JP2014002655A - Recognition device, recognition method, and recognition system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform high-precision input operation to a terminal without forcing a user to perform special operation.SOLUTION: A recognition device 1 achieves input operation to a terminal 3 by recognizing whether or not grip data showing a state of gripping of the terminal 3 by a user corresponds to a state of gripping stored in advance, so that the user is not forced to perform special operation. Further, time variation data on the grip data is calculated, and a feature vector representing similarity between distributions of a probability density function calculated by a statistical method from the calculated time variation data and a reference probability density function is stored as gripping state information for recognizing the state of gripping, so that the gripping state information is robust against a noise due to conditions characteristic of a detection device for detecting the grip data and other unexpected noises. Then the state of gripping is recognized based upon the gripping state information which is robust against various noises, so that input to the terminal based on high-precision recognition processing is achieved.

Description

  The present invention relates to a recognition device, a recognition method, and a recognition system.

  Conventionally, there has been a technique for giving a predetermined command input to a terminal by means other than key input or touch panel operation. For example, a technique is known in which a sensor such as an acceleration sensor or a gyro sensor is provided in a terminal, and a predetermined command input is given to the terminal when a predetermined operation state is detected by the sensor while the user moves while holding the terminal. It has been. Further, a technique is known in which a user's fingerprint is detected and authentication is performed when the fingerprint corresponds to a pre-registered fingerprint. This technique uses a predetermined fingerprint pattern detection as an input command. Also, an authentication method for detecting payment by a sensor provided on a side surface of a mobile phone casing and executing settlement when the detected holding pattern matches a registered pattern registered in advance. Is known (see, for example, Patent Document 1).

JP 2009-77221 A

  In the prior art, in order to give a desired input to the terminal, the user is forced to perform a predetermined action, and thus cannot be used in a situation where the user cannot perform such an action. Also, in the technique of inputting to a terminal by inputting a fingerprint pattern, the user is forced to perform a specific operation for causing the sensor to read the fingerprint, and the operation is a burden on the user. Moreover, since the sensor in the technique described in Patent Document 1 only detects whether or not the sensor has been operated, this technique cannot make a highly accurate determination.

  Therefore, the present invention has been made in view of the above problems, and provides a recognition device, a recognition method, and a recognition system that can perform a highly accurate input operation to a terminal without forcing a user to perform a special operation. For the purpose.

  In order to solve the above-described problem, a recognition apparatus according to an aspect of the present invention is based on a gripping data group that indicates a gripping state of a terminal by a user and includes a plurality of gripping data detected in time series at the terminal. A recognition device for recognizing a gripping state of a terminal, the first gripping data acquiring unit acquiring a gripping data group acquired based on the first gripping state as a first gripping data group, and each of the gripping data groups Time-varying grip data consisting of a group of time-varying data indicating time fluctuations of grip data at time, wherein the time-varying grip data based on the first grip data group acquired by the first grip data acquisition means A first time-variable gripping data calculating means for calculating the gripping data, and a probability density function indicating a fluctuation state of the time fluctuation indicated in the time-varying gripping data, First probability density function generation means for generating a probability density function based on the first time fluctuation gripping data calculated by the time fluctuation gripping data calculation means as a first probability density function, and generation based on reference time fluctuation gripping data as a reference The similarity of the distribution between the reference probability density function that is the calculated probability density function and the first probability density function generated by the first probability density function generating means is calculated, and the calculated similarity of the distribution is calculated as the first The first feature vector defining means that is defined as a feature vector, and the first grip state information including the first feature vector generated by the first feature vector defining means is used as information for recognizing the grip state. Gripping state information storage means to be stored in the information storage means, and second gripping data for acquiring a gripping data group acquired based on the second gripping state as a second gripping data group Acquisition means; second time-variable grip data calculating means for calculating time-variable grip data based on the second grip data group acquired by the second grip data acquisition means as second time-variable grip data; and second time-variable grip Second probability density function generating means for generating a probability density function based on the second time fluctuation grasping data calculated by the data calculating means as a second probability density function, a reference probability density function, and a second probability density function generating means Calculating the similarity of the distribution with the second probability density function generated by the second feature vector defining means for defining the calculated similarity of the distribution as the second feature vector, and defining by the second feature vector defining means A similarity measure indicating the degree of similarity between the second feature vector thus determined and the first feature vector included in the first grip state information stored in the grip state information storage means is calculated. A similarity scale calculating means to be output; and a determining means for determining that the second gripping state corresponds to the first gripping state when the similarity scale calculated by the similarity scale calculating means is equal to or greater than a predetermined threshold. .

  In order to solve the above-described problem, a recognition method according to an aspect of the present invention is based on a gripping data group that includes a plurality of gripping data that indicates a gripping state of a terminal by a user and is detected in time series on the terminal. A recognition method in a recognition device for recognizing a gripping state of a terminal by a user, a gripping data acquisition step for acquiring a gripping data group acquired based on the first gripping state as a first gripping data group, and gripping data Time-variable gripping data composed of a group of time-variable data indicating temporal variation of gripping data at each time included in the group, and time-variable gripping data based on the first gripping data group acquired in the first gripping data acquisition step A first time variation gripping data calculation step for calculating the time variation gripping data as a first time variation gripping data, and a time variation variation indicated in the time variation gripping data. A first probability density function generating step for generating a probability density function based on the first time-variable grasping data calculated in the first time-variable grasping data calculating step as a first probability density function, And calculating the similarity of distribution between the reference probability density function that is a probability density function generated based on the reference time variation grasping data as a reference and the first probability density function generated in the first probability density function generation step. A first feature vector defining step for defining the calculated similarity of the distribution as a first feature vector, and first gripping state information including the first feature vector generated in the first feature vector defining step, As information for recognizing the gripping state, based on the gripping state information storing step stored in the gripping state information storage means and the second gripping state A second grip data acquisition step for acquiring the obtained grip data group as a second grip data group, and time-variable grip data based on the second grip data group acquired in the second grip data acquisition step A second time-variable gripping data calculation step that is calculated as data, and a second probability that generates a probability density function based on the second time-variable gripping data calculated in the second time-variable gripping data calculation step as a second probability density function The distribution similarity between the density function generation step, the reference probability density function, and the second probability density function generated in the second probability density function generation step is calculated, and the calculated distribution similarity is calculated as the second feature vector. A second feature vector defining step defined as: a second feature vector defined in the second feature vector defining step; A similarity scale calculation step for calculating a similarity scale indicating the degree of similarity with the first feature vector included in the first gripping state information stored in the holding state information storage means, and the similarity scale calculated in the similarity scale calculation step include A determination step that determines that the second gripping state corresponds to the first gripping state when it is equal to or greater than a predetermined threshold value.

  In order to solve the above problem, a recognition system according to an aspect of the present invention includes a terminal and a grip data group that includes a plurality of grip data that indicates a grip state of the terminal by a user and is detected in time series at the terminal A recognition device for recognizing a gripping state of a terminal by a user, the terminal including a detection unit that detects gripping data and a plurality of gripping data detected in time series by the detection unit. Transmitting means for transmitting a grip data group including the recognition data to the recognition device, and the recognition device includes a first grip data acquisition means for acquiring the grip data group acquired based on the first grip state as the first grip data group; , Time-varying grip data consisting of a group of time variation data indicating the time variation of grip data at each time included in the grip data group, which is captured by the first grip data acquisition means. First time-variable gripping data calculating means for calculating time-variable gripping data based on the first gripping data group as first time-variable gripping data, and a probability density function indicating a fluctuation state of the time fluctuation indicated in the time-varying gripping data A first probability density function generating means for generating, as a first probability density function, a probability density function based on the first time variable gripping data calculated by the first time variable gripping data calculating means, and a reference time as a reference A distribution similarity between a reference probability density function, which is a probability density function generated based on the fluctuation gripping data, and a first probability density function generated by the first probability density function generating means is calculated, and the calculated distribution First feature vector defining means for defining the similarity of the first feature vector as a first feature vector, and a first feature vector including a first feature vector generated by the first feature vector defining means The gripping state information is stored in the gripping state information storage unit as information for recognizing the gripping state information, and the gripping data group acquired based on the second gripping state is used as the second gripping data group. Second grip data acquisition means for acquiring, and second time variable grip data calculation means for calculating time-variable grip data based on the second grip data group acquired by the second grip data acquisition means as second time-variable grip data; , Second probability density function generating means for generating a probability density function based on the second time fluctuation gripping data calculated by the second time fluctuation gripping data calculating means as a second probability density function, a reference probability density function, The similarity of the distribution with the second probability density function generated by the two probability density function generating means is calculated, and the calculated similarity of the distribution is defined as the second feature vector. Similar to the second feature vector defining means, the second feature vector defined by the second feature vector defining means, and the first feature vector included in the first gripping state information stored in the gripping state information storage means A similarity scale calculating means for calculating a similarity scale indicating a degree, and when the similarity scale calculated by the similarity scale calculating means is equal to or greater than a predetermined threshold value, it is determined that the second gripping state corresponds to the first gripping state Determining means.

  According to the said form, since input operation to a terminal is implement | achieved by recognition of the holding data which shows the holding state of the terminal by a user, a special operation is not forced to a user. Also, grasping state information for recognizing the grasping state using a feature vector representing the similarity between the probability density function calculated from the time variation data indicating the time variation of the grasping data by a statistical method and the reference probability density function. Therefore, it is robust against noise caused by circumstances unique to the detection device for detecting gripping data and other sudden noises. Since the gripping state is recognized based on the gripping state information that is robust against various noises, input to the terminal based on highly accurate recognition processing is realized.

  The recognition device according to another aspect is acquired by an update grip data acquisition unit that acquires a grip data group acquired based on the third grip state as an update grip data group, and an update grip data acquisition unit. Update time fluctuation grip data calculating means for calculating time change grip data based on the update grip data group as update time fluctuation grip data, and update time fluctuation grip data calculated by the update time fluctuation grip data calculation means The update probability density function generating means for generating the probability density function based on the update probability density function, the reference probability density function, and the update probability density function generated by the update probability density function generating means Update feature vector defining means for calculating similarity and defining the similarity of the calculated distribution as an update feature vector; Update verification grip data acquisition means for acquiring the acquired grip data group as an update verification grip data group, and time-varying grip data based on the update verification grip data acquisition acquired by the update verification grip data acquisition means. Update verification for update verification time fluctuation grip data calculation means for calculation as update verification time fluctuation grip data, and update verification of probability density function based on update verification time fluctuation grip data calculation means for update verification Calculate the similarity of distribution between the update verification probability density function generated by the update verification probability density function, the reference probability density function, and the update verification probability density function generated by the update verification probability density function generation means. An update verification feature vector defining means for defining the calculated distribution similarity as an update verification feature vector, and an update verification Calculating a first similarity measure indicating a degree of similarity between the update verification feature vector defined by the collection vector defining unit and the first feature vector included in the first gripping state information stored in the gripping state information storage unit A second similarity measure calculating means for calculating a second similarity measure indicating a degree of similarity between the update verification feature vector and the update feature vector defining means, and a second similarity measure calculating Second determination means for determining whether or not the second similarity measure is larger than the first similarity measure calculated by the means, and the second similarity measure by the second determination means than the first similarity measure Update means for updating the first gripping state information stored in the gripping state information storage unit to the third gripping state information including the update feature vector when it is determined that is large.

  The recognition method according to another embodiment is acquired in an update grip data acquisition step for acquiring a grip data group acquired based on the third grip state as an update grip data group, and an update grip data acquisition step. Time-varying grip data calculation step for calculating time-varying grip data based on the update grip data group as time-varying grip data for updating, and time-varying grip data for updating calculated in the time-varying grip data calculating step for updating Of the update probability density function generation step for generating a probability density function based on the above as an update probability density function, a reference probability density function, and the update probability density function generated in the update probability density function generation step Update feature vector that calculates the similarity and defines the calculated distribution similarity as a feature vector for update The update verification grip data acquisition step for acquiring the grip data group acquired based on the regulation step, the fourth grip state as the update verification grip data group, and the update verification grip data acquisition step acquired in the update verification grip data acquisition step Update verification time fluctuation grip data calculation step for calculating time fluctuation grip data based on the grip data group as update verification time fluctuation grip data, and update verification time fluctuation calculated in the update verification time fluctuation grip data calculation step Update verification probability density function generation step for generating a probability density function based on the grasp data as an update verification probability density function, a reference probability density function, and an update verification probability generated in the update verification probability density function generation step Calculate the similarity of the distribution with the density function and update the calculated similarity of the distribution Update verification feature vector definition step defined as a vector, update verification feature vector definition step defined in the update verification feature vector definition step, and first gripping state information stored in the gripping state information storage means A first similarity measure indicating a degree of similarity with one feature vector is calculated, and a second similarity measure indicating the degree of similarity between the update verification feature vector and the update feature vector defined in the update feature vector defining step A second similarity measure calculating step for calculating the second similarity measure, a second determination step for determining whether or not the second similarity measure is larger than the first similarity measure calculated in the second similarity measure calculating step, When it is determined in the determination step that the second similarity scale is larger than the first similarity scale, the first stored in the gripping state information storage means An update step of updating the gripping state information to the third gripping state information including the update feature vector.

  According to the above aspect, the feature vector based on the third gripping state is also based on the degree of similarity of the feature vector calculated from the update verification gripping data based on the fourth gripping state to the feature vector based on the first gripping state. When the degree of similarity to the vector is higher, the first grip state information stored in the grip state information storage unit as information for recognizing the grip state includes a feature vector based on the third grip state. Updated to third gripping state information. Accordingly, appropriate gripping state information is stored in the gripping state information storage unit as information for recognizing the gripping state. Therefore, for example, by using the second grip data based on the second grip state determined to correspond to the first grip state as the third grip data, the grip referred to for recognition of the grip state The information can be kept as appropriate information according to, for example, changes over time.

  In the recognition device according to another embodiment, before using the grip data group for calculation of time-variable grip data, noise caused by a transient state related to detection of the grip data in the grip data included in the grip data group, It may further comprise noise removing means for removing at least one of pulse noise, noise that can be removed by wavelet shrinkage on the grip data, noise caused by a DC component related to detection of the grip data, and Gaussian noise. Good.

  According to the above aspect, noise is removed from the grip data before calculating the time-varying grip data, so the accuracy of the time-varying grip data and various probability distributions calculated based on the time-varying grip data are improved. Is done.

  In a recognition system according to another aspect, the recognition apparatus further includes recognition information transmission means for transmitting recognition information related to a determination result by the determination means to the terminal, and the terminal receives the recognition information transmitted from the authentication apparatus. It is good also as a recognition information receiving means and an execution means to perform the predetermined process according to the recognition information received by the recognition information receiving means.

  According to the above aspect, for example, a predetermined process such as user authentication at the terminal can be realized without forcing the user to perform a special operation.

  Provided are a recognition device, a recognition method, and a recognition system that enable a highly accurate input operation to a terminal without forcing a user to perform a special operation.

It is a figure which shows the apparatus structure of a recognition system. It is a schematic diagram which shows arrangement | positioning of the pressure sensor in a terminal. It is a block diagram which shows the functional structure of a terminal. It is a block diagram which shows the functional structure of a recognition apparatus. It is a hard block diagram of a recognition device. It is a timing chart which shows the processing content of the recognition method in a recognition system. It is a timing chart which shows the processing content of the recognition method in a recognition system. It is a timing chart which shows the processing content of the recognition method in a recognition system.

  Embodiments of a recognition apparatus, a recognition method, and a recognition system according to the present invention will be described with reference to the drawings. If possible, the same parts are denoted by the same reference numerals, and redundant description is omitted.

  FIG. 1 is a diagram illustrating a device configuration of a recognition system. The recognition system 100 includes a recognition device 1 and a terminal 3. The recognition apparatus 1 is communicable with the terminal 3 via the network N and is data indicating the gripping state of the terminal 3 by the user, and includes a plurality of gripping data detected in time series at the terminal 3 Is a device that recognizes the gripping state of the terminal 3 by the user. The recognition device 1 can be a device that performs user authentication by determining whether or not the user holding the terminal corresponds to a user registered in advance based on the holding state of the terminal 3, for example. .

  The terminal 3 can be a mobile terminal as exemplified by a mobile phone, for example. The terminal 3 is held by the user in the use state. And the terminal 3 is provided with the sensor for detecting the holding state by a user. The sensor provided in the terminal 3 is a pressure sensor, for example.

FIG. 2 is a schematic diagram showing the arrangement of the pressure sensor d in the terminal 3. As illustrated in FIG. 2, the terminal 3 includes a total of N pressure sensors on the bottom surface p _B and the side surfaces p _S1 and p _S2 of the housing. More specifically, provided near an end portion of the bottom _{p B,} a plurality of sensors _{d n1 + 1, d n1 +} 2, d n1 + 3, ··, d n2, d n2 + 1, d n2 + 2, d n2 + 3, ·· d n3 is an array It has been. Further, a plurality of sensors d ₁ , d ₂ , d ₃ ,..., Dn ₁ , dn _{3 + 1} , dn _{3 + 2} , dn _{3 + 3} ,... D _N are provided in an array on each of the side surfaces p _S1 and p _S2. Yes. In the present embodiment, the sensor included in the terminal 3 is a plurality of pressure sensors, but may be an acceleration sensor, a gyro sensor, or the like.

  Subsequently, prior to the description of the recognition device 1, the function of the terminal 3 will be described. FIG. 3 is a block diagram showing a functional configuration of the terminal 3. The terminal 3 includes a detection unit 31 (detection unit), a transmission unit 32 (transmission unit), a recognition information reception unit 33 (recognition information reception unit), and an execution unit 34 (execution unit).

検出部３１は、取得した把持データ群ｆ_ｔを送信部３２に送出する。 The detection unit 31 is a part that detects gripping data indicating a gripping state of the terminal 3 by the user. Specifically, the grip data is detected in time series by each of the _N sensors d _{1 to} d N shown in FIG. The detection unit 31 acquires the grip data fi (t) from the i-th sensor at time t. Then, gripping the data group f _t detected in a time series is expressed as in equation (1).

The detection unit 31 sends the acquired gripping data group _ft to the transmission unit 32.

The transmission unit 32 is a part that transmits the grip data group f _t to the recognition device.

The recognition information receiving unit 33 is a part that receives the recognition information transmitted from the authentication device 1. Recognition information, for example, the gripping position shown by the transmitted gripped data group f _t is, information about advance whether corresponds to a predetermined holding state which is stored in the recognition device 1 the determination result, based on the determination result user Information related to the result of authentication can be included.

The execution unit 34 is a part that executes predetermined processing according to the recognition information received by the recognition information receiving unit 33. For example, when the recognition system 100, the gripping position shown by the transmitted gripped data group f _t is with that corresponding to a predetermined holding state, a system in which the predetermined command input to the terminal 3, The execution unit 34 executes predetermined processing according to the command.

Further, the recognition system 100, when the gripping position shown by the transmitted gripped data group f _t is a system that performs user authentication with that corresponding to a predetermined holding state, the execution unit 34, the terminal 3 Control the login state by the user.

  Next, the function of the recognition apparatus 1 will be described with reference to FIG. FIG. 4 is a block diagram showing a functional configuration of the recognition apparatus 1. The recognition apparatus 1 includes a grip data acquisition unit 11 (first grip data acquisition unit, second grip data acquisition unit, update grip data acquisition unit, update verification grip data acquisition unit), and a noise removal unit 12 (noise removal unit). , Time fluctuation grip data calculation unit 13 (first time fluctuation grip data calculation means, second time fluctuation grip data calculation means, update time fluctuation grip data calculation means, update verification time fluctuation grip data calculation means), probability density function Generation unit 14 (first probability density function generation means, second probability density function generation means, update probability density function generation means, update verification probability density function generation means), feature vector definition section 15 (first feature vector definition means) , Second feature vector defining means, update feature vector defining means, update verification feature vector defining means), gripping state information storage unit 17 (grip state information storing means) , Gripping state information storage unit 18 (gripping state information storage unit), similarity scale calculation unit 19 (similar scale calculation unit), determination unit 20 (determination unit), recognition information transmission unit 21 (recognition information transmission unit), second similarity A scale calculation unit 22 (second similarity scale calculation unit), a second determination unit 23 (second determination unit), and an update unit 24 (update unit) are provided.

  FIG. 5 is a hardware configuration diagram of the recognition device 1. As shown in FIG. 5, the recognition apparatus 1 physically includes a CPU 101, a RAM 102 and a ROM 103 that are main storage devices, a communication module 104 that is a data transmission / reception device, an auxiliary storage device 105 such as a hard disk and a flash memory, and an input device. The computer system includes an input device 106 such as a keyboard and an output device 107 such as a display. Each function shown in FIG. 4 has a communication module 104, an input device 106, and an output device 107 under the control of the CPU 101 by loading predetermined computer software on the hardware such as the CPU 101 and the RAM 102 shown in FIG. This is realized by reading and writing data in the RAM 102 and the auxiliary storage device 105. The terminal 3 shown in FIG. 3 is similarly configured as a computer system. Again, with reference to FIG. 4, each function part of the recognition apparatus 1 is demonstrated.

  Again, with reference to FIG. 4, each function part of the recognition apparatus 1 is demonstrated. First, in the system for recognizing whether or not the gripping state of the terminal 3 by the user corresponds to the predetermined gripping state, the function of each function unit in the process for obtaining gripping state information indicating the predetermined gripping state Will be described. In the following description, the predetermined gripping state is referred to as a “first gripping state”.

  The grip data acquisition unit 11 is a part that acquires a grip data group including time-series grip data transmitted from the terminal 3. Here, the grip data acquisition unit 11 acquires the grip data group acquired based on the first grip state as the first grip data group. The first grip data group acquired based on the first grip state is handled as so-called learning grip data, and is represented by Expression (1).

  The noise removing unit 12 is a part that removes noise of grip data included in the grip data group. The noise removing unit 12 includes noise caused by a transient state related to detection of the gripping data, pulse noise, noise that can be removed by wavelet shrinkage on the gripping data, noise caused by a direct current component related to detection of the gripping data, and At least one of the Gaussian noise can be removed. In the present embodiment, the noise removing unit 12 removes all of these noises from the grip data included in the first grip data group. The noise removal will be specifically described below.

The noise removing unit 12 removes noise caused by a transient state related to detection of the grip data as follows. That is, assuming that the time at which the grip data f _i (t) is acquired is T _S ≦ t ≦ T _e , the noise removing unit 12 acquires data in a transient state in data detection as shown in Expression (2). Data acquired at time δt immediately after the start and immediately before the end of data acquisition is removed.

Next, the noise removing unit 12 removes the pulse noise as follows. That is, when the grip data f _i (t) satisfies the condition shown in Expression (3), it can be determined that the grip data includes a pulse, so the noise removing unit 12 satisfies the condition of Expression (3). All grip data acquired at the time t when the grip data f _i (t) is acquired are excluded from the processing target.

そして、このウェーブレット係数に基づき推定されるノイズの標準偏差δは、式（５）により表される。

また、ノイズの標準偏差δに基づき閾値λを式（６）のように算出する。

ただし、式（６）におけるＮはウェーブレット係数の総数を表す。そして、この閾値λを用いて式（７）に示すＳｏｆｔ Ｔｈｒｅｓｈｏｌｄ処理を施す。

さらに、ノイズ除去部１２は、式（７）により処理されたウェーブレット係数を逆ウェーブレット変換し、式（８）に示されるような、ノイズ除去された把持データを算出する。

なお、式（８）における左辺を、以下の説明において、ｆ＾_ｉ（ｔ）と記す。 Subsequently, the noise removal unit 12 performs noise removal by wavelet shrinkage on the grip data. The noise removal by wavelet shrinkage will be described below. The wavelet coefficient d ^j _k when the gripping data f _i (t) is expanded with the wavelet basis function φ ^j _k (t) is expressed as in Expression (4).

The standard deviation δ of noise estimated based on this wavelet coefficient is expressed by equation (5).

Further, the threshold λ is calculated as shown in Expression (6) based on the standard deviation δ of noise.

However, N in Formula (6) represents the total number of wavelet coefficients. Then, using this threshold λ, the Soft Threshold process shown in Expression (7) is performed.

Further, the noise removing unit 12 performs inverse wavelet transform on the wavelet coefficients processed by Expression (7), and calculates the gripped data from which noise is removed as shown in Expression (8).

In addition, the left side in Formula (8) is described as f ^ _i (t) in the following description.

なお、式（９）におけるＴは、時系列の把持データからなる把持データ群が取得された時間を表す。 Next, the noise removing unit 12 removes noise caused by a DC component related to detection of the grip data. That is, as shown in Expression (9), the noise removing unit 12 subtracts the time average of the grip data and calculates the alternating current component of the grip data.

Note that T in Expression (9) represents a time when a gripping data group including time-series gripping data is acquired.

なお、σはガウス関数のハイパーパラメータを表し、ｔ_ｎｅは時間ｔの近傍を表す。 Further, the noise removing unit 12 removes Gaussian noise. That is, as shown in Expression (10), the noise removing unit 12 smoothes data by convolution with a Gaussian function Gauss (t _ne , σ), and removes Gaussian noise from the grip data.

Note that σ represents a hyperparameter of a Gaussian function, and t _ne represents the vicinity of time t.

そして、Ｎ個のセンサ全てにより取得された把持データの時間変動データは、式（１２）により表される。

The time variation grip data calculation unit 13 is a part that calculates time variation grip data composed of a group of time variation data indicating time variation of grip data at each time included in the grip data group. The time variation data is difference data between the grip data at one time and the grip data acquired immediately before in time series. Here, the time-variable gripping data calculation unit 13 calculates time-variable gripping data based on the first gripping data group from which noise has been removed by the noise removing unit 12 as first time-variable gripping data. For example, the time variation grip data calculation unit 13 calculates time variation data y _i (t) using the grip data obtained from Equation (10) as shown in Equation (11).

Then, the time variation data of the grip data acquired by all the N sensors is expressed by Expression (12).

  The probability density function generation unit 14 is a part that generates a probability density function indicating the variation state of the time variation indicated in the time variation grasping data. Here, the probability density function generation unit 14 generates a probability density function based on the first time variation gripping data calculated by the time variation gripping data calculation unit 13 as a first probability density function. The generation of the first probability density function by the probability density function generation unit 14 will be specifically described below.

The first time-varying grip data calculated by the time-varying grip data calculating unit 13 is expressed by Expression (13).

  In the present embodiment, the probability between the probability density function calculated from the frequency distribution (histogram) of the first time-variable grasping data and the probability density function (reference probability density function) based on the reference time-variable grasping data registered in advance. The amount of JS information that is the dissimilarity of distribution is calculated.

ただし、ｒ（ｋ）は、ヒストグラムにおけるｋ番目の刻み値を表す。そして、確率密度関数生成部１４は、式（１６）に示すように、確率密度関数ＰＤＦを算出する。

First, the probability density function generation unit 14 generates a time direction histogram hist _i (k) of the sensor i as shown in Expression (14) and Expression (15).

Here, r (k) represents the kth step value in the histogram. Then, the probability density function generation unit 14 calculates a probability density function PDF as shown in Expression (16).

  The feature vector defining unit 15 uses the reference probability density function, which is a probability density function generated based on the reference time variation grasping data as a reference, and the probability density function generated by the probability density function generating unit 14 to determine the amount of JS information. Is calculated. The JS information amount is defined as indicating the similarity between the distributions of two probability density functions. Here, the feature vector defining unit 15 calculates a JS information amount defined as the similarity of distribution between the reference probability density function and the first probability density function generated by the probability density function generating unit 14.

なお、式（１７）におけるＲ（ｘ）は、Ｒ（ｘ）＝（Ｐ（ｘ）＋Ｑ（ｘ））／２である。 Specifically, the feature vector defining unit 15 uses the reference probability density function PDF _i (r (k) | Y _Tstd ) and the probability with the first probability density function PDF _i (r (k) | Y _T ). JS information amount D _JS ⁽ⁱ⁾ (PDF _i (r (k) | Y _T ) || PDF _i (r (k) | Y _Tstd )) is calculated as the distribution difference. The JS information amount is defined as in Expression (17).

In addition, R (x) in Formula (17) is R (x) = (P (x) + Q (x)) / 2.

And the feature vector prescription | regulation part 15 prescribes | regulates the similarity of the calculated distribution as a feature vector. Specifically, the feature vector defining unit 15 defines the JS information amount as a feature vector. Here, the feature vector defining unit 15 performs the calculation of the JS information amount based on the first probability density function for all the sensors (i = 1, 2,..., N) for acquiring the grip data. Thus, the JS information amount feature vector JSD (Y _T ) shown in Expression (18) is defined as the first feature vector.

The gripping state information storage unit 16 stores the gripping state information including the feature vector generated by the feature vector defining unit in the gripping state information storage unit 18 as information for recognizing the gripping state. The gripping state information storage unit 18 is a storage unit for storing gripping state information and the like. Here, the gripping state information storage unit 16 uses the first gripping state information including the first feature vector JSD (Y _T ) generated by the feature vector defining unit 15 as collation data (first 1 gripping state information) is stored in the gripping state information storage unit 18.

  Next, processing contents of the recognition method in the recognition system 100 and the recognition apparatus 1 will be described with reference to FIG.

  First, the detection unit 31 detects first grip data (S1). The first grip data is data that is the basis of the verification data when the grip state is recognized, and is acquired based on the first grip state. Subsequently, the transmission unit 32 transmits a first grip data group including time-series grip data to the recognition device 1 (S2).

  When the first grip data group is transmitted from the terminal 3, the grip data acquisition unit 11 acquires the first grip data group acquired based on the first grip state (S3). Next, the noise removing unit 12 removes various noises included in the grip data included in the first grip data group (S4). Subsequently, the time-variable grip data calculation unit 13 calculates time-variable grip data based on the first grip data group from which noise has been removed by the noise removal unit 12 in step S4 as first time-variable grip data (S5).

Next, the probability density function generation unit 14 generates a probability density function based on the first time variation gripping data calculated by the time variation gripping data calculation unit 13 as a first probability density function (S6). Subsequently, the feature vector defining unit 15 calculates the JS information amount defined as the similarity of distribution between the reference probability density function and the first probability density function generated by the probability density function generating unit 14. Subsequently, the feature vector defining unit 15 calculates the JS information amount calculated based on the first probability density function for all sensors (i = 1, 2,..., N) for acquiring grip data. ) To define the JS information feature vector JSD (Y _T ) as the first feature vector (S7).

Then, the gripping state information storage unit 17 uses the first gripping state information including the first feature vector JSD (Y _T ) generated by the feature vector defining unit 15 as information for recognizing the gripping state. The information is stored in the information storage unit 18 (S8).

  Next, in the system for recognizing whether or not the gripping state of the terminal 3 by the user corresponds to the predetermined gripping state, the gripping state of the terminal 3 by the user is stored in the recognition device 1 The function of each functional unit in the process of determining whether or not the above applies will be described. In the following description, the gripping state to be determined and verified is referred to as a “second gripping state”.

  The grip data acquisition unit 11 acquires a grip data group acquired based on the second grip state as a second grip data group. That is, the second grip data group is verification grip data to be determined and verified. The second grip data group is data detected by the detection unit 31 and further transmitted by the transmission unit 32 in the terminal 3 gripped by the user in the second grip state.

  The noise removal unit 12 removes noise included in the grip data included in the second grip data group acquired by the grip data acquisition unit 11 in the same manner as the first grip data group.

The time variation grip data calculation unit 13 calculates time variation grip data based on the second grip data group from which noise has been removed by the noise removal unit 12 as second time variation grip data. The second time fluctuation grip data calculated here is expressed by Expression (19). In Expression (19) and other expressions, it means that the character V relates to data for verification based on the second gripping state.

  The probability density function generation unit 14 generates a probability density function based on the second time variation gripping data calculated by the time variation gripping data calculation unit 13 as a second probability density function.

The feature vector defining unit 15 calculates a JS information amount defined as a distribution similarity between the reference probability density function and the second probability density function generated by the probability density function generating unit 14. Furthermore, the feature vector defining unit 15 performs the calculation of the JS information amount based on the second probability density function for all the sensors (i = 1, 2,..., N) for acquiring the grip data. Similarly to the first feature vector, a JS information amount feature vector JSD (Y _Tv ) consisting of N JS information amounts for all sensors is defined as the second feature vector.

The similarity scale calculation unit 19 determines the degree of similarity between the second feature vector defined by the feature vector defining unit 19 and the first feature vector included in the first gripping state information stored in the gripping state information storage unit 18. Calculate the similarity measure shown. Specifically, the similarity measure calculator 19 calculates the similarity measure Sim (JSD (Y _Tv ), JSD (Y _T )), for example, according to the equation (21). In equation (21), the similarity measure is calculated as the Euclidean distance of the JS information amount feature vector, but the calculation of the similarity measure is not limited to this method.

The determination unit 20 determines that the second gripping state corresponds to the first gripping state when the similarity scale calculated by the similarity scale calculation unit 19 is equal to or greater than a predetermined threshold. Specifically, the determination unit 20 determines whether or not the similarity measure Sim (JSD (Y _Tv ), JSD (Y _T )) is _{equal to} or greater than a predetermined threshold Th _judge .

  The recognition information transmission unit 21 transmits the recognition information regarding the result of determination by the determination unit 20 to the terminal 3. For example, when the recognition system 100 is a system that performs user authentication of the terminal 3, the user's holding state of the terminal 3 is registered in advance in the recognition device as the first holding state, and user authentication is performed. The recognition process in the recognition device 1 is executed with the current gripping state as the second gripping state. In this case, the recognition information can be information related to the result of user authentication.

  Next, processing contents of the recognition method in the recognition system 100 and the recognition apparatus 1 will be described with reference to FIG.

  First, the detection unit 31 detects second grip data (S11). The second grip data is data that is a basis of determination and verification data in the grip state recognition process, and is acquired based on the second grip state. Subsequently, the transmission unit 32 transmits a second grip data group including time-series grip data to the recognition device 1 (S12).

  When the second grip data group is transmitted from the terminal 3, the grip data acquisition unit 11 acquires the second grip data group acquired based on the second grip state (S13). Next, the noise removing unit 12 removes various noises included in the grip data included in the second grip data group (S14). Subsequently, the time-variable grip data calculating unit 13 calculates time-variable grip data based on the second grip data group from which noise has been removed by the noise removing unit 12 in step S14 as second time-variable grip data (S15). Then, the probability density function generation unit 14 generates a probability density function based on the second time variation gripping data calculated by the time variation gripping data calculation unit 13 as a second probability density function (S16).

Next, the feature vector defining unit 15 calculates the JS information amount defined as the similarity of distribution between the reference probability density function and the second probability density function generated by the probability density function generating unit 14. Furthermore, the feature vector defining unit 15 performs the calculation of the JS information amount based on the second probability density function for all the sensors (i = 1, 2,..., N) for acquiring the grip data. Similarly to the first feature vector, a JS information amount feature vector JSD (Y _Tv ) composed of N JS information amounts for all sensors is defined as a second feature vector (S17).

  Next, the similarity scale calculation unit 19 calculates the second feature vector defined by the feature vector defining unit 19 and the first feature vector included in the first gripping state information stored in the gripping state information storage unit 18. A similarity scale indicating the degree of similarity is calculated (S18). Subsequently, the determination unit 20 determines that the second gripping state corresponds to the first gripping state when the similarity scale calculated by the similarity scale calculation unit 19 is equal to or greater than a predetermined threshold (S19). And the recognition information transmission part 21 transmits the recognition information regarding the result of determination by the determination part 20 to the terminal 3 (S18).

  Subsequently, the recognition information receiving unit 33 receives the recognition information transmitted from the authentication device 1 (S19). And the execution part 34 performs the predetermined | prescribed process according to the recognition information received by the recognition information receiving part 33 (S20).

  As described above, since the terminal 3 can execute predetermined processing in accordance with the grip data indicating the grip state of the terminal 3 by the user, only the grip of the terminal 3 can be performed without forcing the user to perform a special operation. Thus, input to the terminal 3 is realized.

  Next, in the system for recognizing whether or not the gripping state of the terminal 3 by the user corresponds to a predetermined gripping state, the function of each functional unit in the update processing of the gripping state information for verification will be described. In the following description, the gripping state used as the basis of the gripping state information for update is referred to as the “third gripping state”, and the gripping state for verifying whether or not updating is performed using the gripping state information for updating is performed. Is the “fourth gripping state”.

  The grip data acquisition unit 11 acquires a grip data group acquired based on the third grip state as an update grip data group. The updated grip data group is data that is detected by the detection unit 31 and further transmitted by the transmission unit 32 in the terminal 3 gripped by the user in the third grip state.

  The noise removal unit 12 removes noise included in the grip data included in the update grip data group acquired by the grip data acquisition unit 11 in the same manner as the first grip data group.

  The time variation grip data calculation unit 13 calculates time variation grip data based on the update grip data group from which noise has been removed by the noise removal unit 12 as update time variation grip data.

  The probability density function generation unit 14 generates a probability density function based on the update time variation grasping data calculated by the time variation grasp data calculation unit 13 as an update probability density function.

The feature vector defining unit 15 calculates a JS information amount defined as the similarity of distribution between the reference probability density function and the update probability density function generated by the probability density function generating unit 14. Further, the feature vector defining unit 15 calculates the JS information amount calculated based on the update probability density function for all sensors (i = 1, 2,..., N) for acquiring grip data. The JS information feature vector JSD (Y _TU ) is defined as an update feature vector.

  The gripping state information storage unit 17 causes the gripping state information storage unit 18 to store third gripping state information including the update feature vector defined by the feature vector defining unit 15.

  Note that the second gripping state determined to correspond to the first gripping state is regarded as the third gripping state, and the grip data described above is used by using the second gripping data group as the update gripping data group. Processing from acquisition of the update grip data group by the acquisition unit 11 to calculation of the update posterior probability distribution by the posterior probability distribution calculation unit 16 may be performed.

  Further, the grip data acquisition unit 11 acquires, as an update verification grip data group, a grip data group acquired based on the fourth grip state for verifying whether or not to update using the grip state information for update. To do. The update verification gripping data group is data detected by the detection unit 31 in the terminal 3 gripped by the user in the fourth gripping state and further transmitted by the transmission unit 32.

  The noise removal unit 12 removes noise included in the grip data included in the update verification grip data group acquired by the grip data acquisition unit 11 in the same manner as the first grip data group.

  The time variation grip data calculation unit 13 calculates time variation grip data based on the update verification grip data group from which noise has been removed by the noise removal unit 12 as update verification time variation grip data.

  The probability density function generation unit 14 generates a probability density function based on the update verification time variation grip data calculated by the time variation grip data calculation unit 13 as an update verification probability density function.

The feature vector defining unit 15 calculates the JS information amount defined as the similarity of distribution between the reference probability density function and the update verification probability density function generated by the probability density function generating unit 14. Further, the feature vector defining unit 15 calculates all the sensors (i = 1, 2,..., N) for acquiring the grip data by calculating the JS information amount calculated based on the update verification probability density function. The JS information feature vector JSD (Y _Tv ) is defined as an update verification feature vector.

The second similarity scale calculation unit 22 includes the update verification feature vector defined by the feature vector definition unit 15 and the first feature vector included in the first gripping state information stored in the gripping state information storage unit 18. A first similarity scale Sim (JSD (Y _Tv ), JSD (Y _T )) indicating the degree of similarity is calculated. In addition, the second similarity scale calculator 22 includes a second similarity scale Sim (JSD (Y _Tv ), which indicates the degree of similarity between the update verification feature vector and the update feature vector defined by the feature vector definition unit 15. JSD (Y _TU )) is calculated. The first and second similarity measures are calculated by, for example, Expression (21).

The second determination unit 23 determines whether or not the second similarity scale is larger than the first similarity scale calculated by the second similarity scale calculation unit 22. Specifically, the second determination unit 23 determines whether or not the second similarity measure is larger than the first similarity measure as shown in Expression (22) and Expression (23).

  When the second determination unit 23 determines that the second similarity scale is larger than the first similarity scale, the update unit 24 stores the first grip state information stored in the grip state information storage unit 18. Then, the information is updated to the third gripping state information including the update feature vector.

  Next, with reference to FIG. 8, the processing content in the update process of the holding | grip state information for collation is demonstrated.

  First, the detection unit 31 detects update grip data (S31). The update grip data is acquired based on the third grip state. Subsequently, the transmission unit 32 transmits an update grip data group including time-series grip data to the recognition device 1 (S32).

  When the update grip data group is transmitted from the terminal 3, the grip data acquisition unit 11 acquires the update grip data group acquired based on the third grip state (S33). Next, the noise removing unit 12 removes various noises included in the grip data included in the update grip data group (S34). Subsequently, the time-variable grip data calculation unit 13 calculates time-variable grip data based on the grip data group from which noise has been removed by the noise removing unit 12 in step S34 as update time-variable grip data (S35).

Next, the probability density function generation unit 14 generates a probability density function based on the update time variation grip data calculated by the time variation grip data calculation unit 13 as an update probability density function (S36). Subsequently, the feature vector defining unit 15 calculates the JS information amount defined as the similarity of distribution between the reference probability density function and the update probability density function generated by the probability density function generating unit 14. Subsequently, the feature vector defining unit 15 calculates the JS information amount calculated based on the update probability density function for all sensors (i = 1, 2,..., N) for acquiring grip data. ) To define the JS information feature vector JSD (Y _TU ) as an update feature vector (S37).

Then, the gripping state information storage unit 17 uses the third gripping state information including the update feature vector JSD (Y _TU ) generated by the feature vector defining unit 15 as information for recognizing the gripping state. The information is stored in the information storage unit 18 (S38).

  On the other hand, the detection unit 31 detects update verification grip data (S41). The update verification grip data is acquired based on the fourth grip state. Subsequently, the transmission unit 32 transmits an update verification grip data group including time-series grip data to the recognition device 1 (S42).

  When the update verification grip data group is transmitted from the terminal 3, the grip data acquisition unit 11 acquires the update verification grip data group acquired based on the fourth grip state (S43). Next, the noise removing unit 12 removes various noises included in the grip data included in the update verification grip data group (S44). Subsequently, the time-variable grip data calculation unit 13 calculates time-variable grip data based on the grip data group from which noise has been removed by the noise removal unit 12 in step S44 as update verification time-variable grip data (S45).

Next, the probability density function generation unit 14 generates a probability density function based on the update verification time variation grip data calculated by the time variation grip data calculation unit 13 as an update verification probability density function (S46). Subsequently, the feature vector defining unit 15 calculates the JS information amount defined as the similarity of distribution between the reference probability density function and the update verification probability density function generated by the probability density function generating unit 14. Further, the feature vector defining unit 15 calculates all the sensors (i = 1, 2,..., N) for acquiring the grip data by calculating the JS information amount calculated based on the update verification probability density function. Then, the JS information feature vector JSD (Y _T ) is defined as a feature vector for update verification (S47).

Next, the second similarity scale calculation unit 22 includes the update verification feature vector defined by the feature vector definition unit 15 and the first feature included in the first gripping state information stored in the gripping state information storage unit 18. A first similarity measure Sim (JSD (Y _Tv ), JSD (Y _T )) indicating a degree of similarity to a vector, and an update verification feature vector, and an update feature vector defined by the feature vector defining unit 15 Second similarity measures Sim (JSD (Y _Tv ), JSD (Y _TU )) indicating the degree of similarity are calculated (S48).

  Subsequently, the second determination unit 23 determines whether or not the second similarity scale is larger than the first similarity scale calculated by the second similarity scale calculation unit 22 (S49). In step S49, when it is determined that the second similarity scale is larger than the first similarity scale, the update unit 24 displays the first gripping state information stored in the gripping state information storage unit 18. Then, the information is updated to the third gripping state information including the update feature vector (S50).

  As described above, when the gripping state information is updated, appropriate gripping state information is stored in the gripping state information storage unit 18 as information for recognizing the gripping state. Therefore, for example, by adopting the second grip data based on the second grip state determined to correspond to the first grip state as the grip data for update, the grip state referred to for recognition of the grip state The information can be kept as appropriate information according to, for example, changes over time.

  According to the recognition device 1, the recognition method, and the recognition system of the present embodiment, the user can perform an input operation to the terminal 3 by recognizing the grip data indicating the grip state of the terminal 3. I can't be forced. Also, grasping state information for recognizing the grasping state using a feature vector representing the similarity between the probability density function calculated from the time variation data indicating the time variation of the grasping data by a statistical method and the reference probability density function. Therefore, it is robust against noise caused by circumstances unique to the detection device for detecting gripping data and other sudden noises. Since the gripping state is recognized based on the gripping state information that is robust against various noises, the input to the terminal 3 based on the highly accurate recognition process is realized.

  The present invention has been described in detail based on the embodiments. However, the present invention is not limited to the above embodiment. The present invention can be variously modified without departing from the gist thereof.

DESCRIPTION OF SYMBOLS 1 ... Recognition apparatus, 100 ... Recognition system, 3 ... Terminal, 11 ... Grasp data acquisition part, 12 ... Noise removal part, 13 ... Time fluctuation grip data calculation part, 14 ... Probability density function generation part, 15 ... Feature vector prescription | regulation part , 16 ... gripping state information storage unit, 18 ... gripping state information storage unit, 19 ... similarity scale calculation unit, 20 ... determination unit, 21 ... recognition information transmission unit, 22 ... second similarity scale calculation unit, 23 ... second determination , 24 ... update unit, 31 ... detection unit, 32 ... transmission unit, 33 ... recognition information reception unit, 34 ... execution unit.

Claims (7)

A recognition device for recognizing a gripping state of a terminal by a user based on a gripping data group including a plurality of gripping data detected in time series at the terminal and indicating a gripping state of the terminal by the user,
First grip data acquisition means for acquiring the grip data group acquired based on the first grip state as a first grip data group;
Time variation grip data comprising a group of time variation data indicating time variation of the grip data at each time included in the grip data group, wherein the first grip data group acquired by the first grip data acquisition means First time-variable gripping data calculating means for calculating the time-variable gripping data based on the first time-variable gripping data;
A probability density function indicating a variation state of time variation indicated by the time variation grasping data, wherein the probability density function based on the first time variation grasping data calculated by the first time variation grasping data calculating means is a first probability. First probability density function generating means for generating as a density function;
Calculating a similarity of distribution between a reference probability density function which is a probability density function generated based on reference time variation gripping data as a reference and the first probability density function generated by the first probability density function generating means; First feature vector defining means for defining the calculated similarity of the distribution as a first feature vector;
Gripping state information storage means for storing, in the gripping state information storage means, first gripping state information including the first feature vector generated by the first feature vector defining means as information for recognizing the gripping state; ,
Second grip data acquisition means for acquiring the grip data group acquired based on the second grip state as a second grip data group;
Second time-variable grip data calculating means for calculating the time-variable grip data based on the second grip data group acquired by the second grip data acquiring means as second time-variable grip data;
Second probability density function generating means for generating the probability density function based on the second time variable gripping data calculated by the second time variable gripping data calculating means as a second probability density function;
Calculating a similarity of distribution between the reference probability density function and the second probability density function generated by the second probability density function generating means, and defining the calculated similarity of the distribution as a second feature vector; Two feature vector defining means;
A similarity measure indicating the degree of similarity between the second feature vector defined by the second feature vector defining unit and the first feature vector included in the first gripping state information stored in the gripping state information storage unit is calculated. A similarity measure calculating means,
A determination unit that determines that the second gripping state corresponds to the first gripping state when the similarity scale calculated by the similarity scale calculating unit is equal to or greater than a predetermined threshold;
A recognition device comprising: Update grip data acquisition means for acquiring the grip data group acquired based on the third grip state as an update grip data group;
Update time-variable grip data calculating means for calculating the time-variable grip data based on the update grip data group acquired by the update grip data acquiring means as update time-variable grip data;
Update probability density function generation means for generating the probability density function based on the update time fluctuation grip data calculated by the update time fluctuation grip data calculation means as an update probability density function;
Update that calculates the similarity of the distribution between the reference probability density function and the update probability density function generated by the update probability density function generation means, and defines the calculated distribution similarity as an update feature vector Feature vector defining means;
Update verification grip data acquisition means for acquiring the grip data group acquired based on a fourth grip state as an update verification grip data group;
Update verification time variation grip data calculation means for calculating the time variation grip data based on the update verification grip data group acquired by the update verification grip data acquisition means as update verification time variation grip data;
Update probability probability density function generation means for generating the probability density function based on the update verification time fluctuation grip data calculated by the update verification time fluctuation grip data calculation means as an update verification probability density function;
The distribution similarity between the reference probability density function and the update verification probability density function generated by the update verification probability density function generation unit is calculated, and the calculated distribution similarity is used as an update verification feature vector. A feature vector defining means for defining update verification;
A first degree of similarity between the update verification feature vector defined by the update verification feature vector defining unit and the first feature vector included in the first gripping state information stored in the gripping state information storage unit Second similarity measure calculating means for calculating a second similarity measure indicating a degree of similarity between the update verification feature vector and the update feature vector defining means. When,
Second determination means for determining whether or not the second similarity scale is larger than the first similarity scale calculated by the second similarity scale calculation means;
When the second determination unit determines that the second similarity scale is larger than the first similarity scale, the first grip state information stored in the grip state information storage unit is updated. Updating means for updating to third gripping state information including a feature vector for use;
The recognition device according to claim 1, further comprising: Before using the grip data group for the calculation of the time-varying grip data, in the grip data included in the grip data group, noise, pulse noise, and the grip data corresponding to the transient state related to detection of the grip data are detected. Noise removing means for removing at least one of noise that can be removed by wavelet shrinkage, noise caused by a direct current component related to detection of the grip data, and Gaussian noise;
The recognition apparatus according to claim 1 or 2. A recognition method in a recognition apparatus for recognizing a gripping state of a terminal by a user based on a gripping data group including a plurality of gripping data detected in time series at the terminal and indicating a gripping state of the terminal by the user,
A first grip data acquisition step of acquiring the grip data group acquired based on the first grip state as a first grip data group;
Time variation grip data composed of a group of time variation data indicating time variation of the grip data at each time included in the grip data group, the first grip data group acquired in the first grip data acquisition step A first time-variable gripping data calculating step for calculating the time-variable gripping data based on the first time-variable gripping data;
A probability density function indicating a variation state of time variation indicated by the time variation grasping data, wherein the probability density function based on the first time variation grasping data calculated in the first time variation grasping data calculating step is a first probability. A first probability density function generating step for generating as a density function;
Calculating a similarity of distribution between a reference probability density function that is a probability density function generated based on reference time variation grasping data as a reference and the first probability density function generated in the first probability density function generation step; A first feature vector defining step for defining the calculated similarity of the distribution as a first feature vector;
A gripping state information storing step of storing in the gripping state information storage means the first gripping state information including the first feature vector generated in the first feature vector defining step as information for recognizing the gripping state; ,
A second grip data acquisition step of acquiring the grip data group acquired based on the second grip state as a second grip data group;
A second time-variable grip data calculation step for calculating the time-variable grip data based on the second grip data group acquired in the second grip data acquisition step as second time-variable grip data;
A second probability density function generating step of generating the probability density function based on the second time variable gripping data calculated in the second time variable gripping data calculating step as a second probability density function;
A similarity degree of distribution between the reference probability density function and the second probability density function generated in the second probability density function generation step is calculated, and a similarity degree of the calculated distribution is defined as a second feature vector. Two feature vector defining steps;
A similarity measure indicating a degree of similarity between the second feature vector defined in the second feature vector defining step and the first feature vector included in the first gripping state information stored in the gripping state information storage unit is calculated. A similarity measure calculating step,
A determination step of determining that the second gripping state corresponds to the first gripping state when the similarity scale calculated in the similarity scale calculating step is equal to or greater than a predetermined threshold;
A recognition method. An update grip data acquisition step for acquiring the grip data group acquired based on the third grip state as an update grip data group;
An update time-variable grip data calculation step for calculating the time-variable grip data based on the update grip data group acquired in the update grip data acquisition step as update time-variable grip data;
An update probability density function generating step for generating, as an update probability density function, the probability density function based on the update time variable grip data calculated in the update time variable grip data calculating step;
Update that calculates the similarity of the distribution between the reference probability density function and the update probability density function generated in the update probability density function generation step, and defines the calculated distribution similarity as an update feature vector A feature vector defining step;
An update verification grip data acquisition step of acquiring the grip data group acquired based on the fourth grip state as an update verification grip data group;
An update verification time variation grip data calculation step for calculating the time variation grip data based on the update verification grip data group acquired in the update verification grip data acquisition step as update verification time variation grip data;
An update verification probability density function generating step for generating the probability density function based on the update verification time variation grasping data calculated in the update verification time variation grasping data calculating step;
The distribution similarity between the reference probability density function and the update verification probability density function generated in the update verification probability density function generation step is calculated, and the calculated distribution similarity is used as the update verification feature vector. A feature vector prescribing step for defining update verification;
A first degree of similarity between the update verification feature vector defined in the update verification feature vector defining step and the first feature vector included in the first gripping state information stored in the gripping state information storage means. A second similarity measure calculating step for calculating a second similarity measure indicating a degree of similarity between the update verification feature vector and the update feature vector defining step When,
A second determination step of determining whether or not the second similarity measure is larger than the first similarity measure calculated in the second similarity measure calculation step;
When it is determined in the second determination step that the second similarity scale is larger than the first similarity scale, the first gripping state information stored in the gripping state information storage unit is updated. An update step for updating to third gripping state information including a feature vector for use;
The recognition method according to claim 4, further comprising: A recognition system comprising: a terminal; and a recognition device that recognizes a gripping state of the terminal by the user based on a gripping data group that indicates a gripping state of the terminal by the user and includes a plurality of gripping data detected in time series by the terminal Because
The terminal
Detecting means for detecting the grip data;
Transmitting means for transmitting a grip data group including a plurality of grip data detected in time series by the detection means to the recognition device;
With
The recognition device is
First grip data acquisition means for acquiring the grip data group acquired based on the first grip state as a first grip data group;
Time variation grip data comprising a group of time variation data indicating time variation of the grip data at each time included in the grip data group, wherein the first grip data group acquired by the first grip data acquisition means First time-variable gripping data calculating means for calculating the time-variable gripping data based on the first time-variable gripping data;
A probability density function indicating a variation state of time variation indicated by the time variation grasping data, wherein the probability density function based on the first time variation grasping data calculated by the first time variation grasping data calculating means is a first probability. First probability density function generating means for generating as a density function;
Calculating a similarity of distribution between a reference probability density function which is a probability density function generated based on reference time variation gripping data as a reference and the first probability density function generated by the first probability density function generating means; First feature vector defining means for defining the calculated similarity of the distribution as a first feature vector;
Gripping state information storage means for storing, in the gripping state information storage means, first gripping state information including the first feature vector generated by the first feature vector defining means as information for recognizing the gripping state; ,
Second grip data acquisition means for acquiring the grip data group acquired based on the second grip state as a second grip data group;
Second time-variable grip data calculating means for calculating the time-variable grip data based on the second grip data group acquired by the second grip data acquiring means as second time-variable grip data;
Second probability density function generating means for generating the probability density function based on the second time variable gripping data calculated by the second time variable gripping data calculating means as a second probability density function;
Calculating a similarity of distribution between the reference probability density function and the second probability density function generated by the second probability density function generating means, and defining the calculated similarity of the distribution as a second feature vector; Two feature vector defining means;
A similarity measure indicating the degree of similarity between the second feature vector defined by the second feature vector defining unit and the first feature vector included in the first gripping state information stored in the gripping state information storage unit is calculated. A similarity measure calculating means,
A determination unit that determines that the second gripping state corresponds to the first gripping state when the similarity scale calculated by the similarity scale calculating unit is equal to or greater than a predetermined threshold;
A recognition system comprising: The recognition device is
Recognizing information transmitting means for transmitting the recognizing information related to the determination result by the determining means to the terminal;
The terminal
Recognition information receiving means for receiving the recognition information transmitted from the recognition device;
Execution means for executing a predetermined process according to the recognition information received by the recognition information receiving means;
The recognition system according to claim 6.

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