JP6317000B1 - Labeling apparatus and program - Google Patents

Abstract

The present invention provides a labeling apparatus and program capable of more appropriately labeling data strings in various fields. A labeling device 10 labels a plurality of data constituting a data string. The labeling device 10 forms a plurality of data sessions based on the data string. Each data session includes two or more pieces of continuous data in the data string, and one or more pieces of data forming the head of adjacent data sessions are separated from each other in the data string. The labeling device 10 assigns a label to each data session based on the data included in each data session, forms a label sequence based on the label assigned to each data session, and starts and ends the section where the same label continues. And label. [Selection] Figure 2

Description

  The present invention relates to a labeling apparatus and a program.

  Entering the IoT era, various devices are connected to the Internet, and an environment in which a large amount of data of various sensors can be acquired as a data string is being prepared. If such data can be used effectively, there is a possibility that beneficial results will be obtained in various applications.

  An example of application is deep learning technology. In recent years, deep learning that uses a large amount of data as teacher data and performs recognition, classification, prediction, etc. with high accuracy has become popular. Patent Documents 1 and 2 also describe a learning technique using a sequence of labeled data.

  However, most of the data strings acquired in large quantities are not labeled, and may not be used as they are. For example, it cannot be deep learning teacher data. For this reason, an operation of assigning a label to the data string is necessary, but such an operation may be difficult.

  For example, in order to create teacher data used for deep learning, for example, if it is an acceleration data string of a wearable device, what the wearer was doing in sequence, or a third party photographed the wearer It is necessary to continue. Further, it is necessary to manually perform labeling by comparing the acceleration data string with the memo or video later. It is not realistic to perform these operations for thousands to 10,000 people because it is too costly.

  A technique using a hidden Markov model is known as a technique for automatically labeling. For example, Patent Document 3 discloses an example.

JP 2008-117293 A JP 2006-50164 A International Publication No. 2007-13885 Pamphlet

  However, the conventional technique has a problem that it is difficult to appropriately label data strings in various fields.

  For example, when a hidden Markov model is used as in Patent Document 3, there is a possibility that relatively good accuracy may be obtained for labeling waveform data, but labeling accuracy for other than waveform data is not always sufficient. There is a case.

  The present invention has been made to solve such problems, and an object of the present invention is to provide a labeling apparatus and program capable of more appropriately labeling data strings in various fields.

In order to solve the above problems, a labeling device according to the present invention is a labeling device that labels a plurality of data constituting a data string,
A data session forming function for forming a plurality of data sessions based on the data string, wherein each data session includes n ₁ pieces of data (where n ₁ is an integer equal to or greater than 2) in the data string. A data session forming function in which data forming the head of adjacent data sessions is separated from each other by u ₁ (where u ₁ is an integer equal to or greater than ₁ ) in the data string;
A data session labeling function that assigns a label to each data session based on the data included in each data session;
A label column forming function for forming a label column based on a label assigned to each data session;
For a section in which the same label continues, a section output function that outputs information representing the start point and end point of the section and information representing the label ;
Forming a plurality of labels sessions based on the previous SL label string, a label session forming function, each said label session includes the n ₂ labels continuous in the label string, forming the beginning of the adjacent label session A label session forming function in which labels are separated from each other by u ₂ (where u ₂ is an integer of 1 or more) in a label row;
A probability distribution calculation function for calculating a probability distribution of labels for each label session;
A label session labeling function for assigning a label to each label session based on the probability distribution;
Obtain Bei the.
According to a specific aspect, the number of label types provided by the label session labeling function is smaller than the number of label types provided by the data session labeling function.
According to a particular aspect,
The label column forming function includes a function of forming a label column based on a label assigned to each label session,
The labeling device is:
A function of executing a loop including the label session forming function, the probability distribution calculating function, and the label session labeling function a plurality of times;
In any of the second and subsequent executions of the loop, the function of n ₂ ≠ 1 or u ₂ ≠ 1,
Is further provided.
According to a specific aspect, each of the data includes information representing acceleration at different times.
According to a particular aspect,
a function of forming the data string by sequentially arranging data included in a series of data each representing a temporal change of p physical variables (where p is an integer of 2 or more);
And a function of determining a value of u ₁ in the data session forming function to be an integer multiple of p.
According to a particular aspect,
The section output function is
A function for identifying a label string extraction section in which the same label is continuous in the label string;
In a data string, a function for specifying a data string extraction section corresponding to the label string extraction section;
With
The start point and the end point represent the start point and end point of the data string extraction section in the data string.
The program according to the present invention causes a computer to function as the above-described labeling device.

  According to the labeling apparatus and program according to the present invention, it is possible to more appropriately label data strings in various fields.

It is a figure which shows the example of a structure of the labeling apparatus which concerns on Embodiment 1 of this invention. It is a figure explaining the term concerning the data etc. which the labeling apparatus of FIG. 1 handles. It is a flowchart which shows the example of the flow of a process of the labeling apparatus of FIG. It is a figure which shows the information regarding the data session produced | generated by step S2 and S3 of FIG. It is a figure which shows the information regarding the label session produced | generated by step S5-S7 of FIG.

Embodiments of the present invention will be described below with reference to the accompanying drawings.
Embodiment 1 FIG.
In FIG. 1, the example of a structure of the labeling apparatus 10 which concerns on Embodiment 1 of this invention is shown. The labeling device 10 is a device that labels a plurality of data constituting a data string. The definition of “labeling” is well known to those skilled in the art. For example, a large number of data can be classified into a plurality of clusters, and each data can be labeled with a label corresponding to the cluster into which the data is classified. .

  The labeling device 10 has a configuration as a known computer, and includes a calculation unit 11 that performs a calculation and a storage unit 12 that stores information. Although not particularly illustrated, the labeling device 10 includes an input unit that receives a user operation and an output unit that outputs information. Further, the labeling device 10 may include a communication unit that inputs and outputs information to an external communication network.

  The storage unit 12 stores data to be subjected to labeling processing and data generated along with the labeling processing. The storage unit 12 stores a program (not shown). When the computing means 11 of the computer executes this program, the computer functions as the labeling device 10. That is, this program makes a computer function as a labeling device described in this specification. Further, the computer implements the functions described in this specification by executing this program.

  With reference to FIG. 2, terms related to data and the like handled by the labeling apparatus 10 of FIG. 1 will be described. A plurality of data forms a data string. The data string is composed of a predetermined number L of data (L = 100 in the example of FIG. 2). Each data is ordered in the data string. In the example of FIG. 2, the first data is referred to as data # 0, the second data as data # 1,..., And the last data as data # 99. Each data is a single numerical value (scalar amount) in the example of FIG. 2, but may be a vector or other types of information.

  The labeling device 10 forms a plurality of data sessions based on the data string as the labeling process is executed. Data sessions have an order relationship with each other and each include a plurality of data. In the example of FIG. 2, the first data session is referred to as data session # 0, the second data session is referred to as data session # 1,..., And the last data session is referred to as data session # 30.

The session width of each data session is a predetermined value n ₁ (where n ₁ is an integer equal to or greater than 2; n ₁ = 10 in the example of FIG. 2). That is, each data session containing a predetermined number n ₁ pieces of continuous data in the data string. In the example of FIG. 2, a total of 31 data sessions are formed, and these data sessions constitute a data session sequence.

The value of n ₁ is the same for all data sessions included in the data session sequence. However, as will be described later, when the process for forming the data session or the data session sequence is executed a plurality of times, the value changes each time. May be.

The data sessions are configured at equal intervals, and the data session interval is a predetermined value u ₁ (where u ₁ is an integer equal to or greater than _1; u ₁ = 3 in the example of FIG. 2). That is, the data forming the head of adjacent data sessions are separated from each other by a predetermined number u ₁ in the data string. More specifically, the data at the head of data session # 0 (ie, data # 0) and the data at the top of data session # 1 (ie, data # 3) differ in the order in the data string by 3 Yes. Note that u ₁ <n ₁ in the example of FIG. 2, but u ₁ = n ₁ may be used. Further, when u ₁ = 1, data forming the heads of adjacent data sessions are adjacent to each other.

The value of u ₁ is constant in the data session sequence (all data sessions are arranged at equal intervals), but the process for forming the data session or data session sequence is executed a plurality of times as described later May change each time.

Here, the number m of data sessions formed is a maximum integer not exceeding (u ₁ + L−n ₁ ) / u ₁ . (In the example of FIG. 2, a total of 31 data sessions # 0 to # 30)

  As is clear from FIG. 2 and the above description, data that is not included in any data session may exist depending on the case.

  The labeling apparatus 10 performs a labeling process to be described later on such a data string, and extracts a section where the same label continues. A label is directly assigned in units of a data session or the like instead of the data itself, but it is possible to specify a data section corresponding to each label in a data string. In the example of FIG. 2, data corresponding to the continuous section assigned the label “label # 2”, data corresponding to the continuous section assigned the label “label # 8”, and “label # 6”. Data corresponding to a continuous section to which a label is assigned is shown.

The operation of the labeling device 10 according to the first embodiment will be described below.
FIG. 3 is a flowchart illustrating an example of a processing flow of the labeling apparatus 10. First, the labeling apparatus 10 acquires a data string (step S1). Next, the labeling apparatus 10 forms a plurality of data sessions based on the data string (step S2, data session forming function). Here, the values of the session width n ₁ and the session interval u ₁ may be specified in advance, or may be input from the user in connection with the execution of step S2.

  FIG. 4 shows an example of data arrangement in the data session formed in step S2. This example corresponds to the data string of FIG. For example, in the data session # 0, the first data is data # 0, that is, data having a value of “5”, and the second data is data # 1, that is, data having a value of “4”.

  Next, the labeling apparatus 10 assigns a label to each data session based on the data included in each data session (step S3, data session labeling function). Any label may be used, but for example, it may be simply a number for identifying a cluster used for clustering processing. In this case, labels such as “Label # 2” and “Label # 8” are given as shown in FIG. For convenience of explanation, in FIG. 4, labels are represented using upper case letters.

A process for determining a label to be assigned to each data session can be arbitrarily designed by those skilled in the art. For example, a known clustering process may be used. For example, for each data session, an n _1- dimensional vector having each data included in the data session as a component may be formed, and the vectors may be clustered using the k-means method or the k-means ++ method. When the clustering process is used, the total number of clusters may be specified in advance, or may be dynamically determined or changed as the clustering process proceeds, and is input from the user in connection with the execution of step S3. May be.

  In the clustering process, an appropriate optimization process may be performed. In the clustering process, duplicate vectors are usually deleted in order to improve calculation efficiency. However, for example, the number of significant digits of data may be determined and rounded off to remove duplicate vectors. By doing so, there is a possibility that the set becomes smaller and the calculation efficiency becomes higher. On the other hand, the granularity of classification may be low.

  Next, the labeling device 10 forms a label row based on the labels given to each data session (step S4, label row forming function). For example, labels obtained by arranging the labels assigned to the data sessions in the order of the data sessions are used as a label string. In the example of FIG. 4, a label string having the contents “A, B, A, C,...” Is formed.

The label string may be expressed in any form. For example, if the same label as the data session is given to the top data of each data session, and a column in which the labels of the data with the labels are arranged in the order of the data is formed, the content is substantially the same as above. Can be obtained. Alternatively, all the data may be given the same label as that at the end of the data session including the data, and a column in which the data labels are arranged in the data order may be formed. (For example, data # 4 in FIG. 2 is included in two data sessions, data session # 0 and data session # 1, of which the label of data session # 1 is given to data # 4. In this way, it is possible to obtain a label string in which the arrangement of labels is repeated n ₁ times for each label. In the example of FIG. 4, “A, A, A, B, B, B, A, A , A, C, C, C,... ”Is formed.

  Next, the labeling apparatus 10 forms a plurality of label sessions based on the label sequence (step S5, label session forming function). The relationship between the label sequence and the label session is the same as the relationship between the data sequence and the data session shown in FIG. 2, as will be described below.

  FIG. 5 shows an example of label arrangement in the label session formed in step S5. This example corresponds to the label row in FIG. For example, the label session # 0 will be described. The first label is the label of the data session # 0, that is, “A”, and the second label is the label of the data session # 1, that is, “B”.

The label sessions are in order with each other and each include a plurality of labels. The session width of each label session is a predetermined value n ₂ (where n ₂ > 0, for example, n ₂ may be an integer greater than or equal to 1. In the example of FIG. 5, n ₂ = 5). That is, each label session containing a predetermined number of the n ₂ labels continuous in the label row. In this example, n ₁ > n ₂ is satisfied, but n ₁ = n ₂ may be satisfied or n ₁ <n ₂ may be satisfied.

The value of n ₂ is the same for all label sessions included in the label session sequence, but changes each time a process for forming a label session or a label session sequence is executed a plurality of times as described later. May be.

The label sessions are configured at equal intervals, and the label session interval is a predetermined value u ₂ (where u ₂ is an integer equal to or greater than 1, u ₂ = 1 in the example of FIG. 2). That is, the labels forming the heads of adjacent label sessions are separated from each other by a predetermined number u ₂ in the label row. For example, u ₂ <n ₂ but u ₂ = n ₂ may be used. In addition, when u ₂ = 1 as shown in FIG. 5, the labels forming the heads of adjacent label sessions are adjacent to each other.

the value of u _2, when is constant in the label session column (all labels sessions are equally spaced) is, as described below, the process of forming a label session or label session string is executed a plurality of times May change each time.

  Next, the labeling apparatus 10 calculates a probability distribution of labels for each label session (step S6, probability distribution calculation function). FIG. 5 also shows the probability distribution of labels in each label session. For example, in label session # 0, since two of the five labels are “A”, the probability corresponding to “A” is 0.4.

  Next, the labeling apparatus 10 assigns a label to each label session based on the probability distribution of each label session calculated in step S6 (step S7, label session labeling function). FIG. 5 also shows labels assigned to each label session.

  Any label may be used. The set of labels (assigned to the data session) used in step S7 is generally different from the set of labels (attached to the label session) used in step S3, but the same set may be used. Good. For example, it may be simply a number for identifying a cluster used for clustering processing. In this case, labels such as “Label # 2” and “Label # 8” are given as shown in FIG. For convenience of explanation, labels are represented using Greek letters in FIG.

A process for determining a label to be assigned to each label session can be arbitrarily designed by those skilled in the art. For example, a known clustering process may be used. For example, for each label session, an n _two- dimensional vector having the appearance probability of each label included in the label session as a component is formed, and each vector is clustered using the k-means method or the k-means ++ method. Good. When the clustering process is used, the total number of clusters may be specified in advance, or may be dynamically determined or changed as the clustering process proceeds, and is input from the user in connection with the execution of step S7. May be.

  In the clustering process, an appropriate optimization process may be performed. In the clustering process, overlapping vectors are usually deleted in order to improve calculation efficiency. However, for example, the number of significant digits of the probability may be determined and rounded off to delete the overlapping vectors. By doing so, there is a possibility that the set becomes smaller and the calculation efficiency becomes higher. On the other hand, the granularity of classification may be low.

  Next, the labeling apparatus 10 outputs information related to a section in which the same label continues (step S8, section output function). More specifically, for a section in which the same label continues, information representing the start point and end point of the section and information representing the label are output. In the example of FIG. 5, since the same label “α” is assigned to the label session # 1 and the label session # 2, information on this portion is output. If there are a plurality of such sections, information may be output for each section.

  The information indicating the start point and end point of the section output in step S8 may be in an arbitrary format, but can be expressed as follows, for example.

  In step S8, the labeling apparatus 10 first identifies a section (label string extraction section) in which the same label continues in the label string. That is, in the example of FIG. 5, it is a section corresponding to label sessions # 1 and # 2.

Next, the labeling apparatus 10 specifies a section (data string extraction section) corresponding to the label string extraction section in the data string. This correspondence can be designed arbitrarily. For example, a predetermined number of continuous data sessions (for example, u ₂ from the beginning) are specified for all the label sessions included in the label string extraction section, and further, continuous for all the data sessions thus identified. A predetermined number of data (for example, u ₁ from the head) to be specified is specified, and a section composed of the data thus specified is set as a data string extraction section. When such a correspondence relationship is used, in the example of FIGS. 4 and 5, the data string extraction section corresponding to the label sessions # 1 to # 2 is data # 3 to # 8, that is, data # 3 is the starting point, and data # 8 is the end point.

Alternatively, a section composed of all data included in all data sessions included in the label string extraction section may be set as the data string extraction section. Since the data sessions # 1 to # 6 are included in the section of the label sessions # 1 to # 2, and the data # 3 to # 27 are included in the data sessions # 1 to # 6, the data # 3 is included in this case. Data # 27 is the start point and data # 27 is the end point. In this case, a predetermined length portion at the head or tail (or both) may be excluded. For example, if the last 19 pieces of data are excluded, the data string extraction section becomes data # 3 to # 8 as in the above example. The predetermined length to be excluded here may be calculated based on at least one of n ₁ , n ₂ , u ₁ , u ₂ .

  In step S8, the labeling device 10 thus identifies the start point and end point of the data string extraction section in the data string, and outputs information representing these. The expression format of the output information can be arbitrarily designed, but may be a numerical value (for example, “3” and “8”), or may be coordinates in the image. In the case of the coordinates in the image, for example, a graph representing the data number on the horizontal axis and the value of the data on the vertical axis, and by coloring the horizontal section corresponding to the data string extraction section to a specific color, the information May be output. When coloring is used, the data string extraction section and other sections may have different colors, and when there are a plurality of data string extraction sections, they may have different colors according to the labels (label # 2 section). Is colored red and the section of label # 8 is colored yellow, etc.).

  As described above, according to the labeling apparatus 10 according to the first embodiment, it is possible to more appropriately label data strings in various fields.

  For example, when there is a pattern or feature in a data string without a label, it is possible to automatically recognize these and attach a label. Even if a label is given to the data string as a correct answer in advance, the appropriateness of the label can be verified. For example, when the same label is given as a correct answer (for example, manually) over a certain section, but actually different features are found in the first half and the second half of the section, the labeling device 10 according to the present invention is used. There is a possibility that different labels may be given to sections having different characteristics. Alternatively, for example, when different labels are given as correct answers to sections showing similar features, the labeling apparatus 10 according to the present invention may give a common label to those sections. is there.

  The result of labeling can be used as teacher data used for deep learning, for example.

  The content of the data string is arbitrary, but may be time series data of acceleration, for example. In other words, each data may include information representing acceleration at different times. In the case of time-series data, there is a meaning of data arrangement, and therefore, more appropriate labeling can be performed by performing a labeling process based on the arrangement of data as in step S3.

  In addition, in the case of using clustering processing by the k-means method or the k-means ++ method, it is possible to deal with a wider variety of types of data as compared with the conventional technique using the hidden Markov model. For example, there is a possibility that a pattern (a section in which the same label continues) can be extracted from a score, a DNA sequence, or the like.

In the first embodiment, the following modifications can be made.
In the first embodiment, the first labeling process (step S3) is performed based on the arrangement of data, and the second labeling process (step S7) is performed based on the probability distribution of the label. As a modification, the number and contents of the labeling process may be changed. Such a modification will be specifically described below.

  As an example, the labeling process based on the probability distribution may be omitted. For example, steps S4 to S7 may be omitted.

As another example, the labeling process based on the data sequence may be omitted. For example, in step S3, an n _1- dimensional vector whose component is the appearance probability of each data value included in each data session is formed, and each vector is clustered using the k-means method or the k-means ++ method. Good. In that case, steps S4 to S7 may be omitted.

  Additional labeling processing may be performed. For example, based on the label given in step S7, the processes in steps S4 to S7 may be executed for the label. In this case, the label row forming function in step S4 includes a function of forming another label row based on the label assigned to each label session.

Such processing of steps S4 to S7 can be executed by looping an arbitrary number of times. Further, in that case, the value of n ₂ or the value of u ₂ may be changed. In other words, the labeling device 10 may further include a function of executing a loop including the label session forming function in step S5, the probability distribution calculating function in step S6, and the label session labeling function in step S7 a plurality of times.

Furthermore, the labeling apparatus 10 may set n ₂ ≠ 1 or u ₂ ≠ 1 in any one of the executions of such a loop (particularly the second and subsequent executions). For example, n ₂ > 1 or u ₂ > 1 may be set.

For example, in any of the second and subsequent executions, if n ₂ = 1, the number of data included in the section corresponding to one label session does not change, but if n ₂ > 1, The number of data included in the section corresponding to one label session will increase, and there is a possibility that labeling processing with an emphasis on larger-scale features may be performed.

In any of the second and subsequent executions, if u ₂ = 1, the overlapping portion of the data included in the section corresponding to the adjacent label session becomes relatively large, but u ₂ > 1. In this case, the overlapping portion of data included in the section corresponding to the adjacent label session is relatively small. Thereby, for example, the calculation amount may be reduced.

  Such additional labeling processing may be arranged after step S8. That is, a label is assigned to each data based on the identified data string extraction section (for data that does not belong to the data string extraction section, a label indicating that it does not belong to any data string extraction section is given), You may perform the process of step S4-S7 based on the label.

  The number of types of labels in each labeling process (for example, the number of clusters in the clustering process) can be arbitrarily designed, but may be designed to decrease as the process proceeds. For example, the number of label types provided by the label session labeling function in step S7 may be smaller than the number of label types provided by the data session labeling function in step S3. Further, when the label session labeling function in step S7 is executed a plurality of times, the number of label types given in the process executed later is the number of label types given in the process executed first. It may be made smaller. In this way, it is possible to more appropriately label a data string having a structure in which small features are aggregated to form a larger feature.

In the first embodiment, there is one type of data string, but it is also possible to handle a data string in which two or more different types of data series are mixed. For example, an acceleration sequence (a ₁ , a ₂ , a ₃ ,...) And an angular velocity sequence (b ₁ , b ₂ , b ₃ ,...) Are alternately arranged to form a data string (a ₁ , B ₁ , a ₂ , b ₂ , a ₃ , b ₃ ,...) May be executed. Further, when there is another physical quantity series (c ₁ , c ₂ , c ₃ ,...), The data series (a ₁ , b ₁ , c ₁ ,. a ₂ , b ₂ , c ₂ , a ₃ , b ₃ , c ₃ ,...)) may be executed. The process for forming such a data string can be inserted after step S1 and before step S2, for example.

  In more general terms, the labeling apparatus 10 arranges data included in a series of data respectively representing temporal changes of p physical variables (where p is an integer of 2 or more) in order. A row may be formed. Here, “arranging in order” means, for example, arranging data at corresponding positions between series in a specific order and repeating this for each position. When the number of series is 2, they are arranged alternately. In such a case, it is preferable that data at the same position in different series is data corresponding to the same time.

In such a case, it is preferable to form a data session so that data at corresponding positions in each data session represents the same physical quantity. For example, when two types of data series are used, it is preferable that u ₁ is an even number. In other words, the labeling device 10 may determine the value of u ₁ in the data session formation function as an integer multiple of p. Similarly, it is more preferable that n ₁ is an even number.

  In steps S2 and S5, a process of excluding some data or labels from the session may be performed. For example, when it is known that a part of the data column or the label column corresponds to a specific label, it is not necessary to form a session for that part (in that case, a separate step S8 is performed for that part). May be executed).

  10 labeling device, S2 data session forming function, S3 data session labeling function, S4 label sequence forming function, S5 label session forming function, S6 probability distribution calculation function, S7 label session labeling function, S8 section output function.

Claims (7)

A labeling device that labels a plurality of data constituting a data string,
A data session forming function for forming a plurality of data sessions based on the data string, wherein each data session includes n ₁ pieces of data (where n ₁ is an integer equal to or greater than 2) in the data string. A data session forming function in which data forming the head of adjacent data sessions is separated from each other by u ₁ (where u ₁ is an integer equal to or greater than ₁ ) in the data string;
A data session labeling function that assigns a label to each data session based on the data included in each data session;
A label column forming function for forming a label column based on a label assigned to each data session;
For a section in which the same label continues, a section output function that outputs information representing the start point and end point of the section and information representing the label ;
A label session forming function for forming a plurality of label sessions based on the label sequence, wherein each label session includes n ₂ consecutive labels in a label sequence and forms a head of an adjacent label session Is a label session forming function separated from each other by u ₂ (where u ₂ is an integer equal to or greater than 1) in the label sequence ;
A probability distribution calculation function for calculating a probability distribution of labels for each label session;
A labeling device , comprising: a label session labeling function that assigns a label to each label session based on the probability distribution . The label number of types of labels applied by the session labeling feature, the smaller than the number of types of labels applied by the data session labeling function, labeling apparatus according to claim 1. The label column forming function includes a function of forming a label column based on a label assigned to each label session,
The labeling device is:
A function of executing a loop including the label session forming function, the probability distribution calculating function, and the label session labeling function a plurality of times;
In any of the second and subsequent executions of the loop, the function of n ₂ ≠ 1 or u ₂ ≠ 1,
Further comprising
The labeling device according to claim 1 or 2 . Each said data includes information indicating the acceleration in different times, the labeling apparatus according to any one of claims 1-3. a function of forming the data string by sequentially arranging data included in a series of data each representing a temporal change of p physical variables (where p is an integer of 2 or more);
Wherein the value of u ₁ in the data session forming function further comprises a function of determining an integral multiple of p, the labeling apparatus according to any one of claims 1-4. The section output function is
A function for identifying a label string extraction section in which the same label is continuous in the label string;
In a data string, a function for specifying a data string extraction section corresponding to the label string extraction section;
With
The start point and the end point represent a start point and an end point of the data string extraction section in the data string,
The labeling apparatus as described in any one of Claims 1-5 . The program which functions a computer as a labeling apparatus as described in any one of Claims 1-6 .

Images