System and method for controlling a functionality of a device based on a user gesture

An embodiment method for controlling at least one functionality of an electronic device on the basis of a gesture of a user comprises detecting, by a sensor, a variation of electrostatic charge of the user during the execution of the gesture and generating a charge variation signal. The gesture includes moving at least one foot upward and, subsequently, downward. The method further includes, by a processing unit, acquiring the charge variation signal, detecting, in the charge variation signal, a characteristic identifying the gesture of moving the foot upward, detecting, in the charge variation signal, a characteristic identifying the gesture of moving the foot downward, and controlling the functionality of the electronic device only in the event that both the first and the second characteristics have been detected.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Italian Patent Application No. 102020000011221, filed on May 15, 2020, which application is hereby incorporated herein by reference.

TECHNICAL FIELD

Embodiments of the present invention relate to a system and a method for controlling at least one functionality of an electronic device based on a gesture of a user of the electronic device.

BACKGROUND

Devices used for the continuous monitoring of a user's body in the environment and during free living conditions are known. Various sensors, for example for detecting the acceleration, the electrical resistance of the skin, the temperature of the skin, the radiated heat flow and the heart rate, are used in various combinations to determine or derive parameters such as calorie burn rate, type and level of activity and sleep status. These devices employ sophisticated algorithms to integrate various acquired data flows, in order to determine output parameters with the best possible precision (e.g., calories burned, type of physical activity, etc.). Additional sensors, to detect additional parameters, provide algorithms with additional data, to improve their accuracy.

In particular, as regards the step counting, the use of triaxial accelerometers (often integrated in portable devices) is known for providing an acceleration signal along three orthogonal axes, and processing the acceleration signal by means of step recognition algorithms in order to identify specific signal patterns that may be related to the execution of a step by the user. However, the acceleration signal processing is calibrated on an “average” or “standard” user and does not take into account specific physical conditions or needs (even if only temporary) that may arise. For example, due to physical problems, the user might take very short steps, or with a different pace from that of the average user taken as a reference in the calibration of the recognition algorithm. Or, the movement of the arms, used by software installed on board wearable devices (e.g., smartwatches), may not be indicative of a step in the event that the user has problems in the movement of the same. Other unpredictable conditions may also prevent a correct step counting.

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.

An electric charge is a fundamental component of nature. The electrons of an element are easily transferred to another element in conditions of direct contact between the elements or at a distance. When the charge is transferred between two electrically insulated objects, a static charge is generated whereby the object with an excess of electrons is negatively charged and the object with a deficiency of electrons is positively charged.

Electrons move within an object in different ways depending on whether the object is a conducting or insulating object. In a conductor, electrons are more or less evenly distributed throughout the material and may easily move based on the influence of external electric fields. In an insulator, the charge mainly exists on the surface. The charge may however be movable, depending on the properties of the material and other environmental factors.

Devices detecting the variation of the electric field generated by a man during the movements of the same, or exploiting a capacitive-type detection are known. Technologies using the latter type of detection include, for example, touch screens, systems for detecting the occupant position 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 US2014/0232516 proposes the use of an electrostatic charge sensor to derive a physiological parameter or a user's activity, such as walking, cycling or energy consumption from a field or a capacitive sensor.

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 technology feasibility to step counting. This document does not teach a technique applicable in real life conditions, wherein the subject executes, in addition to steps, a plurality of other activities, each of which causes a variation of the electrostatic charge detected by the sensor. In these conditions, the detection of signal components due exclusively to the subject's steps is complex and does not guarantee high reliability on the accuracy of the detection and consequent counting.

SUMMARY

The need is therefore felt to make up for the shortcomings of the prior art by providing a system and a method for controlling at least one functionality of an electronic device based on a gesture of a user of the electronic device that is economical but reliable, and that requires a reduced computational load.

Embodiments of the present invention relate to a system and a method for controlling at least one functionality of an electronic device based on a gesture of a user of the electronic device. Embodiments of the present invention also relate to the electronic device which comprises the system. In particular, the gesture includes moving at least one foot upward and, subsequently, downward by the user, detecting as a consequence of this gesture a variation of the electrostatic charge of the user's body.

According to embodiments of the present invention, a system and a method for controlling at least one functionality of an electronic device based on a gesture of a user of the electronic device, and an electronic device that includes the system are provided, as defined in the attached claims.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIG.1illustrates, schematically, a movement detection system1according to an aspect of the present invention. The movement detection system1comprises a processing unit2of an electronic device, or system,4and an electrostatic charge variation sensor6coupled to the processing unit2. In one embodiment, the electrostatic charge variation sensor6is integrated in the device4. The processing unit2receives a charge variation signal SQfrom the electrostatic charge variation sensor6and generates, according to the charge variation signal SQ, a command or control signal SC.

The processing unit2implements, in use, a method for recognizing a gesture by a user of the device4. The recognition of this gesture causes the generation of the command or control signal SC, which is for controlling or commanding at least one functionality of the device4(for example, enabling or disabling a functionality).

This method is represented by means of a block diagram inFIG.2.

With reference toFIG.2, block B1, the functionality to be controlled is considered off or disabled, waiting for the command signal SC.

Then, block B2, a charge variation signal SQgenerated by the electrostatic charge variation sensor6is acquired.

Then, block B3, a step of processing the charge variation signal SQfor extracting significant parameters and parameters identifying the gestures to be detected is carried out.

In the event that such gestures are detected, block B4, the aforementioned functionality of the device4is enabled (block B5); otherwise the method returns to block B2by continuing the acquisition of the charge variation signal SQ.

In one embodiment of the present invention, the gestures to be detected are the upward movement from the ground and the successive downward movement to the ground of at least one foot, or leg, of the user.

Each upward and downward movement generates a variation of electrostatic charge through the user's body, due to an exchange of charges with the ground/floor, which is detected by the electrostatic charge variation sensor6. The sequence, within a predefined time, of upward and downward movements of the user's foot or leg identifies the intention of the user of enabling the aforementioned functionality of the device4and involves, therefore, the generation of the command signal SCwhich enables such functionality. These steps are an implementation of block B4, which therefore includes a sub-step of recognizing the upward movement of the foot or leg and, only in the positive case, the passage to a sub-step of recognizing the downward movement of the foot or leg within a first time interval. If the downward movement of the leg occurs within this time interval, then the method moves on to block B5; conversely it returns to block B2.

Byway of non-limiting example, the device4is a portable electronic device, such as a smartphone, and the functionality to be controlled is the taking of a photo (for example, in selfie mode) by means of a photo- or video-camera of the device4.

The processing unit2is, in one embodiment, a microcontroller integrated in the device4.

The movement detection system1has at least one sensitive element, or electrode (identified with the reference number5only inFIG.3), with which a part of the user's body (e.g., hand or finger) may be placed in direct electrical contact. The electrode5may be integrated in the case of the device4, or connected to the device4in another way, for example by means of an external cable or wireless connection (for example, integrated in a smartwatch connected to the device4). Other embodiments are possible, as apparent to the skilled in the art, so that the electrode5is in electrical contact with a region of the user's body during the step of controlling and commanding the aforementioned functionality.

The sensitive element (electrode)5that collects the external charge may be a metal surface or an electrode coated with dielectric material or again a metal surface placed under the case of the device integrating it. In any case, in use, the user is required to place a finger (or hand, or other portion of the body) in contact with this sensitive element5.

The movement detection system1is affected by the variation of electrostatic charge due to movements of the user. The signal deriving from specific movements (in particular, due to the upward and downward movements of a foot or leg) may be isolated and identified with respect to other movements that are not of interest and with respect to the background noise present in case of user inactivity. According to the present invention, however, it is assumed that the functionality is enabled starting from a condition of substantial stationarity of the user and therefore it is not necessary to carry out a recognition of shapes of the signal SQto identify the shape deriving from the upward/downward movement of the foot or leg with respect, for example, to that deriving from a complex movement of the arms.

FIG.3illustrates an exemplary and non-limiting embodiment of an electrostatic charge variation sensor6. The electrostatic charge variation sensor6comprises an input8a, electrically coupled to the electrode5which, in turn, is contactable by a portion of a user's body; the electrostatic charge variation sensor6also comprises an input8b, electrically coupled to the input8aby means of a resistor Ro and a capacitor Co in parallel with each other. The pair Ro, Co, connected to the electrode5and placed between the two non-inverting inputs of the two operational amplifiers OP1and OP2, has the function of accumulating the charges collected by the electrode5and of managing the band of the input signal (to filter signals and noises at unwanted frequency).

The values of the capacitance of Co and the resistance of Ro may be chosen according to the type of filter that it is desired to form, for example a low-pass filter, with a cut-off frequency of a few tens of Hz, for example 20 Hz. For example, the capacitance of Co is chosen in the range 5 pF-5 nF. For example, the resistance of Ro is chosen in the range 500 MOhm-50 GOhm. The values of the capacitance of Co and the resistance of Ro may also be chosen according to the impedance of the stage to which they are connected, the usable frequency of the signal Vd and that of the interferences to be filtered (e.g., frequency of the power grid, high frequency electrical noises of the power supply circuits, etc.).

The voltage (or electric potential) Vd that is established, in use, between the inputs8aand8brepresents the differential input of an instrumentation amplifier12.

The instrumentation amplifier12includes two operational amplifiers OP1and OP2. A biasing stage (buffer) OP3is used for biasing the instrumentation amplifier12to a common mode voltage VCM.

The inverting terminals of the operational amplifiers OP1and OP2are connected to each other by means of a resistor R2. As the two inputs of each operational amplifier OP1, OP2are to be at the same potential, the input voltage Vd is also applied to the ends of R2and causes, through this resistor R2, a current equal to I2=Vd/R2. This current I2does not come from the input terminals of the operational amplifiers OP1, OP2and therefore runs through the two resistors R1connected between the outputs of the operational amplifiers OP1, OP2, in series with the resistor R2; the current I2, therefore running through the series of the three resistors R1-R2-R1, produces an output voltage Vd′ given by Vd′=I2·(2R1+R2)=Vd·(1+2R1/R2). Therefore, the overall gain of the circuit ofFIG.3is Ad=(1+2R1/R2). The differential gain depends on the value of the resistor R2and may therefore be modified by acting on the resistor R2.

The differential output Vd′, therefore being proportional to the potential Vd between the inputs8a,8b, is input to an analog-to-digital converter14, which outputs the charge variation signal SQfor the processing unit2. The charge variation signal SQis, for example, a high-resolution digital stream (16 bits or 24 bits). The analog-to-digital converter14is optional, since the processing unit2may be configured to work directly on the analog signal, or may itself comprise an analog-to-digital converter for converting the signal Vd′.

FIG.4Aillustrates an example of the charge variation signal SQ. The values of the potential difference Vd, induced by the charge variation, between the inputs8a,8bare represented on the ordinate axis of the charge variation signal SQ. This value is expressed here in LSB (“Least Significant Bit”), i.e., the minimum digital value output from the analog-to-digital converter, which is proportional to the potential difference Vd present at the inputs8a,8b. Typically 1 LSB corresponds to a value ranging between a few V and a few tens of μV. The constant of proportionality (or sensitivity) depends on the gain of the amplifier, the resolution of the analog-to-digital converter and any digital processing (e.g., oversampling, decimation etc.). The representation in LSB is common in the art and disregards a quantification in physical units, since the aim is typically to detect relative variations, with respect to a steady state or base state.

The progressive numbers of the acquired sample are represented on the abscissa axis of the charge variation signal SQ. The reported measurements have been made with a sampling frequency equal to 50 Hz, thus each sample is temporally spaced from the following and the previous by 20 ms.

As may be seen fromFIG.4A, the charge variation signal SQhas a plurality of peaks p1-p10that follow each other temporally with amplitude much greater than the background noise.

Each peak p1-p10is caused by a respective upward movement of the user's foot or downward movement of the user's foot. The peaks p1-p10are identified in the signal Vd′ (then sampled generating the signal SQ), but what described here applies in an equivalent manner to the signal Vd. In fact the amplification stage (instrumentation amplifier ofFIG.2) is useful, but unnecessary. If the charge variation signal to be acquired (caused by the movement of the user) is sufficiently large or if the electrical characteristics of the analog-to-digital converter allow it (e.g., high input impedance, high resolution, full scale suitable for the signal to be converted, etc.), then this amplification stage may be omitted, and the inputs8aand8bare the inputs of the analog-to-digital converter.

Negative peaks p1, p3, p5, p7, p9are generated when the user performs an upward movement of his/her foot, while positive peaks p2, p4, p6, p8, p10are generated when the user performs a downward movement of his/her foot previously moved upward. By way of example when, starting from time 0, the user moves his/her foot upward for the first time, the negative peak p1of the signal SQis generated and, when the same foot is subsequently moved downward, the positive peak p2in the signal SQis generated. The same situation occurs for the generation of the other pairs of negative and positive peaks (foot that is moved upward: generation of p3, foot that is moved downward: generation of p4, etc.).

According to an aspect of the present invention, a sampling of the voltage Vd′ is provided at a sampling rate of 50 Hz, generating the signal SQ. The sampling rate is for example equal to 50 Hz, but may be different, for example chosen in the range 25 Hz-1 kHz.

Optionally, a filtering of the signal SQis also provided, to remove or attenuate any spectral components not related to the movement to be detected. For example, it is possible to carry out a low-pass filtering to attenuate the components of the signal SQgreater than 30 Hz, in order to reduce the noise induced by the electric field of the power grid (usually to 50 Hz or 60 Hz) as much as possible.

According to the present invention, parameters of interest are also extracted from the signal SQ(hereinafter, reference is made to “signal SQ” to indifferently identify such signal SQwith or without filtering) which are used to detect the gestures of the user (as said, movement of the foot upward from/downward to the ground). This operation provides for calculating the derivative with respect to the time of the signal SQ(hereinafter, identified as SQ′); the signal SQ′ is illustrated inFIG.4B.

In the previously illustrated embodiment, the voltage Vd′ is, as said, sampled at 50 Hz. This means representing 2 seconds of the signal Vd′ with N=100 samples SQ(1), . . . , SQ(N) of the signal SQ. For each sample SQ(1), . . . , SQ(N) of the signal SQ, a derivative value is calculated (or estimated) according to a known method, for example according to the “forward difference method” which provides for calculating the difference between two consecutive samples divided by time unit.

Other methods allow the derivative to be estimated with higher accuracy and less sensitivity to noise, employing a greater number of points. In the method known as “second order central difference” it is yi=(xi+1−xi−1)/2dt, that is for estimating the value yi of the derivative y(n) of a function, at the i-th sampling step and of duration dt, it is necessary to know the values xi+1and xi−1of the function x(n), respectively at the following and previous sampling step. In the method known as “fourth order central difference” it is yi=(−xi+2+8xi+1−8xi−1+xi−2)/12dt: in this case there is a better estimate of the derivative, at the expense of a greater complexity of calculation and use of memory, as it is necessary to know the values of the function x(n) with 4 sampling steps.

The components of the derivative SQ′ of the charge variation signal SQwhich identify the execution, by the user, of the gesture to be detected, are then extracted.

To this end, a comparison by means of double threshold ±ThQis used to identify the components of the signal SQ′ identifying the expected gesture.

In particular, the variation of the signal SQofFIG.4Adue to the upward movement of the foot generates the negative peak p1, whose derivative over time is represented inFIG.4Bby a sequence of two peaks, of which a first negative peak p1′ and then a positive peak p1″; similarly, the variation of the signal SQofFIG.4Adue to the successive downward movement of the foot generates the positive peak p2whose derivative over time is represented, inFIG.4B, by a sequence of two peaks, of which a first positive peak p2′ and then a negative peak p2″.

InFIG.4B, the values of the derivative calculated on the basis of the signal SQusing the same representation in LSB ofFIG.4Aare represented on the ordinate axis. The derivative provides a measure of the “speed”, or of the rate, with which the signal SQvaries. The progression of the acquired samples is represented on the abscissa axis ofFIG.4B, similarly toFIG.4B.

The exceeding (towards negative values) of the threshold −ThQby the signal SQ′ (peak p1′) followed by the successive exceeding (towards positive values) of the threshold +ThQby the same signal SQ′ (peak p1″) is associated with the execution of an upward movement of the foot by the user. The exceeding (towards positive values) of the threshold +ThQby the signal SQ′ (peak p2′) followed by the exceeding (towards negative values) of the threshold −ThQby the same signal SQ′ (peak p2″) is associated with the execution of a downward movement of the foot by the user. By recognizing therefore this sequence of events in the signal SQ′ it is possible to identify the expected upward and successive downward movements of the foot by the user.

Similar considerations apply to the pairs of peaks (p3′, p3″) and (p4′, p4″), identifying the upward movement of the foot identified by peak p3and, respectively, the downward movement of the foot identified by peak p4, and so on.

As noted fromFIG.4B, where there are no movements of the user, the signal SQvaries slightly, and is substantially constant (unless of a variation due to electrostatic noise phenomena). The derivative SQ′ of the signal SQis, in this context, substantially equal to zero (or with a mean value equal to zero). On the contrary, in the presence of the expected gesture, the derivative SQ′ shows an apparent variation, with positive peaks p1″, p2′, and negative peaks p1′, p2″ with respect to the zero value. By “negative peaks” it is therefore intended variations of the signal SQ′ towards negative values (smaller than the mean value that the signal SQ′ assumes in the condition of absence of movement) reaching values lower than the threshold −ThQ(exceeding of the threshold −ThQtowards negative values); by “positive peaks” it is intended variations of the signal SQ′ towards positive values (greater than the mean value that the signal SQ′ assumes in the condition of absence of movement) reaching values higher than the threshold +ThQ(exceeding of the threshold +ThQtowards positive values).

The Applicant has also verified that other movements, such as the oscillation of the user's arm that supports the device4during the enabling of the functionality according to the present invention, do not generate an electrostatic charge variation having a trend, in the signals SQand SQ′, comparable to that of the upward/downward movement of the foot. In fact, the electrostatic charge variation due to the movement of the arm is much lower than, and different from that, caused by the upward movement of a foot; in fact it does not arise from an exchange of charges with the ground/floor, but from a variation of the electrostatic field, induced by the movement of the body (this effect prevails with respect to the generation or transfer of charges as a result of arm-air friction).

The thresholds ±ThQare, in one embodiment, of a fixed and preset type. In particular, the Applicant has verified that the signal produced by the movement of the leg is so strong with respect to the background noise of the signal SQ′ (and, correspondingly, SQ) that each threshold ±ThQmay be preset to a value (in modulus) equal to 20-50 times as much as the maximum value reached by the background noise of the signal SQ′, without the risk of acquiring false positives.

In one embodiment, the thresholds ±ThQmay be defined as fixed levels, based on the full scale of the sensor (¼, ½, ¾, etc.), or may be programmed by the user (e.g., by creating a calibration routine of the “move foot upward” and “move foot downward” gestures, with a user interface asking the user to perform a certain number of repeated actions to estimate the levels of optimal threshold, as well as, possibly, of maximum time between the two events).

The thresholds ±ThQmay be defined for example by calculating a mean and a standard deviation of the maxima (in the case of positive peaks) and the minima (in the case of negative peaks) and choosing the threshold as the mean decreased or increased (respectively for maxima or minima) by 3 times as much as the standard deviation.

Byway of example, considering that the background noise of the signal SQ′, when the user is substantially stationary, oscillates between values ±5 LSB, a choice of the threshold +ThQof value 100 LSB and of the threshold −ThQof value −100 LSB allows for distinguish between peaks actually due to the expected movements by the user and the background noise.

In a further embodiment, the threshold ±ThQis of adaptive type, that is, it varies according to the trend of the signal SQ′. The calculation of the threshold ±ThQof the adaptive type may be performed exploiting techniques known in the state of the art. For example, sliding windows or overlapping windows may be used. Other techniques for real-time calculation of adaptive threshold may be used.

As discussed with reference toFIG.2, in order to distinguish between commands actually given by the user and upward/downward movements of the foot for other reasons, it is possible, optionally, to detect a time tOFFelapsing between the detection of the positive peak p1″ and the detection of the following positive peak p2′. The time tOFFis, for example, the residence time of the signal SQ′ below the threshold +ThQ.

If the time tOFFelapsing between these two peaks p1″, p2′ is greater than a reference time interval ΔTOFF, then the command (or trigger) to enable the aforementioned functionality of the device4is not generated; conversely, the command to enable the aforementioned functionality of the device4is generated.

The choice of the value of this time interval ΔTOFFshould take into account the speed of upward/downward movement of the foot, as the rising and falling edges of the peaks p1-p10of the signal SQ(and, consequently, of the derivative thereof) vary according to the speed of the upward/downward movement of the foot.

FIG.5illustrates, by means of a flow chart, a method for calculating and evaluating the time tOFFelapsing between the upward movement of the foot and the following downward movement of the foot. These steps are part of step B4ofFIG.2.

With reference to the flowchart ofFIG.5, when the upward movement of the foot is detected at step B4a(according to the modes already described), step B4bis carried out wherein a timer TQwhich counts the time elapsed from the upward movement of the foot to the following resting of the foot on the ground is started.

At each acquisition iteration, the condition of “foot resting on the ground” is assessed, step B4c: if the foot has been moved downward, the acquisition of the signal SQof step B2stops and step B5ofFIG.2(enable functionality) is carried out.

If the resting of the foot on the ground is not detected at step B4c, it is verified, at step B4d, whether the timer TQhas exceeded the predefined reference interval ΔTOFF(for example 1 second, which may be set or modified by the user).

If not (TQ<ΔTOFF), the timer TQis increased (step B4e), and the method returns to step B4a, to detect the upward movement of the foot based on new samples of the signal SQacquired.

In case the threshold of ΔTOFFis exceeded (exit YES from block B4d), the method returns to step B4a.

Therefore, based on the steps ofFIG.5, it is that toffis the time elapsing between the instant of detection of the foot moved upward and the instant of detection of the foot moved downward.

FIG.6Aexemplary illustrates the trend of the signals SQand SQ′ in the case of rapid movement, whileFIG.6Bexemplary illustrates the trend of the signals SQand SQ′ in the case of slow movement. As noted, in the case ofFIG.6Athe time tOFFis substantially less than the corresponding time tOFFof the situation ofFIG.6B. The applicant has however verified that the choice of a reference interval ΔTOFFof a value equal to 1-2 seconds, with a threshold +ThQequal to or greater than 100, covers most of the situations that occur in practice, managing to identify both slow and fast commands.

Finally, it is apparent that changes and variations may be made to what discussed above, without departing from the scope of the present invention.

For example, the step of detecting peaks p1-p10by means of a threshold may be replaced by a step of recognizing the shape of the signal SQor the signal SQ′. For example, machine learning and/or artificial intelligence techniques may be used for the automatic recognition of specific patterns of the signal SQor SQ′ associated with the gesture to be detected (upward/downward movement of the leg), so as to distinguish between different types of gestures, including a step up, a step down, a foot tap on the ground, etc.

Furthermore, it is noted that a charge variation sensor of a type not wearable by the user, but configured to remotely detect electrostatic variations generated following the execution of a step by the user, may be used. A system of this type is a distributed system and may be used, for example, in gaming or augmented reality applications, wherein the user performs his/her movements in a delimited environment, for example a room.

The advantages achieved by the present invention are apparent from the preceding description.

For example, the present invention offers an opportunity of controlling a device without using the hands, which might be engaged in other purposes (for example to support the device itself). Furthermore, the present invention is not affected by the ambient brightness (critical parameter in application of facial recognition or gesture recognition according to the prior art) and is not affected by the acoustic noise (interfering in the case of voice commands). Even in the case of hands-free, the control through movement of the foot allows for not exerting pressure or movements (some unwanted) directly on the device to be controlled (key pressing, screen touching etc.) with the advantage of not altering the position, the target or the focus (in the case of a photo-camera) thereof.