This invention relates to a theft detection system which can be attached to valuable objects such as laptop computers, other electronic devices, and even works of fine art.
Computers have conveniently become smaller and smaller in size. There are now notebook computers, hand held personal computers, and personal data assistants in addition to laptop computers.
However, because of their smaller size, computers are now easier to steal, for example, when left unattended for even a brief moment at an airport.
In U.S. Pat. No. 5,574,786, incorporated herein by this reference, a motion detector is coupled to a computer and the computer is disabled whenever it is moved.
The primary problem with this device is that the computer is disabled whenever it is moved. Therefore, if the owner of the computer enables the motion detector and then accidentally moves the computer, her computer will be disabled. Another problem with the device of the '786 patent is that it is an integral component of the computer and thus cannot be used in combination with other objects of value, for example, cellular telephones, other electronic devices, or works of fine art.
It is therefore an object of this invention to provide a more versatile theft detection system.
It is a further object of this invention to provide such a theft detection system for objects of value including computers, works of fine art, cellular telephones, and other electronic devices.
It is a further object of this invention to provide such a theft detection system that can be attached to the housing of any object of value.
It is a further object of this invention to provide such a theft detection system which is self-contained and can be easily attached to an object of value by the user, incorporated on a PC card, or added to the existing circuit board of a computer.
It is a further object of this invention to provide such a theft detection system which filters out any movement of the object which does not constitute a theft of the object thus eliminating false alarms.
It is a further object of this invention to provide a method of detecting the theft of objects of value.
This invention results from the realization that a theft of an object such as a laptop computer can be more accurately determined by attaching an accelerometer to the object and analyzing the frequency of the resulting acceleration signal to effectively filter out movement of the object which is not indicative of a theft (e.g., by filtering out any acceleration signals which cannot be the result of human movement) and then activating an alarm only when the analysis of the acceleration signal reveals a possible theft event. The resulting system thus intelligently differentiates between theft events and non-theft events.
This invention features a theft detection system comprising an accelerometer attachable to an object, the accelerometer providing an acceleration signal in response to movement of the object: an alarm mechanism responsive to the accelerometer for providing an alarm signal in response to movement of the object; and a filter for preventing false alarms, the filter including means for determining the frequency of the acceleration signal and providing an output to activate the alarm mechanism only when the frequency of the acceleration signal meets a predetermined criteria.
The security mechanism may be an audible alarm with three modes, a slow mode, a fast mode and a siren mode. The means for determining the frequency of the acceleration signal may include means for calculating the deviation of the amplitude of the acceleration signal in a predetermined time frame and the filter then includes means for activating the security mechanism only when the deviation of the amplitude of the acceleration signal in a predetermined time frame exceeds a predetermined threshold. The filter typically also further includes means for counting how often the deviation of the amplitude of the acceleration signal exceeds the predetermined threshold.
Alternatively, or in addition, the means for determining the frequency of the acceleration signal includes means for performing a spectral analysis of the acceleration signal and the filter includes means for activating the security mechanism only when the frequency of the acceleration signal is within a specified range and also means for counting how often the frequency of the acceleration signal is within the specified range.
In one embodiment of the theft detection system of this invention, an accelerometer provides an acceleration signal in response to movement of the object; an alarm mechanism provides an alarm signal in response to movement of the object; and a processor is programmed to determine the frequency of the acceleration signal by calculating the deviation of the amplitude of the acceleration signal in a predetermined time frame and to provide an output to activate the alarm mechanism only when the deviation of the amplitude of the acceleration signal exceeds a predetermined threshold. In the preferred embodiment, the processor is further programmed to count how often the deviation of the amplitude of the acceleration signal exceeds the predetermined threshold.
In another embodiment, the processor is programmed to determine the frequency of the acceleration signal by performing a spectral analysis of the acceleration signal and to provide an output to activate the alarm mechanism only when the frequency of the acceleration signal is within a specified range. In the preferred embodiment, the processor is further programmed to count how often the frequency of the acceleration signal is within the specified range.
A method of detecting the theft of an object in accordance with this invention features the steps of employing an accelerometer to provide an acceleration signal in response to movement of an object; determining the frequency of the acceleration signal and providing an output to activate an alarm mechanism only when the frequency of the acceleration signal meets a predetermined criteria. Determining the frequency of the acceleration signal may include calculating the deviation of the amplitude of the acceleration signal in a predetermined time frame and comparing the deviation to a predetermine threshold. The method may further include the step of counting how often the deviation of the amplitude of the acceleration signal exceeds the predetermined threshold. Determining the frequency of the acceleration signal may instead or also include performing a spectral analysis of the acceleration signal and calculating whether the frequency of the acceleration signal is within a specified range. This method may further include the step of counting how often the frequency of the acceleration signal is within the specified range.
In accordance with another aspect of this invention, the theft detection method includes attaching an accelerometer to an object, the accelerometer providing an acceleration signal in response to movement of the object and programming a processor to be responsive to the acceleration signal and to determine the frequency of the acceleration signal by calculating the deviation of the amplitude of the acceleration signal in a predetermined time frame and to provide an output to activate an alarm mechanism only when the deviation of the amplitude of the acceleration signal exceeds a predetermined threshold. Typically, the processor is further programmed to count how often the deviation of the amplitude of the acceleration signal exceeds the predetermined threshold and to activate the alarm mechanism in different modes depending on the count of how often the deviation exceeds the predetermined threshold.
In still another aspect of this invention, the theft detection method comprises attaching an accelerometer to an object, the accelerometer providing an acceleration signal in response to movement of the object; and programming a processor to be responsive to the acceleration signal and to determine the frequency of the acceleration signal by performing a spectral analysis of the acceleration signal and to provide an output to activate the alarm mechanism only when the frequency of the acceleration signal is within specified range. Typically, the processor is further programmed to count how often the frequency of the acceleration signal is within the specified range and to actuate the alarm mechanism in different modes depending on the count of how often the frequency is within the specified range.
Other objects, features and advantages will occur to those skilled in the art from the following description of a preferred embodiment and the accompanying drawings in which:
Theft detection system 10,
The primary components of the preferred theft detection system 10, in all embodiments, include a motion sensor such as accelerometer 20,
In the preferred embodiment, microprocessor 22 is programmed to determine the frequencies of the acceleration signal provided by accelerometer 20 and to filter out any frequencies indicative of movement of computer 12,
Microprocessor 22 is typically programmed to include five primary routines or “circuits”: arming circuit 30 which allows the user to arm the theft detection system, sampling circuit 32 which samples the signal from accelerometer 20 at a predetermined rate (e.g. 32 Hz), windowing circuit 34 which breaks the sampled data into predefined windows, and filtering circuit 36 and motion classifying circuit 38 defined infra.
In general, filtering circuit 36 determines the frequency of the acceleration signal output from accelerometer 20 either by performing a spectral analysis of the sampled varying amplitude acceleration signal to determine the frequency content of the acceleration signal or, more typically (or in addition), by calculating the amplitude deviation of the acceleration signal in a predetermined time frame, e.g. from one sample window to the next.
In this invention, it was determined that human movement typically falls into a frequency range between 0.5 to 2 Hz. Any frequency component less than 0.5 Hz is due to the effects of gravity and any frequency component greater than 2 Hz cannot normally be attributed to human movement. Thus, by filtering out any acceleration signal output from accelerometer 20 which does not fall within this range, theft detection system 10,
System 10,
System 10 supplies a stream of continuously sampled accelerometer outputs. The algorithm initially processes the 2-element time varying discreet data stream into a 1-element stream that is used in subsequent calculations. Next, the processed sensor data is windowed into overlapping finite sets (windows) of data. The algorithm may employ two separate calculation processes on the windowed data, each to detect suspect motion. Finally, a characterization stage uses the string of the most recent processed windows of data to determine whether or not potentially hostile motion is taking place. The process is then repeated, indefinitely, until either the system is unarmed or it is deemed that hostile motion is occurring.
In sample step 40,
In step 42,
A[n]=√{square root over (X[n]2+Y[n]2)}. (1)
That magnitude is then detrended (its DC component is removed) and filtered by a first difference discrete time filter kernel:
a[n]=A[n]−A[n−1]. (2)
In step 44, the windowing circuit algorithm uses the last 10 seconds of data (320 data points, a[−319] . . . a[0] for analysis. These 320 points are broken into 9 smaller windows of data. Each window is two seconds long (64 samples) and overlaps the previous window by one second. Thus, if the ten second set of data covers from −10 to 0 seconds, the 9 windows will cover the following time ranges: −10 to −8, −9 to −7, −8 to −6, −7 to −5, −6 to −4, −5 to −3, −4 to −2, −3 to −1, and −2 to 0.
Filtering circuit 36,
The deviation value Da is proportioned to the overall amount of motion occurring in a given window. For each window, the deviation is compared with a threshold step 48, to determine whether or not the window represents suspicious data.
Alternatively, or in parallel with steps 46 and 48, microprocessor filter circuit 36,
At this stage in the processing, there is a 33 point PSD of each of the nine windows of data. For each of the nine PSD, the low frequency content (0.5 to 2 Hz) or the sum of the second through the fifth elements of the PSD's (L) is calculated. A high value of the low frequency content metric (L) is indicative of walking or carrying motion.
When the low frequency content (L) of nine windows of data (or the last ten seconds) and/or the deviation (D) are above a predetermined threshold, step 57, a hostile motion (a theft) may possibly be taking place and the hostility state is incremented, step 58. Alternatively, if (L) or (D) are not above their respective thresholds, the hostility state is decremented, step 60 and processing returns to step 40 as shown.
When the hostility state is incremented past a first threshold, a first alarm signal may be output to multi-mode alarm 62,
In the preferred embodiment, accelerometer 20,
Alarm 24, as explained supra, may be replaced or supplemented with a device or programming which renders laptop computer 12,
The operation of filtering circuit 36,
If a thief takes computer 12 from a table in an airport, however, the acceleration signal output by accelerometer 20,
Deviation analysis filtering step 46,
Alternatively, or in addition, signal 72,
If, instead of a theft of laptop computer 12,
In this way, by carefully choosing values for the acceptable amplitude deviation (D),
The current algorithm has several routines. The basic idea is that the accelerometer 20 output (X, Y) is sampled continuously at 32 Hz, step 32,
The accelerometer output is sampled at 32 Hz. Both the X axis output and the Y axis output are sampled each time. Each (X, Y) pair is combined into a single magnitude metric that will further be used by the algorithm. The procedure for computing the magnitude metric is to sample the X and Y accelerometer outputs at 32 Hz (X[n], Y[n]); and to calculate the difference between the current sample and the last sample for both the X and Y samples:
Xdiff[n]=X[n−1]−X[n], Ydiff[n]=y[n−1]−Y[n]. (4)
A “magnitude” value is calculated by summing the absolute values of the two difference signals:
AbsMag[n]=|Xdiff[n]|+|Ydiff [n] |. (5)
The magnitude value is compressed into an 8 bit number. Currently the magnitude value AbsMag is an 11 bit quantity. Because of hardware limitations the signal is compressed into 8 bits. This is something that is not fundamental to the algorithm and may not be implemented on some platforms:
Small magnitudes are pinned to zero thus:
if (AbsMag8[n]<=2)AbsMag8[n]=0 (7)
The algorithm next combines multiple samples of the AbsMag8 data stream. This is done by creating windows of data. Currently each window consists of 32 consecutive samples from the AbsMag8 data stream. The rate at which the data is windowed can be varied throughout an effective range of 1 Hz to 32 Hz. The amount of overlap between windows is determined by this rate. At a window rate of 1 Hz, the windows will not overlap. At a window rate of 32 Hz, 31 of the 32 values in each epoch will overlap. A window rate of 2 Hz is currently used. A single window summary value metric is computed for each window of data.
A create current window is created:
The mean of each window is then calculated:
WindowMean[i]=sum(WindowArray[i][ . . . ]−WindowMean)/32). (9)
A binary window summary value for each window summary value is calculated by comparing each WindowSummaryValue to a threshold value:
If (WindowMean[i]>=WindowThreshold) then BinaryWindowSummary[i]=1; Else BinaryWindowSummary[i]=0. (10)
This BinaryWindowSummary stream is then further used to determine if the system has been stolen. Note that the frequency that the BinaryWindowSummary is created at is different than the rate at which the data is sampled. Currently the accelerometer is sampled at 32 Hz, while window summary values are computed at a rate of 2 Hz.
The algorithm next looks at a finite number of the most recent samples from the BinaryWindowSummary stream. This is the BinaryWindowHistoryArray. This history is updated each time a new window summary value is computed. The metrics WindowsAbove and WindowsBelow are computed based on the BinaryWindowHistoryArray and are used as inputs to a theft detection state machine. Transitions between states happen when WindowsAbove or WindowsBelow exceed state dependent thresholds. After a state transition, the BinaryWindowHistoryArray is set to be empty. The number of states can be varied. A system employing 4 states has been used. State 1 would be the resting state, States 2 and 3 are intermediate states and State 4 is the alarm state. Once State 4 has been reached, the system is considered stolen. It should also be noted that many of the parameters discussed previously can be state dependent. Examples include WindowThreshold, thresholds for WindowsAbove and WindowsBelow, and the frequency at which window summary values are computed.
Currently the algorithm keeps track of the last 10 BinaryWindowSummary values thus:
BinaryWindowHistory[1.10]={BinaryWindowSummary[i] . . . BinaryWindowSummary[i]}; (11)
In the current system, the magnitude value is 11 bits nominally. Because of the processors limitations, it is desirable to compress and scale this magnitude into 8 bits. The absolute magnitude is compressed into an 8 bit value using the following monotonically increasing, sigmoidal scaling function:
The first term on the right hand side of this equation is a sigmoidal function. The parameter B can be predetermined or used as a ‘sensitivity’ variable. The second term on the right hand side is a linear function added to the sigmoid to allow the scaling function to continue to rise even thought the sigmoid has approached its maximum. Examples of the effect of this scaling function are plotted in
if (AbsMag8[n]<=2) AbsMag8[n]=0. (13)
In summary, the frequency of the resulting acceleration signal emitted by accelerometer 20,
As a result, theft detection system 10,
Therefore, although specific features of the invention are shown in some drawings and not in others, this is for convenience only as each feature may be combined with any or all of the other features in accordance with the invention. Moreover, other embodiments will occur to those skilled in the art and are within the following claims:
This application claims priority of U.S. Provisional Application Nos. 60/164,709 filed Nov. 11, 1999; 60/157,766 filed Oct. 5, 1999; 60/134,575 filed May 17, 1999; and 60/154,818 filed Sep. 20, 1999.
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