9 , Figure 10A the vibration signal S A obtained by calculating the modulus of the three detection components of a three-axis accelerometer is shown. Figure 10A The signal of MAX is temporarily limited to the signal detected during the execution of a single step.

[0165] Figure 10B The AC component of the signal of Figure 10A is shown (which in fact represents the envelope of the signal of Figure 10A ). The maximum value XLPK XLPK of the peak amplitude, here equal to 101.9 LSB, the calculated value XLPK25= 25.5 LSB shows on the signal of Figure 10B .

[0166] Figure 10C The signal resulting from the operation XLPKi-XLPK25is shown (performed on each i-th sample of the signal SA), in which Figure 10B the peak value is now equal to 76.4, the value of the area AXLis equal to 517.9, the ratio R XLPK has a value equal to 6.78, and the threshold RXLPK THRES is set to the value 15. Therefore R XLPK <RXLPK THRES the evaluation of gives a positive result, i.e. R XLPK <RXLPK THRES .

[0167] With reference to Figure 11 and Figure 12 , a respective method for removing the baseline applicable to the present disclosure is now described.

[0168] With reference to Figure 11 , the algorithm operates in real time, similar to the method of Figure 6 or Figure 7 , i.e. processing the data during the same acquisition step. Assuming that the signal received at the input (which can be the vibration signal S A , the pressure signal S P and the electrostatic charge variation signal SQ of any of the moduli have been converted into numbers, therefore, hereinafter, the term "data" identifies the sample values or the numerical values of the considered signal (e.g. signal amplitudes in LSBs or pressure values in mbar).

[0169] In each iteration, the following operations are performed.

[0170] If the input data xi (i-th data) is contained between the thresholds BL THRES_H and BL THRES_L (step 110, output is), the data xi is accumulated in a shift buffer having size N BLBUFF (e.g. N BLBUFF = 10) (step 111).

[0171] In the early iteration steps (first start of the algorithm), the thresholds BL THRES_H and BL THRES_L (i.e. the output of block 110 is "yes") are ignored until the buffer is completely filled (using and, in some embodiments, possibly requiring a number of iterations equal to N BLBUFF ). In other words, all input samples xi will fill the buffer, as shown by the dashed arrow 110a.

[0172] The variable BL storing the current baseline value is then updated with a value equal to the average of the samples present in the buffer (step 112a), while the standard deviation value of the samples present in the buffer is calculated (step 112b). The new thresholds BL THRES_H and BL THRES_L (step 113) are calculated, respectively equal to the value of the variable BL increased and decreased by a multiple of the standard deviation of the samples present in the buffer. The parameter k adjusts the width of the band defined by the two thresholds BL THRES_H and BL THRES_L : the greater the value of k, the greater the variation of the input data to be absorbed in the baseline. The variable k is selected in the range, for example, 3-6.

[0173] After the respective baseline value BL has been calculated for each input sample xi, the output data yi = xi - BL is generated (step 114), i.e. the data to be formed into the respective vibration signal S A , pressure signal S P or electrostatic charge signal S Q is deprived of the respective baseline.

[0174] If the input data is not contained between the thresholds BL THRES_H and BL THRES_Lbetween them (step 110, output NO), then the baseline and the threshold are not modified. In any case, the output data yi is equal to the input value xi minus the value BL calculated as the average of the samples present in the buffer. The operations of steps 112a, 112b are repeated until the buffer is completely filled.

[0175] Figure 12 Another method is shown, alternative to the method of Figure 11 for calculating the baseline and subtracting it from the corresponding signal.

[0176] This algorithm operates in real time, similarly to the method of Figure 11 i.e. processing the data during the same acquisition step. It is assumed that the signal received at the input (which can be any one of the modulus of the vibration signal S A , the pressure signal S P and the electrostatic charge variation signal S Q has been converted into digital, therefore, hereinafter, the term "data" identifies the sample values or the numerical values of the signal under consideration (e.g. signal amplitude in LSB or pressure value in mbar).

[0177] In each i-th iteration, the processing unit 2 acquires the i-th data xi of the corresponding signal (step 120). Then, the first derivative xi' is calculated (step 121). Then, the absolute value of the first derivative xi' is calculated (step 122). The calculated absolute value xi' is then input into a buffer of size NB LBUFF (e.g. equal to 10) (step 123).

[0178] If (step 124) all the values contained in the buffer are lower than the threshold BL THRES (output YES from step 124), the input data xi is input into a second buffer of size M BLBUFF (step 125). As a derivative, the threshold BL THRES represents the rate at which the signal increases (or decreases). This value depends on the type of quantity analyzed, on the data rate and on the "noise" of the environment and of the sensor itself. For example, in the case of the charge variation signal, the threshold BL THRES may be comprised between 8000 and 16000.

[0179] The baseline BL is then updated to the new value given by the average of the elements present in this second buffer (step 126).

[0180] After calculating the corresponding baseline value BL for each input sample xi, the output data yi = xi - BL is generated (step 127), i.e. the vibration signal S A , the pressure signal SP or electrostatic charge signal S Q The data are deprived of the corresponding baseline.

[0181] If at least one element of the first buffer exceeds or is equal to the threshold value BL THRES , the baseline variable BL is not updated (step 124 output NO).

[0182] However, the output value yi is equal to the input value xi minus the value BL.

[0183] At the first start, the algorithm ignores the check for the threshold value BL BLBUFF of the first buffer of size N THRES sufficient to completely fill it for a number of iterations equal to the size N A of the first buffer. In this initial condition, all the input samples |xi'| are used to fill the first buffer and the generation of the output data yi is not performed.

[0184] Figure 13 The steps of a peak finding method, which can be used in the context of the present disclosure to identify positive and negative peaks, are illustrated by means of a block diagram, for example applicable in the context of steps 62 and 73 previously referred to Figure 6 and Figure 7 respectively.

[0185] With reference to Figure 13 , the algorithm operates in real time, similarly to the method of Figure 6 or Figure 7 , i.e. processing the data during the same acquisition step. Assuming that the signal received at the input (which can be any one of the signals S A , S P and S Q , modulus of the electrostatic charge variation) has been converted into digital, therefore, hereinafter, the term "data" identifies the sample values or the digital values or the samples of the signal under consideration (for example, signal amplitude in LSB or pressure value in mbar).

[0186] With reference to the algorithm of Figure 13 , the following variables are defined and used:

[0187] xi = amplitude in LSB or pressure value in mbar of the current data (sample) (i-th data);

[0188] ti = instant of time related to the current data xi;

[0189] 2N + 1 = width of the peak under consideration, expressed in number of samples (encompassing the signal portion rising to the maximum peak value, the maximum value reached and the signal portion falling from the maximum value);

[0190] PK THRES= comparison threshold for detecting the presence of a positive peak;

[0191] VL THRES = comparison threshold for detecting the presence of a negative peak;

[0192] xj = local maximum reached by a positive peak;

[0193] xk = local minimum reached by a negative peak;

[0194] pka = amplitude in LSBs or pressure value in mbar of the local maximum reached by the considered positive peak;

[0195] pkt = instant related to the local maximum pka reached by the considered positive peak;

[0196] vla = amplitude in LSBs or pressure value in mbar of the local minimum reached by the considered negative peak;

[0197] vlt = instant related to the local minimum vla reached by the considered negative peak;

[0198] PKF = variable or "flag" for identifying a "positive peak found" event;

[0199] VLF = variable or "flag" for identifying a "negative peak found" event.

[0200] In each iteration, the amplitude and the time index of the input data are inputted (steps 130a and 130b) into two buffers X PBUFF (containing the data xi) and T PBUFF (containing the data ti). Subsequently, the maximum xj and the minimum xk of all the elements of the buffer X PBUFF are calculated (steps 131a and 131b).

[0201] If the time index pkt of the found local maximum xj is not equal to the value of the index N, it means that the sample corresponding to the local maximum xj is not placed in the middle of the buffer X PBUFF ; in this case, no peak is found and PKF = "false" (step 132a outputs NO).

[0202] On the contrary, if the time index pkt of the found local maximum xj is equal to the value of the index N (step 132a outputs YES), it means that the sample corresponding to the local maximum xj is placed in the middle of the buffer X PBUFF ; if the found local maximum xj is higher than PK THRES (for example, a value equal to 15000 is chosen for the electrostatic charge variation signal PK THRES), then (step 133a) it occurs. If so, it is confirmed that there is a peak with width 2N+1 and the variable PKF is set to "TRUE" (step 134a).

[0203] The amplitude of the peak found and confirmed is x N , with time index t N .

[0204] A double check can be made for the search of negative peaks.

[0205] In this case, if the time index vlt of the local minimum found xk is not equal to the value of the index N, it means that the sample corresponding to the local minimum xk is not placed in the middle of the buffer T PBUFF ; in this case, no peak is found and VLF = "FALSE" (step 132b output NO).

[0206] On the contrary, if the time index vlt of the local minimum found xk is equal to the value of the index N (step 132b output YES), it means that the sample corresponding to the local minimum xk is placed in the middle of the buffer T PBUFF ; if the local minimum found xk exceeds (for negative values) the threshold value VL THRES (eg, VL THRES equal to -15000 is chosen for the electrostatic charge variation signal), then (step 133b) it occurs. If so, it is confirmed that there is a peak with width 2N+1 and the variable VLF is set to "TRUE" (step 134b).

[0207] The amplitude of the negative peak found and confirmed is x N , with time index t N .

[0208] At the first start, the algorithm is not operated for a number of iterations equal to 2N+1, that is, until the buffers X PBUFF and T PBUFF are filled. In this step, all the input samples will fill the buffers and the outputs are set to PKF = "FALSE" and VLF = "FALSE".

[0209] Figure 14 An algorithm or method is shown for calculating the first derivative of the signals S P , S A and S Q , which is applicable in the context of the present disclosure.

[0210] Figure 14 The algorithm operates in real time, similar to the method described earlier, that is, processing the data during the same acquisition step. It is assumed that the signal received at the input (which can be a vibration signal S A , pressure signal SP and the electrostatic charge variation signal S Q Any of the signals (any of the modulus of the signals S

[0211] By definition, the output y is delayed with respect to the input of 2 samples; the first derivative of the input signal computed at time ti is related to the input signal at time t(i-2). Therefore, the two streams are aligned in time before computing the relative time distance.

[0212] Reference is made to Figure 15 for extracting the envelope of the considered signal (any of the signals S P , S A and S Q ) or for obtaining the above-mentioned “AC component” (e.g. with reference to step 92). The method is illustrated by means of a block diagram.

[0213] Reference is made to Figure 15 The digital samples xi of the signal being processed are acquired and stored in a buffer 150. In particular, the buffer 150 is designed to store a plurality of samples (e.g. 25 samples, for a sampling rate of 50 Hz and a time window of 0.5 seconds). In any case, the number of samples can vary according to needs, considering that the greater this number, the smoother the signal generated at the output of the block chain of Figure 15 of the following. For example, the number of samples in the buffer 150 is selected in the range 10-30.

[0214] The samples stored in the buffer 150 are sent to a first input of a subtraction block 152. The other input of the subtraction block 152 receives samples further processed (filtered) by a branch 154, as described below.

[0215] The branch 154 first comprises a processing block 155 using a Hann window 156 or Hann function, of the type known per se and implementing the following function:

[0216]

[0217] where xi = [x0,..., x K-1 ] are the samples at the input in the processing block 155 (the index “i = 0,..., K-1” identifies the i-th sample) and yi = [y0,..., y K-1 ] are the samples output by the processing block 155.

[0218] The branch 154 comprises an averaging block 157 which receives the samples yi = [y0,..., y K-1and performs an arithmetic mean operation of the values of said samples.

[0219] The branch 154 also comprises a multiplication block 158 which receives at the input the mean value generated at the output of the block 157 and performs an operation of multiplying said mean value by 2 (since the Hann window of the block 156 halves the average amplitude of the signal, the introduced attenuation is compensated by this operation), thus generating an output which is provided to the second input of the subtraction block 152.

[0220] At the output of the subtraction block 152, the signal at the input of the subtraction block 152, from which the mean value has been subtracted, is thus oscillating around zero, without any significant offset. The output of the subtraction block 152 is then further processed by the block 159, which implements a further Hann window, as described for the block 156. This further Hann window 159 has the function of smoothing the signal, smoothing the peaks and the discontinuities at the ends of the analysis window.

[0221] The block 160 receives at the input the array generated at the output of the block 159 and performs an estimation of the variance of said array in a manner known per se. The output of the block 160 is thus a scalar.

[0222] Finally, the square root operation of the variance value (block 162) has the function of compressing the dynamic range of the output signal and restoring it to the initial physical dimension. In other words, the variance increases according to powers of 2 and the square root restores the physical dimension. For example, for a signal S A If the physical dimension at the input is denoted by V, after the variance calculation it is denoted by V 2 .

[0223] The advantages achieved by the present disclosure are evident from the foregoing description.

[0224] In particular, with respect to the prior art, the following advantages are obtained:

[0225] Not sensitive to environmental conditions;

[0226] Very low consumption compared to other technologies (infrared, microwave, etc.);

[0227] Small size, easy to integrate and install;

[0228] Unlike the common detectors which provide a "lens" or an antenna which limits its spatial shape / arrangement, the present disclosure can be physically organized on the basis of the application;

[0229] Reduction of costs with respect to known technologies.

[0230] Further variations with respect to what has been described can also be provided.

[0231] For example, the present disclosure can be modified to what has been described by excluding one of the pressure sensor 4 and the vibration sensor 7. In this case, the presence of a human in the environment to be monitored is confirmed by the analysis step of only one of the electrostatic charge variation signal S Q in combination with the vibration signal S A and the pressure signal S P . If the excluded or absent sensor is the pressure sensor, the environment in which the presence of the subject is detected can not be a closed environment.

[0232] Furthermore, although the present disclosure has been described with explicit reference to the processing of digital signals, what has been described applies in a manner that is per se evident to analog signals.

[0233] A system for detecting the presence in an environment to be monitored can be summarized as comprising: a processing unit (2); an electrostatic charge variation sensor (6) coupled to the processing unit (2) configured to detect a variation of electrostatic charge in said environment and to generate an electrostatic charge variation signal (S Q ); and one of a vibration sensor (7) and an environmental pressure sensor (4), wherein the vibration sensor is operatively coupled to the environment to be monitored to detect an environmental vibration in the environment to be monitored and to generate a vibration signal (S A ), and wherein the environmental pressure sensor (4) is operatively coupled to the environment to be monitored to detect an environmental pressure in the environment to be monitored and to generate a pressure signal (S P ), wherein the processing unit (2) is configured to acquire the electrostatic charge variation signal (S Q ) from the electrostatic charge variation sensor (6); to detect in said electrostatic charge variation signal (S Q ) a first signal feature indicative of the presence of a human in said environment to be monitored; to acquire the vibration signal (S A ) or the pressure signal (S P ) from one of the vibration sensor (7) and the environmental pressure sensor (4), respectively; to detect in said acquired vibration signal (S A ) or pressure signal (S P ) a respective second signal feature indicative of the presence of a human in said environment to be monitored; to generate a warning signal if both the first signal feature and the second signal feature are detected.

[0234] The system can also comprise the other of the vibration sensor (7) and the environmental pressure sensor (4), wherein the processing unit (2) is further configured to acquire the vibration signal (S A ) or the pressure signal (S P ) from the other of said vibration sensor (7) and environmental pressure sensor (4), respectively; to acquire the vibration signal (S A ) and the pressure signal (SP In another item of the above, a corresponding third signal feature indicating the presence of humans in the environment to be monitored is detected; if the first signal feature, the second signal feature and the third signal feature are all detected, a warning signal is generated.

[0235] The operation of detecting the first signal feature can include the electrostatic charge change signal (S Q In the detection of the following features that follow each other in a time sequence: first rising edge; first local maximum; first falling edge; first local minimum; second rising edge; or, in the electrostatic charge change signal (S Q The following features are detected in chronological order: falling edge; first local minimum; first rising edge; first local maximum; second falling edge.

[0236] The operation of detecting the first signal feature may further include performing a comparison of the local maximum and minimum values ​​with corresponding thresholds; and evaluating the steepness or rate of rise of the first rising edge and the second rising edge, as well as the steepness or rate of fall of the falling edge, by comparing with the corresponding thresholds.

[0237] In the static charge change signal (S Q The operation of detecting features that follow each other in a time sequence can include: calculating the electrostatic charge change signal (S Q The first derivative of ) (S Q '); In the static charge change signal (S) Q ) and the first derivative signal (S Q The corresponding positive and negative peaks are identified in the diagram; detect one of the following time series a) and b), where the multiple positive and negative peaks follow each other over time: a) First derivative signal (S Q The first positive peak (P1) in the ') signal; the electrostatic charge change signal (S Q The second positive peak (P2) in the signal; the first derivative signal (S) Q The first negative peak (P3) in the '); the electrostatic charge change signal (S Q The second negative peak (P4) in the signal; the first derivative signal (S) Q The third positive peak (P5) in the signal; b) the first derivative signal (S) Q The third negative peak in '); electrostatic charge change signal (S Q The fourth negative peak in the signal; the first derivative signal (S) Q The fourth positive peak in '); the electrostatic charge change signal (S Q The fifth positive peak in the signal; the first derivative signal (S) Q The fifth negative peak in ').

[0238] The operation of detecting the first signal feature may also include detecting one or more of the following time relationships:

[0239] T1 = T3 + T4,

[0240] T6 = T2 + T3,

[0241] T7 = T4 + T5,

[0242] T8 = T6 + T7,

[0243] wherein T1 can be the time interval between the second positive peak (P2) and the second negative peak (P4), T2 can be the time interval between the second positive peak (P2) and the first positive peak (P1), T3 can be the time interval between the second positive peak (P2) and the first negative peak (P3), T4 can be the time interval between the second negative peak (P4) and the first negative peak (P3), T5 can be the time interval between the second negative peak (P4) and the third positive peak (P5), T6 can be the time interval between the first positive peak (P1) and the first negative peak (P3), T7 can be the time interval between the first negative peak (P3) and the third positive peak (P5), and T8 can be the time interval between the first positive peak (P1) and the third positive peak (P5).

[0244] The time intervals T1-T7 can be the respective time distances between the respective maximum or minimum points of the positive and negative peaks.

[0245] The operation of detecting the first signal feature can further comprise detecting one or more of the following temporal relationships:

[0246] T2 TH_L T2 < T2 TH_H T3 TH_L T3 < T3 TH_H T4 TH_L T4 < T4 TH_H ,

[0247] T5 TH_L T5 < T5 TH_H wherein T2 TH_L T3 TH_L T4 TH_L and T5 TH_L are respective lower threshold values that can comprise respective values comprised between 30 and 70 ms, and T2 TH_H T3 TH_H T4 TH_H and T5 TH_H are respective upper threshold values that comprise respective values comprised between 150-250 ms.

[0248] The second signal feature can belong to a pressure signal (S P and the pressure signal (S PThe operation of detecting a second signal feature in the pressure signal (S P ) can comprise detecting a time-amplitude value and a maximum value of a pressure peak present in the pressure signal (S

[0249] Detecting a time-amplitude value can comprise calculating an integral or an area subtended by a pressure peak present in the pressure signal (S P ) and the first comparison parameter can be calculated by dividing the result of the integral of the pressure peak or the value of the area subtended by the pressure peak by the maximum value of the pressure peak.

[0250] The third signal feature belongs to a vibration signal (S A ) and the operation of detecting a third signal feature in the vibration signal (S A ) can comprise detecting a time-amplitude value and a maximum value of a vibration peak present in the vibration signal (S A ) and calculating a second comparison parameter that is a function of the ratio between the time-amplitude value and the maximum value of the vibration peak.

[0251] Detecting a time-amplitude value can comprise calculating an integral or an area subtended by a vibration peak present in the vibration signal (S A ) and the second comparison parameter can be calculated by dividing the result of the integral of the vibration peak or the value of the area subtended by the vibration peak by the maximum value of the vibration peak.

[0252] A method for detecting a presence in an environment to be monitored can be summarized as comprising the steps of: detecting electrostatic charge variations in the environment by means of an electrostatic charge variation sensor (6) and generating an electrostatic charge variation signal (S Q ); detecting environmental vibrations in the environment to be monitored by means of a vibration sensor (7) operatively coupled with the environment to be monitored and generating a vibration signal (S A ); or, detecting environmental pressure in the environment to be monitored by means of an environmental pressure sensor (4) operatively coupled to the environment to be monitored and generating a pressure signal (S P ); acquiring, by means of a processing unit (2), the electrostatic charge variation signal (S Q ) from the electrostatic charge variation sensor (6); detecting, by means of the processing unit (2), a first signal feature in the electrostatic charge variation signal (S Q ) indicative of a presence of an object in the environment to be monitored; acquiring, by means of the processing unit (2), the vibration signal (S A ) or the pressure signal (SP ); The vibration signal (S) acquired by the processing unit (2) A ) or pressure signal (S P The first signal feature is detected in the processing unit (2) to indicate the existence of an object in the environment to be monitored; if both the first signal feature and the second signal feature have been detected, a warning signal is generated by the processing unit (2).

[0253] The method may also include detecting environmental vibrations and generating vibration signals (S). A ) or detect environmental pressure and generate a pressure signal (S P Another step in the process, and further includes the step performed by the processing unit (2): acquiring vibration signals (S) from another of the vibration sensor (7) and the ambient pressure sensor (4). A ) or pressure signal (S P ); in the acquired vibration signal (S A ) and pressure signal (S P In another item of the above, a corresponding third signal feature indicating the presence of an object in the environment to be monitored is detected; if the first signal feature, the second signal feature and the third signal feature are all detected, a warning signal is generated.

[0254] The step of detecting the first signal feature may include detecting the electrostatic charge change signal (S Q In the detection of the following features that follow each other in a time sequence: first rising edge; first local maximum; first falling edge; first local minimum; second rising edge; or, in the electrostatic charge change signal (S Q The following features are detected in chronological order: falling edge; first local minimum; first rising edge; first local maximum; second falling edge.

[0255] The step of detecting the first signal feature may further include comparing the local maximum and minimum values ​​with corresponding thresholds; and by comparing with the corresponding thresholds, evaluating the steepness or rise rate of the first rising edge and the second rising edge, as well as the steepness or fall rate of the falling edge.

[0256] In the static charge change signal (S Q Detecting features that follow each other in a time sequence can include calculating the electrostatic charge change signal (S Q The first derivative of ) (S Q '); In the static charge change signal (S) Q ) and the first derivative signal (S Q The corresponding positive and negative peaks are identified in the diagram; detect one of the following time series a) and b), where the multiple positive and negative peaks follow each other over time: a) First derivative signal (S Qthe first positive peak (P1) in the first derivative signal (S Q the second positive peak (P2) in the first derivative signal (S Q the first negative peak (P3) in the first derivative signal (S Q the second negative peak (P4) in the first derivative signal (S Q the third positive peak (P5) in the first derivative signal (S Q the third negative peak in the first derivative signal (S Q the fourth negative peak in the first derivative signal (S Q the fourth positive peak in the first derivative signal (S Q the fifth positive peak in the first derivative signal (S Q the fifth negative peak in the first derivative signal (S

[0257] The step of detecting the first signal features can further comprise detecting one or more of the following temporal relationships:

[0258] T1 = T3 + T4,

[0259] T6 = T2 + T3,

[0260] T7 = T4 + T5,

[0261] T8 = T6 + T7,

[0262] wherein T1 can be the time interval between the second positive peak (P2) and the second negative peak (P4), T2 can be the time interval between the second positive peak (P2) and the first positive peak (P1), T3 can be the time interval between the second positive peak (P2) and the first negative peak (P3), T4 can be the time interval between the second negative peak (P4) and the first negative peak (P3), T5 can be the time interval between the second negative peak (P4) and the third positive peak (P5), T6 can be the time interval between the first positive peak (P1) and the first negative peak (P3), T7 can be the time interval between the first negative peak (P3) and the third positive peak (P5), and T8 can be the time interval between the first positive peak (P1) and the third positive peak (P5).

[0263] The time intervals T1-T7 can be the respective time distances between the respective maxima or minima of the positive and negative peaks.

[0264] The step of detecting the first signal features can further comprise detecting one or more of the following temporal relationships:

[0265] T2 TH_L T2 < T2 TH_H T3 TH_L T3 < T3 TH_H T4 TH_L< T4 < T4 TH_H 、

[0266] T5 TH_L < T5 < T5 TH_H wherein T2 TH_L , T3 TH_L , T4 TH_L and T5 TH_L are respective lower threshold values which can comprise respective values comprised between 30 and 70 ms, T2 TH_H , T3 TH_H , T4 TH_H and T5 TH_H are respective upper threshold values which can comprise respective values comprised between 150 and 250 ms.

[0267] The second signal feature belongs to the pressure signal (S P ) and wherein said step of detecting the second signal feature in the pressure signal (S P ) can comprise detecting a time amplitude value and a maximum value of a pressure peak present in the pressure signal (S P ); calculating a first comparison parameter which is a function of the ratio between said time amplitude value and said maximum value of the pressure peak; verifying whether said first comparison parameter is in a predetermined relationship with a first threshold value.

[0268] Detecting the time amplitude value can comprise calculating an integral or an area subtended by the pressure peak present in the pressure signal (S P ) and said first comparison parameter can be calculated by dividing the result of said integral or the value of said area subtended by the pressure peak by the maximum value of the pressure peak.

[0269] The third signal feature belongs to the vibration signal (S A ) and said step of detecting the third signal feature in the vibration signal (S A ) can comprise detecting a time amplitude value and a maximum value of the vibration peak present in the vibration signal (S A ); calculating a second comparison parameter which can be a function of the ratio between said time amplitude value and said maximum value of the vibration peak; verifying whether said second comparison parameter is in a predetermined relationship with a second threshold value.

[0270] Detecting the time amplitude value can comprise calculating an integral or an area subtended by the vibration peak present in the vibration signal (S A ) and the second comparison parameter can be calculated by dividing the result of said integral or the value of said area subtended by the vibration peak by the maximum value of the vibration peak.

[0271] The various embodiments described above can be combined to provide further embodiments. All patents, patent applications, publications, etc. cited above are incorporated herein by reference to the extent that the incorporated material is consistent with the disclosure of this application. To the extent of any conflict between the disclosure of this application and the incorporated material, the disclosure of this application will control.

[0272] In light of the above detailed description, those skilled in the art can make changes and modifications to the embodiments. In general, the terms used in the following claims should not be construed to limit the claims to the specific embodiments disclosed in the specification and claims, but should be construed to include all possible embodiments and the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.

Claims

1. A system for detecting a presence in an environment to be monitored, comprising: a processor; an electrostatic charge variation sensor coupled to the processor and configured to detect a variation of an electrostatic charge in the environment and to generate an electrostatic charge variation signal; and one of a vibration sensor operatively coupled to the environment to be monitored and configured to detect an environmental vibration in the environment to be monitored and to generate a vibration signal, or an environmental pressure sensor operatively coupled to the environment to be monitored and configured to detect an environmental pressure in the environment to be monitored and to generate a pressure signal, wherein the processor is configured to: obtain the electrostatic charge variation signal from the electrostatic charge variation sensor; detect a first signal feature indicative of the presence of an object in the environment to be monitored in the electrostatic charge variation signal; obtain the vibration signal or the pressure signal, respectively, from one of the vibration sensor or the environmental pressure sensor; detect a respective second signal feature indicative of the presence of the object in the environment to be monitored in the obtained vibration signal or pressure signal; and generate a warning signal if both the first signal feature and the second signal feature have been detected.

2. The system of claim 1, further comprising the other one of the vibration sensor or the environmental pressure sensor, wherein the processor is further configured to: obtain the vibration signal or the pressure signal, respectively, from the other one of the vibration sensor or the environmental pressure sensor; detect a respective third signal feature indicative of the presence of the object in the environment to be monitored in the other one of the obtained vibration signal or pressure signal; and generate the warning signal if all of the first signal feature, the second signal feature, and the third signal feature are detected.

3. The system of claim 1, wherein detecting the first signal feature comprises: detecting, in the electrostatic charge variation signal, the following features in temporal succession following each other: a first rising edge; a first local maximum; a first falling edge; a first local minimum; a second rising edge; and alternatively, detecting, in the electrostatic charge variation signal, the following features in temporal succession following each other: a falling edge; a first local minimum; a first rising edge; a first local maximum; a second falling edge.

4. The system of claim 3, wherein detecting the first signal feature further comprises: performing a comparison of the local maximum and the local minimum with respective threshold values; and evaluating, by comparison with the respective threshold values, a value of a steepness or rise rate of the first rising edge and the second rising edge and a value of a steepness or fall rate of the falling edge.

5. The system of claim 3, wherein detecting the features in temporal succession following each other in the electrostatic charge variation signal comprises: calculating a first derivative of the electrostatic charge variation signal; identifying a respective plurality of positive and negative peaks in the electrostatic charge variation signal and the first derivative signal; and detecting, in the electrostatic charge variation signal, a first local maximum and a first local minimum between which a first rising edge is detected, and a second local maximum and a second local minimum between which a falling edge is detected. detecting one of the following time sequences a) and b) in which the plurality of positive and negative peaks follow each other over time: a) a first positive peak in the first derivative signal; a second positive peak in the static charge variation signal; a first negative peak in the first derivative signal; a second negative peak in the static charge variation signal; a third positive peak in the first derivative signal, b) a third negative peak in the first derivative signal; a fourth negative peak in the static charge variation signal; a fourth positive peak in the first derivative signal; a fifth positive peak in the static charge variation signal; a fifth negative peak in the first derivative signal.

6. The system according to claim 5, wherein detecting the first signal features further comprises detecting one or more of the following time relationships: T1 = T3 + T4, T6 = T2 + T3, T7 = T4 + T5, T8 = T6 + T7, wherein: T1 is the time interval between the second positive peak and the second negative peak, T2 is the time interval between the second positive peak and the first positive peak, T3 is the time interval between the second positive peak and the first negative peak, T4 is the time interval between the second negative peak and the first negative peak, T5 is the time interval between the second negative peak and the third positive peak, T6 is the time interval between the first positive peak and the first negative peak, T7 is the time interval between the first negative peak and the third positive peak, T8 is the time interval between the first positive peak and the third positive peak.

7. The system according to claim 6, wherein the time intervals T1 to T7 are the respective time distances between the respective maximum points or minimum points of the positive peaks and the negative peaks.

8. The system according to claim 6, wherein detecting the first signal features further comprises detecting one or more of the following time relationships: T2 TH_L <T2<T2 TH_H , T3 TH_L <T3<T3 TH_H , T4 TH_L <T4<T4 TH_H , T5 TH_L <T5<T5 TH_H , wherein T2 TH_L , T3 TH_L , T4 TH_L and T5 TH_L are respective lower threshold values of the respective values between 30 ms and 70 ms, and T2 TH_H , T3 TH_H , T4 TH_H and T5 TH_H are respective upper threshold values of the respective values between 150 ms and 250 ms, respectively.

9. The system according to claim 1, wherein the second signal features belong to the pressure signal, and wherein detecting the second signal features in the pressure signal comprises: detecting a time amplitude value of a pressure peak present in the pressure signal and a maximum amplitude value of the pressure peak; calculating a first comparison parameter which is a function of the ratio between the time amplitude value of the pressure peak and the maximum amplitude value of the pressure peak; and verifying whether the first comparison parameter is in a predetermined relationship with a first threshold value.

10. The system according to claim 9, wherein detecting the time amplitude value comprises calculating an integral of the pressure peak present in the pressure signal or an area subtended by the pressure peak present in the pressure signal, and wherein the first comparison parameter is calculated by dividing the result of the integral of the pressure peak or the value of the area subtended by the pressure peak by the maximum amplitude value of the pressure peak; wherein calculating the integral of the pressure peak comprises determining a fractional value of the maximum amplitude value of the pressure peak and iteratively performing the following steps: subtracting the fractional value from the pressure peak present in the pressure signal; and if the result of the subtraction of the fractional value is positive, adding the result of the subtraction of the fractional value to the result from the previous iteration.

11. The system of claim 2, wherein the third signal feature belongs to the vibration signal, and wherein detecting the third signal feature in the vibration signal comprises: detecting a time amplitude value of a vibration peak present in the vibration signal and a maximum amplitude value of the vibration peak; computing a second comparison parameter that is a function of a ratio between the time amplitude value of the vibration peak and the maximum amplitude value of the vibration peak; and verifying whether the second comparison parameter is in a predetermined relationship with a second threshold value.

12. The system of claim 11, wherein detecting the time amplitude value comprises computing an integral of the vibration peak present in the vibration signal or an area subtended by the vibration peak present in the vibration signal, and wherein the second comparison parameter is computed by dividing a result of the integral of the vibration peak or a value of the area subtended by the vibration peak by the maximum amplitude value of the vibration peak; wherein computing the integral of the vibration peak comprises determining a fractional value of the maximum amplitude value of the vibration peak and iteratively performing the following steps: subtracting the fractional value from the vibration peak present in the vibration signal; and if the result of subtracting the fractional value is positive, adding the result of subtracting the fractional value to the result from a previous iteration.

13. A method of detecting a presence in an environment to be monitored, comprising: detecting, by an electrostatic charge variation sensor, a variation of an electrostatic charge in the environment and generating an electrostatic charge variation signal; detecting, by one of a vibration sensor or an environmental pressure sensor operatively coupled to the environment to be monitored, an environmental vibration in the environment to be monitored and generating a vibration signal or an environmental pressure and generating a pressure signal, respectively; acquiring, by a processor, the electrostatic charge variation signal from the electrostatic charge variation sensor; detecting, by the processor, a first signal feature in the electrostatic charge variation signal indicative of the presence of an object in the environment to be monitored; acquiring, by the processor, the vibration signal or the pressure signal, respectively, from the one of the vibration sensor or the environmental pressure sensor; detecting, by the processor, a respective second signal feature in the acquired vibration signal or pressure signal indicative of the presence of the object in the environment to be monitored; and generating, by the processor, a warning signal if both the first signal feature and the second signal feature have been detected.

14. The method of claim 13, further comprising: detecting, by the other of the vibration sensor and the environmental pressure sensor, the environmental vibration and generating the vibration signal or detecting the environmental pressure and generating the pressure signal; acquiring the vibration signal or the pressure signal, respectively, from the other of the vibration sensor and the environmental pressure sensor; detecting a respective third signal feature in the other of the acquired vibration signal and pressure signal indicative of the presence of the object in the environment to be monitored; and generating the warning signal if all of the first signal feature, the second signal feature and the third signal feature have been detected.

15. The method according to claim 13, wherein detecting the first signal feature comprises: detecting in the electrostatic charge variation signal the following features following each other in time sequence: a first rising edge; a first local maximum; a first falling edge; a first local minimum; a second rising edge. alternatively detecting in the electrostatic charge variation signal the following features following each other in time sequence: a falling edge; a first local minimum; a first rising edge; a first local maximum; a second falling edge.

16. The method according to claim 15, wherein detecting the first signal feature further comprises: performing a comparison of the local maximum and the local minimum with respective threshold values; and evaluating a value of a steepness or rise rate of the first rising edge and the second rising edge and a value of a steepness or fall rate of the falling edge by comparison with the respective threshold values.

17. The method according to claim 15, wherein detecting the features following each other in time sequence in the electrostatic charge variation signal comprises: calculating a first derivative of the electrostatic charge variation signal; identifying in the electrostatic charge variation signal and the first derivative signal a respective plurality of positive and negative peaks; detecting one of the following time sequences a) and b) in which the plurality of positive and negative peaks follow each other over time: a first positive peak in the first derivative signal; a second positive peak in the electrostatic charge variation signal; a first negative peak in the first derivative signal; a second negative peak in the electrostatic charge variation signal; a third positive peak in the first derivative signal, b) a third negative peak in the first derivative signal; a fourth negative peak in the electrostatic charge variation signal; a fourth positive peak in the first derivative signal; a fifth positive peak in the electrostatic charge variation signal; a fifth negative peak in the first derivative signal.

18. The method according to claim 17, wherein detecting the first signal feature further comprises detecting one or more of the following time relationships: T1 = T3 + T4, T6 = T2 + T3, T7 = T4 + T5, T8 = T6 + T7, wherein: T1 is a time interval between the second positive peak and the second negative peak, T2 is a time interval between the second positive peak and the first positive peak, T3 is a time interval between the second positive peak and the first negative peak, T4 is a time interval between the second negative peak and the first negative peak, T5 is a time interval between the second negative peak and the third positive peak, T6 is a time interval between the first positive peak and the first negative peak, T7 is a time interval between the first negative peak and the third positive peak, T8 is a time interval between the first positive peak and the third positive peak.

19. The method according to claim 18, wherein the time intervals T1 to T7 are respective time distances between respective maximum points or minimum points of the positive peaks and the negative peaks.

20. The method according to claim 18, wherein detecting the first signal feature further comprises detecting one or more of the following time relationships: T2 TH_L <T2<T2 TH_H , T3 TH_L <T3<T3 TH_H , T4 TH_L <T4<T4 TH_H , T5 TH_L <T5<T5 TH_H , wherein, T2 TH_L , T3 TH_L , T4 TH_L and T5 TH_L are respective lower threshold values of the respective values between 30 ms and 70 ms, and T2 TH_H , T3 TH_H , T4 TH_H and T5 TH_H are respective upper threshold values of the respective values between 150 ms and 250 ms, respectively.

21. The method according to claim 13, wherein said second signal feature belongs to said pressure signal, and wherein detecting said second signal feature in said pressure signal comprises: detecting a time amplitude value of a pressure peak present in said pressure signal and a maximum amplitude value of said pressure peak; computing a first comparison parameter which is a function of a ratio between said time amplitude value of said pressure peak and said maximum amplitude value of said pressure peak; and verifying whether said first comparison parameter is in a predetermined relationship with a first threshold value.

22. The method according to claim 21, wherein detecting said time amplitude value comprises computing an integral of said pressure peak present in said pressure signal or a value of an area subtended by said pressure peak present in said pressure signal, and wherein said first comparison parameter is computed by dividing a result of said integral of said pressure peak or said value of said area subtended by said pressure peak by said maximum amplitude value of said pressure peak; wherein computing said integral of said pressure peak comprises determining a fractional value of said maximum amplitude value of said pressure peak and iteratively performing the following steps: subtracting said fractional value from said pressure peak present in said pressure signal; and if said result of subtracting said fractional value is positive, adding the result of subtracting said fractional value to the result from a previous iteration.

23. The method according to claim 14, wherein said third signal feature belongs to said vibration signal, and wherein detecting said third signal feature in said vibration signal comprises: detecting a time amplitude value of a vibration peak present in said vibration signal and a maximum amplitude value of said vibration peak; computing a second comparison parameter which is a function of a ratio between said time amplitude value of said vibration peak and said maximum amplitude value of said vibration peak; and verifying whether said second comparison parameter is in a predetermined relationship with a second threshold value.

24. The method according to claim 23, wherein detecting said time amplitude value comprises computing an integral of said vibration peak present in said vibration signal or a value of an area subtended by said vibration peak present in said vibration signal, and wherein said second comparison parameter is computed by dividing a result of said integral of said vibration peak or said value of said area subtended by said vibration peak by said maximum amplitude value of said vibration peak; wherein computing said integral of said vibration peak comprises determining a fractional value of said maximum amplitude value of said vibration peak and iteratively performing the following steps: subtracting said fractional value from said vibration peak present in said vibration signal; and if said result of subtracting said fractional value is positive, adding the result of subtracting said fractional value to the result from a previous iteration.

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