JP2022059422A - Arithmetic method and arithmetic device - Google Patents

Abstract

To provide an arithmetic device capable of more accurately calculating a level of kinesia.SOLUTION: An arithmetic device for calculating an estimated value of kinesia from an input value comprises: a sensor I/F 14 that functions as an input unit for inputting a first body motion value showing motion stimulation given to a person's body at a first point of time; and an arithmetic unit 110 for calculating an estimated value of kinesia of the person. The arithmetic unit uses a history of body motion values showing motion stimulation given to the person's body at points of time before the first point of time to predict a second body motion value, a prediction value for a body motion value showing motion stimulation to be given to the person's body at the first point of time; takes a value based on the first body motion value and the second body motion value as the input value and inputs the value to an arithmetic model for calculating the estimated value of kinesia from the input value to calculate the estimated value of kinesia of the person at the first point of time.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to an arithmetic method and an arithmetic unit.

Motion sickness is a disease caused by imbalance and includes motion sickness and sea sickness. Therefore, in order to improve the comfort (ride quality) of vehicles such as vehicles and ships, it is effective to suppress the occurrence and degree of motion sickness.

In this regard, Japanese Patent Application Laid-Open No. 2020-18770 (hereinafter referred to as Patent Document 1) by the inventor of the present application was constructed from a mathematical model that imitates the vestibular sensation (semicircular canals, otoliths) and vision, and their integration process. , Discloses a mathematical model based on the "sensory contradiction theory" that is said to be the cause of agitation. By using this mathematical model, it is possible to calculate the degree of motion sickness and control the shaking of the vehicle.

Japanese Unexamined Patent Publication No. 2020-18770

In order to further improve the comfort of the vehicle, it is desirable to calculate the degree of motion sickness with high accuracy. Therefore, the inventor has repeatedly developed to calculate the degree of motion sickness with higher accuracy based on the mathematical model he found earlier.

According to a certain embodiment, the calculation method is a calculation method using a calculation model for calculating an estimated value of motion sickness from an input value, and is a first method representing a motor stimulus given to a human body at a first time point. The second physical exercise value, which is a predicted value of the physical exercise value representing the exercise stimulus given to the human body at the first time point, is applied to the human body at the time point before the first time point. Prediction is made using the history of body exercise values representing a given exercise stimulus, and a value based on the first body exercise value and the second body exercise value is input to the calculation model as an input value, and the first person's first body exercise value. Includes calculating estimates of motion sickness at a time point.

According to another embodiment, the arithmetic device is an arithmetic device that calculates an estimated value of motion sickness from an input value, and is a first physical exercise representing a motor stimulus given to a human body at a first time point. It includes an input unit for inputting a value and an arithmetic unit for calculating an estimated value of motion sickness, and the arithmetic unit is a predicted value of a physical exercise value representing an exercise stimulus given to a human body at a first time point. The second physical exercise value is predicted using the history of the physical exercise value representing the exercise stimulus given to the human body before the first time point, and the first physical exercise value and the second physical exercise are predicted. It includes inputting a value based on a value into an arithmetic model for calculating an estimated value of motion sickness from an input value and calculating an estimated value of motion sickness at a person's first time point.

Further details will be described in the embodiments described below.

FIG. 1 is a block diagram showing an outline of an apparatus configuration of an arithmetic unit according to an embodiment. FIG. 2 is a diagram showing an outline of arithmetic processing in the arithmetic unit. FIG. 3 is a block diagram showing an example of the functional configuration of the arithmetic unit of the arithmetic unit. FIG. 4 is a block diagram showing an example of detailed processing of each function of FIG. FIG. 5 is an example of the history of exercise stimuli given to a person. FIG. 6 shows the reliability of the predicted value obtained from the history of the motor stimulus of FIG. FIG. 7 is an example of the history of exercise stimuli given to a person. FIG. 8 shows the reliability of the predicted value obtained from the history of the motor stimulus of FIG. 7. 9 is an estimated value using the predicted value obtained from the history of the motor stimulus of FIG. 5 and the reliability of FIG. FIG. 10 is an estimated value using the predicted value obtained from the history of the motor stimulus of FIG. 7 and the reliability of FIG.

<1. Overview of arithmetic method and arithmetic unit>

(1) The calculation method according to the embodiment is a calculation method using a calculation model for calculating an estimated value of motion sickness from an input value, and is a first calculation method representing a motor stimulus given to a human body at a first time point. The second physical exercise value, which is a predicted value of the physical exercise value representing the exercise stimulus given to the human body at the first time point, is obtained from the body exercise value of the human body before the first time point. Prediction is made using the history of the physical exercise value representing the exercise stimulus given to, and the value based on the first physical exercise value and the second physical exercise value is input to the calculation model as the input value, and the first of the person Includes calculating estimates of motion sickness at the time of.

The motor stimulus given to the human body is, for example, the motor stimulus given to the head. The estimated value of motion sickness is, for example, the incidence of motion sickness (MSI: Motion Sickness Incidence). MSI is the ratio of the number of people leading to vomiting to physical stimuli.

The first time point is, for example, the present time. In this case, the first physical exercise value refers to the physical exercise value obtained at the present time. The history of physical exercise values is one or more physical exercise values representing the exercise stimuli given to the human body before the first time point. The plurality of physical exercise values at the time points before the first time point may include the physical exercise values at the first time point. The second physical exercise value refers to the predicted value of the current physical exercise value predicted from the past physical exercise value.

By inputting the input value obtained by using the first body exercise value and the second body exercise value into the calculation model, the body exercise value at the first time point (first body exercise value) can be obtained. , An estimated value of motion sickness can be obtained in consideration of the predicted value of the physical exercise value at the first time point (second physical exercise value). As a result, it is possible to obtain an estimated value of motion sickness, which indicates that the movement given to the human body is less sick when it is easy to predict, and it is easy to get sick when it is difficult to predict.

(2) Preferably, predicting the second physical exercise value includes analyzing the history of the physical exercise value. This makes it possible to calculate the second physical exercise value.

(3) Preferably, in predicting the second physical exercise value, environmental information that affects the motor stimulus given to the human body at the first time point is used. This makes it possible to calculate the second physical exercise value.

(4) Preferably, the first body exercise value and the history of the body exercise value include the acceleration, and the second body exercise value includes the predicted value of the acceleration. This makes it possible to calculate the predicted value of acceleration.

(5) Preferably, the input value includes a value obtained by using the predicted value of acceleration as the first body exercise value. The value obtained by using the predicted value of acceleration as the first body movement value includes, for example, the first body movement value and the predicted value of acceleration in consideration of reliability. This makes it possible to calculate an estimated value of motion sickness in consideration of the predicted value.

(6) Preferably, the first body exercise value and the history of the body exercise value include the angular velocity, and the second body exercise value includes the predicted value of the angular velocity. This makes it possible to calculate the predicted value of the angular velocity.

(7) Preferably, the input value includes a value obtained by using the predicted value of the angular velocity as the first body exercise value. The value obtained by using the predicted value of acceleration as the first body movement value includes, for example, the first body movement value and the predicted value of the angular velocity. This makes it possible to calculate an estimated value of motion sickness in consideration of the predicted value.

(8) Preferably, predicting the second physical exercise value includes using a prediction algorithm for predicting the physical exercise value from the history of the physical exercise value. The prediction algorithm is, for example, Gaussian process regression. This makes it possible to calculate the second physical exercise value.

(9) Preferably, the arithmetic model corresponds to a value corresponding to the amount of motion sensation in the sensory organ and a motion estimator which is the amount of motion sensation estimated by the internal model of the sensory organ constructed in the central nervous system. The estimated value of sway disease is calculated based on the difference between the value and the value. As a result, the estimated value of motion sickness can be calculated with high accuracy.

(10) Preferably, inputting the input value into the calculation model includes adjusting and inputting the second body exercise value according to the reliability of the prediction of the second body exercise value. Thereby, the second physical exercise value can be appropriately reflected in the estimated value of motion sickness.

(11) Preferably, adjusting the second body exercise value includes increasing the degree of input to the calculation model when the reliability is high, and decreasing the degree of input when the reliability is low. Thereby, the second physical exercise value can be appropriately reflected in the estimated value of motion sickness.

(12) The calculation device according to the embodiment is a calculation device that calculates an estimated value of motion sickness from an input value, and is a first physical exercise that represents an exercise stimulus given to a human body at a first time point. It includes an input unit for inputting values and a calculation unit for calculating an estimated value of human motion sickness, and the calculation unit is a predicted value of a physical exercise value representing an exercise stimulus given to the human body at a first time point. The second physical exercise value is predicted by using the history of the physical exercise value representing the exercise stimulus given to the human body before the first time point, and the first physical exercise value and the second physical exercise value are predicted. This includes inputting a value based on the physical exercise value into an arithmetic model for calculating an estimated value of motion sickness from the input value, and calculating an estimated value of motion sickness at a person's first time point. As a result, the arithmetic unit can calculate the estimated value of motion sickness by the arithmetic method described in (1) to (11).

<2. Calculation method and example of arithmetic unit>

[First Embodiment]

The arithmetic unit according to this embodiment calculates the degree of motion sickness. The degree of motion sickness is represented by an index value such as an estimated value of motion sickness as an example. The arithmetic unit according to this embodiment calculates an estimated value of motion sickness. The estimated value of motion sickness is, for example, the incidence of motion sickness (MSI: Motion Sickness Incidence). MSI is the ratio of the number of people leading to vomiting to physical stimuli.

MSI can be used, for example, for control to prevent or reduce motion sickness in occupants of vehicles such as vehicles. Vehicles are, for example, ships, trains, aircraft, and the like. The comfort of the vehicle is improved by preventing or reducing the degree of motion sickness. Therefore, it is desirable to prevent or reduce the degree of motion sickness more effectively. Therefore, as an example, let the occupant of the vehicle be a person who measures data for calculating MSI with an arithmetic unit.

The inventor focused on less sickness when the movement given to the occupant's body is easy to predict, and easy to get sick when it is difficult to predict. Therefore, in the arithmetic unit according to the present embodiment, the MSI is calculated by adding the ease of predicting such a motion to the conventional mathematical model.

With reference to FIG. 1, the arithmetic unit 1 according to the present embodiment is composed of a general computer or the like having a processor 11 and a storage unit 12. The processor 11 is, for example, a CPU (Central Processing Unit).

The arithmetic unit 1 includes a sensor interface (I / F) 14. The sensor I / F14 is connected to each of the first sensor SE1 and the second sensor SE2 by wire or wirelessly, and receives the input of the sensor signal. The sensor I / F 14 inputs the input sensor signal to the processor 11.

The sensor SE1 is a sensor corresponding to the vestibular sensory organ. The sensor SE1 detects the motor stimulus given to the occupant's body, for example, the head, and outputs the motor information corresponding to the motor stimulus. The output signal I1 of the sensor SE1 represents motion information. Hereinafter, the output from the sensor SE1 is also referred to as motion information I1.

The motion information I1 is the head acceleration and the head angle. In addition, the head speed or a combination of two or more of these may be used. The sensor SE1 is, for example, an acceleration sensor attached to the occupant's head (for example, a hat) and measuring the head acceleration, and a gyro sensor for measuring the head angle.

The motion information I1 may be information about a motion considered to be a motion stimulus given to the head of the occupant, and may be, for example, the acceleration and the angle of the vehicle V itself. This is because it is estimated that the acceleration and angle of the vehicle V are the same as those of the occupants of the vehicle V. In this case, the first sensor SE1 is an in-vehicle acceleration sensor and gyro sensor.

The sensor SE1 executes sensing at predetermined intervals (for example, 10 msec intervals) and outputs motion information I1 to the arithmetic unit 1.

The storage unit 12 includes a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), a ROM, a RAM (Random Access Memory), and the like. The storage unit 12 stores an arithmetic program 121 composed of one or a plurality of programs.

The processor 11 functions as a calculation unit 110 that reads out the calculation program 121 stored in the storage unit 12 in response to the sensor signal and executes the calculation process 111. The arithmetic program 121 can be transferred in a state of being recorded on a recording medium such as a CD-ROM, (Compact Disc Read only memory) or DVD-ROM (Digital Versatile Disk ROM), or can be transferred from a computer device such as a server computer. It can also be transferred by downloading. The arithmetic program 121 may be a so-called web application that operates on a web browser, or may be a so-called dedicated application that operates only on the processor 11.

Preferably, the arithmetic unit 1 is connected to the output unit 3. The output device 3 is a device capable of outputting the calculation result of the calculation device 1, and is, for example, a display. The processor 11 passes the calculation result of the calculation process 111 to the output device 3 and outputs the result.

The arithmetic processing 111 is a processing for calculating MSI using a mathematical model M which is an arithmetic model. The mathematical model M is an arithmetic model that calculates MSI as an estimated value of motion sickness in a person from a body movement value representing a movement stimulus given to the human body.

The mathematical model M has a value corresponding to the amount of motion sensation in the sensory organ and a value corresponding to the amount of motion estimation which is the amount of motion sensation estimated by the internal model of the sensory organ constructed in the central nervous system. The MSI is calculated based on the difference. Sensory organs include vestibular sensations.

With reference to FIG. 2, the calculation unit 110 that executes the calculation process 111 has a body movement value (first body movement value) that represents a human movement stimulus obtained at the first time point by the sensor SE1. Entered. The exercise stimulus here includes a physical exercise stimulus, and the body exercise value includes the exercise information I1 corresponding to the exercise stimulus.

The arithmetic processing 111 includes a sensory organ processing (step S1). The sensory organ processing includes processing using the motion information I1 and outputting the motion sensory information I3 indicating the amount of motion sensory. The processing here corresponds to the processing of the motion perception of the vestibular sensory organ.

The arithmetic processing 111 includes an internal model processing (step S2). The internal model processing includes processing using the motion information I1 and outputting the motion estimation information I4 indicating the motion estimation amount which is the estimated motion sensation amount. The processing here corresponds to the processing of motion estimation in the internal model of the vestibular sensory organ.

The calculation process 111 includes an error calculation process (step S3). The error calculation process includes calculating an error between the motion sensation amount output as the motion sensation information I3 and the motion estimation amount output as the motion estimation information I4.

The calculation process 111 includes a motion sickness degree calculation process (step S4). The motion sickness degree calculation process includes calculating MSI from the error calculated by the error calculation process. The calculation result obtained in the motion sickness degree calculation process is passed to the output device 3. As a result, the output device 3 outputs MSI as a calculation result.

The arithmetic process 111 further includes a motion prediction process (step S5). The exercise prediction process includes calculating a predicted value of the body movement value (second body movement value) at the first time point when the sensor SE1 obtains the first body movement value, and outputting the movement prediction value I5. .. The predicted value is calculated using a plurality of physical exercise values representing the exercise stimuli (history of exercise stimuli) given to the human body at each of the plurality of time points prior to the first time point. In the internal model processing, the processing is further performed using the motion prediction value I5.

Preferably, the motion prediction process further includes calculating the reliability of the predicted value and outputting the reliability I6. In the internal model processing, the motion prediction value I5 may be used in consideration of the reliability I6.

Here, the principle of estimating the degree of motion sickness adopted in the mathematical model M will be described. Head movement is detected in the vestibular organ, which is one of the sensory organs, and a signal corresponding to the head movement (motion sensory signal) is generated. Further, it is considered that an internal model simulating a similar process is constructed in the central nervous system, and the internal model generates a signal (motion estimation signal) indicating the amount of motor sensation of head movement. If there is an error between the kinesthetic signal and the kinesthetic estimation signal, that is, if there is an error between the kinesthetic amount obtained by the sensory organ and the kinesthetic amount estimated by the internal model, the error is sensory. It becomes a contradiction. Sensory contradictions are said to cause motion sickness.

Further, in the arithmetic processing 111, in order to take into account the ease of predicting the movement, the body movement value (first body movement value) obtained at the first time point is used as the input value to be input to the mathematical model M. The value obtained by using the predicted value of the physical exercise value at the first time point (second physical exercise value) is used. The predicted value may be used by multiplying it by a predetermined gain. The predetermined gain may be based on the reliability of the predicted value as an example. In the following description, the input value includes a physical exercise value and a value obtained by adjusting the predicted value according to the reliability.

FIG. 3 will explain the functional configuration of the arithmetic unit 110 of the arithmetic unit 1 that executes the arithmetic processing 111 according to the mathematical model M. Each part shown in FIG. 3 is realized by the processor 11.

With reference to FIG. 3, the arithmetic unit 110 includes a processing unit 101. The processing unit 101 executes a process corresponding to the process of motion perception in the vestibular sensory organ as a sensory organ. The motion information I1 obtained at the first time point is input to the processing unit 101. The motion information I1 includes a head acceleration, which is a resultant force of gravity and inertial force of the head as seen from the head coordinate system, and a head angular velocity.

The process in the processing unit 101 includes a process corresponding to the motion perception in the otolith device and a process corresponding to the motion perception in the semicircular canal. When the motor information I1 is input, the processing unit 101 outputs the motor sensory information corresponding to the motor sensory signal indicating the motor sensory amount output from the vestibular sensory organ.

The calculation unit 110 includes a motion prediction unit 120. The motion information I1 is input to the motion prediction unit 120. When the exercise information I1 is input, the exercise prediction unit 120 executes the exercise prediction process and outputs the predicted value of the physical exercise (second physical exercise value) at the first time point. Preferably, the reliability of the predicted value may be further output. The predicted values of physical exercise and the reliability of the predicted values correspond to the predicted exercise values I5 and the reliability I6 in FIG.

The calculation unit 110 includes a body movement estimation unit 103. The body movement estimation unit 103 executes a process corresponding to a process of generating information (hereinafter, abbreviated as copy information) corresponding to an efferent copy of a stimulus given to the body in the nervous system, somatosensory, and the like. Specifically, the exercise information I1 is input to the body exercise estimation unit 103. Further, the exercise prediction value I5 and the reliability I6 are also input to the body exercise estimation unit 103.

The body movement estimation unit 103 multiplies the motion prediction value I5 by the gain K. The gain K is stored in advance as an example. As a result, the motion prediction value I5 is multiplied by a constant and input to the internal model described later.

Preferably, the body movement estimation unit 103 stores the relationship between the reliability I6 and the gain K in advance, and the body movement estimation unit 103 determines the gain K according to the input reliability I6. Then, the determined gain K is multiplied by the motion prediction value I5. The relationship between the reliability I6 and the gain K may be an arithmetic expression with the reliability as a parameter. Here, the relationship that the gain increases as the reliability increases is stored.

The body movement estimation unit 103 multiplies the movement information I1 by a gain stored in advance. Then, the body exercise estimation unit 103 uses the value based on the value obtained from the exercise information I1 and the value obtained from the exercise prediction value I5 as copy information. That is, the body movement estimation unit 103 uses the information obtained by adjusting the movement prediction value I5 with the reliability I6 as the above-mentioned copy information.

Information output from the adaptive processing unit 109, which will be described later, is input to the body movement estimation unit 103. The body movement estimation unit 103 executes an integrated process of integrating the copy information and the information output from the adaptive processing unit 109. The integrated process is a process corresponding to sensory integration that integrates various sensory information performed by a person in order to grasp one's own physical movement. The body movement estimation unit 103 outputs the information output by the integrated processing to the internal model. Therefore, the larger the gain, the larger the degree to which the motion prediction value I5 is input to the internal model, and the smaller the gain, the smaller the degree to which the motion prediction value I5 is input to the internal model. The copy information corresponds to the motion information I1 including the error.

As for the adjustment method using reliability, any other method can be used as long as the adjustment method is such that the higher the reliability, the larger the degree of input, and the lower the reliability, the smaller the degree of input. There may be.

The arithmetic unit 110 includes a vestibular sensory organ model 107 that executes a process corresponding to the process of motion estimation in the internal model. Information output from the body movement estimation unit 103 is input to the vestibular sensory organ model 107. When the information is input from the physical motion estimation unit 103, the vestibular sensory organ model 107 outputs the information corresponding to the motion estimation information I4 in FIG.

Specifically, the vestibular sensory organ model 107 executes processing corresponding to the processing of motion estimation in the vestibular sensory organ model of the internal model using the information input from the body movement estimation unit 103. The vestibular sensory organ model 107 estimates the kinesthetic amount by this processing, and outputs the motion estimation information indicating the motion estimation amount which is the estimated motion sensation amount.

The arithmetic unit 110 includes a comparison unit 108. The comparison unit 108 is an adder. The kinesthetic information output from the processing unit 101 and the kinesthetic estimation information output from the vestibular sensory organ model 107 are input to the comparison unit 108.

The comparison unit 108 executes a comparison process using the motion sensory information and the motion estimation information. As a result, the comparison unit 108 compares the kinesthetic amount obtained by the processing unit 101 with the kinesthetic sensory amount (motor estimation amount) estimated by the vestibular sensory organ model 107, and calculates the difference (error) between them. .. The calculated error is output as sensory contradiction information.

The arithmetic unit 110 includes an adaptive processing unit 109. The sensory contradiction information output from the comparison unit 108 is input to the adaptation processing unit 109. The adaptation processing unit 109 accumulates the input sensory contradiction information and outputs the information subjected to the adaptation processing for adapting to the processing in the internal model.

The arithmetic unit 110 includes a sensory contradiction processing unit 125. The sensory contradiction information output from the comparison unit 108 is input to the sensory contradiction processing unit 125. The sensory contradiction processing unit 125 stores in advance an arithmetic expression for calculating an index value indicating the degree of agitation using the sensory contradiction information. The sensory contradiction processing unit 125 calculates the MSI by substituting the sensory contradiction information input from the comparison unit 108 into the stored arithmetic expression.

With reference to FIG. 4, the arithmetic unit 110 that executes the arithmetic processing 111 according to the mathematical model M has the head, which is the sum of the gravitational acceleration g applied to the head and the inertial acceleration a, as the motion information I1 from the sensor SE1. The sensing results of the acceleration f (f = g + a), the head angular velocity ω, and the inertial acceleration a at the first time point are input.

The processing unit 101 includes a processing unit OTO and a processing unit SCC. The head acceleration f is input to the processing unit OTO. The processing unit OTO executes a process corresponding to the process of motion perception in the otolith device. The head angular velocity ω is input to the processing unit SCC. The processing unit SCC executes processing corresponding to the processing of motion perception in the semicircular canals.

The transmission characteristics of the processing unit OTO and the processing unit SCC are both expressed by the equations described in the head fixed coordinate system. The transfer characteristics of the processing unit OTO are represented by an identity matrix. The transmission characteristics of the processing unit SCC are represented by the equation (1) in the figure. Note that τa and τd in Eq. (1) are time constants.

The signal f'is output from the processing unit OTO. The processing unit SCC outputs the sensory amount ω'of the head angular velocity ω. By applying the low-pass filter LP to the signal f'and the signal ω', the perceived amount vs. the velocity in the direction of gravity is calculated. The sensory quantity as of the inertial acceleration a is calculated using the sensory quantity vs. The characteristics of the low-pass filter LP are represented by the equation (2) in the figure. Note that τ in Eq. (2) is a time constant. The sensory amount as, the sensory amount ωs, and the sensory amount vs. correspond to the kinesthetic information.

The vestibular sensory organ model 107 includes a processing unit <OTO> corresponding to an internal model of an otolith and a processing unit <SCC> corresponding to an internal model of a semicircular canal. The code sandwiched between the notation "<>" refers to the processing unit that performs the processing corresponding to the internal model. The same applies to the following description.

The transfer characteristics of the processing unit <OTO> are represented by an identity matrix. The transmission characteristics of the processing unit <SCC> are represented by the equation (3) in the figure. By applying the low-pass filter <LP> to the signals output from the processing unit <OTO> and the processing unit <SCC>, the estimated amount of inertial acceleration a, the estimated amount of head angular velocity ω, ωs ^, And the estimator vs ^ of the velocity in the direction of gravity is obtained. The characteristics of the low-pass filter <LP> are also represented by the equation (2) in the figure. The signals output from each of the processing unit <OTO> and the processing unit <SCC> correspond to motion estimation information.

The comparison unit 108 includes the sensory amount as, the sensory amount vs, and the sensory amount ωs output from each of the processing unit OTO and the processing unit SCC, and the estimated amount output from each of the processing unit <OTO> and the processing unit <SCC>. The errors Δa, Δv, and Δω with as ^, the estimator ωs ^, and the estimator vs ^ are calculated, and signals indicating each are output. The output signal from the comparison unit 108 corresponds to sensory contradiction information.

The adaptive processing unit 109 integrates the errors Δa, Δv, and Δω shown in the output signal from the comparison unit 108, respectively, and multiplies the gains Kac, Kωc, and Kvc, respectively, to obtain the error after processing Δa', Δv'. , ΔΩ'is input to the body movement estimation unit 103.

The head acceleration f is input to the motion prediction unit 120. The motion prediction unit 120 executes a process of predicting the motion stimulus at the first time point. The process of predicting motor stimuli will be described later. As an example, the motion prediction unit 120 predicts the inertial acceleration a in the motion stimulus. In this case, the motion prediction unit 120 outputs the predicted inertial acceleration a', which is the predicted inertial acceleration by the processing. Further, the motion prediction unit 120 calculates the reliability of the predicted inertial acceleration a'and outputs a signal d indicating the reliability.

The predicted inertial acceleration a'and the signal d are input to the body motion estimation unit 103. Further, the head angular velocity ω is input to the body movement estimation unit 103. The body movement estimation unit 103 determines the gain Ka based on the signal d. The body movement estimation unit 103 obtains the head angular velocity ω ~ by multiplying the head angular velocity ω by the gain Kω stored in advance. The body motion estimation unit 103 obtains the inertial acceleration a ~ by multiplying the predicted inertial acceleration a'by the determined gain Ka. The head angular velocity ω ~ and the inertial acceleration a ~ correspond to copy information.

The integrated process executed by the body movement estimation unit 103 is, for example, an addition process. Therefore, the body movement estimation unit 103 further includes adders 64, 66, 67. The adder 64 adds the inertial acceleration a ~ and the error Δa'and passes it to the adder 66. The adder 66 inputs to the processing unit <OTO> a signal indicating the addition result of the inertial acceleration a ~ and the error Δa'and the result obtained by adding the error Δv'. The adder 67 inputs the result of adding the head angular velocity ω ~ and the error Δω'to the processing unit <SCC>.

The error Δv output from the comparison unit 108 is input to the sensory contradiction processing unit 125. The error Δv is also called the Subjective Vertical Conflict. The sensory contradiction processing unit 125 executes a process of applying a quadratic Hill function and a quadratic lag transfer function to the error Δv. By this process, MSI is output.

In this example, it is assumed that the mathematical model M uses one sensory organ represented by the vestibular sensory organ. As another example, the mathematical model M may also use other sensory organs. That is, in addition to the vestibular sensory organ, the kinesthetic amount may be calculated for other sensory organs, and the MSI may be calculated using the value corresponding to the error. This is because humans have not only vestibular sensory organs but also multiple types of sensory organs, and it is thought that humans integrate various sensory information in the nervous system to grasp their own physical movements. Other sensory organs are, for example, visual organs. In the visual organ, a signal (visual sensory signal) corresponding to the amount of kinesthetic sensory in the visual organ to the visual stimulus is generated. In this case, the internal model is considered to include an internal model corresponding to other sensory organs such as a visual organ. By doing so, it is considered that MSI is calculated with higher accuracy.

A process of predicting the motion stimulus in the motion prediction unit 120 and calculating the reliability of the predicted value will be described. As an example, the processing in the exercise prediction unit 120 includes analysis of the history of human exercise stimulation. It refers to the motor stimulus given to a person at a previous time point, and is one or more physical exercise values. The plurality of physical exercise values may include the physical exercise values at the first time point.

As an example, the exercise prediction unit 120 stores in advance a prediction algorithm for obtaining a predicted value of exercise stimulus from a history of physical exercise values. Preferably, the prediction algorithm for obtaining the reliability of the predicted value is also stored. The prediction algorithm for obtaining the predicted value and its reliability is, for example, Gaussian process regression.

For example, it is assumed that the history of exercise stimulation as shown in FIG. 5 is input. FIG. 5 shows the time change of the inertial acceleration given to the human body. In the history of FIG. 5, the time change of the inertial acceleration is patterned, and the same change is repeated at a fixed cycle.

In this case, the Gaussian process regression model gives the reliability of FIG. With reference to FIG. 6, the reliability is low in the first cycle of the history of inertial acceleration, but since the second and subsequent cycles are periodic changes in inertial acceleration, the predicted values based on the pattern are obtained. It can be seen that the reliability is high. In this case, the gain for multiplying the predicted value determined by the body movement estimation unit 103 becomes large.

As another example, it is assumed that the history of the motor stimulus shown in FIG. 7 is input. FIG. 7 also shows the time change of the inertial acceleration given to the human body. In the history of FIG. 7, the time change of the inertial acceleration is not patterned although the change of the acceleration occurs periodically.

In this case, the Gaussian process regression model gives the reliability of FIG. With reference to FIG. 8, in this case, it can be seen that the reliability is not constant as a whole and is lower than the reliability of FIG. In this case, the gain for multiplying the predicted value determined by the body movement estimation unit 103 is smaller than that in the case of FIG.

When the MSI for the same exercise stimulus was calculated using the history of the exercise stimuli of FIGS. 5 and 7, the results of FIGS. 9 and 10 were obtained, respectively. That is, since the history of the motor stimulus in FIG. 5 is patterned, the reliability is high as shown in FIG. As a result, the determined gain is large. That is, the degree of input to the internal model of the estimated value is high. In this case, as shown in FIG. 9, the size of MSI is as low as 0.6 or less as a whole, and the inclination with the passage of time becomes gentle.

On the other hand, in the case of the history of the motor stimulus of FIG. 7, the reliability is low as shown in FIG. 8 because it is not patterned. As a result, the determined gain becomes smaller. That is, the degree of input to the internal model of the estimated value is low. In this case, as shown in FIG. 10, the size of the MSI is generally higher than that of the MSI of FIG. 0, and the inclination with the passage of time is also large.

From the calculation results of FIGS. 9 and 10, the exercise prediction unit 120 calculates the predicted value and its reliability, and the body exercise estimation unit 103 uses the predicted value as described above according to the reliability, thereby performing arithmetic processing. It was found that the arithmetic processing of 111 can obtain MSI indicating that the movement given to the body is less sick when it is easy to predict, and it is easy to get sick when it is difficult to predict.

[Second Embodiment]

In the process of calculating the predicted value in the exercise prediction unit 120, environmental information that affects the exercise stimulus at the first time point may be further used. Similarly, in the process of calculating the reliability of the predicted value, environmental information that affects the motor stimulus at the first time point may be used.

The environmental information that affects the motor stimulus at the first time point is, for example, a traffic situation image, a traffic information, a map information, a traveling plan, etc. obtained at the first time point. This information is obtained from a sensor attached to a occupant or a vehicle at the first time point, or is obtained from another device such as a server using a communication device (not shown).

Specifically, in the case of a traffic situation image, the arithmetic unit 1 is further connected to the sensor SE2 for obtaining the traffic situation image, as shown in FIG. The sensor SE2 is, for example, a camera, which is attached to the front of a vehicle such as a occupant's head (for example, a hat) or a vehicle, and photographs the direction in which the occupant's face faces. The sensor SE2 takes an image at a predetermined timing and outputs the image information I2 to the arithmetic unit.

The image information I2 is used in the sensory organ processing (step S1) and the motion prediction processing (step S5) as shown in FIG. In this case, the image information I2 is input to the processing unit 101, the body exercise estimation unit 103, and the exercise prediction unit 120 together with the exercise information I1.

In this case, the motion prediction unit 120 executes an image analysis process on the image represented by the image information I2. Here, as an example, the motion prediction unit 120 may extract the traveling direction. For example, the motion prediction unit 120 identifies the presence / absence and size of a curve by extracting the center line. This makes it possible to predict the motor stimulus given to the occupant's body.

For example, when it is identified that the vehicle is curved to the right, the motion prediction unit 120 determines that the direction of the inertial acceleration given to the occupant's body is to the left. Further, the motion prediction unit 120 determines the magnitude of the inertial acceleration according to the curvature so that the smaller the curvature of the identified curve, the larger the magnitude of the inertial acceleration given to the occupant's body. By doing so, the motion prediction unit 120 can predict the motion stimulus.

In the above description, the body movement value representing the movement stimulus predicted by the movement prediction unit 120 is defined as the inertial acceleration, but it may be replaced with or in addition to the inertial acceleration as another index. Other indicators are, for example, head acceleration and angular velocity. In this case as well, the motor stimulus can be predicted in the same manner as described above. Further, the reliability can be calculated.

<3. Addendum>
The present invention is not limited to the above embodiment, and various modifications are possible.

1: Arithmetic logic unit 3: Output device 11: Processor 12: Storage unit 14: Sensor I / F
64: Adder 66: Adder 67: Adder 101: Processing unit 103: Body movement estimation unit 107: Front yard sensory device model 108: Comparison unit 109: Adaptive processing unit 110: Calculation unit 111: Calculation processing 120: Motion prediction unit 121: Arithmetic program 125: Sensory inconsistency processing unit I1: Motion information I2: Image information I3: Motion sensory information I4: Motion estimation information I5: Motion prediction value I6: Reliability K: Gain Ka: Gain Kac: Gain Kvc: Gain Kω : Gain Kωc: Gain LP: Low pass filter M: Mathematical model OTO: Processing unit SCC: Processing unit SE1: Sensor SE2: Sensor a: Inertial acceleration a': Predicted inertial acceleration as: Sensory amount as ^: Estimated amount d: Signal f : Head acceleration f': Signal g: Gravity acceleration vs: Sensory amount vs ^: Estimated amount Δa: Error Δa': Error Δv: Error Δv': Error Δω': Error ω: Head angular velocity ω': Sensory amount ωs : Sensory amount ωs ^: Estimated amount

Claims (12)

In the calculation method using the calculation model that calculates the estimated value of motion sickness from the input value
Obtain the first physical exercise value representing the exercise stimulus given to the human body at the first time point,
A second body exercise value, which is a predicted value of a body exercise value representing an exercise stimulus given to the person's body at the first time point, was given to the person's body at a time point before the first time point. Predict using the history of physical exercise values that represent exercise stimuli,
A value based on the first physical exercise value and the second physical exercise value is input to the calculation model as the input value, and an estimated value of the motion sickness at the first time point of the person is calculated. Calculation method including that. The calculation method according to claim 1, wherein predicting the second physical exercise value includes analyzing the history of the physical exercise value. The calculation method according to claim 1, further comprising environmental information affecting the exercise stimulus given to the human body at the first time point in predicting the second physical exercise value. The first physical exercise value and the history of the physical exercise value include acceleration, and the history includes acceleration.
The calculation method according to any one of claims 1 to 3, wherein the second body exercise value includes a predicted value of the acceleration. The calculation method according to claim 4, wherein the input value includes a value obtained by using the predicted value of the acceleration in the first body exercise value. The first physical exercise value and the history of the physical exercise value include the angular velocity.
The calculation method according to any one of claims 1 to 3, wherein the second physical exercise value includes a predicted value of the angular velocity. The calculation method according to claim 6, wherein the input value includes a value obtained by using the predicted value of the angular velocity in the first body exercise value. The calculation method according to any one of claims 1 to 7, wherein predicting the second physical exercise value includes using a prediction algorithm for predicting the physical exercise value from the history of the physical exercise value. The arithmetic model includes a value corresponding to the amount of motor sensation in the sensory organ, and a value corresponding to the amount of motor sensation estimated by the internal model of the sensory organ constructed in the central nervous system. The calculation method according to any one of claims 1 to 8, wherein the estimated value of the sway disease is calculated based on the difference between the two. Inputting the input value into the calculation model includes adjusting and inputting the second body exercise value according to the reliability of prediction of the second body exercise value. Claims 1 to 9. The calculation method described in any one of the above items. The tenth aspect of claim 10, wherein adjusting the second body exercise value includes increasing the input degree to the calculation model when the reliability is high and decreasing the input degree when the reliability is low. Calculation method. It is an arithmetic unit that calculates the estimated value of motion sickness from the input value.
An input unit for inputting a first physical exercise value representing an exercise stimulus given to the human body at the first time point,
A calculation unit for calculating the estimated value of the motion sickness of the person is provided.
The arithmetic unit
A second body exercise value, which is a predicted value of a body exercise value representing an exercise stimulus given to the person's body at the first time point, was given to the person's body at a time point before the first time point. Predict using the history of physical exercise values that represent exercise stimuli,
The value based on the first physical exercise value and the second physical exercise value is used as the input value, and is input to the arithmetic model for calculating the estimated value of the motion sickness from the input value, and the person's first body exercise value is calculated. An arithmetic unit that includes calculating the estimated value of the motion sickness at the time point in time.

Images