Patent Publication Number: US-2020289029-A1

Title: Walking vibration analysis apparatus, walking vibration analysis method, and computer-readable recording medium

Description:
TECHNICAL FIELD 
     The example embodiments relates to a walking vibration analysis apparatus and a walking vibration analysis method that are for analyzing vibration that occurs due to walking, and furthermore relates to a computer-readable recording medium that includes a program recorded thereon for realizing the apparatus and method. 
     BACKGROUND ART 
     In recent years, systems that install sensors in a residence for purposes such as watching over elderly people when they are at home have been proposed. With such systems, it is necessary to both protect the privacy of the resident and identify the resident. Thus, in view of protecting privacy, methods for identifying a resident using a vibration sensor rather than using a camera sensor have been proposed. That is, such methods perform identification by extracting a feature of an individual from vibration associated with walking (hereinafter, “walking vibration”) measured with a vibration sensor installed in the residence (e.g., see Patent Document 1 and Non-Patent Document 1). 
     Specifically, Patent Document 1 discloses a system that specifies an individual from walking vibration. In the system disclosed in Patent Document 1, a frequency of vibration produced due to an action of a person within a space or a period of multiple steps is extracted as a feature amount of an individual from measured walking vibration, and collated with frequencies or periods of individuals collected in advance to specify an individual. 
     Non-Patent Document 1 also discloses a system that specifies an individual from walking vibration. The system disclosed in Non-Patent Document 1 specifies an individual by utilizing the fact that vibration patterns differ between individuals. 
     LIST OF RELATED ART DOCUMENTS 
     Patent Document 
     
         
         Patent Document 1: Japanese Patent Laid-Open Publication No. 2004-227053 
       
    
     Non-Patent Document 
     
         
         Non-Patent Document 1: Kazuki ITO et al., “Construction of Individual Identification System using Gait Vibration Data” [online], Symposium on the Living Body, Sensibility and Advanced Information Processing, 2017 [viewed on Aug. 31, 2015], Internet URL: http://pelican.nagaokaut.acp/2017symposium/pdf/09-S-1%E3%80%80%E4%BC%8A%E8%97%A4%E3%80%80%E5%92%8C%E8%BC%9D%EF%BC%88%E9%98%BF%E5%8D%97%E5%B7%A5%E6%A5%AD%E9%AB%98%E7%AD%8 9%E5%B0%82%E9%96%80%E5%AD%A6%E6%A0%A1%EF%BC%89.pdf 
       
    
     SUMMARY 
     Problems 
     However, in actual fact, patterns of walking vibration are not constant but vary, even when the same person walks through the same place. Thus, there is a problem in that both the system disclosed in Patent Document 1 and the system disclosed in Non-Patent Document 1 have difficulty in specifying an individual. 
     An example object of the example embodiments is to solve the above problems and provide a walking vibration analysis apparatus, a walking vibration analysis method, and a computer-readable recording medium that can enable an individual to be specified from walking vibration, even in the case where variation occurs in the walking vibration. 
     Means for Solving the Problems 
     A walking vibration analysis apparatus according to an example aspect of the example embodiments includes: 
     a binarization processing unit configured to acquire walking vibration data specifying walking vibration produced in association with walking, and convert the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     an individual determination unit configured to specify an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     Also, a walking vibration analysis method according to an example aspect of the example embodiments includes: 
     (a) a step of acquiring walking vibration data specifying walking vibration produced in association with walking, and converting the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     (b) a step of specifying an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     Furthermore, a computer-readable recording medium according to an example aspect of the example embodiments includes a program recorded thereon, the program including instructions that cause the computer to carry out: 
     (a) a step of acquiring walking vibration data specifying walking vibration produced in association with walking, and converting the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     (b) a step of specifying an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     Advantageous Effects 
     As described above, according to the example embodiments, an individual can be specified from walking vibration, even in the case where variation occurs in the walking vibration. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a schematic configuration of a walking vibration analysis apparatus in an example embodiment. 
         FIG. 2  is a block diagram more specifically showing the configuration of the walking vibration analysis apparatus in the example embodiment. 
         FIG. 3  is a diagram showing an example of walking vibration data that is acquired in the example embodiment. 
         FIG. 4  is a diagram showing an example of binary data that is obtained in the example embodiment. 
         FIG. 5  is a diagram showing an example of sample data that is acquired in the example embodiment. 
         FIG. 6  is a diagram showing a state where the sample data shown in  FIG. 5  has been binarized. 
         FIG. 7  is a diagram showing an example of composited sample data in the example embodiment. 
         FIG. 8  is a diagram showing a state after filtering of the sample data shown in  FIG. 7 . 
         FIG. 9  are diagrams showing an example of created high-level data in the example embodiment.  FIGS. 9 a  to 9 d    respectively show the high-level data of different individuals. 
         FIG. 10  is a diagram showing an example of a table that consolidates individual specification data that is created in the example embodiment. 
         FIG. 11  is a diagram showing an example of binary data targeted for determination in the example embodiment. 
         FIG. 12 a    is a diagram showing a state where the binary data shown in  FIG. 11  and the individual specification data of person A are superimposed, and  FIG. 12 b    is a diagram showing a state where the binary data shown in  FIG. 11  and the individual specification data of person B are superimposed. 
         FIG. 13  is a flow diagram showing operations at the time of individual specification data creation processing by the walking vibration analysis apparatus according to the example embodiment. 
         FIG. 14  is a flow diagram showing operations at the time of individual specification processing by the walking vibration analysis apparatus according to the example embodiment. 
         FIG. 15  is a block diagram showing an example of a computer that realizes the walking vibration analysis apparatus according to the example embodiment. 
     
    
    
     EXAMPLE EMBODIMENTS 
     Example Embodiment 
     Hereinafter, a walking vibration analysis apparatus according to an example embodiment will be described, with reference to  FIGS. 1 to 15 . 
     [Apparatus Configuration] 
     Initially, a schematic configuration of the walking vibration analysis apparatus according to the example embodiment will be described using  FIG. 1 .  FIG. 1  is a block diagram showing a schematic configuration of the walking vibration analysis apparatus according to the example embodiment. 
     A walking vibration analysis apparatus  10  according to the example embodiment shown in  FIG. 1  is an apparatus for specifying an individual by analyzing vibration that occurs due to walking. As shown in  FIG. 1 , the walking vibration analysis apparatus  10  includes a binarization processing unit  11  and an individual determination unit  12 . 
     The binarization processing unit  11  acquires walking vibration data specifying walking vibration produced in association with walking, and converts the acquired walking vibration data into binary data. The binary data is data representing the value of vibration over time from the start of walking with two values, namely, high level and low level. 
     The individual determination unit  12  specifies the individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data. The individual specification data is data created, for each individual in advance, by converting the walking vibration data of the individual into binary data. 
     In this way, in the example embodiment, the walking vibration analysis apparatus  10  extracts features of an individual in the walking vibration through binarization, and specifies an individual by the extracted features. Thus, according to the example embodiments, an individual can be specified from walking vibration, even in the case where variation occurs in the walking vibration. 
     Next, the configuration of the walking vibration analysis apparatus  10  according to the example embodiment will be more specifically described using  FIGS. 2 to 12 .  FIG. 2  is a block diagram more specifically showing the configuration of the walking vibration analysis apparatus according to the example embodiment. 
     First, in the example embodiment, as shown in  FIG. 2 , the walking vibration analysis apparatus  10  is connected to a vibration sensor  20  for detecting walking vibration. An acceleration sensor, for example, is used as the vibration sensor  20 . Also, the vibration sensor  20  is attached to the floor of a building  22  such as a residence, for example, and detects vibration that occurs due to walking of a person  21  who is in the building  22 . 
     The binarization processing unit  11 , first, acquires sensor data output by the vibration sensor  20 . The sensor data is walking vibration data specifying walking vibration produced in association with walking. The binarization processing unit  11  then calculates a standard deviation σ of the walking vibration data (sensor data), and converts the walking vibration data into binary data, using the calculated standard deviation σ as a threshold value. 
     Specifically, the binarization processing unit  11  acquires M steps (M: any natural number) of walking vibration data of a given interval shown in  FIG. 3 .  FIG. 3  is a diagram showing an example of walking vibration data that is acquired in the example embodiment. In the example of  FIG. 3 , the given interval is the interval of one step, and includes from the point in time (time point A) at which the heel of one foot of the person contacts the floor until the point in time (time point C) at which the toe of the person contacts the floor. Also, in the example in  FIG. 3 , time point B is the point in time at which the toe of the other foot lifts off the floor. The walking vibration data shown in  FIG. 3  is one step of walking vibration data. 
     Also, in the example in  FIG. 3 , the sampling rate of the vibration sensor  20  is set to 1 ksps. Furthermore, the number of pieces N of walking vibration data in the given interval is set to 299, and the length is set to 230 ms. Accordingly, the binarization processing unit  11  acquires a m0  to a mN  as values of the walking vibration data of the mth step of the given interval, where “a mn ” (0≤n≤N) is the value of each piece of walking vibration data at any given point in time of the mth step (m=1 to M). 
     Next, the binarization processing unit  11  calculates the standard deviation σ, based on the following equation 1, for each piece of acquired walking vibration data (a m0  to a mN ). Note that, in the following equation 1, ā is the average value of a m0  to a mN . 
     
       
         
           
             
               
                 
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     Furthermore, the binarization processing unit  11 , as shown in  FIG. 4 , converts the walking vibration data into binary data, using the calculated standard deviation σ as a threshold value. M steps of walking vibration data are thereby binarized.  FIG. 4  is a diagram showing an example of binary data that is obtained in the example embodiment. In the binary data thus obtained, the horizontal axis shows time and the vertical axis shows signal probability. 
     Specifically, the binarization processing unit  11  calculates an average value Av of the walking vibration data every 10 ms, for example, and compares the calculated average value with the standard deviation σ. The binarization processing unit  11 , as shown in the following equation 2, then sets the value a mn  of the walking vibration data targeted for comparison to “b mn =0”, in the case where the result of comparison indicates that the average value Av is smaller than the standard deviation σ. On the other hand, in the case where the average value Av is greater than or equal to the standard deviation σ, the binarization processing unit  11  sets the value a mn  of the walking vibration data targeted for comparison to “b mn =1”. 
         Av&lt;σ:a   mn   →b   mn =0 
         Av≥σ:a   mn   →b   mn =1  [Equation 2]
 
     Also, as shown in  FIG. 2 , in the example embodiment, the walking vibration analysis apparatus  10  includes an individual specification data creation unit  13  and a data storage unit  14 , in addition to the binarization processing unit  11  and the individual determination unit  12 . The individual specification data creation unit  13  creates, for each individual, individual specification data of the individual. Also, the individual specification data creation unit  13  stores the created individual specification data in the data storage unit  14 . 
     Specifically, the individual specification data creation unit  13 , first, acquires, for each individual, a plurality of steps of walking vibration data of the individual. The walking vibration data that is acquired at this time is data to be used in creating individual specification data, and will be notated hereinafter as “sample data”. Also, the individual specification data creation unit  13  converts, for each individual, the acquired plurality of steps of sample data of the individual into binary data. Note that, at this time, the individual specification data creation unit  13  may perform binarization with similar processing to the binarization processing unit  11 , or may instruct the binarization processing unit  11  to binarize the sample data. 
       FIG. 5  is a diagram showing an example of sample data that is acquired in the example embodiment.  FIG. 6  is a diagram showing a state where the sample data shown in  FIG. 5  has been binarized. In the example in  FIG. 5 , the sample data obtained with the first step and the sample data obtained with the second step of walking by a certain individual are shown. Since these pieces of sample data do not coincide despite having been acquired from the same person, the data will also differ from each other after binarization as shown in  FIG. 6 .  FIG. 6  similarly shows time on the horizontal axis and signal probability on the vertical axis. 
     Next, the individual specification data creation unit  13  calculates the probability of occurrence of high-level portions in association with elapsed time from the start of walking, based on the plurality of steps of walking vibration data converted into binary data, and takes the calculated probability of occurrence as the individual specification data of the individual. This point will be described in detail using  FIGS. 7 to 10 . 
     As shown in  FIG. 7 , the individual specification data creation unit  13 , first, composites the M steps of sample data that are acquired for a specific individual. Composition is performed by calculating an average value c n  every piece of sample data whose n value is the same (value=b mn ) using the following equation 3.  FIG. 7  is a diagram showing an example of composited sample data in the example embodiment.  FIG. 7  similarly shows time on the horizontal axis and signal probability on the vertical axis. 
         c   n   =Σb   mn   /M   [Equation 3]
 
     Next, as shown in  FIG. 8 , the individual specification data creation unit  13  filters the sample data after composition, using a threshold value α shown in the following equation 4. The threshold value α is used for filtering parts that have a low signal probability and possibly contain noise. The value c n  of the sample data is thereby converted into d n  based on the threshold value α.  FIG. 8  is a diagram showing a state after filtering of the sample data shown in  FIG. 7 .  FIG. 8  similarly shows time on the horizontal axis and signal probability on the vertical axis. In  FIG. 8 , the threshold value α is set to 0.25. 
         c   n   ≤α→d   n =0 
         c   n   &gt;α→d   n   =c   n   [Equation 4]
 
     The obtained data after filtering shown in  FIG. 8  will indicate the probability of occurrence of high-level portions in the walking vibration data of the specific individual. In other words, in  FIG. 8 , the non-zero portions indicate portions where high-level portions occur with high probability. Hereinafter, the data shown in  FIG. 8  will be notated as “high-level data”. Also, high-level data is created for each individual, as shown in  FIG. 9 .  FIG. 9  are diagrams showing an example of created high-level data in the example embodiment.  FIGS. 9 a  to 9 d    respectively show the high-level data of different individuals. 
     Also, among the high-level portions respectively shown in  FIGS. 9 a  to 9 d   , portions corresponding to time point A shown in  FIG. 3  will be notated as “peak  1 ”, portions corresponding to time point B shown in  FIG. 3  will be notated as “peak  2 ”, and portions corresponding to time point C shown in  FIG. 3  will be notated as “peak  3 ”. 
     It is evident from  FIG. 9 a    that, with person A, the probability of occurrence of peaks  1  and  2  is high. Similarly, it is evident from  FIG. 9 b    that, with person B, the probability of occurrence of peaks  1 ,  2  and  3  is high. Also, it is evident from  FIG. 9 c    that, with person C, the probability of occurrence of peaks  1  and  3  is high. Furthermore, it is evident from  FIG. 9 d    that, with person D, the probability of occurrence of peak  1  is high. 
     The high-level data, respectively shown in  FIGS. 9 a  to 9 d   , obtained in this manner will be the individual specification data of each individual. In the individual specification data, the probability of occurrence of high-level portions is associated with elapsed time from the start of walking. In this way, in the example embodiment, individual specification data is created, by superimposing a plurality of steps of walking vibration, and indexing the probability of a high-level portion occurring at respective timings. 
     Also, the individual specification data creation unit  13  creates a table shown in  FIG. 10  from the individual specification data of each individual.  FIG. 10  is a diagram showing an example of a table consolidating individual specification data that is created in the example embodiment. In the table shown in  FIG. 10 , persons A to D are sorted into one of categories 1 to 4, depending on the combination of peaks with a high probability of occurrence. 
     Also, in the example embodiment, the individual determination unit  12 , first, acquires the individual specification data of each individual created by the individual specification data creation unit  13  from the data storage unit  14 . Next, the individual determination unit  12  acquires binary data targeted for determination from the binarization processing unit  11 . The individual determination unit  12  then contrasts, for each piece of individual specification data, high-level portions of the acquired binary data with the probability of occurrence of high-level portions of the individual specification data. 
     For example, assume that the acquired binary data targeted for determination is the binary data shown in  FIG. 11 . The result of superimposing the binary data shown in  FIG. 11  and the individual specification data shown in  FIGS. 9 a  to 9 d    will be as shown in  FIGS. 12 a  and 12 b   .  FIG. 11  is a diagram showing an example of binary data targeted for determination in the example embodiment.  FIG. 12 a    is a diagram showing a state where the binary data shown in  FIG. 11  and the individual specification data of person A are superimposed, and  FIG. 12 b    is a diagram showing a state where the binary data shown in  FIG. 11  and the individual specification data of person B are superimposed. 
     The individual determination unit  12  then calculates, for each piece of individual specification data, the probability of the individual from which the walking vibration data was acquired being the individual corresponding to the individual specification data, based on the results of contrasting the binary data and the individual specification data. 
     Specifically, the individual determination unit  12 , first, calculates the sum of products S of the values (d 0  to d N ) of the individual specification data and the values (s 0  to s N ) of the binary data, based on the following equation 5, where so to s N  are the respective values of the binary data targeted for determination. 
         S =Σ( d   n   ×s   n )  [Equation 5]
 
     Next, the individual determination unit  12  normalizes the sum of products S calculated by the above equation 5, based on the following equation 6, in order to eliminate deviation of the distribution of the probabilities of occurrence for each person. The value s obtained through normalization indicates the probability of the individual from which the walking vibration data targeted for determination was acquired being the respective individual corresponding to the individual specification data. 
         s=S /(Σ c   n )  [Equation 6]
 
     For example, assume that the individual specification data is the data shown in  FIGS. 9 a  to 9 d   , and the binary data of the walking vibration data of the individual targeted for determination is the binary data shown in  FIG. 11 . In this case, the calculation results of the probability s by the individual determination unit  12  will be as follows. Accordingly, the individual determination unit  12  determines that the individual from which the walking vibration data targeted for determination was acquired is “person A”. In this way, in the example embodiment, the individual is specified by contrasting the obtained binary data of walking vibration data with individual specification data, and verifying the similarity. 
     Person A: 0.786 
     Person B: 0.448 
     Person C: 0.000 
     Person D: 0.000 
     Also, the individual determination unit  12  may determine the individual from which the walking vibration data targeted for determination was acquired, by deriving the positions of the peaks in the binary data shown in  FIG. 11 , and contrasting the derived positions of the peaks with the table shown in  FIG. 10 . 
     [Apparatus Operations] 
     Next, the operations of the walking vibration analysis apparatus  10  according to the example embodiment will be described using  FIGS. 13 and 14 . In the following description,  FIGS. 1 to 12  will be taken into consideration as appropriate. Also, in the example embodiment, the walking vibration analysis method is implemented by operating the walking vibration analysis apparatus  10 . Therefore, description of the walking vibration analysis method according to the example embodiment will be replaced by the following description of the operations of the walking vibration analysis apparatus  10 . 
     Initially, processing for creating individual specification data by the walking vibration analysis apparatus  10  will be described using  FIG. 13 .  FIG. 13  is a flow diagram showing operations at the time of individual specification data creation processing by the walking vibration analysis apparatus according to the example embodiment. 
     As shown in  FIG. 13 , initially, the individual specification data creation unit  13  acquires, for each individual, a plurality of steps of walking vibration data of the individual as sample data (step A 1 ). The sample data may be acquired from the vibration sensor  20 , or may be input from another terminal device or the like. 
     Next, the individual specification data creation unit  13  converts, for each individual, the plurality of steps of sample data of the individual acquired in step A 1  into binary data (step A 2 ). 
     Next, the individual specification data creation unit  13  creates, for each individual, individual specification data from the binary data, and stores the created individual specification data in the data storage unit  14  (step A 3 ). 
     Specifically, the individual specification data creation unit  13  composites, for each individual, the sample data using the abovementioned equation 3. The individual specification data creation unit  13  then creates high-level data from the sample data after composition, further using equation 4, and calculates the probability of occurrence of high-level portions. Thereafter, the individual specification data creation unit  13  stores, for each individual, the calculated probability of occurrence in the data storage unit  14  as the individual specification data of the individual. 
     Next, processing for specifying an individual by the walking vibration analysis apparatus  10  will be described using  FIG. 14 .  FIG. 14  is a flow diagram showing operations at the time of individual specification processing by the walking vibration analysis apparatus according to the example embodiment. 
     As shown in  FIG. 14 , initially, the binarization processing unit  11  acquires sensor data output by the vibration sensor  20 , as walking vibration data (step B 1 ). 
     Next, the binarization processing unit  11  converts the walking vibration data acquired in step B 1  into binary data (step B 2 ). Also, the binarization processing unit  11  passes the binary data obtained through conversion to the individual determination unit  12 . 
     Next, the individual determination unit  12 , upon receiving the binary data, acquires the individual specification data of each individual created by the individual specification data creation unit  13  from the data storage unit  14  (step B 3 ). 
     Next, the individual determination unit  12  contrasts, for each piece of individual specification data, high-level portions of the binary data acquired in step B 1  with the probability of occurrence of high-level portions of the individual specification data acquired in step B 3 . The individual determination unit  12  then calculates the probability of the individual from which the walking vibration data was acquired being the individual corresponding to the individual specification data, based on the contrast results (step B 4 ). 
     Thereafter, the individual determination unit  12  specifies the individual from which the walking vibration data acquired in step B 1  was acquired, using the probabilities calculated in step B 4  (step B 5 ). 
     [Effects of the Example Embodiment] 
     In this way, in the example embodiment, individual specification data is created, by superimposing a plurality of steps of walking vibration, and indexing the probability of a high-level portion occurring at respective timings. Because the probability of occurrence of high-level portions is a value unique to each individual, an individual can be specified by contrasting obtained binary data of walking vibration data with individual specification data. Thus, according to the example embodiment, an individual can be specified from walking vibration, even in the case where variation occurs in the walking vibration. 
     [Program] 
     A program according to the example embodiment need only be a program that causes a computer to carry out steps A 1  to A 3  shown in  FIG. 13  and steps B 1  to B 5  shown in  FIG. 14 . The walking vibration analysis apparatus  10  and the walking vibration analysis method according to the example embodiment can be realized, by this program being installing on a computer and executed. In this case, a processor of the computer performs processing while functioning as the binarization processing unit  11 , the individual determination unit  12 , and the individual specification data creation unit  13 . 
     Also, the program according to the example embodiment may be executed by a computer system constructed from a plurality of computers. In this case, for example, each computer may respectively function as one of the binarization processing unit  11 , the individual determination unit  12 , and the individual specification data creation unit  13 . 
     Here, a computer that realizes the walking vibration analysis apparatus  10  by executing a program according to the example embodiment will be described using  FIG. 15 .  FIG. 15  is a block diagram showing an example of a computer that realizes the walking vibration analysis apparatus according to the example embodiment. 
     As shown in  FIG. 15 , a computer  110  includes a CPU (Central Processing Unit)  111 , a main memory  112 , a storage device  113 , an input interface  114 , a display controller  115 , a data reader/writer  116 , and a communication interface  117 . These units are connected to each other in a manner that enables data communication, via a bus  121 . Note that the computer  110  may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array), in addition to the CPU  111  or instead of the CPU  111 . 
     The CPU  111  implements various computational operations, by extracting programs (code) according to the example embodiment that are stored in the storage device  113  to the main memory  112 , and executing these programs in predetermined order. The main memory  112 , typically, is a volatile storage device such as a DRAM (Dynamic Random Access Memory). Also, programs according to the example embodiment are provided in a state of being stored in a computer-readable recording medium  120 . Note that programs according to the example embodiment may be distributed over the Internet connected via the communication interface  117 . 
     Also, a semiconductor storage device such as a flash memory is given as a specific example of the storage device  113 , other than a hard disk drive. The input interface  114  mediates data transmission between the CPU  111  and input devices  118  such as a keyboard and a mouse. The display controller  115  is connected to a display device  119  and controls display by the display device  119 . 
     The data reader/writer  116  mediates data transmission between the CPU  111  and the recording medium  120 , and executes readout of programs from the recording medium  120  and writing of processing results of the computer  110  to the recording medium  120 . The communication interface  117  mediates data transmission between the CPU  111  and other computers. 
     Also, a general-purpose semiconductor storage device such as a CF (Compact Flash (registered trademark)) card or an SD (Secure Digital) card, a magnetic recording medium such as a flexible disk, and an optical recording medium such as a CD-ROM (Compact Disk Read Only Memory) are given as specific examples of the recording medium  120 . 
     Note that the walking vibration analysis apparatus  10  according to the example embodiment is also realizable by using hardware corresponding to the respective units, rather than by a computer on which programs are installed. Furthermore, the walking vibration analysis apparatus  10  may be realized in part by programs, and the remaining portion may be realized by hardware. 
     The example embodiments described above can be partially or wholly realized by supplementary notes 1 to 12 described below, but the example embodiments is not limited to the following description. 
     (Supplementary Note 1) 
     A walking vibration analysis apparatus including: 
     a binarization processing unit configured to acquire walking vibration data specifying walking vibration produced in association with walking, and convert the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     an individual determination unit configured to specify an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     (Supplementary Note 2) 
     The walking vibration analysis apparatus according to supplementary note 1, 
     in which the binarization processing unit calculates a standard deviation of the acquired walking vibration data, and converts the acquired walking vibration data into the binary data, using the calculated standard deviation as a threshold value. 
     (Supplementary Note 3) 
     The walking vibration analysis apparatus according to supplementary note 1 or 2, further including: 
     an individual specification data creation unit configured to create, for each of the individuals, the individual specification data of the individual, 
     in which the individual specification data creation unit acquires, for each of the individuals, a plurality of steps of walking vibration data of the individual, converts the acquired plurality of steps of walking vibration data into the binary data, calculates a probability of occurrence of high-level portions in association with elapsed time from the start of walking, based on the plurality of steps of walking vibration data after conversion, and takes the calculated probability of occurrence as the individual specification data of the individual. 
     (Supplementary Note 4) 
     The walking vibration analysis apparatus according to supplementary note 3, 
     in which the individual determination unit contrasts, for each piece of the individual specification data, high-level portions of the binary data obtained through conversion with the probability of occurrence of high-level portions of the individual specification data, and calculates a probability of the individual from which the walking vibration data was acquired being the individual corresponding to the individual specification data. 
     (Supplementary Note 5) 
     A walking vibration analysis method comprising: 
     (a) a step of acquiring walking vibration data specifying walking vibration produced in association with walking, and converting the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     (b) a step of specifying an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     (Supplementary Note 6) 
     The walking vibration analysis method according to supplementary note 5, 
     in which, in the (a) step, a standard deviation of the acquired walking vibration data is calculated, and the acquired walking vibration data is converted into the binary data, using the calculated standard deviation as a threshold value. 
     (Supplementary Note 7) 
     The walking vibration analysis method according to supplementary note 5 or 6, further including: 
     (c) a step of creating, for each of the individuals, the individual specification data of the individual, 
     in which, in the (c) step, for each of the individuals, a plurality of steps of walking vibration data of the individual are acquired, the acquired plurality of steps of walking vibration data are converted into the binary data, a probability of occurrence of high-level portions is calculated in association with elapsed time from the start of walking, based on the plurality of steps of walking vibration data after conversion, and the calculated probability of occurrence is taken as the individual specification data of the individual. 
     (Supplementary Note 8) 
     The walking vibration analysis method according to supplementary note 7, 
     in which, in the (b) step, for each piece of the individual specification data, high-level portions of the binary data obtained through conversion are contrasted with the probability of occurrence of high-level portions of the individual specification data, and a probability of the individual from which the walking vibration data was acquired being the individual corresponding to the individual specification data is calculated. 
     (Supplementary Note 9) 
     A computer-readable recording medium that includes a program recorded thereon, the program including instructions that cause the computer to carry out: 
     (a) a step of acquiring walking vibration data specifying walking vibration produced in association with walking, and converting the acquired walking vibration data into binary data representing a value of vibration over time from a start of walking with two values, being high level and low level; and 
     (b) a step of specifying an individual from which the walking vibration data was acquired, by collating the binary data obtained through conversion with individual specification data created, for each individual in advance, by converting the walking vibration data of the individual into the binary data. 
     (Supplementary Note 10) 
     The computer-readable recording medium according to supplementary note 9, 
     in which, in the (a) step, a standard deviation of the acquired walking vibration data is calculated, and the acquired walking vibration data is converted into the binary data, using the calculated standard deviation as a threshold value. 
     (Supplementary note 11) 
     The computer-readable recording medium according to supplementary note 9 or 10, the program further including instructions that cause the computer to carry out: 
     (c) a step of creating, for each of the individuals, the individual specification data of the individual, 
     in which, in the (c) step, for each of the individuals, a plurality of steps of walking vibration data of the individual are acquired, the acquired plurality of steps of walking vibration data are converted into the binary data, a probability of occurrence of high-level portions is calculated in association with elapsed time from the start of walking, based on the plurality of steps of walking vibration data after conversion, and the calculated probability of occurrence is taken as the individual specification data of the individual. 
     (Supplementary Note 12) 
     The computer-readable recording medium according to supplementary note 11, in which, in the (b) step, for each piece of the individual specification data, high-level portions of the binary data obtained through conversion are contrasted with the probability of occurrence of high-level portions of the individual specification data, and a probability of the individual from which the walking vibration data was acquired being the individual corresponding to the individual specification data is calculated. 
     Although the example embodiments of the present application has been described above with reference to example embodiments, the example embodiments is not limited to the example embodiments described above. Various modifications apparent to those skilled in the art can be made to the configurations and details of the example embodiments within the scope of the example embodiments. 
     This application is based upon and claims the benefit of priority from Japanese application No. 2017-212737, filed on Nov. 2, 2017, the disclosure of which is incorporated herein in its entirety by reference. 
     INDUSTRIAL APPLICABILITY 
     As described above, according to the example embodiments, an individual can be specified from walking vibration, even in the case where variation occurs in the walking vibration. The example embodiments is useful in systems that seek to specify individuals from walking vibration, such as systems that watch over elderly people and crime prevention systems, for example. 
     LIST OF REFERENCE SIGNS 
     
         
         
           
               10  Walking vibration analysis apparatus 
               112  Binarization processing unit 
               12  Individual determination unit 
               13  Individual specification data creation unit 
               14  Data storage unit 
               20  Vibration sensor 
               21  Person 
               22  Building 
               110  Computer 
               111  CPU 
               112  Main memory 
               113  Storage device 
               114  Input interface 
               115  Display controller 
               116  Data reader/writer 
               117  Communication interface 
               118  Input device 
               119  Display device 
               120  Recording medium 
               121  Bus