Patent Grant
11861998
The present disclosure relates to a system and a method for detecting a presence in an environment to be monitored, for example for anti-theft or anti-intrusion purpose.
Electric field sensors are used in alternative or in addition to accelerometer sensors for determining a user's activity, or for helping interpret the signals generated by other sensor devices.
In conductors, the electrical charges have a certain freedom of movement, and for this reason they will tend to position so as to remain as far away as possible from each other, distributing over the entire surface of the conductor.
In the presence of an external electric field, the electrons move until they reach a steady condition; the electric field inside the conductor is zero and immediately outside the conductor is perpendicular to it. The charges on the surface crowd where the radius of curvature is smaller (point effect). The electric charges may be transferred from one conductor to another by contact. Furthermore, the electric charges may be generated on a conductor by induction.
In insulators, instead, the atomic structure does not allow the charges to move, on the contrary it tends to retain them in the place where they were produced: the charge will therefore be localized. The insulators may be charged by rubbing (triboelectric effect). In the presence of an external electric field there are no charges free to move, but the molecules of the dielectric “deform” due to the repulsion between charges having the same sign and dipoles are created (bias), which make the dielectric macroscopically charged.
Nowadays there are many technologies and products that deal with anti-intrusion application and presence detection. Here is a list of the most common approaches for detecting an intrusion: Thermal image of a subject through infrared sensors; Passive infrared reacting to temperature variation (PIR); Active infrared wherein a ray from an emission point and a reception point is interrupted; Emission of microwaves being reflected by a subject, with the possibility of also measuring the speed of the subject; Ultrasound; use of beam-type photoelectric devices; use of microphones; use of cameras.
All the above-mentioned methods offer strengths and weaknesses in detecting an unwanted intrusion. This is the reason why the most robust and sophisticated systems combine multiple technologies together. For example, the passive infrared sensors are sensitive to environmental temperatures, while microwave anti-intrusion systems are unable to detect behind metal objects. In addition, a fluorescent light or a slight movement may trigger alarms. For this reason, dual technology based on the combination of PIR and microwaves is quite common. By crossing both information and alarms, an anti-intrusion system becomes more reliable with respect to false positives and unwanted alarms, and attains further advantages such as immunity with respect to pets. Below are some examples of the prior art.
Patent document EP2533219 describes an anti-intrusion system comprising at least one microwave detection device, for detecting the unauthorized entry of a subject into an area under surveillance; the detection device comprising an emitting antenna for emitting microwaves and a receiving antenna for receiving the reflected signal.
Patent document U.S. Pat. No. 6,188,318 describes a microwave plus PIR dual technology intrusion detector with immunity to pets.
Patent document EP1587041 describes an intrusion detection system comprising a passive infrared optic and a microwave transceiver.
Devices detecting the variation of the electric field generated by a person during the movements of the same, or exploiting a capacitive-type detection are also known. Technologies using the latter type of detection include, for example, touch screens, systems for detecting the position of the occupants in automobiles, and devices for determining the position, the orientation and the mass of an object, such as, for example, described in patent document U.S. Pat. No. 5,844,415 regarding an electric field detection device for determining the position, the mass distribution and the orientation of an object within a defined space, arranging a plurality of electrodes within the defined space. This technical solution could also be used to recognize a user's gestures, hand position and orientation, for example for interactive use with a processing system, in place of a mouse or a joystick.
Patent document KR20110061750 refers to the use of an electrostatic sensor in association with an infrared sensor for detecting the presence of an individual. The specific application relates to the automatic opening/closing of a door. Patent document EP2980609 relates to the use of an electrostatic field sensor in addition to a magnetic sensor for detecting a human presence in an environment.
The scientific document by K. Kurita, “Development of Non-Contact Measurement System of Human Stepping,” SICE Annual Conference 2008, Japan, illustrates a system and a method for counting the steps taken by a subject exploiting a contactless technique. This technique provides for detecting the electrostatic induction current, generated as a direct consequence of the movement of the subject in the environment, through an electrode placed at a distance of 1.5 m from the subject. However, the experiment illustrated in this document is carried out under ideal conditions and is a mere demonstration of the applicability of the technology to step counting.
Some disadvantages of prior approaches are already highlighted in the background section above. Moreover, none of the above-mentioned documents teaches a system and/or a method for detecting a presence in an environment to be monitored, in particular for anti-intrusion or anti-theft purposes, for being implemented minimizing the number of sensors cooperating with each other, while ensuring high reliability.
The need is therefore felt to make up for the shortcomings of the prior art by providing a system and a method for detecting a presence in an environment to be monitored.
According to the present disclosure, a system and a method for detecting a presence in an environment to be monitored are provided.
In at least one embodiment, a system for detecting a presence in an environment to be monitored is provided that includes 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 generate an electrostatic charge variation signal. The system further includes 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 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 generate a pressure signal. The processor is configured to: acquire, from the electrostatic charge variation sensor, the electrostatic charge variation signal; detect, in said electrostatic charge variation signal, first signal characteristics indicative of the presence of a subject in said environment to be monitored; acquire, from said one of the vibration sensor or the environmental pressure sensor, respectively the vibration signal or the pressure signal; detect, in said vibration signal or pressure signal acquired, respective second signal characteristics indicative of the presence of the subject in said environment to be monitored; and generate a warning signal if both the first and the second signal characteristics have been detected.
In at least one embodiment, a method for detecting a presence in an environment to be monitored is provided that includes: detecting, by an electrostatic charge variation sensor, a variation of electrostatic charge in said environment and generating an electrostatic charge variation signal; detecting, by one of a vibration sensor or an environmental sensor operatively coupled to the environment to be monitored, respectively, an environmental vibration in the environment to be monitored and generating a vibration signal or an environmental pressure in the environment to be monitored and generating a pressure signal; acquiring, by a processor, from the electrostatic charge variation sensor, the electrostatic charge variation signal; detecting, by the processor, in said electrostatic charge variation signal, first signal characteristics indicative of the presence of a subject in said environment to be monitored; acquiring, by the processor, from said one of the vibration sensor or the environmental pressure sensor, respectively the vibration signal or the pressure signal; detecting, by the processor, in said vibration signal or pressure signal acquired, respective second signal characteristics indicative of the presence of the subject in said environment to be monitored; and generating, by the processor, a warning signal if both the first and the second signal characteristics have been detected.
For a better understanding of the disclosure, embodiments thereof are now described, purely by way of non-limiting example and with reference to the attached drawings, wherein:
The processing unit 2 is configured to receive, and receives during use: a signal SQ, correlated to a variation of the environmental electrical charge in the monitored environment, from the electrostatic charge variation sensor 6; a signal SA, indicative of a vibration detected in the environment monitored by the accelerometer 7; and a signal SP, indicative of a pressure (or variation of pressure) detected in the environment monitored by the accelerometer 7.
The pressure sensor 4 is arranged in the environment wherein it is desired to detect a human presence, or operatively coupled to this environment, to detect a variation of environmental pressure caused for example by the opening of a door or a window, or indicative of the entry of a foreign subject into this environment. In this case, therefore, the environment to be monitored is a closed environment, such as, for example, a room in an apartment or home. In fact, it should be remembered that the system 1 according to the present disclosure has the objective of identifying an unwanted entry into an environment to be protected, in particular for anti-theft purpose. When the system 1 is operative, the pressure detected is the environmental pressure present therein, which typically varies relatively slowly between day and night hours, due to air heating, or in conjunction with a variation in weather/climatic conditions. Any significant disturbance of this pressure may be indicative of an infringement.
Similarly, the accelerometer 7 is also arranged in the environment wherein it is desired to detect a human presence, to detect any vibrations in this environment that might be correlated to an intruder's footsteps, in particular footsteps caused by a person who has entered such an environment.
Similarly, the electrostatic charge variation sensor 6 is also arranged in the environment wherein it is desired to detect the human presence, or operatively coupled to this environment, to detect a variation of the environmental electrostatic charge caused by the entry of a foreign subject into this environment.
The analysis of the signals generated by the aforementioned sensors, and their suitable combination, allows the entry, in the environment to be monitored, of a subject or an intruder to be detected, discriminating with respect to false positives.
The two electrodes may be connected to a differential input (i.e., to the positive/negative “+”/“−” input pair of an amplifier stage or an ADC converter). Particular cases of this general configuration (which do not require changes to the electric diagram of
In this embodiment, one of the electrodes E1, E2 (e.g., E2) is coupled to a reference potential, having constant value (e.g., common mode voltage, or VCM, typically half of the supply voltage of the device), while the other electrode of the electrodes E1, E2 (e.g., E1) is, for example, made of conductive material and 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 of square shape, with a side equal to about 2-10 cm, for example 5 cm. Other examples comprise electrodes made using conductive wires coated with insulator, having a length equal to a few centimeters or a few tens of centimeters, e.g., 10 cm-20 cm.
In particular, the input electrodes E1, E2 are arranged in the environment wherein it is desired to detect a human presence, while the rest of the electrostatic charge variation sensor 6 (e.g., the amplification stage, described hereinafter) may also be arranged outside the environment to be monitored, or inside this environment, indifferently.
The pair of input terminals 8a, 8b receives an input voltage Vd (differential signal), being supplied to an instrumentation amplifier 12. In a per se known manner, a human presence generates a variation of the environmental electrostatic charge which, in turn, after having been detected by the electrode E1, generates the input voltage Vd.
The instrumentation amplifier 12 comprises, in an exemplary embodiment, two operational amplifiers OP1 and OP2 and a biasing stage (buffer) OP3 having the function of biasing the instrumentation amplifier 12 to a common mode voltage VCM.
The inverting terminal of the amplifier OP1 is connected to the inverting terminal of the amplifier OP2 by means of a resistor R2 having a voltage equal to the input voltage Vd at its ends; therefore, a current equal to I2=Vd/R2 will flow through this resistor R2. This current I2 does not come from the input terminals of the operational amplifiers OP1, OP2 and therefore flows through the two resistors R1 connected between the outputs of the operational amplifiers OP1, OP2, in series with the resistor R2; the current I2, therefore flowing through the series of the three resistors R1−R2-R1, produces an output voltage Vd′ given by Vd′=(2R1+R2)I2=(2R1+R2)Vd/R2. Therefore, the overall gain of the circuit of
The differential output Vd′, therefore being proportional to the potential Vd between the input electrodes 8a, 8b, is provided at input to an analog-to-digital converter 14, which provides at output the charge variation signal SQ for the processing unit 2. The charge variation signal SQ 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 may be configured to work directly on the analog signal, or may itself comprise an analog-to-digital converter for converting the signal Vd′.
Alternatively, in presence of the analog-to-digital converter 14, the instrumentation amplifier 12 may be omitted, so that the analog-to-digital converter 14 receives the differential voltage Vd between the electrodes E1, E2 and samples this signal Vd directly.
The pressure sensor 4 is for example a pressure sensor made using MEMS technology. Examples of pressure sensors usable in the context of the present disclosure include pressure sensors with a measuring range of 200 mbar-2000 mbar and with accuracy (absolute precision) of a few mbar units; however, operating around the environmental pressure, approximately 1000 mbar, and observing relative values around it, a relevant parameter is the ability to detect variations around a working point, that is high resolution over time and amplitude and low inherent noise. Examples of sensors for this purpose include sensors with a resolution of 1 Pascal ( 1/100 of mbar), data rate equal to 200 Hz, RMS noise level equal to 0.5 Pascal (without filters applied).
In respective embodiments, other pressure sensors (other than MEMS sensors) are however usable.
The vibration sensor 7 is, as mentioned and in one embodiment, an accelerometer, for example of three-axis or six-axis type made using MEMS technology, or a sensor including the combination of accelerometer and gyroscope.
As can be seen from
SA=√{square root over (SAx2+SAy2+SAz2)}
As can be seen from
In particular:
A method is now described, with reference to
The signal portions of
With reference to
P1: is temporally the first identified peak, here a positive peak, which occurs on the first derivative signal SQ′ at about 24.3 s and has an amplitude value equal to about +30000 LSB.
P2: is temporally the second identified peak, here a positive peak, which occurs on the signal SQ at about 24.4 s and has an amplitude value equal to about +42000 LSB.
P3: is temporally the third identified peak, here a negative peak, which occurs on the first derivative signal SQ′ at about 24.55 s and has an amplitude value equal to about −38000 LSB.
P4: is temporally the fourth identified peak, here a negative peak, which occurs on the signal SQ at about 24.65 s and has an amplitude value equal to about −65000 LSB.
P5: is temporally the first identified peak, here a positive peak, which occurs on the first derivative signal SQ′ at about 24.75 s and has an amplitude value equal to about +18000 LSB.
As apparent to the skilled in the art, the positive peak P1 present on the signal SQ′ of the first derivative identifies a rising edge of the signal SQ which culminates at the positive peak P2 of the signal SQ; similarly, the negative peak P3 present on the signal SQ′ of the first derivative identifies a falling edge of the signal SQ which culminates at the negative peak P4 of the signal SQ; then, the signal SQ starts to grow again, and this new rising edge is identified by the positive peak P5 present on the signal SQ′ of the first derivative. Therefore, the aforementioned assessment of the succession of positive and negative peaks of the signals SQ and SQ′ has the function of identifying or detecting a specific trend of the signal generated by the electrostatic charge variation sensor 6, which the Applicant has identified as specific or significant of the presence (in particular, of the execution of a step) of a human subject in the monitored environment.
Summarizing, considering the time course of the electrostatic charge variation signal SQ and of the first derivative SQ′ of the electrostatic charge variation signal SQ in conjunction, the following time succession of positive and negative peaks is observed:
However, the Applicant notes that, with a different arrangement of the electrodes E1, E2, the aforementioned time sequence (succession) may be inverted, that is the following time succession is observed:
The configuration of the electrodes may in fact have an effect on the detection of an electrostatic charge variation signal. While the geometry (firstly the surface) and the materials of the electrodes determine the sensitivity of the same, their arrangement in space and their distance affects the directionality or the cancellation ability of certain unwanted signal sources. On this last point it is noted that the two electrodes E1, E2 are coupled to the differential inputs of the differential amplifier (also referred to as instrumentation amplifier) or of the 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 suitably dimensioning and positioning the electrodes, the common mode signals, that is, those signals that occur at both inputs with the same intensity, may be cancelled (or attenuated). Based on this, embodiments of the present disclosure include configurations with a single electrode, with two electrodes being equal but spaced apart from each other, with two electrodes having different geometries, etc. If the most stressed input is the “+” one, the signal shown in the figure is found; vice versa in the case of greater stress of the input “−.” In this context, the most stressed electrode is the one that detects potential variations (due to a variation of charge in the environment) that are more intense with respect to the other electrode; this may happen due to a different geometry and/or to a different installation point of the two electrodes.
The Applicant has verified that, when the above identified time succession (one of the two) is observed, it can be concluded that the signal portion 18a of
In order to identify whether a variation of the signals SQ and SQ′ is one of the peaks sought, respective thresholds (positive or negative) A1TH-A5TH are provided, to be compared with the trend of the signals SQ and SQ′.
The thresholds A1TH-A5TH have a predefined/default value, identified empirically on the basis of observations of the trend of the signals SQ and SQ′, and for example are defined as follows:
In the example of
Alternatively to what has been described, the value of the thresholds A1TH-A5TH may be chosen as a function of the background noise of the respective signals SQ and SQ′, for example equal to 8-12 times (e.g., 10 times) the average value of the noise.
The following comparisons are then performed for each threshold:
To improve the robustness of the method proposed herein, by improving the discrimination between actual step and environmental noise or other perturbation, it is possible, again with reference to
The existence of the following relationships is verified:
T1=T3+T4
T6=T2+T3
T7=T4+T5
T8=T6+T7
Additionally, or alternatively, the existence of the following relationships is verified, to verify that the duration of the time intervals T2-T5 complies with that expected for the signal shape that may be associated with a step of a subject:
T4TH_L<T4<T4TH_H, where T4TH_L and T4TH_H represent the boundaries of a range of time values within which T4 needs to be comprised (e.g., T4TH_L=30-70 ms and T4TH_H=150-250 ms); and
T5TH_L<T5<T5TH_H, where T5TH_L and T5TH_H represent the boundaries of a range of time values within which T5 needs to be comprised (e.g., T5TH_L=30-70 ms and T5TH_H=150-250 ms).
In one embodiment, the values of T1TH_L-T5TH_L are equal to each other and equal to 50 ms; and the values of T1TH_H-T5TH_H are equal to each other and equal to 200 ms.
The choice of the values of T1TH_H-T5TH_H may vary with respect to what is described herein and set on an empirical basis after experimental observations.
At step 60 the processing unit 2 acquires the raw signal SQ from the electrostatic charge variation sensor 6.
At step 61 the raw signal SQ is processed to remove the baseline or background signal, as previously mentioned.
At step 62 the method for searching the positive/negative peaks on the electrostatic charge variation signal SQ is performed, identifying for example the time succession of the peaks P2 and P4 of
At step 63 the first derivative signal SQ′ of the electrostatic charge variation signal SQ is calculated.
Then, at step 64 the method for searching positive/negative peaks on the first derivative signal SQ′ is performed, identifying for example the time succession of the peaks P1, P3 and P5 of
The aforementioned conditions are then assessed on the amplitude A1-A5 of the detected peaks and on the time intervals T2-T5. The method proposed herein is performed in real time, that is by acquiring the samples of the raw signal SQ and assessing the conditions previously described as these samples are generated by the electrostatic charge variation sensor 6.
A counter PCOUNT (initialized for example to zero) stores the number of identified peaks (five peaks P1-P5 may be utilized and, in some embodiments, may be required to confirm the identification of a step in this embodiment). At an initial instant where no peaks have yet been detected, PCOUNT=0.
With reference to blocks 65-69 of
At block 65 the presence of the peak P1 in the first derivative signal SQ′ is assessed by comparing the amplitude value A1 with the respective threshold A1TH. If the comparison with the threshold determines the presence of the peak P1, then the counter PCOUNT is updated (PCOUNT=1) and a new datum is acquired from the raw signal SQ. Otherwise, the counter PCOUNT is reset to a zero value and a new datum is acquired from the raw signal SQ.
Steps 60-64 are then performed again.
If the presence of the peak P1 has been confirmed, the assessment of the value of the counter PCOUNT determines passing from step 64 to step 66, wherein the presence of the peak P2 in the electrostatic charge variation signal SQ is assessed by comparing the amplitude value A2 with the respective threshold A2TH. If the comparison with the threshold determines the presence of the peak P2, and the time conditions on the value of the interval T2 are met, such that T2TH_L<T2<T2TH_H, then the counter PCOUNT is updated (PCOUNT=2) and a new datum is acquired from the raw signal SQ. Otherwise, the counter PCOUNT is reset to a zero value and a new datum is acquired from the raw signal SQ.
Steps 60-64 are then performed again.
If the presence of the peak P2 has been confirmed, the assessment of the value of the counter PCOUNT determines passing from step 64 to step 67, wherein the presence of the peak P3 in the first derivative signal SQ′ is assessed by comparing the amplitude value A3 with the respective threshold A3TH. If the comparison with the threshold determines the presence of the peak P3, and the time conditions on the value of the interval T3 are met, such that T3TH_L<T3<T3TH_H, then the counter PCOUNT is updated (PCOUNT=3) and a new datum is acquired from the raw signal SQ. Otherwise, the counter PCOUNT is reset to a zero value and a new datum is acquired from the raw signal SQ.
Steps 60-64 are then performed again.
If the presence of the peak P3 has been confirmed, the assessment of the value of the counter PCOUNT determines passing from step 64 to step 68, wherein the presence of the peak P4 in the electrostatic charge variation signal SQ is assessed by comparing the amplitude value A4 with the respective threshold A4TH. If the comparison with the threshold determines the presence of the peak P4, and the time conditions on the value of the interval T4 are met, such that T4TH_L<T4<T4TH_H, then the counter PCOUNT is updated (PCOUNT=4) and a new datum is acquired from the raw signal SQ. Otherwise, the counter PCOUNT is reset to a zero value and a new datum is acquired from the raw signal SQ.
Steps 60-64 are then performed again.
If the presence of the peak P4 has been confirmed, the assessment of the value of the counter PCOUNT determines passing from step 64 to step 69, wherein the presence of the peak P5 in the first derivative signal SQ′ is assessed by comparing the amplitude value A5 with the respective threshold A5TH. If the comparison with the threshold determines the presence of the peak P5, and the time conditions on the value of the interval T5 are met, such that T5TH_L<T5<T5TH_H, then the analysis of the relative portion 18a, 18b of the signals SQ and SQ′ is concluded and a warning, or trigger signal, may be generated, which confirms the identification of a step in the signal generated by the electrostatic charge variation sensor 6.
The counter PCOUNT is reset and a new datum is acquired from the raw signal SQ, to identify the presence of a successive step.
The confirmation of human presence in the environment occurs, according to an aspect of the present disclosure, after the identification of a plurality of steps, for example of five steps. However, it is apparent that, in order to speed up the detection, it is possible to report the presence of the subject even only after the identification of a single step.
As previously mentioned, to generate the actual alarm or final confirmation of the human presence, the present disclosure provides for the joint analysis of the signals SP, SA generated by the pressure sensor 4 and by the vibration sensor 7.
In one embodiment, the algorithm of
At each iteration, after the acquisition of the pressure signal SP (step 70), the i-th pressure datum Pi (amplitude value) is deducted of its baseline (step 71) and is stored in a buffer PBUFF (step 72); at the same time, or at previous or subsequent time instants, indifferently, the search for possible peaks in the pressure signal SP (step 73) is carried out, using algorithms known for this purpose, or specifically provided for this purpose. If a peak is detected (step 74, output YES), the value PK25 equal to 25% of the amplitude of the detected peak is calculated (step 75) (this percentage value is indicative and may vary for example in the range 10%-50%). Iteratively, this value PK25 is subtracted (step 76) to each pressure datum (i-th datum PKi) contained in the buffer PBUFF (operation PKi-PK25). If the value resulting from this subtraction is positive (step 77, output YES), then this value is added to a variable PAREA (representative of the area of the plane portion, between the peak and 25% of its value), to perform a calculation, in digital form, of the integral of the signal around the detected peak (step 79). The integral may be calculated as an area (variable A in step 79) subtended by the signal relating to the peak, that is in digital format by adding the amplitude values Pi-PK25, only if this difference is greater than 0. At step 76 the value PK25 is in fact subtracted to each sample Pi; if the result of this operation of step 76 is positive, then this result is added to the previous area value A (where A is initialized to 0 at the beginning of the method); if the result of step 76 is negative, this result is ignored. This operation of addition is carried out for a maximum of N iterations; the count of these N iterations is carried out by increasing an index j, regardless of the value of the result of the operation of step 76 (the increase of j allows to go through the entire buffer 72).
The steps 76,77,78,79 have the function of quantifying the portion of the area subtended by the curve only in the presence of a peak, so as to be capable of performing the operation, of the successive step 80, of assessing the peak itself.
Finally, the ratio RPK between PAREA and PKi-PK25 is calculated (step 80) (obtaining a result which is greater than 1), which is indicative of the “steepness” of the peak: the smaller the value of this ratio RPK, the greater the steepness and vice versa. The value of the ratio RPK is compared with a threshold RPTHRES (step 81): if RPK<RPTHRES then the peak is sufficiently steep to be similar to that generated by the opening of a door, and a signal, or trigger, indicative of this event is generated (step 82); vice versa, the method returns to step 70 by resetting the variables j and A. The choice of the threshold RPTHRES includes, for example, values comprised between 10 and 30; the smaller this value, the steeper and more time-limited the detected peak.
For greater clarity of the operation of the method of
In one embodiment, the algorithm of
At each iteration, after having acquired the vibration signal relating to the detection axes of the accelerometer (SAx, SAy, SAz) by the processing unit 2 (step 90), the modulus XLM (i.e., the signal SA previously discussed) of the acceleration is calculated (step 91) on the basis of the signals acquired from the three axes of the accelerometer (assuming here to use a triaxial accelerometer).
The AC component is then obtained (step 92) (i.e., amount correlated to the variation of the signal with respect to the average value of this signal, whose i-th value is indicated as XLPKi), which is stored in a buffer XLACBUFFER; at the same time the search for possible signal peaks is carried out on such data (step 93). If a peak is detected (step 93, output YES), the value XLPK25 (step 95) is calculated equal to 25% of the peak amplitude (this percentage value may be chosen differently, for example in the range 10%-50%). Otherwise, the method returns to step 90.
Iteratively, this value XLPK25 is subtracted to all the values XLPKi contained in the buffer XLACBUFFER (step 96). If, for each sample, the value resulting from this subtraction is positive, then (step 97, output YES) this value is added to the variable XLA (representative of the area of the plane portion, between the peak and 25% of its value), implementing an operation of calculation of the integral in digital format (step 98).
If the result of the foregoing step is negative, this result is ignored. This operation of adding and updating the variable XLA is carried out for a maximum of N iterations; the count of these N iterations is carried out by increasing an index k, regardless of the result of the assessment of step 97 (the increase of k allows to go through the entire buffer XLACBUFFER)
The ratio RXLPK between the area AXL and XLPKi-XLPK25 (greater than 1), indicative of the steepness of the peak, is then calculated (step 100), for each i-th datum: the smaller the value of this ratio, the greater the steepness of rising of the signal, and vice versa.
RXLPK is compared (step 101) with a threshold RXLPKTHRES: if RXLPK<RXLPKTHRES then the identified peak is steep and similar to that generated by a subject's step (step 103) and a suitable signal, or trigger, is generated which confirms the presence of the subject in the considered environment.
In order to increase the reliability of the proposed method, so that the vibration signal is validated as generated by a subject's steps, it is optionally possible to verify (step 102) the repetition of a certain number of peaks over time (e.g., by setting a comparison threshold CountTHRES, for example equal to 2), with the condition that a time which is longer than a predefined value TTHRES (for the choice of this value, considerations similar to those previously made for the pressure signal Sp are valid) does not elapse between an event of single step and the successive.
For improved clarity of the operation of the method of
With reference to
With reference to
At each iteration, the following operations are performed.
If the input datum xi (i-th datum) is comprised between the thresholds BLTHRES_H and BLTHRES_L (step 110, output YES) the datum xi is accumulated (step 111) in a shift buffer having size NBLBUFF (e.g., NBLBUFF=10).
In the early iteration steps (first start of the algorithm), the thresholds BLTHRES_H and BLTHRES_L are ignored (i.e., the output from block 110 is “YES”), until the buffer is completely filled (a number of iterations equal to NBLBUFF are utilized and in some embodiments may be required). In other words, all the incoming samples xi will fill the buffer, as identified by the arrow 110a in dashed line.
A variable BL that stores the current baseline value is then updated with a value equal to the average value of the samples present in the buffer (step 112a) at the same time, the standard deviation value of the samples present in the buffer is calculated (step 112b). New thresholds BLTHRES_H and BLTHRES_L are calculated (step 113), respectively equal to the value of the variable BL increased and decreased by a multiple amount 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 BLTHRES_H and BLTHRES_L: the greater the value of k, the greater the variations of the input datum that will be absorbed in the baseline. The variable k is chosen, for example, in the range 3-6.
After calculating, for each input sample xi, the respective baseline value BL, the output datum yi=xi=BL is generated (step 114), that is the datum that will form the respective vibration SA, pressure SP or electrostatic charge signal SQ deprived of the respective baseline.
If the input datum is not comprised between the thresholds BLTHRES_H and BLTHRES_L (step 110, output NO), the baseline and the thresholds are not modified. The output datum yi is, in any case, equal to the input value xi deducted of the value BL calculated as the average of the samples present in the buffer. It is repeated that the operations of steps 112a, 112b are not performed until the buffer is completely filled.
The algorithm operates in real time, similarly to the method of
At each i-th iteration, the i-th datum xi of the respective signal is acquired by the processing unit 2 (step 120). Then, the first derivative xi′ is calculated (step 121). Then, the absolute value |xi′| of the first derivative xi′ is calculated (step 122). The absolute value |xi′| which is calculated is then entered into a buffer (step 123) having size NBLBUFF′(for example equal to 10).
If (step 124) all the values contained in the buffer are lower than a threshold BLTHRES then (output YES from step 124) the input data xi is entered into a second buffer with size MBLBUFF (step 125). Being a derivative, exceeding the threshold BLTHRES is indicative of 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 “noisiness” of the environment and of the sensor itself. For example, in the case of the charge variation signal, the threshold BLTHRES may be comprised between 8000 and 16000.
The baseline BL is then updated to the new value, given by the average of the elements present in this second buffer (step 126).
After calculating, for each input sample xi, the respective baseline value BL, the output datum yi=xi−BL is generated (step 127), that is the datum that will form the respective vibration SA, pressure Sp or electrostatic charge signal SQ deprived of the respective baseline.
If at least one element of the first buffer exceeds or is equal to the threshold BLTHRES, the baseline variable BL is not updated (output NO from step 124).
The output value yi is, however, equal to the input value xi deducted of the value BL.
At the first start, the algorithm ignores the check of the threshold BLTHRES for a number of iterations sufficient to completely fill the first buffer of size NBLBUFF′ In this starting condition, all the input samples |xi′| are used to fill this first buffer and the generation of an output datum yi is not performed.
With reference to
With reference to the algorithm of
xi=amplitude in LSB or pressure value in mbar of the current datum (sample) (i-th datum);
ti=time instant associated with the current datum xi;
2N+1=width, expressed in number of samples, of a considered peak (comprehensive of the signal portion rising towards a maximum peak value, the maximum value reached, and the signal portion falling from the maximum value);
PKTHRES=comparison threshold to detect the presence of a positive peak;
VLTHRES=comparison threshold to detect the presence of a negative peak;
xj=local maximum reached by the positive peak;
xk=local minimum reached by the negative peak;
pka=amplitude in LSB or pressure value in mbar of the local maximum reached by the considered positive peak;
pkt=time instant associated with the local maximum pka reached by the considered positive peak;
vla=amplitude in LSB or pressure value in mbar of the local minimum reached by the considered negative peak;
vlt=time instant associated with the local minimum vla reached by the considered negative peak;
PKF=variable, or “flag,” which identifies the “positive-peak-found” event;
VLF=variable, or “flag,” which identifies the “negative-peak-found” event. At each iteration, the amplitude and the time index of the input datum are entered (steps 130a and 130b), respectively, into the two buffers XPBUFF (buffer that contains the data xi) and TPBUFF (buffer that contains the data ti). Subsequently, the maximum xj and the minimum xk of all the elements of the buffer XBUFF are calculated (steps 131a and 131b).
If the time index pkt of the local maximum xj found 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 XPBUFF; in this case no peak was found and PKF=“FALSE” (output NO from step 132a).
On the contrary, if the time index pkt of the local maximum xj found is equal to the value of the index N (output YES from step 132a), it means that the sample corresponding to the local maximum xj is placed in the middle of the buffer XPBUFF; it occurs (step 133a) if the local maximum xj found is higher than PKTHRES (e.g., PKTHRES chosen of a value equal to 15000 for the electrostatic charge variation signal). If so, the presence of a peak of width 2N+1 is confirmed and the variable PKF is set to “TRUE” (step 134a).
The amplitude of the peak found and confirmed is xN, and the time index is tN.
Dual considerations may be made for searching the negative peak.
In this case, if the time index vlt of the local minimum xk found 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 TPBUFF; in this case no peak was found and VLF=“FALSE” (output NO from step 132b).
On the contrary, if the time index vlt of the local minimum xk found is equal to the value of the index N (output YES from step 132b), it means that the sample corresponding to the local minimum xk is placed in the middle of the buffer TPBUFF; it occurs (step 133b) if the local minimum xk found exceeds (for negative values) the threshold VLTHRES (e.g., VLTHRES chosen of a value equal to −15000 for the electrostatic charge variation signal). If so, the presence of a peak of width 2N+1 is confirmed and the variable VLF is set to “TRUE” (step 134b).
The amplitude of the negative peak found and confirmed is xN, and the time index is tN.
At the first start, the algorithm is not operative for a number of iterations equal to 2N+1, that is, until the buffers XPBUFF and TPBUFF are filled. In this step all the input samples will fill the buffers and the outputs are set to PKF=“FALSE” and VLF=“FALSE.”
The algorithm of
By definition, the output y is delayed by 2 samples with respect to the input; the first derivative of the input signal, calculated at time ti, relates to the input signal at time t(i−2). The two flows are therefore temporally aligned before calculating relative time distances.
With reference to
With reference to
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 which are further processed (filtered) through the branch 154, as described hereinafter.
The branch 154 first comprises a processing block 155 which uses a Hann window 156, or Hann function, which is of a per se known type and implements the following function:
where xi=[x0, . . . , xK−1] are the samples at the input in the processing block 155 (the subscript “i=0, . . . , K−1” identifies the i-th sample) and yi=[y0, . . . , yK−1] are the samples at the output from the processing block 155.
The branch 154 comprises an averaging block 157, which receives the samples y1=[y0, . . . , yK−1] and performs an arithmetic average operation of the value of said samples.
The branch 154 further comprises a multiplication block 158, which receives at input the average value generated at the output of block 157 and performs an operation of multiplication by 2 of said average value (since the Hann window of block 156 halves the average amplitude of the signal, the attenuation introduced is compensated with this operation), generating an output which is supplied to the second input of the subtraction block 152.
At the output of the subtraction block 152, the signal at the input in the subtraction block 152 minus its average value is obtained, therefore a signal which on average oscillates around zero, without any significant offset. The output of the subtraction block 152 is then further processed through a block 159 which implements a further Hann window, as it has been described for block 156. This further Hann window 159 has the function of smoothing the signal, smoothing the peaks and discontinuities at the ends of the analysis window.
A block 160 receives at input the array generated at the output of block 159, and performs an estimation of the variance of said array, in a per se known manner. The output from block 160 is consequently scalar.
Finally, a square root operation (block 162) of the variance value has the function of compressing the dynamic range of the output signal, as well as of bringing it back to the initial physical dimensions. In other words, the variance increases according to a power of two, and the square root restores the physical dimensions. For example, for the signal SA, if the physical dimension at the input is expressed in V, after the calculation of the variance, it is expressed in V2.
The advantages achieved by the present disclosure are apparent from the foregoing description.
In particular, the following advantages are obtained with respect to the prior art:
Further variations, with respect to what has been described, may also be provided.
For example, the present disclosure may be modified with respect to what has been described by excluding one of the pressure sensor 4 and the vibration sensor 7; in this case, the confirmation of human presence in the monitored environment is provided by the analysis steps of the electrostatic charge variation signal SQ combined with only one of the vibration signal SA and the pressure signal SP. If the sensor excluded or not present is the pressure sensor, the environment wherein the presence of the subject is detected may not be a closed environment.
Furthermore, while the present disclosure has been described with explicit reference to the processing of digital signals, what has been described applies, in a per se apparent manner, to analog signals.
A system for detecting a presence in an environment to be monitored, may be summarized as including 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 generate an electrostatic charge variation signal (SQ); 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 generate a vibration signal (SA), 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 generate a pressure signal (SP), wherein the processing unit (2) is configured to acquire, from the electrostatic charge variation sensor (6), the electrostatic charge variation signal (SQ); detect, in said electrostatic charge variation signal (SQ), first signal characteristics indicative of the presence of a subject in said environment to be monitored; acquire, from said one of the vibration sensor (7) and the environmental pressure sensor (4), respectively the vibration signal (SA) or the pressure signal (SP); detect, in said vibration signal (SA) or pressure signal (SP) acquired, respective second signal characteristics indicative of the presence of the subject in said environment to be monitored; and generate a warning signal if both the first and the second signal characteristics have been detected.
The system may further include the other of the vibration sensor (7) and the environmental pressure sensor (4), wherein the processing unit (2) is further configured to acquire, from the other of said vibration sensor (7) and environmental pressure sensor (4), respectively the vibration signal (SA) or the pressure signal (SP); detect, in said other of the vibration signal (SA) and the pressure signal (SP) acquired, respective third signal characteristics indicative of the presence of the subject in said environment to be monitored; and generate the warning signal if all the first, the second and the third signal characteristics have been detected.
The operation of detecting the first signal characteristics may include detecting, in the electrostatic charge variation signal (SQ), the following characteristics which follow each other in temporal order: 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 (SQ), the following characteristics which follow each other in temporal order: a falling edge; a first local minimum; a first rising edge; a first local maximum; a second falling edge.
The operation of detecting the first signal characteristics nay further include performing a comparison of said local maximums and minimums with respective thresholds; and assessing, through comparison with respective thresholds, a value of steepness or rising rate of the first and the second rising edges and of steepness or falling rate of the falling edge.
The operation of detecting, in the electrostatic charge variation signal (SQ), the characteristics that follow each other in temporal order may include calculating the first derivative (SQ′) of the electrostatic charge variation signal (SQ); identifying, in the electrostatic charge variation signal (SQ) and in the first derivative signal (SQ′), a respective plurality of positive and negative peaks; detecting one of the following time series a) and b) with which said plurality of positive and negative peaks follow each other over time a) a first positive peak (P1) in the first derivative signal (SQ′); a second positive peak (P2) in the electrostatic charge variation signal (SQ); a first negative peak (P3) in the first derivative signal (SQ′); a second negative peak (P4) in the electrostatic charge variation signal (SQ); a third positive peak (P5) in the first derivative signal (SQ′), b) a third negative peak in the first derivative signal (SQ′); a fourth negative peak in the electrostatic charge variation signal (SQ); a fourth positive peak in the first derivative signal (SQ′); a fifth positive peak in the electrostatic charge variation signal (SQ); a fifth negative peak in the first derivative signal (SQ′).
The operation of detecting the first signal characteristics may further include detecting one or more of the following time relationships:
T1=T3+T4
T6=T2+T3
T7=T4+T5
T8=T6+T7
wherein T1 may be a time interval between the second positive peak (P2) and the second negative peak (P4), T2 may be a time interval between the second positive peak (P2) and the first positive peak (P1), T3 may be a time interval between the second positive peak (P2) and the first negative peak (P3), T4 may be a time interval between the second negative peak (P4) and the first negative peak (P3), T5 may be a time interval between the second negative peak (P4) and the third positive peak (P5), T6 may be a time interval between the first positive peak (P1) and the first negative peak (P3), T7 may be a time interval between the first negative peak (P3) and the third positive peak (P5), T8 may be a time interval between the first positive peak (P1) and the third positive peak (P5).
Said time intervals T1-T7 may be respective time distances between respective maximum or minimum points of the positive and negative peaks.
The operation of detecting the first signal characteristics may further include detecting one or more of the following time relationships: T2TH_L<T2<T2TH_H, T3TH_L<T3<T3TH_H, T4TH_L<T4<T4TH_H, T5TH_L<T5<T5TH_H, where T2TH_L, T3TH_L, T4TH_L and T5TH_L may be respective lower thresholds of respective value including between 30 and 70 ms, and T2TH_H, T3TH_H, T4TH_H and T5TH_H are respective higher thresholds of respective value including between 150-250 ms.
The second signal characteristics may belong to the pressure signal (SP), and said operation of detecting in said pressure signal (SP) the second signal characteristics may include detecting a time amplitude value and maximum value of a pressure peak present in said pressure signal (SP); calculating a first comparison parameter which is a function of the ratio between said time amplitude value and maximum value of the pressure peak; verifying whether said first comparison parameter is in a predetermined relationship with a first threshold.
Detecting a time amplitude value may include calculating an integral of, or an area subtended by, the pressure peak present in said pressure signal (SP), and said first comparison parameter may be calculated by dividing the result of said integral of the pressure peak, or the value of said area subtended by the pressure peak, by the maximum value of the pressure peak.
The third signal characteristics belong to the vibration signal (SA), and said operation of detecting in said vibration signal (SA) the third signal characteristics may include detecting a time amplitude value and maximum value of a vibration peak present in said vibration signal (SA); calculating a second comparison parameter which is a function of the ratio between said time amplitude value and maximum value of the vibration peak; verifying whether said second comparison parameter is in a predetermined relationship with a second threshold.
Detecting a time amplitude value may include calculating an integral of, or an area subtended by, the vibration peak present in said vibration signal (SA), and said second comparison parameter may be calculated by dividing the result of said integral of the vibration peak, or the value of said area subtended by the vibration peak, by the maximum value of the vibration peak.
A method for detecting a presence in an environment to be monitored, may be summarized as including the steps of detecting, through an electrostatic charge variation sensor (6), a variation of electrostatic charge in said environment and generating an electrostatic charge variation signal (SQ); detecting, through a vibration sensor (7) operatively coupled to the environment to be monitored, an environmental vibration in the environment to be monitored and generating a vibration signal (SA); alternatively detecting, through an environmental pressure sensor (4) operatively coupled to the environment to be monitored, an environmental pressure in the environment to be monitored and generating a pressure signal (SP), acquiring, through a processing unit (2) from the electrostatic charge variation sensor (6), the electrostatic charge variation signal (SQ); detecting, through the processing unit (2) in said electrostatic charge variation signal (SQ), first signal characteristics indicative of the presence of a subject in said environment to be monitored; acquiring, through the processing unit (2) from said one of the vibration sensor (7) and the environmental pressure sensor (4), respectively the vibration signal (SA) or the pressure signal (SP); detecting, through the processing unit (2), in said vibration signal (SA) or pressure signal (SP) acquired, respective second signal characteristics indicative of the presence of the subject in said environment to be monitored; and generating, through the processing unit (2), a warning signal if both the first and the second signal characteristics have been detected.
The method may further include the other step of detecting the environmental vibration and generating a vibration signal (SA) or detecting the environmental pressure and generating a pressure signal (SP),
and further including the steps, performed by the processing unit (2), of acquiring, from the other of said vibration sensor (7) and environmental pressure sensor (4), respectively the vibration signal (SA) or the pressure signal (SP); detecting, in said other of the vibration signal (SA) and the pressure signal (SP) acquired, respective third signal characteristics indicative of the presence of the subject in said environment to be monitored; and generating the warning signal if all the first, the second and the third signal characteristics have been detected.
The step of detecting the first signal characteristics may include detecting, in the electrostatic charge variation signal (SQ), the following characteristics which follow each other in temporal order: 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 (SQ), the following characteristics which follow each other in temporal order: a falling edge; a first local minimum; a first rising edge; a first local maximum; a second falling edge.
The step of detecting the first signal characteristics may further include performing a comparison of said local maximums and minimums with respective thresholds; and assessing, through comparison with respective thresholds, a value of steepness or rising rate of the first and the second rising edges and of steepness or falling rate of the falling edge.
Detecting, in the electrostatic charge variation signal (SQ), the characteristics that follow each other in temporal order may include calculating the first derivative (SQ′) of the electrostatic charge variation signal (SQ); identifying, in the electrostatic charge variation signal (SQ) and in the first derivative signal (SQ′), a respective plurality of positive and negative peaks; detecting one of the following time series a) and b) with which said plurality of positive and negative peaks follow each other over time a) a first positive peak (P1) in the first derivative signal (SQ′); a second positive peak (P2) in the electrostatic charge variation signal (SQ); a first negative peak (P3) in the first derivative signal (SQ′); a second negative peak (P4) in the electrostatic charge variation signal (SQ); a third positive peak (P5) in the first derivative signal (SQ′), b) a third negative peak in the first derivative signal (SQ′); a fourth negative peak in the electrostatic charge variation signal (SQ); a fourth positive peak in the first derivative signal (SQ′); a fifth positive peak in the electrostatic charge variation signal (SQ); a fifth negative peak in the first derivative signal (SQ′).
The step of detecting the first signal characteristics may further include detecting one or more of the following time relationships:
T1=T3+T4
T6=T2+T3
T7=T4+T5
T8=T6+T7
wherein T1 may be a time interval between the second positive peak (P2) and the second negative peak (P4), T2 may be a time interval between the second positive peak (P2) and the first positive peak (P1), T3 may be a time interval between the second positive peak (P2) and the first negative peak (P3), T4 may be a time interval between the second negative peak (P4) and the first negative peak (P3), T5 may be a time interval between the second negative peak (P4) and the third positive peak (P5), T6 may be a time interval between the first positive peak (P1) and the first negative peak (P3), T7 may be a time interval between the first negative peak (P3) and the third positive peak (P5), T8 may be a time interval between the first positive peak (P1) and the third positive peak (P5).
Said time intervals T1-T7 may be respective time distances between respective maximum or minimum points of the positive and negative peaks.
The step of detecting the first signal characteristics may further include detecting one or more of the following time relationships: T2TH_L<T2<T2TH_H, T3TH_L<T3<T3TH_H, T4TH_L<T4<T4TH_H, T5TH_L<T5<T5TH_H, where T2TH_L, T3TH_L, T4TH_L and T5TH_L may be respective lower thresholds of respective value including between 30 and 70 ms, and T2TH_H, T3TH_H, T4TH_H and T5TH_H are respective higher thresholds of respective value including between 150-250 ms.
The second signal characteristics belong to the pressure signal (SP), and wherein said step of detecting in said pressure signal (SP) the second signal characteristics may include detecting a time amplitude value and maximum value of a pressure peak present in said pressure signal (SP); calculating a first comparison parameter which is a function of the ratio between said time amplitude value and maximum value of the pressure peak; verifying whether said first comparison parameter is in a predetermined relationship with a first threshold.
Detecting a time amplitude value may include calculating an integral of, or an area subtended by, the pressure peak present in said pressure signal (SP), and said first comparison parameter may be calculated by dividing the result of said integral of the pressure peak, or the value of said area subtended by the pressure peak, by the maximum value of the pressure peak.
The third signal characteristics belong to the vibration signal (SA), and said step of detecting in said vibration signal (SA) the third signal characteristics may include detecting a time amplitude value and maximum value of a vibration peak present in said vibration signal (SA); calculating a second comparison parameter which may be a function of the ratio between said time amplitude value and maximum value of the vibration peak; verifying whether said second comparison parameter is in a predetermined relationship with a second threshold.
Detecting a time amplitude value may include calculating an integral of, or an area subtended by, the vibration peak present in said vibration signal (SA), and said second comparison parameter may be calculated by dividing the result of said integral of the vibration peak, or the value of said area subtended by the vibration peak, by the maximum value of the vibration peak.
The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments.
These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.
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| 102021000012665 | May 2021 | IT | national |
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