Information Processing Apparatus, Information Processing Method, and Storage Medium

Abstract

The present invention provides an information processing device, an information processing method and a storage medium. When one of the multiple parameters representing the acquired motion state changes or is assumed to change, the values ​​of other parameters that change in conjunction with the change can be estimated. A parameter acquisition unit (211) acquires the value of a first parameter and the value of a second parameter that is an indicator different from the first parameter and has a correlation as an indicator representing the motion state of the subject. A parameter estimation unit (213) estimates the value of a second parameter corresponding to other first parameters in accordance with a model, wherein the model is generated based on a group of values ​​of a third parameter of the same type as the first parameter and a fourth parameter of the same type as the second parameter that represent the motion states of each of the multiple subjects stored in a motion state storage DB (222), with the third parameter as input and the fourth parameter as output.

Description

Technical Field

[0001] The present invention relates to an information processing device, an information processing method and a storage medium. Background Art

[0002] It is known that various indicators (parameters) representing the motion state of a user are measured by wearable devices, motion sensors, etc. equipped on the user's body. In the patent publication No. 2018-026149, an invention is disclosed for displaying an animation representing the user's activity based on various indicators representing the acquired motion state. In the invention described in patent document 1, if the user manually changes the value of the indicator, the animation can be changed corresponding to the changed value.

[0003] Among the parameters that characterize the state of motion, there are known parameters whose values ​​change in conjunction with changes in other parameters. In the invention described in Patent Document 1, even if the user manually changes the value of a certain parameter, the values ​​of other parameters that are estimated to change in conjunction with it do not change, so the animation after the change may move unnaturally. Summary of the invention

[0004] The present invention is made in view of the above-mentioned facts, and its purpose is to provide an information processing device, an information processing method and a storage medium that can estimate the values ​​of other parameters that change in conjunction with the change when one of the multiple parameters representing the obtained motion state changes or is assumed to have changed.

[0005] In order to achieve the above-mentioned purpose, the information processing device involved in the present invention comprises: an acquisition unit, which acquires the value of a first parameter and the value of a second parameter which is an indicator different from and correlated with the first parameter as an indicator representing the motion state of a certain subject; a parameter acquisition unit, which acquires other first parameters of the first parameter which are different from the value of the first parameter acquired in the acquisition unit; a first derivation unit, which follows a model and derives a first reference value as the second parameter based on the first parameter acquired in the acquisition unit, wherein the model is based on a first parameter representing the same type as the certain subject. A group of values ​​of a third parameter of the same type as the first parameter and values ​​of a fourth parameter of the same type as the second parameter for the motion states of multiple subjects respectively is generated by taking the value of the third parameter as input and the value of the fourth parameter as output; a second derivation unit, which follows the model and derives a second reference value as the second parameter based on the other first parameters obtained in the parameter acquisition unit; and a parameter estimation unit, which estimates the value of the second parameter corresponding to the other first parameter based on the value of the second parameter obtained in the acquisition unit, the first reference value and the second reference value.

[0006] Effects of the Invention

[0007] According to the present invention, when one of the plurality of parameters representing the acquired motion state changes or is assumed to change, the values ​​of other parameters that change in conjunction with the change can be estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a diagram showing a configuration example of an animation generation system according to an embodiment.

[0009] Figure 2 FIG. 2 is a diagram showing an example of information stored in the motion state storage DB.

[0010] Figure 3 This is a flowchart of the parameter acquisition process involved in the implementation mode.

[0011] Figure 4 This is a flowchart of the animation generation process involved in the embodiment.

[0012] Figure 5 The diagram shows an example of a screen displaying a generated animation.

[0013] Figure 6 This is a flowchart of the parameter estimation process involved in the embodiment.

[0014] Figure 7 This is a diagram showing the distribution of the acquired speed and pitch value pairs and a model generated based on the distribution.

[0015] Figure 8 is a diagram for explaining an example of estimating parameters.

[0016] Figure 9 This is a diagram showing the distribution of the acquired speed and up and down motion value combinations and a model generated based on the distribution.

[0017] Figure 10 This is a diagram showing the distribution of the acquired combinations of the cadence and the vertical activity value, and a model generated based on the distribution. DETAILED DESCRIPTION

[0018] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In addition, the same reference numerals are given to the same or corresponding parts in the figures.

[0019] (Implementation Method)

[0020] The animation generation system 1000 according to the embodiment of the present invention is a system for generating an animation representing the activity (behavior) of a user (subject) who is performing a moving sport such as running. Figure 1It is equipped with a data transmission device 100 and an animation generation device 200 as shown. In fact, multiple data transmission devices 100 are provided for each subject.

[0021] The data transmission device 100 is, for example, a small wearable device equipped near the waist along the torso of the subject. As shown Figure 1 The data transmission device 100 is equipped with a control unit 110, a storage unit 120, a communication unit 131, an input unit 132, an output unit 133, and a sensor unit 134. The control unit 110, the storage unit 120, the communication unit 131, the input unit 132, the output unit 133, and the sensor unit 134 are connected to each other via a bus BL. The data transmission device 100 transmits data representing the activities of the subject detected by the sensor unit 134 to the animation generation device 200 via the communication unit 131.

[0022] The control unit 110 includes at least one CPU (Central Processing Unit, central processor) as a processor, etc. The control unit 110 functions as a motion data transmission unit 111 described later by executing a program stored in the storage unit 120.

[0023] The storage unit 120 includes at least one ROM (Read Only Memory), RAM (Random Access Memory), flash memory, etc. as a memory, and stores the program executed by the CPU of the control unit 110 and the required data. In addition, data that is desired to be saved even after the power of the data transmission device 100 is turned off is saved to a non-volatile memory such as a flash memory.

[0024] The communication unit 131 includes a wireless communication module and an antenna, and performs wireless data communication with the animation generation device 200. In addition, the communication unit 131 is not limited to a wireless method in the data communication with the animation generation device 200, and a wired interface such as USB (Universal Serial Bus) can also be used for data communication.

[0025] The input unit 132 includes a push button switch, etc., and accepts input instructions from the subject such as "measurement start" and "data transmission".

[0026] The output unit 133 includes an LED (Light Emitting Diode), a liquid crystal display panel, an organic EL (Electro-Luminescence) display panel, etc., and displays the operation status (power on, measuring, data transmitting, etc.) of the data transmission device 100. In addition, the output unit 133 includes a sound output device such as a speaker, and outputs information indicating the operation status of the data transmission device 100 as sound information.

[0027] The sensor unit 134 includes an acceleration sensor, a gyro (angular velocity) sensor, a GPS (Global Positioning System) receiver, etc., and detects the activities of the object under test equipped with the data transmission device 100, the current position of the object under test, etc. The sensor unit 134 sends the acceleration data detected by the acceleration sensor, the angular velocity data detected by the gyro sensor, the time data and position data received by the GPS receiver, etc. to the control unit 110. These data sent by the sensor unit 134 to the control unit 110 are hereinafter collectively referred to as "motion data" because they are used to characterize the motion state of the object under test equipped with the data transmission device 100. In addition, the data transmission device 100 (sensor unit 134) may also be equipped on parts of the object under test other than the waist (such as the wrist and ankle). In addition, the data transmission device 100 may include, for example, multiple sensor units 134 such as a sensor unit 134 equipped on the waist of the object under test, a sensor unit 134 equipped on the wrist, and a sensor unit 134 equipped on the ankle.

[0028] Next, the functions of the control unit 110 will be described. The control unit 110 functions as a motion data transmission unit 111 by executing the program stored in the storage unit 120.

[0029] The motion data transmission unit 111 sends the motion data detected by the sensor unit 134 (acceleration data, angular velocity data, time data, position data, etc. indicating the activities of the object under test) to the animation generation device 200 via the communication unit 131. The motion data transmission unit 111 may further send the moving distance data calculated based on the position data, the speed data calculated based on the time data and position data, the acceleration data, etc. to the animation generation device 200 as motion data.

[0030] Next, the animation generation device 200 will be described. The animation generation device 200 is, for example, a terminal device such as a PC (Personal Computer), a smart phone, or a tablet computer. The animation generation device 200 is as Figure 1As shown in the figure, the apparatus 200 includes a control unit 210, a storage unit 220, a communication unit 231, an input unit 232, and an output unit 233. The control unit 210, the storage unit 220, the communication unit 231, the input unit 232, and the output unit 233 are connected to each other via a bus BL. The animation generating device 200 calculates the values ​​of a plurality of different types of parameters based on the motion data transmitted by the data transmitting device 100, generates an animation representing the running activity of the subject as an index representing the motion state of running, and presents it to the subject.

[0031] The control unit 210 includes a CPU, etc. The control unit 210 executes a program stored in the storage unit 220 to function as each unit (a parameter acquisition unit 211 , an animation generation unit 212 , a parameter estimation unit 213 ) described later.

[0032] The storage unit 220 includes ROM, RAM, flash memory, etc., and stores programs and necessary data executed by the CPU of the control unit 210. In addition, data that is desired to be saved even after the power of the animation generating device 200 is turned off is saved in a non-volatile memory such as a flash memory. In addition, the storage unit 220 stores a user DB (DataBase) 221 and a motion state storage DB 222.

[0033] The user DB 221 is a database for registering information related to the subject of the data transmission device 100. Specifically, in the user DB 221, information indicating the user ID, name, gender, physique (height, weight, etc.), running history, best time, etc. that uniquely identify the subject of each data transmission device 100 is stored.

[0034] The motion state storage DB 222 is a database storing a plurality of parameters characterizing the running motion state of the subject measured so far. Specifically, in the motion state storage DB 222, for example Figure 2 As shown, a plurality of motion parameter information is stored, wherein the user ID of the subject, the speed of the subject's moving motion as parameters characterizing the subject's motion state, the step frequency (number of steps per unit time), each value of the up and down movement, and information indicating the measurement date and time of measuring these parameters are established in correspondence. In addition, it is known from experience that the speed, step frequency, and each value of the up and down movement as parameters characterizing the user's motion state are related to each other, and change in conjunction with each other as the motion state of the subject changes. For example, if the speed of the subject changes, the step frequency and the up and down movement also change in conjunction.

[0035] The communication unit 231 includes a wireless communication module and an antenna, and performs data communication with the data transmission device 100 wirelessly. In addition, the communication unit 231 is not limited to the wireless method in the data communication with the data transmission device 100, and a wired interface such as USB can also be used for data communication.

[0036] The input unit 232 includes a switch, a touch panel, a keyboard, a mouse, etc., and accepts input instructions from the subject such as "animation generation" and "parameter change".

[0037] The output unit 233 includes a liquid crystal display panel, an organic EL display panel, etc., and displays, for example, the animation generated in the animation generation process described later, or a screen for changing parameters. In addition, the output unit 233 may also include a sound output device such as a speaker, and outputs sounds related to the animation generated in the animation generation process.

[0038] Next, the functions of the control unit 210 will be described. The control unit 210 functions as a parameter acquisition unit 211, an animation generation unit 212, and a parameter estimation unit 213 by executing the program stored in the storage unit 220.

[0039] The parameter acquisition unit 211 acquires motion data (acceleration data, angular velocity data, time data, position data, moving distance data, speed data, etc.) representing the activity of the subject from the data transmission device 100 via the communication unit 231. Then, the parameter acquisition unit calculates a plurality of relevant parameters (speed, cadence, vertical movement) characterizing the running motion state of the subject based on the obtained motion data, and registers them in the motion state storage DB222. The parameter acquisition unit 211 functions as an acquisition unit.

[0040] In addition, regarding the method by which the parameter acquisition unit 211 calculates each parameter based on the motion data, a known method described in, for example, Japanese Patent No. 6648439 or Japanese Unexamined Patent Application Publication No. 2019-216798 can be adopted.

[0041] For example, the parameter acquisition unit 211 can calculate the speed based on the time change of the position data represented by the motion data. In addition, the parameter acquisition unit 211 can obtain the period (running period) of the waveform of the vertical direction component of the acceleration represented by the motion data, and calculate the cadence based on the running period. In addition, the parameter acquisition unit 211 can integrate the vertical direction component of the acceleration represented by the motion data to calculate the vertical movement as the difference between the highest point and the lowest point of the orientation (the position of the waist of the subject equipped with the data transmission device 100) from the landing of one foot to the landing of the other foot.

[0042] The animation generation unit 212 generates an animation representing the corresponding motion of the subject based on the values ​​of the plurality of parameters designated as the target of the animation. The animation generation unit 212 functions as an animation generation unit.

[0043] The parameter estimation unit 213 generates a model that defines the relationship between the values ​​of the plurality of parameters based on the plurality of motion parameter information stored in the motion state storage DB 222 when one value among the plurality of parameters designated as the object of the animation is changed, and estimates the value of the parameter that is considered to change in linkage. The parameter estimation unit 213 functions as a parameter acquisition unit, a first derivation unit, a second derivation unit, and a parameter estimation unit.

[0044] Next, the processing performed by the animation generating device 200 will be described. First, the parameter acquisition processing performed by the animation generating device 200 will be described. The subject of the data transmitting device 100 is equipped with the data transmitting device 100, and after inputting the instruction of "starting the motion data measurement" via the input unit 132, performs, for example, running or walking as exercise. As a result, the sensor unit 134 of the data transmitting device 100 continuously measures the motion data of the subject who is running or walking at given intervals (for example, every 1 second). After that, the subject who has finished running indicates "end of motion data measurement" via the input unit 132. As a result, the motion data transmitting unit 111 transmits the continuously measured motion data together with the user ID of the subject to the animation generating device 200. Upon receiving the motion data from the data transmitting device 100, the parameter acquisition unit 211 of the animation generating device 200 executes Figure 3 The parameter acquisition process shown.

[0045] First, the parameter acquisition unit 211 divides the received motion data into 12 parts according to a given measurement time (e.g., 5 minutes) (step S101). For example, when the given measurement time is 5 minutes and the received motion data is 1 hour of motion data, the motion data is divided into 12 parts in step S101. In addition, the parameter acquisition unit 211 can also determine the timing when the speed changes by more than a certain threshold value based on the change in the speed of the subject represented by the received motion data, and divide the motion data at this timing.

[0046] Next, the parameter acquisition unit 211 calculates the values ​​of various parameters (speed, cadence, up-and-down movement, etc.) indicating the exercise state of the subject from each exercise data divided in step S101 (step S102 ).

[0047] Next, the parameter acquisition unit 211 registers the motion parameter information including the calculated parameters in the motion state storage DB222 (step S103). For example, if the motion data is divided into 12 in step S101, 12 pieces of motion parameter information are registered in the motion state storage DB222. In addition, the user ID included in the motion parameter information is set to the user ID received together with the motion data. Through the above, the parameter acquisition process ends.

[0048] In addition, in the above parameter acquisition process, the animation generation device 200 calculates each parameter based on the motion data received from the data transmission device 100. However, it may also be that the data transmission device 100 calculates each parameter based on the motion data acquired by the sensor unit 134 and transmits it to the animation generation device 200.

[0049] Next, the animation generation process executed by the animation generation device 200 will be described. In addition, before the animation generation process, the above parameter acquisition process is executed for a plurality of subjects, and a sufficient number (for example, 100 or more) of motion parameter information is stored in the motion state storage DB222. If the subject inputs an instruction of "animation generation" via the input unit 232 of the animation generation device 200, the animation generation unit 212 starts Figure 4 the animation generation process shown.

[0050] First, the animation generation unit 212 receives the values of the parameters (speed, cadence, vertical movement) that are the objects of the animation from the subject (step S201). For example, the animation generation unit 212 receives, via the input unit 232, the selection of the motion parameter information stored in the motion state storage DB222 from the subject, and receives the values of the parameters included in the selected motion parameter information as the objects of the animation. In addition, the animation generation device 200 may also receive the values of the parameters directly input by the subject via the input unit 232.

[0051] Next, the animation generation unit 212 creates an animation representing the running motion state of the subject based on the received parameter values, as Figure 5 shown, and displays it on the output unit 233 (step S202). In this screen, the generated animation is displayed on the right side, and the values of the parameters (speed, cadence, vertical movement) that are the basis of the animation and the sliders for changing the values of the parameters respectively are displayed on the left side.

[0052] Return to Figure 4When the subject wishes to change one of the parameters that are the basis of the displayed animation, the subject moves the slide bar next to the parameter that is desired to be changed to a position corresponding to the amount of change that is desired via the input unit 232. If an operation to change a parameter is received ("Yes" in step S203), the parameter estimation unit 213 performs parameter estimation processing to estimate the values ​​of other parameters that change in conjunction with the parameter that is to be changed (step S204).

[0053] refer to Figure 6 In the following description, a parameter whose value changes due to the operation of the subject is defined as a first parameter, and another parameter whose value changes in conjunction with the first parameter is defined as a second parameter. Figure 5 When an operation to change the speed is performed on the screen shown, the speed becomes the first parameter, and the cadence or up and down movement becomes the second parameter.

[0054] When the parameter estimation process is started, the parameter estimation unit 213 obtains the value of the first parameter and the value of the second parameter before being changed by the operation of the subject (step S301).

[0055] Next, the parameter estimation unit 213 creates a model with the value of the third parameter as input and the value of the fourth parameter as output based on all the groups of the values ​​of the third parameter of the same type as the first parameter and the values ​​of the fourth parameter of the same type as the second parameter respectively represented by the plurality of motion parameter information stored in the motion state storage DB 222 (step S302). This model is, for example, a function (regression formula) of a regression curve created by the least square method. In addition, the parameter estimation unit 213 may also obtain a reliability interval representing the range of the value of the fourth parameter of the group that is a certain proportion or more (for example, more than 60%) of all the groups including the value of the third parameter and the value of the fourth parameter as a model.

[0056] For example, Figure 7 The distribution of the groups of speed as the third parameter and step frequency as the fourth parameter respectively represented by the multiple motion parameter information stored in the motion state storage DB222 is represented. In the figure, one "※" corresponds to the group of speed and step frequency contained in one motion parameter information. In step S302, based on the distribution, a model with speed as input and step frequency as output is created by the least squares method. The solid line curve F in the figure is equivalent to the regression curve of the model created based on the distribution. In addition, the two dot-dashed curves A and B respectively represent the upper and lower limits of the reliability interval. 60% of the speed and step frequency groups represented by "※" are included in the range between the dot-dashed curves A and B. In addition, based on the relationship between the curve F and the distribution of the speed and step frequency groups, the curves A and B that define the reliability interval are obtained by a known method.

[0057] Back to Figure 6 Then, the parameter estimation unit 213 inputs the value of the first parameter before the value change obtained in step S301 into the prepared model to derive the first reference value as the value of the second parameter (step S303).

[0058] Next, the parameter estimation unit 213 inputs the value of the first parameter whose value has changed due to the operation of the subject into the created model, and derives a second reference value which is the value of the second parameter (step S304).

[0059] Next, the parameter estimation unit 213 estimates the value of the second parameter that changes in conjunction with the changed first parameter based on the value of the second parameter before change acquired in step S301 , the first reference value, and the second reference value (step S305 ). The parameter estimation process ends as described above.

[0060] Here, the processing of step S305 is described by taking an example. Here, it is assumed that the first parameter and the third parameter are speed, and the second parameter and the fourth parameter are step frequency. Figure 8 The model is generated as shown in the curve F. Then, consider the case where the speed value is changed to V2 from the state where the speed value is V1 and the step frequency value is P1 by the operation of the subject. In this case, the parameter estimation unit 213 estimates that the step frequency value changes along the curve F. That is, the parameter estimation unit 213 estimates the value P2 of the step frequency as the second parameter that changes in conjunction with the speed value V1 and the step frequency value P1 by the following formula.

[0061] P2=S2+K*(PI-S1)

[0062] like Figure 8 As shown in FIG. 1 , in the formula, P1 represents the value of the step frequency corresponding to the speed V1 before the speed is changed to V2, S1 represents the first reference value, and S2 represents the second reference value. In addition, K in the formula is a coefficient arbitrarily set within the range of 0 to 1, and K is usually set to 1.0. When it is desired to make the estimated value of the second parameter after the change, that is, P2, close to the second reference value S2, K is set to a value close to 0.0.

[0063] When the reliability interval is calculated as a model, the parameter estimation unit 213 may estimate P2 by taking into account the ratio (T2 / T1) of the lengths of the reliability intervals as shown in the following equation.

[0064] P2=S2+K*(P1-S1)*T2 / T1

[0065] like Figure 8As shown in the figure, T1 in the formula represents the length of the reliability interval at the speed V1 before the change. T2 represents the length of the reliability interval at the speed V2 after the change. For example, as a function of speed v, the formula of curve A is represented by a(v), and the formula of curve B is represented by b(v). In this case, T1 and T2 can be calculated by the formula shown below.

[0066] T1=a(V1)-b(V1)

[0067] T2=a(V2)-b(V2)

[0068] In addition, here, an example is shown in which the step frequency as the second parameter is estimated when the speed as the first parameter is changed, but a group of other parameters having correlated values ​​may be set as the first parameter and the second parameter. For example, the first parameter and the second parameter may be reversed, and the speed as the second parameter may be estimated by the same transmission and reception when the step frequency as the first parameter is changed.

[0069] For example, there is also a correlation between speed and up and down activity, e.g. Figure 9 As shown in FIG. 1 , when a regression curve F' and a reliability interval (interval between curve A' and curve B') can be obtained as a model from the distribution of the group of speed and up and down movement, the model can be used to estimate the up and down movement as the second parameter in the same manner when the speed as the first parameter is changed. Alternatively, the first parameter and the second parameter can be reversed, and the speed as the second parameter can be estimated in the same manner when the up and down movement as the first parameter is changed.

[0070] For example, there is also a correlation between step frequency and up and down activity, e.g. Figure 10 As shown, when the regression curve F" and the reliability interval (the interval between curve A" and curve B") can be obtained as a model based on the distribution of the group of step frequency and up and down movement, using this model, when the step frequency as the first parameter is changed, the up and down movement as the second parameter can be estimated in the same way. Alternatively, the first parameter and the second parameter can be reversed, and when the up and down movement as the first parameter is changed, the step frequency as the second parameter can be estimated in the same way.

[0071] Back to Figure 4 When the parameter estimation process (step S204) is completed, the animation generation unit 212 creates an animation reflecting the estimation result and updates the display (step S205). For example, the speed value is changed by the operation of the subject, and the step frequency and the up and down movement values ​​that change in conjunction with it are estimated in the parameter estimation process. In this case, the animation generation unit 212 generates an animation reflecting the estimation result and updates the display (step S205). Figure 5The parameter values ​​and the position of the slider on the left side of the screen shown change to become the values ​​of the speed at which the subject changes, the value of the step frequency estimated in the parameter estimation process, and the value of the up and down movement, and an animation is created based on these values ​​to update the animation displayed on the right side.

[0072] After the display update in step S205 is completed, or when the operation to change the parameter is not received from the subject (step S203: No), the animation generator 212 determines whether an instruction to end the animation generation process is received from the subject via the input unit 232 (for example, Figure 5 If the instruction to end is not received ("No" in step S206), the process returns to step S203. On the other hand, if the instruction to end is received ("Yes" in step S206), the animation generation process ends.

[0073] Thus, according to the present embodiment, a model (regression line) is generated with one parameter (e.g., speed) as input and another parameter (e.g., step frequency) as output based on a group of parameters representing the motion state of multiple subjects stored in the motion state storage DB 222 (e.g., a group of speed and step frequency). Then, based on the generated model, when the value of the acquired parameter is changed, it is estimated how the value of the other parameter changes. That is, according to the present embodiment, when one of the multiple parameters representing the motion state changes, or when it is assumed to change, it is possible to estimate how the values ​​of the other parameters change.

[0074] In addition, according to the present embodiment, when one value among a plurality of parameters representing the motion state is changed by the operation of the subject, etc., it is estimated how the values ​​of other parameters change in conjunction with the change, and an animation representing the motion state is created based on the estimated parameter value. Therefore, even when only the value of one parameter changes, an animation without a sense of incongruity can be created.

[0075] In addition, in this embodiment, in addition to the regression line, a reliability interval is further obtained as a model for estimating parameters, and the length of the reliability interval can be taken into consideration when estimating parameters. Therefore, the estimation accuracy of the parameters can be improved.

[0076] (Variation Example)

[0077] In addition, the present invention is not limited to the above-mentioned embodiment, and various modifications can be made without departing from the gist of the present invention.

[0078] For example, in the above-mentioned embodiment, in the parameter estimation process, the model is created based on all the motion parameter information stored in the motion state storage DB 222, but the model may also be created based on the motion parameter information corresponding to the motion state of the subject having the same attributes as the subject to be animated. For example, in the case of estimating the parameters representing the motion state of the subject who is a female in the parameter estimation process, the parameter estimation unit 213 may also refer to the user DB 221, extract the motion parameter information corresponding to the motion state of the female from the motion state storage DB 222, and create the model based on the extracted motion parameter information.

[0079] In the above-mentioned embodiment, the speed, step frequency, and up and down movement of the subject are shown as examples of parameters that characterize the motion state that changes in linkage with each other, but the parameters are not limited to these, and other parameters may be used as the first parameter and the second parameter. For example, the speed, step frequency, and up and down movement may be further added with step length, step height ratio, up and down movement height ratio, left and right movement, front and back movement, contact time, leg swing time, contact time rate, deceleration, sinking amount, sinking amount height ratio, stop time, landing impact, kicking acceleration, kicking time, pelvic rotation amount, stiffness, stiffness weight ratio, contact angle, and kicking angle. Two parameters with correlation may be selected and set as the first parameter and the second parameter. In addition, these parameters, like the speed, step frequency, and up and down movement, can be calculated by a known method based on the motion data obtained by the data transmission device 100.

[0080] For example, the stride length is the width of each step, and can be obtained by dividing the speed per minute by the cadence. The stride length to height ratio can be obtained by dividing the stride length by the height of the subject. The up-and-down movement to height ratio can be obtained by dividing the up-and-down movement by the height of the subject.

[0081] The left-right movement is the left-right variation of the position from the landing of one foot to the landing of the other foot, and can be calculated by integrating the left-right component of the acceleration represented by the motion data. The front-back movement is the front-back variation of the position from the landing of one foot to the landing of the other foot, and can be calculated by integrating the front-back component of the acceleration represented by the motion data and subtracting the moving distance at the average speed.

[0082] The contact time is the time from when a foot touches the ground to when the foot leaves the ground. The contact time can be calculated by determining the timing of contact and departure based on the acceleration represented by the motion data. The swing time is the time from when a foot leaves the ground to when the foot touches the ground. The contact time rate can be calculated by contact time / (contact time + swing time).

[0083] Based on the acceleration represented by the motion data, the deceleration amount can be obtained by integrating the magnitude of the acceleration vector in the backward direction during the grounded period over one cycle of a single foot. The sinking amount is the difference between the orientation at the time of touchdown of a single foot and the lowest point thereafter, and can be obtained by integrating the vertical component of the acceleration represented by the motion data from the time of touchdown to the lowest point. The sinking amount to height ratio can be obtained by dividing the sinking amount by the height of the subject.

[0084] The stopping time is the time from touchdown to when the forward and backward components of the acceleration change to the propulsion direction, and can be obtained by determining the touchdown timing and the timing when the forward and backward components of the acceleration represented by the motion data change to the propulsion direction. The touchdown impact is the amount of impact at touchdown, and can be characterized by the magnitudes of the respective components of the acceleration vector represented by the motion data immediately after touchdown.

[0085] The kicking acceleration is the magnitude of the acceleration during propulsion, and can be characterized by the magnitude of the forward and backward components of the acceleration vector represented by the motion data. The kicking time is the time during touchdown when the acceleration in the propulsion direction is generated, and can be obtained by measuring the time when the forward and backward components of the acceleration represented by the motion data are generated. Alternatively, the kicking time can also be obtained by measuring the time from the lowest point to the lift-off of a single foot based on the vertical component of the acceleration represented by the motion data.

[0086] The pelvic rotation amount is the amount of lumbar rotation between the touchdown of a single foot and the next touchdown of the same foot (2-step cycle), or between the touchdown of one foot and the touchdown of the other foot (1-step cycle), and can be obtained based on the rotational speed represented by the motion data. The stiffness is the spring constant when the foot is regarded as a spring, and can be obtained based on the change in the vertical component of the acceleration represented by the motion data. The stiffness to weight ratio can be obtained by dividing the stiffness by the weight of the subject.

[0087] The touchdown angle is the angle between the velocity vector at touchdown and the horizontal plane or the ground, and the kick-off angle is the angle between the velocity vector at lift-off and the horizontal plane or the ground. The touchdown angle and the kick-off angle can be calculated based on the components of the acceleration in each direction represented by the motion data.

[0088] In the above-described embodiment, in the parameter estimation process, the first parameter value is input to the created regression equation, i.e., the model, to derive the first reference value and the second reference value. However, the method of deriving the first reference value and the second reference value from the model is not limited to this. For example, a value obtained by multiplying a certain coefficient by the value of the first parameter may be input to the model to derive the first reference value and the second reference value.

[0089] In the above-described embodiment, the animation generation device 200 for creating an animation representing the running motion state of a subject has been described. However, the motion state is not limited to running. For example, the present invention can also be applied to an animation generation device for creating an animation of a baseball pitching action, etc.

[0090] In the above-described embodiment, the animation generation device 200 for creating an animation representing the motion state of a human subject has been described as an example. However, the object of the created animation is not limited to humans, and the present invention can also be applied to an animation generation device for creating an animation representing the motion state of a subject other than a human. For example, the present invention can also be applied to an animation generation device for generating an animation representing the running state of a racehorse, the action state of a robot, etc.

[0091] The present invention is not limited to the animation generation device 200. For example, the present invention can also be applied to an information processing device that only performs parameter estimation processing without creating an animation.

[0092] In the above-described embodiment, in the parameter estimation process, the animation generation device 200 generates a model for estimating parameters. However, the model can also be pre-generated in an external server or the like, and in the parameter estimation process, the animation generation device 200 estimates parameters based on the model obtained from an external server or the like. In this way, since the process of generating the model can be omitted in the animation generation device 200, the burden on the animation generation device 200 can be reduced, and the processing time of the parameter estimation process can be shortened.

[0093] Each function of the animation generation device 200 can also be implemented by a computer such as a general-purpose PC. Specifically, in the above-described embodiment, it has been described that the program for the animation generation process performed by the animation generation device 200 is pre-stored in the ROM of the storage unit 220. However, the program can also be stored in a computer-readable recording medium such as a floppy disk, a CD-ROM (Compact Disc Read Only Memory), a DVD (Digital Versatile Disc), an MO (Magneto-Optical disc), a memory card, a USB (Universal Serial Bus) memory, etc. and distributed, and a computer that can implement the above-described various functions can be configured by reading the program into the computer and installing it.

[0094] The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the related specific embodiments, and the present invention includes the invention described in the claims and its equivalent scope. The invention described in the claims of this application at the time of filing is noted below.

Claims

1. An information processing apparatus, characterized in that, it comprises: an acquisition unit that acquires the value of a first parameter and the value of a second parameter that is an index different from and correlated with the first parameter as an index characterizing the motion state of a certain subject; a parameter acquisition unit that acquires other first parameters of the first parameter that are different values from the value of the first parameter acquired by the acquisition unit; a first derivation unit that derives a first reference value of the second parameter based on the first parameter acquired by the acquisition unit in accordance with a model, where the model is generated by taking the value of the third parameter of the same type as the first parameter and the value of the fourth parameter of the same type as the second parameter, which characterize the motion states of a plurality of subjects of the same type as the certain subject, as input and output respectively; a second derivation unit that derives a second reference value of the second parameter based on the other first parameter acquired by the parameter acquisition unit in accordance with the model; and a parameter estimation unit that estimates the value of the second parameter corresponding to the other first parameter based on the value of the second parameter acquired by the acquisition unit, the first reference value, and the second reference value.

2. The information processing apparatus according to claim 1, characterized in that, the information processing apparatus further comprises: an animation generation unit that generates an animation characterizing the motion state of the certain subject based on the value of the other first parameter acquired by the parameter acquisition unit and the value of the second parameter estimated by the parameter estimation unit.

3. The information processing apparatus according to claim 1 or 2, characterized in that, the model further includes a reliability interval that represents a range of values of the fourth parameter that includes a proportion or more of the groups of the values of the third parameter and the values of the fourth parameter that characterize the motion states of the plurality of subjects respectively, the parameter estimation unit estimates the value of the second parameter based on the ratio of the length of the reliability interval when the value of the third parameter is the value of the first parameter acquired by the acquisition unit to the length of the reliability interval when the value of the third parameter is the value of the other first parameter acquired by the parameter acquisition unit.

4. The information processing apparatus according to claim 1 or 2, characterized in that, the model is generated based on the groups of the values of the third parameter and the values of the fourth parameter that characterize the motion states of a plurality of subjects of the same type as the certain subject and having the same attributes.

5. The information processing apparatus according to claim 1 or 2, characterized in that, the first parameter and the second parameter are indices related to the translational motion of the certain subject. The first parameter and the second parameter are respectively any one of the speed, cadence, vertical movement, step length, step length to height ratio, vertical movement to height ratio, lateral movement, forward and backward movement, ground contact time, swing leg time, ground contact time ratio, deceleration amount, sinking amount, sinking amount to height ratio, stop time, landing impact, kicking acceleration, kicking time, pelvic rotation amount, stiffness, stiffness to body weight ratio, ground contact angle, and kicking angle of the movement.

6. An information processing method, characterized in that, it has: an acquisition step of acquiring a value of a first parameter and a value of a second parameter that is an index different from and correlated with the first parameter as an index characterizing the motion state of a certain subject; a parameter acquisition step of acquiring another first parameter of the first parameter that is a value different from the value of the first parameter acquired in the acquisition step; a first derivation step of deriving a first reference value of the second parameter based on the first parameter acquired in the acquisition step according to a model, where the model is generated by taking a value of a third parameter of the same type as the first parameter and a value of a fourth parameter of the same type as the second parameter, which characterize the motion states of a plurality of subjects of the same type as the certain subject, as input and output respectively; a second derivation step of deriving a second reference value of the second parameter based on the other first parameter acquired in the parameter acquisition step according to the model; and a parameter estimation step of estimating a value of the second parameter corresponding to the other first parameter based on the value of the second parameter, the first reference value, and the second reference value acquired in the acquisition step.

7. A storage medium, characterized in that, it stores a program that causes a computer to function as the following units: an acquisition unit that acquires a value of a first parameter and a value of a second parameter that is an index different from and correlated with the first parameter as an index characterizing the motion state of a certain subject; a parameter acquisition unit that acquires another first parameter of the first parameter that is a value different from the value of the first parameter acquired by the acquisition unit; a first derivation unit that derives a first reference value of the second parameter based on the first parameter acquired by the acquisition unit according to a model, where the model is generated by taking a value of a third parameter of the same type as the first parameter and a value of a fourth parameter of the same type as the second parameter, which characterize the motion states of a plurality of subjects of the same type as the certain subject, as input and output respectively; a second derivation unit that derives a second reference value of the second parameter based on the other first parameter acquired by the parameter acquisition unit according to the model; and a parameter estimation unit that estimates a value of the second parameter corresponding to the other first parameter based on the value of the second parameter, the first reference value, and the second reference value acquired by the acquisition unit.

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