Disease state determination device, disease state determination method, program for disease state determination device, and disease state determination system

Abstract

Provided are a disease condition determination device, a disease condition determination method, a program for a disease condition determination device, and a disease condition determination system which can determine the condition of epilepsy.　Sight line data which indicate the position of a sight line of a subject T as measured when the subject T drives a vehicle V is acquired (S11), then driving characteristic data which indicate a driving characteristic of the subject T on the vehicle V is acquired (S10), and then the condition of epilepsy in the subject T is determined in accordance with the relationship between the sight line data and the driving characteristic data (S18).

Description

Technical Field

[0001] The present invention relates to the technical field of a disease state determination device, a disease state determination method, a program for a disease state determination device, and a disease state determination system for determining the epileptic state of a subject.

Background Art

[0002] Various determination systems for detecting the state of a driver driving a vehicle have been developed. For example, in Patent Document 1, devices for measuring the driving ability and health information of a vehicle driver and transmitting the driving ability check result and the health information check result to a server are provided at a plurality of points along the vehicle movement route. The server evaluates the driving risk of the vehicle driver based on the driving ability check result, the health information check result, and the evaluation criteria, and diagnoses an increase in the driving risk of the vehicle driver by comparing the results of the driving ability check and the health information check over time. A safe driving support system is disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the prior art such as Patent Document 1, in order to measure the health state, special devices such as an exhaled gas component measuring instrument are required. In combination with the motor ability, it simply determines the driving risk without directly relating to a specific disease. Therefore, it has been difficult to simply determine the epileptic state.

[0005] Therefore, an example of the problem of the present invention is to provide a disease state determination device or the like capable of determining the epileptic state.

Means for Solving the Problems

[0006] In order to solve the above problems, the invention according to claim 1 includes a line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle, a driving characteristic data acquisition means for acquiring driving characteristic data which is at least one of operation amount data of the subject operating the vehicle and behavior amount data of the behavior of the vehicle, and a disease state determination means capable of determining the epileptic state of the subject according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration during which the line of sight indicated by the line-of-sight data is continuously located outside the central part of the subject's field of view in the traveling direction of the vehicle and a first threshold value of the duration during non-epileptic seizure and a second threshold value of the duration during epileptic seizure. Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, and pressure distribution calculation means for calculating the seat pressure area as the magnitude of the seat pressure distribution and the center position of the seat pressure distribution, is provided with , the disease state determination means determines the epilepsy state of the subject according to the change in the seat pressure area and the change in the center position and is characterized by this.

[0007] Claim 5 The invention according to claim includes a line-of-sight data acquisition step in which the line-of-sight data acquisition means acquires line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle, a driving characteristic data acquisition step in which the driving characteristic data acquisition means acquires driving characteristic data which is at least one of operation amount data of the subject operating the vehicle and behavior amount data of the behavior of the vehicle, and a disease state determination step in which the disease state determination means can determine the epileptic state of the subject according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration during which the line of sight indicated by the line-of-sight data is continuously located outside the central part of the subject's field of view in the traveling direction of the vehicle and a first threshold value of the duration during non-epileptic seizure and a second threshold value of the duration during epileptic seizure. A seat pressure distribution acquisition step in which the seat pressure distribution acquisition means acquires data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, and a pressure distribution calculation step in which the pressure distribution calculation means calculates the seat pressure area as the magnitude of the seat pressure distribution and the center position of the seat pressure distribution, includes , the disease state determination means determines the epilepsy state of the subject according to the change in the seat pressure area and the change in the center position and is characterized by this.

[0008] Claim 6The invention described in is characterized in that a computer is caused to function as: a line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of a target person measured when the target person is driving a vehicle; a driving characteristic data acquisition means for acquiring driving characteristic data which is at least one of operation amount data of the target person operating the vehicle and behavior amount data of the behavior of the vehicle; and a disease state determination means capable of determining the epileptic state of the target person according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration for which the line of sight indicated by the line-of-sight data is continuously located outside the central portion of the field of view of the target person in the traveling direction of the vehicle and a first threshold value for the duration during non-epileptic seizure and a second threshold value for the duration during epileptic seizure. , seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, and pressure distribution calculation means for calculating the seat pressure area as the magnitude of the seat pressure distribution and the center position of the seat pressure distribution function as , the disease state determination means determines the epilepsy state of the subject according to the change in the seat pressure area and the change in the center position characterized by

[0009] Claim 7 The invention described in is characterized in that, in a disease state determination system including a terminal device for collecting data regarding a target person driving a vehicle and a disease state determination device for determining the epileptic state of the target person based on the data regarding the target person, the disease state determination device includes: a line-of-sight data acquisition means for acquiring, from the terminal device, line-of-sight data indicating the position of the line of sight of the target person measured when the target person is driving the vehicle; a driving characteristic data acquisition means for acquiring, from the terminal device, driving characteristic data which is at least one of operation amount data of the target person operating the vehicle and behavior amount data of the behavior of the vehicle; and a disease state determination means capable of determining the epileptic state of the target person according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration for which the line of sight indicated by the line-of-sight data is continuously located outside the central portion of the field of view of the target person in the traveling direction of the vehicle and a first threshold value for the duration during non-epileptic seizure and a second threshold value for the duration during epileptic seizure. Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, and pressure distribution calculation means for calculating the seat pressure area as the magnitude of the seat pressure distribution and the center position of the seat pressure distribution, comprising , the disease state determination means determines the epilepsy state of the subject according to the change in the seat pressure area and the change in the center position characterized by

[0010] Claim 8 The invention described in is characterized in that it includes a seat pressure distribution acquisition means for acquiring data of the seat pressure distribution of a seat surface on which a target person sits in a vehicle, and as the magnitude of the seat pressure distribution ofSeated pressure area and the center position of the seat pressure distribution pressure distribution calculation means for calculating, and the above-mentioned Change in seat pressure area and the center position change and According to, disease state determination means capable of determining the epileptic state of the subject, characterized by comprising.

[0011] Claim 9 The invention described in is a seated pressure distribution acquisition step in which the seated pressure distribution acquisition means acquires data on the seated pressure distribution of the seat surface on which the subject sits in the vehicle, and the pressure distribution calculation means calculates the of seated pressure area and the center position of the seat pressure distribution as the magnitude of the seated pressure distribution, and a disease state determination step in which the disease state determination means can determine the epileptic state of the subject according to the change of the above-mentioned Change in seat pressure area and the center position change and characterized by including.

[0012] Claim 10 The invention described in causes a computer to function as seated pressure distribution acquisition means for acquiring data on the seated pressure distribution of the seat surface on which the subject sits in the vehicle, pressure distribution calculation means for calculating the of seated pressure area and the center position of the seat pressure distribution as the magnitude of the seated pressure distribution, and disease state determination means capable of determining the epileptic state of the subject according to the change of the above-mentioned Change in seat pressure area and the center position change and characterized by.

[0013] Claim 11 In the disease state determination system comprising a terminal device for collecting data on the subject driving the vehicle and a disease state determination device for determining the epileptic state of the subject based on the data on the subject, the seated pressure distribution acquisition means for acquiring data on the seated pressure distribution of the seat surface on which the subject sits in the vehicle from the terminal device, the pressure distribution calculation means for calculating the of seated pressure area and the center position of the seat pressure distribution as the magnitude of the seated pressure distribution, and disease state determination means capable of determining the epileptic state of the subject according to the change of the above-mentioned Change in seat pressure area and the center position change and characterized by having.

Advantages of the Invention

[0018] According to the present invention, according to the relationship between the line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle and the driving characteristic data indicating the driving characteristics of the subject's vehicle, by determining the disease state of the subject such as epilepsy, even without special equipment, it is possible to determine the state of epilepsy from easily measurable data such as line-of-sight data and driving characteristic data.

Brief Description of the Drawings

[0019]

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MODE FOR CARRYING OUT THE INVENTION

[0020] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the embodiments described below are embodiments when the present invention is applied to a disease state determination system.

[0021] [1. Configuration and Functional Outline of Disease State Determination System] First, the configuration of the disease state determination system S according to the present embodiment will be described with reference to FIGS. 1 and 2. FIG. 1 is a diagram showing an example of the schematic configuration of the disease state determination system S according to the present embodiment. FIG. 2 is a schematic diagram showing an example of the state of the subject T who operates the vehicle V.

[0022] As shown in FIG. 1, the disease state determination system S includes an information processing server device 10 (an example of a disease state determination device) that determines the disease state of the subject T, such as epilepsy, from various data for each subject T who drives the vehicle V, a mobile terminal device 20 carried by the subject T that transmits the physiological data of the subject T to the information processing server device 10, an in-vehicle terminal device 30 that collects data on the operations and behaviors of the vehicle V driven by the subject T from a plurality of sensors, a home terminal device 40 that collects physiological data and the like when the subject T is at home H, and a medical institution server device 50 of a medical institution used by the subject T.

[0023] Here, examples of the vehicle V include automobiles such as passenger cars, taxis, high-ers, trucks, trailers (including tractors alone), buses, motorcycles (motorcycles with sidecars, trikes, reverse trikes), bicycles, electric carts, and trains such as railway vehicles.

[0024] Examples of the subject T include a person who drives the above vehicle.

[0025] The information processing server device 10, the mobile terminal device 20, the in-vehicle terminal device 30, the home terminal device 40, and the medical institution server device 50 can transmit and receive data to and from each other via the network N, for example, using a communication protocol such as TCP / IP. The network N is constructed by, for example, the Internet.

[0026] The network N is connected to a road information providing server device (not shown) that provides road information on road conditions such as traffic jams and construction work, and a weather server device (not shown) that provides weather data to the information processing server device 10.

[0027] Note that the network N may be constructed by a dedicated communication line, a mobile communication network, and a gateway or the like. Further, the network N may have an access point Ap. The mobile terminal device 20, the in-vehicle terminal device 30, etc. may be connected to the network N via the access point Ap.

[0028] The information processing server device 10 has the functions of a computer. The information processing server device 10 acquires driving characteristic data indicating the driving characteristics of the vehicle V with respect to the subject T measured when each subject T is driving the vehicle V. Examples of the driving characteristic data include operation amount data for each subject T to operate the vehicle V, and behavior amount data for the behavior of each vehicle V. The information processing server device 10 acquires, for example, the operation amount data and the behavior amount data from the in-vehicle terminal device 30.

[0029] In addition, the information processing server device 10 acquires data obtained by sensing the subject T who operates each vehicle V from the mobile terminal device 20 or the in-vehicle terminal device 30. For example, this sensing data is line-of-sight data indicating the position of the line of sight of the subject T measured when the subject T is driving the vehicle V, data on the seat pressure distribution of the seat surface on which the subject T who operates each vehicle V sits, turning data of the arm of the subject T who is operating the vehicle V, and the like.

[0030] The information processing server device 10 acquires weather data from the weather server device. The information processing server device 10 acquires road information from the road information providing server device.

[0031] The mobile terminal device 20 has the functions of a computer. The mobile terminal device 20 is, for example, a smartphone or a tablet terminal. The mobile terminal device 20 collects data from each sensor that senses the target person T. As shown in FIG. 2, inside the vehicle V, the target person T has the mobile terminal device 20 in their pocket, in their bag, or somewhere else inside the vehicle V.

[0032] The in-vehicle terminal device 30 has the functions of a computer. The in-vehicle terminal device 30 is, for example, the navigation device of the vehicle V. As shown in FIG. 2, the in-vehicle terminal device 30 is installed in the vehicle V driven by the target person T. The vehicle V is, for example, a vehicle owned by the target person T themselves, their family, acquaintance, or company, or a rented vehicle.

[0033] As shown in FIG. 2, the target person T operates the vehicle V by means of the steering wheel sw, accelerator pedal (not shown), and brake pedal (not shown) of the vehicle V.

[0034] As shown in FIG. 2, when measuring the rotation data of the rotation of both arms of the target person T, the target person T wears a wristband-type wearable terminal device w1 on each arm. When measuring the gaze data indicating the position of the target person T's line of sight, the target person T wears a glasses-type wearable terminal device w2.

[0035] As shown in FIG. 2, the seat sensor ss measures the seat pressure distribution of the seat surface on which the target person T who operates the vehicle V sits. The seat sensor ss is a sheet-like sensor in which pressure sensor elements are two-dimensionally distributed in order to measure the body pressure distribution. The seat sensor ss measures the position and pressure where it contacts the surface of the seat. Note that the seat sensor ss may be installed on the backrest of the seat.

[0036] The mobile terminal device 20 and the in-vehicle terminal device 30 can communicate with each other via wireless communication. The wearable terminal devices w1 and w2 can communicate with the mobile terminal device 20 and the in-vehicle terminal device 30 via wireless communication. The seat sensor ss has an interface that enables communication with the outside. The seat sensor ss can communicate with the mobile terminal device 20 and the in-vehicle terminal device 30 via wireless communication. The seat sensor ss may be able to communicate with the in-vehicle terminal device 30 by wire.

[0037] The home terminal device 40 has the functions of a computer. The home terminal device 40 is installed in a home H such as the subject T, a workplace, etc. The home terminal device 40 is, for example, a personal computer. The mobile terminal device 20 and the home terminal device 40 can communicate with each other via wireless communication.

[0038] The medical institution server device 50 has the functions of a computer. The medical institution server device 50 is set in, for example, a medical institution such as a hospital, a center that is the core of regional medical care, etc. The medical institution server device 50 has electronic medical record information recording information such as examination results, examination orders, examination results, health checkups, etc. for the subject T.

[0039] Here, the operation amount is the amount of operation that the subject T performs when driving the vehicle V. Examples of the operation amount of the vehicle include the steering angle of the steering wheel of the vehicle V, the accelerator stroke of the accelerator of the vehicle V, the operation amount of the brake pedal, etc. The operation amount may be the steering angular velocity of the steering wheel of the vehicle V calculated from the time derivative of the steering angle data, the steering torque corresponding to the angular acceleration of the steering. The operation amount may be data that can measure the operation performance of the subject T.

[0040] Also, the behavior amount is an amount related to the movement of the vehicle V. Examples of the behavior amount of the vehicle include the sway of the vehicle V, the inter-vehicle distance from the vehicle ahead, the speed of the vehicle, the acceleration of the vehicle, the position within the lane, etc. The acceleration includes the acceleration in the traveling direction of the vehicle V, the lateral acceleration in the lateral direction with respect to the traveling direction, etc. The behavior amount may be data that can measure the behavior state of the vehicle V due to the driving of the subject T.

[0041] The operation amount and the behavior amount are amounts indicating the driving characteristics of the vehicle V with respect to the subject T.

[0042] Further, as an example of data obtained by sensing the subject T who operates the vehicle V, there are line-of-sight data indicating the position of the line of sight of the subject T measured when the subject T is driving the vehicle V, data on the seating pressure distribution of the seating surface on which the subject T who operates the vehicle V sits, turning data of the turning of the arm of the subject T who is operating the vehicle V, and the like.

[0043] Next, as an example of the disease state, with respect to a predetermined disease, there are the degree of the disease such as whether the state of the disease is mild enough to drive or severe enough not to drive. As an example of the disease state, there are disease types such as epilepsy, stroke, and epileptic seizures. Further, as the degree of the disease, in the case of epilepsy, it may be partial seizure or general seizure. In the case of stroke, it may be unilateral paralysis or bilateral paralysis, right-sided paralysis or left-sided paralysis.

[0044] Examples of the disease type include circulatory diseases such as stroke, epileptic seizure, myocardial infarction, hypertension, and arrhythmia, sleep apnea syndrome, dementia, and reduced consciousness level due to diabetes.

[0045] Further, the disease type may include the type of symptoms. Examples of the symptoms include the degree of paralysis, palpitations, shortness of breath, constipation, fever, chills, diarrhea, numbness, pain, and the like. The disease type may include the degree of the disease. For example, when the disease is stroke, there are levels such as having a stroke but no paralysis, mild paralysis, and having paralysis. Further, the degree of the disease may be a level ID different from the disease ID. For example, in the case of epileptic seizures, there is a difference between partial seizures and general seizures.

[0046] Further, as an example of the disease state, with respect to a predetermined disease, there are signs of the disease, the risk of disease onset, and the risk of symptom onset. As an example of the disease state, there may be the degree of the signs of the disease and the value of the onset risk.

[0047] Regarding the determination of the precursors of symptoms, it may be determined using a single indicator or a combination of multiple indicators. For example, palpitations may be determined only by the heart rate, shortness of breath may be determined unambiguously by the respiratory rate (measured by movements of the chest, etc.), and further, blood pressure may be added to determine the "impact due to shortness of breath".

[0048] In addition, the type of disease may include the type of organ or viscera and the type of biological function. As an example of the disease state, it may be the level of the state of each organ or viscera, or the level of the state of each biological function (e.g., digestive function, circulatory function, nervous system function, metabolic function, cognitive function, etc.). These levels may be levels corresponding to specific numerical values obtained from blood tests, etc., taking into account the age, weight, etc. of the subject T.

[0049] As an example of the disease state, it may be the occurrence probability (onset risk) of a predetermined disease. Instead of the probability value, it may be expressed as "disease A is less likely to occur", "disease A is slightly more likely to occur", "disease A is likely to occur", "disease A has manifested", etc.

[0050] As an example of the disease state, it may be multiple diseases, for example, "disease A and disease B are likely to occur", etc. The type of disease may also be a combination of multiple diseases.

[0051] As an example of the disease state, it may be "the onset risk of disease A exceeds the first threshold", "the onset risk of disease B exceeds the first threshold", ···, "the onset risk of disease A exceeds the nth threshold", "the onset risk of disease B exceeds the nth threshold".

[0052] As an example of the disease state, it may be the level of physical condition. For example, regarding the physical condition, it may be "healthy", "poor physical condition", or regarding the physical condition, it may also be leveled as "good, slightly good, slightly abnormal, abnormal", etc. When indicating the level of physical condition, the disease name, etc. may not be specified. Risk and level are examples of quantitative evaluations. In these cases, it is difficult to specify the type of disease, but it may be a preliminary state.

[0053] As the level of the physiological state, for each disease, it may be based on the number exceeding the threshold value or the combination of diseases exceeding the threshold value.

[0054] Also, as the level of the physiological state, the value of predetermined physiological data (or the driving characteristic data of the subject T while the subject T is driving the vehicle V) may be "exceeding the first threshold value", ··· "exceeding the nth threshold value". The level of the physiological state may be based on the combination of a plurality of data.

[0055] Also, not only can each physiological state be grasped individually, but each physiological state may also be handled simultaneously in a vector space (the feature space of the feature vector). The index of each physiological state may be grasped in an n-dimensional vector space and treated like the level of the physiological state in terms of the positional relationship in the vector space.

[0056] [2. Configuration and Functions of the Information Processing Server Device and Each Terminal Device] (2.1 Configuration and Functions of the Information Processing Server Device 10) Next, the configuration and functions of the information processing server device 10 will be described with reference to FIGS. 3 to 21.

[0057] FIG. 3 is a block diagram showing an example of the schematic configuration of the information processing server device 10. FIG. 4 is a diagram showing an example of the data stored in the subject information database. FIG. 5 is a diagram showing an example of the data stored in the operation amount database. FIG. 6 is a diagram showing an example of the data stored in the behavior amount database. FIG. 7 is a diagram showing an example of the data stored in the subject sensing database. FIG. 8 is a diagram showing an example of the data stored in the disease determination database.

[0058] Figures 9A to 11C are graphs showing an example of line-of-sight data and driving characteristic data during driving. Figures 12A to 12C are diagrams showing an example of the degree of deviation between the line-of-sight movement and the driving characteristics and the elapsed time after the deviation. Figures 13A to 13C are graphs showing an example of driving characteristic data. Figures 14A to 16B are graphs showing an example of the time during which the viewpoints are continuously located outside the central portion. Figures 17A and 17B are graphs showing an example of the average time during which the viewpoints are continuously located outside the central portion. Figures 18A and 18B are graphs showing an example of the ratio during which the viewpoints are continuously located outside the central portion. Figure 19 is a schematic diagram showing an example of data on the seat pressure distribution. Figures 20 and 21 are schematic diagrams showing an example of the time series of the seat pressure center data and the magnitude of the seat pressure distribution.

[0059] As shown in FIG. 3, the information processing server device 10 includes a communication unit 11, a storage unit 12, an output unit 13, an input unit 14, an input / output interface unit 15, and a control unit 16. The control unit 16 and the input / output interface unit 15 are electrically connected via a system bus 17. The information processing server device 10 also has a clock function.

[0060] The communication unit 11 is electrically or electromagnetically connected to the network N to control the communication state with the mobile terminal device 20 and the like.

[0061] The storage unit 12 is constituted by, for example, a hard disk drive, a solid state drive, or the like. The storage unit 12 stores data related to each vehicle V, data sensed for each target person T, and the like. The storage unit 12 also stores various programs such as an operating system and a server program, and various files. The various programs may be acquired from another server device or the like via the network N, or may be recorded on a recording medium and read via a drive device.

[0062] In addition, the memory unit 12 constructs a subject information database 12a (hereinafter referred to as the "subject information DB 12a"), an operation amount database 12b (hereinafter referred to as the "operation amount DB 12b"), a behavior amount database 12c (hereinafter referred to as the "behavior amount DB 12c"), a driving environment information database 12d (hereinafter referred to as the "driving environment information DB 12d"), a subject sensing database 12e (hereinafter referred to as the "subject sensing DB 12e"), a disease determination database 12f (hereinafter referred to as the "disease determination DB 12f"), and the like.

[0063] The subject information DB 12a stores information about each subject T. For example, as shown in FIG. 4, the subject information DB 12a stores the name, gender, date of birth of the subject T, the vehicle ID used by the subject T, etc. in association with the subject ID for identifying each subject T.

[0064] The operation amount DB 12b stores various operation amount data of the subject T of each vehicle V operating the vehicle V. For example, as shown in FIG. 5, the operation amount DB 12b stores the measurement time when various operation amounts of the subject T driving the vehicle V are measured, the position information of the vehicle V, operation amount data, etc. in association with the subject ID and the operation amount ID for identifying each operation amount. The operation amount ID is assigned an ID corresponding to each operation amount such as the steering angle of the steering wheel sw of the vehicle V, the accelerator stroke of the accelerator of the vehicle V, and the operation amount of the brake stroke. Instead of the subject ID, the vehicle ID for identifying each vehicle V may also be used. The position information of the vehicle V is latitude and longitude information or link information.

[0065] Here, the accelerator stroke is the movement amount of the accelerator pedal. As the operation amount of the accelerator pedal, the number and frequency of sudden accelerations (the number and frequency of accelerations equal to or greater than a predetermined value) may also be used.

[0066] The brake stroke is the amount of movement of the brake pedal. As the amount of operation of the brake pedal, the number and frequency of hard braking (the number and frequency of deceleration rates equal to or higher than a predetermined value), the time from when braking is required until the brake is depressed, the time from when the accelerator pedal is released until the brake pedal is depressed, etc. may also be used.

[0067] The behavior amount DB12c stores behavior amount data indicating the behavior of each vehicle V driven by the subject T. For example, as shown in FIG. 6, the behavior amount DB12c associates the subject ID with the behavior amount ID for specifying each behavior amount, and stores the measurement time when the behavior amount data of the vehicle V driven by the subject T was measured, the position information of the vehicle V, the behavior amount data, etc. An ID is assigned corresponding to each behavior amount such as the sway of the vehicle V, the inter-vehicle distance from the vehicle ahead, the lateral acceleration of the vehicle V, the speed of the vehicle, and the acceleration in the traveling direction of the vehicle. Instead of the subject ID, a vehicle ID for specifying each vehicle V may be used.

[0068] The driving environment information DB12d stores driving environment information such as map information, the attributes or types of roads such as whether it is a highway or a general road, the degree of curvature of the road, and road information on road conditions such as traffic congestion and construction work.

[0069] The map information may include link information. Here, a link is a line segment of a road connecting nodes such as road intersections and structural change points of the road.

[0070] Examples of the degree of curvature of a road include the curvature of a road curve, the average curvature in a certain section of a road, the proportion or number of roads with a curvature greater than or equal to a predetermined value, etc. As an example of the degree of curvature of a road, it may simply be a road with many curves or a road with few curves. As an example of the degree of curvature of a road, it may be a pattern of the degree of bending of a road. Also, as an example of the degree of curvature of a road, it may be a distinction between roads with a high degree of curvature, such as the Metropolitan Expressway in Tokyo, and roads with relatively many straight sections. As an example of the degree of curvature of a road, it may be a classification of road types such as general roads, highways, the Metropolitan Expressway in Tokyo, and mountain roads. Also, a distinction may be made between a standard highway with relatively few curves and a highway with relatively many curves, such as the Metropolitan Expressway in Tokyo. Also, a distinction may be made between a highway with frequent intersections, such as the Metropolitan Expressway in Tokyo, and a highway without such intersections. Also, a collection of road portions with a curvature within a predetermined range may be used as a road classification.

[0071] In the driving environment information DB12d, the degree of curvature of a road, the type of road, etc. are stored in association with a road classification ID indicating the classification of the road.

[0072] Examples of driving environment information include, in addition to the above, road information such as stop places, one-way roads, two-lane roads, roads with a median strip, etc. Also, examples of driving environment information include whether the road width is narrow or wide, whether it is a road that is always used or a road that is used for the first time, whether there are many or few pedestrians, (even if not to the extent of congestion) whether the vehicle traffic volume is large or small, etc. Also, examples of driving environment information include information such as a road where sunlight is dazzling depending on the time of day, a road where a driver is likely to be nervous, a road where the heart rate is likely to increase, the length of driving time, the probability of an accident occurring at each location, etc. Congestion information may be information on whether there was congestion, time periods such as rush hours, infrastructure information such as road construction or accidents. Note that the information processing server device 10 Latest acquires the road information from the road information providing server device. Also, the information processing server device 10 may store past congestion information.

[0073] Next, the target person sensing DB 12e stores data obtained by sensing each target person T driving each vehicle V with various sensors. For example, as shown in FIG. 7, the target person sensing DB 12e stores the measurement time when the target person T is measured by each sensor, the position information of the vehicle V, the sensing data, etc. in association with the target person ID and the sensor ID for specifying each sensor.

[0074] As an example of the data obtained by sensing the target person T operating the vehicle V, there are line-of-sight data indicating the position of the line of sight of the target person T measured when the target person T is driving the vehicle V, data on the seating pressure distribution of the seating surface on which the target person T operating the vehicle V sits, turning data of the arm of the target person T operating the vehicle V, and the like.

[0075] Here, the target person sensing data may be biological, chemical, or physical data of the target person T that can be measured by sensors or the like.

[0076] For example, as an example of the target person sensing data, there are the body temperature and body temperature distribution of the target person T. As an example of the target person sensing data, there are data related to blood and the circulatory system, such as blood pressure values, heart rate, pulse wave, pulse wave propagation velocity, electrocardiogram, arrhythmia state, blood flow volume, blood glucose level, and the like. As components of blood, there are red blood cell count, white blood cell count, platelet count, pH value, types of electrolytes, amount of electrolytes, types of hormones, amount of hormones, uric acid level, various markers, and the like.

[0077] Also, as an example of the target person sensing data, there are sweating amount, sweating distribution, skin resistance value, components of body odor, amount of digestive fluid such as saliva volume, and components of digestive fluid such as saliva components. As an example of the target person sensing data, there are data related to the brain, such as brain waves and cerebral blood flow distribution. As an example of the target person sensing data, there are data related to respiration, such as respiration rate, respiration volume, and components of exhaled air.

[0078] Examples of subject sensing data include data related to the eyes, such as the number of blinks, the amount of tears, and eye movements (eye position, pupil diameter, etc.). Examples of subject sensing data include electromyogram data of each part of the body. Examples of subject sensing data include data such as facial color and facial expressions.

[0079] Examples of subject sensing data include data related to sleep, such as bedtime, wake-up time, sleep time, sleep pattern, presence or absence of snoring, intensity of snoring, frequency of snoring, duration of snoring, breathing state, number of turns in bed, sleeping posture, and depth of sleep. The quality of sleep may be determined, for example, from electroencephalogram, eye movements, breathing, sleeping posture, etc.

[0080] Examples of subject sensing data include weight, height, etc. Also, examples of subject sensing data may include data obtained by quantifying symptoms such as pain and numbness.

[0081] Also, as an example of the measurement time, in order to obtain a single value of subject sensing data, the time when the measurement starts, the time when the measurement ends, or a time in between these may be mentioned. The measurement time may be any time associated with the measurement of a certain value. For example, when calculating the heart rate every minute, any time within this one minute may be used. Also, when calculating the heart rate from the length of time between R waves in an electrocardiogram, the time of the peak of the R wave, the time of the Q wave or S wave, the time of the peak of the P wave, etc. may be mentioned. Instead of the time between R waves, the time between P waves, Q waves, S waves, T waves, etc. may also be used. Also, not limited to electrocardiograms, the same applies to pulse wave graphs, and it may be the time when common feature points appear or an intermediate value. Also, when measuring blood pressure using Korotkoff sounds, the measurement time may be any time within the measurement period when calculating the maximum blood pressure and the minimum blood pressure.

[0082] The above-mentioned subject sensing data can also be said to be the physiological data of subject T. Note that the data of seat pressure distribution may or may not be included in the physiological data.

[0083] Next, the disease determination DB12f stores data necessary for the determination of a predetermined disease. For example, as shown in FIG. 8, the disease determination DB12f stores data necessary for the determination of a predetermined disease in association with a disease ID indicating the type of the disease and the level of the disease.

[0084] The data necessary for the determination of a predetermined disease is, for example, an operation value, a behavior value, a relational value between subject sensing data and driving characteristic data, a time when the position of the line of sight deviates from the center of the field of view of the subject T in the traveling direction of the vehicle V, and a value of the magnitude of the seat pressure distribution, etc., which are statistically calculated from the data of a plurality of subjects having the same disease ID and the level of the disease.

[0085] The data necessary for the determination of a predetermined disease includes, for example, operation values and the like, and also includes thresholds for these values. The data necessary for the determination of a predetermined disease includes, for example, the frequency range at the time of frequency analysis and includes a predetermined frequency. The data necessary for the determination of a predetermined disease may be, for example, a statistical value of these values.

[0086] Based on operation amount data, behavior amount data, subject sensing data, etc., processes such as spectrum analysis and time series analysis are performed, and statistical amounts are calculated for a plurality of data. Examples of the statistical amounts include representative values such as average values (arithmetic mean, geometric mean, harmonic mean, median, mode, maximum value, minimum value, etc.), variance, standard deviation, skewness, and flatness. Note that a plurality of measurements may be performed on an individual subject to calculate a statistical amount. For the operation amount data, behavior amount data, or subject sensing data classified by road section, a relational value, an operation value, a behavior value, a time when the position of the line of sight deviates from the center of the field of view of the subject T in the traveling direction of the vehicle V, and a value of the magnitude of the seat pressure distribution may be calculated for each road section.

[0087] Here, the operation value is operation-related data in a component of a predetermined frequency range calculated from the operation amount data, and is a value calculated from the operation-related data. The behavior value is a value calculated from the behavior data.

[0088] The operation-related data is data related to the operation amount data calculated from the operation amount data. For example, as an example of the operation-related data, data obtained by performing discrete Fourier transform on the operation amount data, power spectral density, time-differentiated data obtained by differentiating the operation amount data with respect to time, time-integrated data obtained by integrating the operation amount data with respect to time, and the like can be mentioned. As an example of the operation-related data in the components within a predetermined frequency range, data obtained by extracting frequency components within a predetermined frequency range from a spectrum or a power spectrum, and the like can be mentioned. The predetermined frequency range may be one frequency or all frequencies within a range determined by sampling of the data. The operation-related data in the components within a predetermined frequency range may be data obtained by applying a filter such as a low-pass filter, a high-pass filter, or a band-pass filter to the raw operation amount data. For example, data obtained by cutting noise from the raw operation amount data, data obtained by emphasizing a predetermined frequency component, and the like can be mentioned. The operation-related data in the components within a predetermined frequency range may be data obtained by performing Fourier transform on the operation amount data or the power spectrum itself. Note that the behavior-related data is data related to the behavior amount data calculated from the behavior amount data. Also, the subject sensing-related data is data related to the subject sensing data calculated from the subject sensing data. The above regarding the operation-related data is the same for the behavior-related data and the subject sensing-related data.

[0089] The value of the relationship between the subject sensing data and the driving characteristic data may be, for example, the degree of deviation between the line-of-sight data and the operation amount data, the degree of deviation between the line-of-sight data and the behavior amount, or the value of the relationship may be the correlation coefficient between the subject sensing data and the driving characteristic data.

[0090] The time when the position of the line of sight deviates from the center of the field of view of the subject T in the traveling direction of the vehicle V is the duration during which the viewpoint is continuously (for example, 50 ms, 100 ms or more) located outside the center, the average time thereof, and the like.

[0091] The magnitude value of the seat pressure distribution is the seat pressure area, ratio, etc. where the seat pressure is equal to or higher than a predetermined value.

[0092] The data in the disease determination DB 12f may be data when the subject T drives on a driving simulator or data when driving on an actual road. For example, the course of a standard highway driving simulator has a total course length of 15.2 km, an average radius of curvature of 1640 m, and a height difference of 0.0 m. The course of the driving simulator on the Tokyo Metropolitan Expressway has a total course length of 13.2 km, an average radius of curvature of 257 m, and a height difference of 17.5 m.

[0093] Here, FIGS. 9 to 22 are used to explain the data in the disease determination DB 12f when the operation amount data, behavior amount data, and subject sensing are measured for a healthy person and a patient with epilepsy, where the disease type is epilepsy.

[0094] Measurement examples when driving on a predetermined course such as the Tokyo Metropolitan Expressway where the road section is shown in FIGS. 9A to 21.

[0095] FIGS. 9A to 9C are graphs showing an example of gaze data and steering angle data when driving on a predetermined course where the road section is the Tokyo Metropolitan Expressway using a driving simulator. FIG. 9A shows data of a healthy subject. FIG. 9B shows data of a subject with epilepsy during a non-seizure period. FIG. 9C shows data of a subject with epilepsy when a seizure occurs.

[0096] The gaze data is shown by a solid line in the figure. The steering angle data is shown by a broken line in the figure. The horizontal axis represents time. In the case of gaze data, the vertical axis (X coordinate of the gaze point: X - AXIS OF GAZE POINT) is the value of the position of the gaze in the lateral direction with respect to the traveling direction of the vehicle V. In the figure, the upward direction of the vertical axis indicates the right direction of the gaze, and the downward direction of the vertical axis in the figure indicates the left direction of the gaze. In the case of steering angle data, the vertical axis (steering wheel angle: STEERING WHEEL ANGLE) is the angle of the steering wheel angle. In the figure, the upward direction of the vertical axis is plus in the clockwise direction, and the downward direction of the vertical axis in the figure is minus in the counterclockwise direction.

[0097] As shown in FIGS. 9A and 9B, from the measurement results, it was found that both healthy subjects and subjects with epilepsy had similar movements in the position of the line of sight and the steering angle with respect to time. The relatively slow (high-frequency components of the line-of-sight data removed) lateral movement of the line of sight almost coincides with the movement of the steering angle. It was found that there is a high correlation between the line-of-sight data indicating the lateral line of sight and the steering angle data. Note that the spiky movement of the line of sight is a temporary movement of the line of sight due to checking the rearview mirror or the like.

[0098] However, as shown in FIG. 9C, when a seizure starts at time t0, in the seizure period, it was observed that the change in the steering angle weakens and the line of sight rapidly deviates greatly from the center of the visual field. Note that the seizure was identified by measuring the electroencephalogram.

[0099] Data necessary for epilepsy determination such as the relationship between the line-of-sight data and the steering angle data is stored in the disease determination DB12f in association with the index ID of the index indicating the relationship between the line-of-sight data and the steering angle data, the sensor ID corresponding to the line-of-sight data, the operation amount ID of the steering angle, the road section ID indicating the road section, and the disease ID indicating epilepsy. Note that the threshold value of the relationship between the line-of-sight data and the steering angle data may be set from the average value during non-seizure or seizure and stored in the disease determination DB12f.

[0100] FIGS. 10A to 10C are graphs showing an example of line-of-sight data and steering torque data when driving a predetermined course on the Metropolitan Expressway in Tokyo, where the road section is the Metropolitan Expressway in Tokyo, using a driving simulator. FIG. 10A shows data of a healthy subject. FIG. 10B shows data of a subject with epilepsy during non-seizure. FIG. 10C shows data of a subject with epilepsy when a seizure occurs.

[0101] The line-of-sight data is shown by a solid line in the figure, similar to FIG. 9. The steering torque data is shown by a dashed line in the figure. The horizontal axis represents time. In the case of the steering torque data, the vertical axis (steering torque: STEERING WHEEL TORQUE) represents the value of the steering torque, with the upward direction of the vertical axis in the figure being positive in the clockwise direction and the downward direction of the vertical axis in the figure being negative in the counterclockwise direction.

[0102] As shown in FIGS. 10A and 10B, from the measurement results, it was found that for both healthy subjects and subjects with epilepsy, the position of the line of sight and the steering torque with respect to time move in a similar manner. The relatively slow (high-frequency components of the line-of-sight data removed) horizontal movement of the line of sight almost coincides with the movement of the steering torque. It was found that there is a high correlation between the line-of-sight data indicating the horizontal line of sight and the steering torque data.

[0103] However, as shown in FIG. 10C, when a seizure starts at time t0, it was observed that in the seizure occurrence interval, the change in the steering torque weakens, and the line of sight rapidly deviates greatly from the center of the visual field.

[0104] Data necessary for the determination of epilepsy, such as the relationship between the line-of-sight data and the steering torque data, is stored in the disease determination DB12f in association with the index ID of the index indicating the relationship between the line-of-sight data and the steering torque data, the sensor ID corresponding to the line-of-sight data, the operation amount ID of the steering torque, the road section ID indicating the road section, and the disease ID indicating epilepsy. Note that the threshold value of the relationship between the line-of-sight data and the steering torque data may be set from the average values during non-seizure and seizure times and stored in the disease determination DB12f.

[0105] FIGS. 11A to 11C are graphs showing an example of the line-of-sight data and the vehicle lateral acceleration data when driving a predetermined course where the road section is the Metropolitan Expressway in Tokyo on a driving simulator. FIG. 11A shows the data of a healthy subject. FIG. 11B shows the data of a subject with epilepsy during non-seizure. FIG. 11C shows the data of a subject with epilepsy when a seizure occurs.

[0106] The line-of-sight data is shown by a solid line in the figure, similar to FIG. 9. The vehicle lateral acceleration data is shown by a dashed line in the figure. The horizontal axis represents time. In the case of the vehicle lateral acceleration data, the vertical axis (vehicle lateral acceleration: VEHICLE LATERAL ACCELERATION) represents the acceleration in the lateral direction of vehicle V. The upward direction of the vertical axis in the figure is the right direction with respect to the traveling direction of vehicle V, and the downward direction of the vertical axis in the figure is the left direction with respect to the traveling direction of vehicle V.

[0107] As shown in FIGS. 11A and 11B, from the measurement results, it was found that both in healthy subjects and subjects with epilepsy, the position of the line of sight and the vehicle lateral acceleration with respect to time showed similar movements. The relatively slow (high-frequency components of the line-of-sight data removed) lateral movement of the line of sight almost coincides with the movement of the vehicle lateral acceleration. It was found that there is a high correlation between the line-of-sight data indicating the lateral line of sight and the vehicle lateral acceleration data.

[0108] However, as shown in FIG. 11C, when a seizure starts at time t0, it was observed that in the seizure occurrence interval, the vehicle lateral acceleration becomes almost zero, and the line of sight rapidly deviates greatly from the center of the visual field.

[0109] Data necessary for epilepsy determination such as the relationship between the line-of-sight data and the vehicle lateral acceleration data is stored in the disease determination DB12f in association with the index ID of the index indicating the relationship between the line-of-sight data and the vehicle lateral acceleration data, the sensor ID corresponding to the line-of-sight data, the behavior amount ID of the vehicle lateral acceleration, the road section ID indicating the road section, and the disease ID indicating epilepsy. Note that the threshold value of the relationship between the line-of-sight data and the vehicle lateral acceleration data may be set from the average values during non-seizure and seizure times and stored in the disease determination DB12f.

[0110] Next, as shown in FIG. 12A, regarding the graph of the elapsed time after the degree of deviation (an example of the value of the relationship) obtained by indexing the relationship between the line of sight and the steering angle, a comparison was made among the cases of healthy subjects, the cases before the onset of seizures in subjects with epilepsy, and the cases during seizure occurrence.

[0111] Here, the vertical axis is the elapsed time, and the horizontal axis is the degree of dissociation. The degree of dissociation between the line of sight and the steering angle is, for example, the difference value between the two values calculated every 0.5 seconds after converting both the position of the line of sight and the steering angle so that the maximum value becomes 100. The elapsed time is, for example, the time elapsed since the degree of dissociation exceeded D0th. In the figure, white circles indicate the degree of dissociation before the onset of the seizure (when not having a seizure), black circles indicate the degree of dissociation at the time of seizure onset, and crosses indicate the degree of dissociation in the case of healthy individuals.

[0112] As shown in FIG. 12A, even when the elapsed time value is short during non-seizure periods, the degree of dissociation may be high. However, by looking at both the degree of dissociation and the elapsed time since the dissociation, it was found that the characteristics during a seizure can be captured and the non-seizure period and the seizure period can be clearly distinguished. As shown in FIG. 12A, for example, when the degree of dissociation is equal to or higher than the threshold value Dth and the elapsed time is equal to or higher than the threshold value Tth, the seizure period and the non-seizure period can be distinguished.

[0113] Data necessary for the determination of epilepsy, such as the degree of dissociation between the line-of-sight data and the steering angle data and the elapsed time of this degree of dissociation, are associated with the index ID of the index indicating the degree of dissociation between the line-of-sight data and the steering angle data and its elapsed time, the sensor ID corresponding to the line-of-sight data, the operation amount ID of the steering angle, the road section ID indicating the road section, and the disease ID indicating epilepsy, and stored in the disease determination DB12f as reference values. Note that the threshold value of the degree of dissociation between the line-of-sight data and the steering angle data and the threshold value of the elapsed time of this degree of dissociation may be set from the average values during non-seizure and seizure periods and stored in the disease determination DB12f.

[0114] Next, as shown in FIG. 12B, regarding the graph of the elapsed time since the degree of dissociation, which is an index of the relationship between the line of sight and the steering torque, a comparison was made among the cases of healthy individuals, the cases before the onset of a seizure in subjects having epilepsy, and the cases at the time of seizure onset.

[0115] Here, the degree of deviation between the line of sight and the steering torque is, for example, the difference value between the two values calculated every 0.5 seconds after converting both the position of the line of sight and the steering torque so that their maximum values become 100. The elapsed time is, for example, the time elapsed since the degree of deviation exceeded D0th.

[0116] As shown in FIG. 12B, similar to the case of the steering angle, even when not having an attack, if the value of the elapsed time is short, the degree of deviation may be high. However, by looking at both the degree of deviation and the elapsed time since the deviation, it was found that the characteristics during an attack can be captured and the non-attack time and the attack time can be clearly distinguished. As shown in FIG. 12B, for example, when the degree of deviation is equal to or greater than the threshold value Dth and the elapsed time is equal to or greater than the threshold value Tth, the attack time and the non-attack time can be distinguished.

[0117] The degree of deviation between the line-of-sight data and the steering-torque data, and data necessary for the determination of epilepsy such as the elapsed time of this degree of deviation, are associated with the index ID of the index indicating the degree of deviation between the line-of-sight data and the steering-torque data and its elapsed time, the sensor ID corresponding to the line-of-sight data, the operation amount ID of the steering torque, the road section ID indicating the road section, and the disease ID indicating epilepsy, and are stored in the disease determination DB12f as reference values. Note that the threshold value of the degree of deviation between the line-of-sight data and the steering-torque data and the threshold value of the elapsed time of this degree of deviation may be set from the average values during non-attack and attack times and stored in the disease determination DB12f.

[0118] Next, as shown in FIG. 12C, regarding the graph of the elapsed time since the degree of deviation between the line of sight and the lateral acceleration of the vehicle, which is indexed, a comparison was made among the cases of healthy subjects, the cases before the onset of an attack of subjects having the disease of epilepsy, and the cases during the occurrence of an attack.

[0119] Here, the degree of deviation between the line of sight and the lateral acceleration of the vehicle is, for example, the difference value between the two values calculated every 0.5 seconds after converting both the position of the line of sight and the lateral acceleration so that their maximum values become 100. The elapsed time is, for example, the time elapsed since the degree of deviation exceeded D0th.

[0120] As shown in FIG. 12C, similar to the case of the steering angle and the steering torque, if the elapsed time value is short even during the non-seizure period, the degree of deviation may be high. However, when considering both the degree of deviation and the elapsed time since the deviation, it was found that the characteristics during the seizure can be captured and the non-seizure period and the seizure period can be clearly distinguished. As shown in FIG. 12C, for example, when the degree of deviation is equal to or greater than the threshold value Dth and the elapsed time is equal to or greater than the threshold value Tth, the seizure period and the non-seizure period can be distinguished.

[0121] The degree of deviation between the line-of-sight data and the vehicle lateral acceleration data, and data necessary for the determination of epilepsy such as the elapsed time of this degree of deviation, are associated with the index ID of the index indicating the degree of deviation between the line-of-sight data and the vehicle lateral acceleration data and its elapsed time, the sensor ID corresponding to the line-of-sight data, the behavior amount ID of the vehicle lateral acceleration, the road section ID indicating the road section, and the disease ID indicating epilepsy as reference values, and stored in the disease determination DB12f. Note that the threshold value of the degree of deviation between the line-of-sight data and the vehicle lateral acceleration data and the threshold value of the elapsed time of this degree of deviation may be set from the average values during the non-seizure period and the seizure period and stored in the disease determination DB12f.

[0122] As described above, regarding the degree of deviation between the driving characteristics of the subject T and the line of sight, it can be said that the seizure period and the non-seizure period can be distinguished when the degree of deviation is equal to or greater than a predetermined degree of deviation and the elapsed time is equal to or greater than a predetermined elapsed time.

[0123] FIGS. 13A to 13C are graphs showing an example of driving characteristic-related data obtained by frequency-analyzing driving characteristic data. The horizontal axis represents frequency, and the vertical axis represents the power spectral density (PSD) on a logarithmic scale. Here, in the figure, symbol (a) represents data from the start of the vehicle's departure to before the start of the subject's seizure, symbol (b) represents data in the section where the seizure occurred, symbol (c) represents data during the non-seizure period of the same subject, and symbol (d) represents data of a healthy person.

[0124] FIG. 13A is a graph showing an example of operation-related data obtained by Fourier-transforming steering angle data. As shown in FIG. 13A, for both the subject with epilepsy and the healthy subject, the power spectral density during non-seizure was almost the same (symbols (a), (c), (d)). However, as shown by symbol (b), during a seizure, the power spectral density decreased across the entire frequency range.

[0125] FIG. 13B is a graph showing an example of operation-related data obtained by Fourier-transforming steering torque data. As shown in FIG. 13B, for both the subject with epilepsy and the healthy subject, the power spectral density during non-seizure was almost the same (symbols (a), (c), (d)). However, as shown by symbol (b), during a seizure, the power spectral density decreased across the entire frequency range.

[0126] FIG. 13C is a graph showing an example of behavior-related data obtained by Fourier-transforming vehicle lateral acceleration data. As shown in FIG. 13C, for both the subject with epilepsy and the healthy subject, the power spectral density during non-seizure was almost the same (symbols (a), (c), (d)). However, as shown by symbol (b), during a seizure, the power spectral density decreased across the entire frequency range.

[0127] Therefore, for example, as shown in FIG. 13A, the power spectral density p0 (an example of an operation value) for frequency f0, the power spectral density p1 for frequency f1, etc. may be set as determination thresholds. Also, as shown in FIG. 13B, the power spectral density p2 (an example of an operation value) for frequency f2, etc. may be set as determination thresholds. As shown in FIG. 13C, the power spectral density p3 (an example of a behavior value) for frequency f3, etc. may be set as determination thresholds.

[0128] Alternatively, instead of a specific frequency, a frequency range may be set in advance. Also, the difference between power spectral densities may be used. Based on the non-seizure state or the case of a healthy person, the square root of the integrated value of the difference in power spectral density in a specific frequency range may be used. As long as the disease state can be differentiated, instead of calculating the square root, the integrated value or total of the power spectral density may be used.

[0129] Data necessary for epilepsy determination, such as these operation values or behavior values, frequency values or frequency ranges, are stored in the disease determination DB12f in association with an operation amount ID or behavior amount ID, a road section ID indicating the road section, and a disease ID indicating epilepsy, as reference operation values or behavior values. Note that the threshold value of the power spectral density for a specific frequency may be set from the average value during non-seizure or seizure, and may be stored in the disease determination DB12f.

[0130] Next, FIGS. 14A to 18B are graphs showing an example of the time when the viewpoint is continuously located outside the central part of the visual field. Here, the visual field is, for example, the visual field of the subject T in the traveling direction of the vehicle V. When the subject T sits on the seat of the vehicle V, the central part of the visual field indicates the range of the central part centered on the front of the vehicle V.

[0131] Figures 14A to 14D show the case where the range of the central part is set narrow. Figures 15A to 15D show the case where the range of the central part is set wide. In Figures 14A to 15D, the vertical axis (Duration) represents the duration during which the viewpoint is continuously (e.g., 50 ms, 100 ms or more) outside the central part. The horizontal axis (Number of Times of Gazing Outside Forward View) is the count of the cases where the viewpoint is continuously outside the central part arranged in order. That is, the horizontal axis is the total number of times the viewpoint has deviated from the set central range. The scale on this horizontal axis is indicated by "few" and "many". Figures 14A and 15A show the data from the start of the vehicle to before the onset of the seizure of the subject, Figures 14B and 15B show the data during the period when the seizure occurred, Figures 14C and 15C show the data of the same subject when not having a seizure, and Figures 14D and 15D show the data of a healthy person.

[0132] Here, the shape of the central part of the subject's visual field includes a circle, an ellipse, a square, a rectangle, etc. The size of the central part of the subject's visual field is the diameter of a circle, the major axis and minor axis of an ellipse, the length of one side or the diagonal of a square, the length and width of a rectangle, the diagonal length of a rectangle, etc.

[0133] As shown in Figures 14A to 14D and Figures 15A to 15D, it can be seen that after a seizure occurs, the viewpoint may continuously be located outside the central part for a long time. Here, when the range of the central part is set small, the probability of detecting that "the viewpoint is continuously outside the central part" (the detection sensitivity of epilepsy seizures) increases, but if it is set too small, there is a possibility of false detection as an epilepsy seizure. On the other hand, when the range of the central part is set wide, the possibility of false detection is low, but it is considered that the detection sensitivity decreases. Therefore, it is better not to limit the setting of the central part, and epilepsy may be detected by changing between the case of setting it small and the case of setting it large, or by their combination.

[0134] Figures 16A and 16B are diagrams plotting the respective durations in the cases of starting a vehicle, the onset of a seizure (BEFORE SEIZURE) in a subject, the period during which a seizure occurs (SEIZURE PERIOD), when the same subject is not having a seizure (DRIVING WITHOUT SEIZURE), and a healthy subject (HEALTHY SUBJECT). Figure 16A shows the case where the range of the central part of the visual field is set narrow. Figure 16B shows the case where the range of the central part of the visual field is set wide.

[0135] As shown in Figure 16A, when the range of the central part of the visual field is narrowed, during the duration when the viewpoint is continuously located outside the central part when not having a seizure, it does not exceed Tth1, but during a seizure, it exceeds Tth2. As shown in Figure 16B, when the range of the central part of the visual field is widened, the time when the viewpoint is continuously located outside the central part when not having a seizure does not exceed Tth1, but during a seizure, it exceeds Tth2. Thus, as shown in Figures 16A and 16B, with the duration thresholds Tth1 and Tth2, the onset of epilepsy can be distinguished.

[0136] Data necessary for the determination of epilepsy such as these durations are stored in the disease determination DB12f in association with the sensor ID corresponding to the wearable terminal device w2, the road section ID, and the disease ID indicating epilepsy as the reference subject sensing values. Note that the duration thresholds Tth1, Tth2, etc. are set from the average values during non-seizure and seizure times and may also be stored in the disease determination DB12f.

[0137] Figures 17A and 17B are diagrams showing the average time (AVERAGE DURATION) during which the viewpoint is continuously (for example, 50 ms, 100 ms or more) located outside the central part in the cases of starting a vehicle, the onset of a seizure in a subject, the period during which a seizure occurs, when the same subject is not having a seizure, and a healthy subject. Figure 17A shows the case where the range of the central part is set narrow. Figure 17B shows the case where the range of the central part is set wide.

[0138] As shown in FIGS. 17A and 17B, it was found that the average time during which the viewpoints were continuously located outside the center was long during seizures. For example, as shown in FIGS. 17A and 17B, it is possible to distinguish the onset of epilepsy from the average time threshold Tth.

[0139] Data necessary for epilepsy determination such as these average times are stored in the disease determination DB12f in association with the sensor ID corresponding to the wearable terminal device w2, the road section ID, and the disease ID indicating epilepsy, as the reference subject sensing values. Note that the average time threshold Tth and the like may be set from average values during non-seizure or seizure times and stored in the disease determination DB12f.

[0140] FIGS. 18A and 18B are diagrams showing the ratio (RATE OF DURATION) in which the viewpoints are continuously located outside the center part (for example, 50 ms, 100 ms or more) from the start of the vehicle to the start of the seizure of the subject, the section in which the seizure occurs, the non-seizure time of the same subject, and the case of a healthy person. FIG. 18A shows the case where the range of the center part is set narrow. FIG. 18B shows the case where the range of the center part is set wide.

[0141] Here, the ratio is calculated, for example, from the number of frames outside the center part / the total number of frames in the video frames of the video of the camera that captures the eyes of the subject T. Alternatively, the ratio may be calculated from the time during which the viewpoint is located outside the center part / the total time of the section.

[0142] As shown in FIGS. 18A and 18B, during non-seizure, the ratio of the time during which the viewpoints are continuously located outside the center part does not exceed R1, but during the seizure section, the ratio of the time during which the viewpoints are continuously located outside the center part becomes about R2. For example, as shown in FIGS. 18A and 18B, it is possible to distinguish the onset of epilepsy from the thresholds R1 and R2.

[0143] Data necessary for the determination of epilepsy such as these ratios is stored in the disease determination DB12f in association with the sensor ID corresponding to the wearable terminal device w2, the road section ID, and the disease ID indicating epilepsy as the reference subject sensing value. Note that the ratio threshold values R1, R2, etc. may be set from the average values during non-seizure and seizure times and stored in the disease determination DB12f.

[0144] Next, FIG. 19 shows an example of data on the seat pressure distribution measured by the seat sensor ss2.

[0145] The skewness (an example of the subject sensing value) calculated from the data on the seat pressure distribution as shown in FIG. 19 is averaged over multiple measurements. The multiple measurements may be multiple measurements for the same person or measurements for multiple people with the same attributes. Data necessary for the determination of a predetermined disease such as the average skewness is stored in the disease determination DB12f in association with the sensor ID corresponding to the seat sensor ss, the road section ID, and the disease ID as the reference subject sensing value.

[0146] Here, the skewness is calculated from the center position of the seat pressure distribution. The center position is, for example, the center of position COP (Center of Position) of the seat pressure distribution calculated from the seat pressure distribution. The center of position of the seat pressure is, for example, the center of gravity (Gx, Gy) of the seat pressure distribution. The center of gravity of the seat pressure distribution is the position obtained by weighted averaging the positions of each point of the heat map of the seat pressure with the value of the heat map at that position as the weight. In this case, the skewness is the value Gx of the position of the center of gravity on the x-axis starting from the origin (0, 0) of the seat sensor ss. Note that the center of position of the seat pressure may be any value calculated from the entire points of the heat map of the seat pressure distribution.

[0147] The central position may be, for example, the shape center position calculated from the distribution shape of the seat pressure distribution. The shape center position is, for example, the COB (Center of Body). In this case, the degree of deviation is the value of the position of the COB on the x-axis starting from the origin (0,0) of the seat sensor ss which is an example of the left-right position center of the seat of the vehicle V. Note that the COB is determined from the constricted part of the distribution shape. Also, the front side, rear side, left side, and right side in the figure represent the orientation of the subject T when the subject T is sitting on the seat.

[0148] Also, the degree of deviation may be the difference between the shape center position and the seat pressure center position. For example, the degree of deviation is Gx - COB, or COB - Gx.

[0149] Figures 20 and 21 are schematic diagrams showing an example of the time series of the seat pressure center data and the magnitude of the seat pressure distribution. In the figure, graph (a) is the x-coordinate position of the COP (COP COORDINATE), graph (b) is the y-coordinate position of the COP (COP COORDINATE), and graph (c) is an example of the magnitude of the seat pressure distribution is It is the seat pressure area (AREA OF SEAT PRESSURE). Here, the seat pressure area is indicated, for example, by the ratio of the portion where pressure is applied in the entire seat surface. In FIG. 19, the seat pressure area is calculated as the ratio where the seat pressure is equal to or higher than a predetermined value. FIG. 20 shows an example of data of a healthy subject. FIG. 21 shows an example of data of a subject having epilepsy when epilepsy occurs at the seizure time t0.

[0150] As shown in FIG. 20, in healthy subjects, even when the COP changes, there was not much change in the seat pressure area. However, as shown in FIG. 21, at the epilepsy seizure time t0, the COP changed greatly, and the seat pressure area narrowed, that is, it was below the threshold value Sth of the seat pressure area has become .

[0151] For the seat pressure distribution data as well, the seat pressure area is averaged over multiple measurements. Data necessary for determining epilepsy diseases, such as the threshold of the average seat pressure area, is stored in the disease determination DB12f in association with the sensor ID corresponding to the seat sensor ss, each road section ID, and each disease ID, as the reference subject sensing value. Note that the threshold Sth of the seat pressure area, etc., may be set from the average values during non-seizure or seizure times and stored in the disease determination DB12f.

[0152] The subject information DB12a, the operation amount DB12b, the behavior amount DB12c, the driving environment information DB12d, the subject sensing DB12e, and the disease determination DB12f may be within the information processing server device 10, or in another server connected to the information processing server device 10 via a network, or may be distributed and exist in the network N. These may be separate databases or may be within the same database.

[0153] When the output unit 13 outputs video, for example, it has a liquid crystal display element or an EL (Electro Luminescence) element, etc. When the output unit 13 outputs sound, it has a speaker.

[0154] The input unit 14 has, for example, a keyboard and a mouse, etc.

[0155] The input / output interface unit 15 is configured to perform interface processing between the communication unit 11, the storage unit 12, etc. and the control unit 16.

[0156] The control unit 16 has a CPU (Central Processing Unit) 16a, a ROM (Read Only Memory) 16b, a RAM (Random Access Memory) 16c, etc. And the control unit 16 determines the disease state of each subject T by the CPU 16a reading and executing the codes of various programs stored in the ROM 16b and the storage unit 12.

[0157] (2.2 Configuration and Functions of the Portable Terminal Device 20) Next, the configuration and functions of the mobile terminal device 20 will be described with reference to FIG. 22.

[0158] FIG. 22 is a block diagram showing an example of the schematic configuration of the mobile terminal device 20.

[0159] As shown in FIG. 22, the mobile terminal device 20 includes an output unit 21, a storage unit 22, a communication unit 23, an input unit 24, a sensor unit 25, an input / output interface unit 26, and a control unit 27. The control unit 27 and the input / output interface unit 26 are electrically connected via a system bus 28. Each mobile terminal device 20 is assigned a mobile terminal ID. The mobile terminal device 20 has a clock function. The mobile terminal device 20 may have a vibration function that vibrates the mobile terminal device 20.

[0160] The output unit 21 has, for example, a liquid crystal display element or an EL element as a display function. The output unit 32 has a speaker for outputting sound.

[0161] The storage unit 22 is composed of, for example, a hard disk drive, a solid state drive, etc. The storage unit 22 stores various programs such as an operating system and applications for the mobile terminal device 20. Note that the various programs may be acquired from another server device or the like via the network N, or may be recorded on a recording medium and read via a drive device. Further, the storage unit 22 may have information of a database such as the storage unit 12 of the information processing server device 10.

[0162] The communication unit 23 is electrically or electromagnetically connected to the network N to control the communication state with the information processing server device 10 and the like. Further, the communication unit 23 is electrically or electromagnetically connected to the information processing server device 10 to control the communication state with the information processing server device 10.

[0163] The communication unit 23 has a function of wireless communication for communicating with a terminal device by radio waves or infrared rays. The mobile terminal device 20 communicates with the in-vehicle terminal device 30 and the home terminal device 40 via the communication unit 23. Also, as shown in FIG. 2, the mobile terminal device 20 carried by the subject T communicates with the seat sensor ss installed in the seat on which the subject T is sitting, and the wearable terminal devices w1 and w2 worn by the subject T via the communication unit 23. Note that the mobile terminal device 20 may communicate with the in-vehicle terminal device 30, the home terminal device 40, the seat sensor ss, and the wearable terminal devices w1 and w2 by wire.

[0164] The communication unit 23 may communicate with an IC tag as a reader of the IC tag.

[0165] The input unit 24 has a touch switch type display panel such as a touch panel, for example. The input unit 24 acquires the position information of the output unit 21 where the finger of the user touches or approaches. The input unit 24 has a microphone for inputting voice.

[0166] The sensor unit 25 has various sensors such as a GPS (Global Positioning System) sensor, an azimuth sensor, an acceleration sensor, a gyro sensor, a pressure sensor, a temperature sensor, and a humidity sensor. The sensor unit 25 has imaging elements such as a CCD (Charge Coupled Device) image sensor and a CMOS (Complementary Metal Oxide Semiconductor) image sensor of a digital camera. The mobile terminal device 20 acquires the current position information of the mobile terminal device 20 by the GPS sensor. Note that a unique sensor ID is assigned to each sensor.

[0167] The input / output interface unit 26 performs interface processing between the output unit 21, the storage unit 22, etc. and the control unit 27.

[0168] The control unit 27 is composed of a CPU 27a, a ROM 27b, a RAM 27c, etc. Then, in the control unit 27, the CPU 27a reads and executes various programs stored in the ROM 27b and the storage unit 22.

[0169] Here, the wearable terminal device w1 is a wristband-type wearable computer. The wearable terminal device w1 includes an output unit, a storage unit, a communication unit, an input unit, a sensor unit, an input / output interface unit, a control unit, and a timer unit (not shown).

[0170] The sensor unit of the wearable terminal device w1 measures various physiological data of the subject T.

[0171] The sensor unit includes an acceleration sensor, a gyro sensor, a temperature sensor, a pressure sensor, an ultrasonic sensor, an optical sensor, an electrical sensor, a magnetic sensor, an image sensor, etc. Note that a unique sensor ID is assigned to each sensor.

[0172] The acceleration sensor measures the acceleration of the wearable terminal device w1. From the measurement data of the acceleration sensor, the movement of the subject T's arm is measured. The gyro sensor measures the angular acceleration of the wearable terminal device w1. From the measurement data of the gyro sensor, the rotation of the subject T's arm is measured. The wearable terminal device w1 may measure the sleeping posture, the number of turns in bed, the number of steps, etc. using the acceleration sensor and the gyro sensor.

[0173] The temperature sensor measures the temperature of the contacted part or the part imaged by thermography. The pressure sensor measures, for example, the pulse wave. The optical sensor detects the response to irradiating electromagnetic waves on the skin or the like, that is, at least one of the reflected wave and the transmitted wave. The optical sensor measures the blood flow velocity, blood components, etc.

[0174] The ultrasonic sensor detects the response to irradiating ultrasonic waves, that is, at least one of the reflected wave and the transmitted wave.

[0175] An electrical sensor measures voltage, current, impedance, etc. The electrical sensor measures the electric field generated by muscle activity, blood flow, nerve excitation, etc. The electrical sensor also combines with electrodes to detect components of sweat, etc., and functions as a chemical sensor, pH sensor, etc.

[0176] A magnetic sensor measures the magnetic field generated by muscle activity, blood flow, nerve excitation, etc.

[0177] An image sensor detects skin color, surface temperature, surface movement, blood flow, sweating conditions, etc.

[0178] Also, the sensor unit includes a GPS sensor, an azimuth sensor, a barometric pressure sensor, etc. The wearable terminal device w1 may measure the moving distance, momentum, etc. with these sensors.

[0179] Also, the microphone of the input unit may capture the snoring and breathing sounds of the subject T during sleep.

[0180] The subject sensing data measured by the sensor unit of the wearable terminal device w1 or the sensor embedded in the contact part with the subject such as the steering wheel is transmitted to the mobile terminal device 20 via the communication unit. Note that the wearable terminal device w1 may transmit the subject sensing data measured to the in-vehicle terminal device 30.

[0181] Also, the wearable terminal device w2 is a glasses-type wearable computer. The wearable terminal device w2 has an output unit, a storage unit, a communication unit, an input unit, a sensor unit, an input / output interface unit, a control unit, and a timer unit (not shown), similar to the wearable terminal device w1.

[0182] The sensor unit of the wearable terminal device w2 further includes a sensor for measuring the movement of the viewing point. For example, in the case of the corneal reflection method, the sensor unit includes an LED that irradiates light such as far-infrared rays onto the eyeball, and a camera for eye tracking that captures the eyes of the subject T. The control unit of the wearable terminal device w2 calculates the position of the subject T's line of sight from the reflection point on the cornea and the position of the pupil in the image, and outputs line-of-sight data from the output unit.

[0183] The wearable terminal device w2 mainly measures the position of the line of sight, blinking, the size of the pupil, etc.

[0184] Note that the wearable terminal device w2 may not irradiate light such as far-infrared rays from the LED of the sensor unit, and may identify the position of the line of sight by performing image processing on the image of the camera that captures the eyes of the subject T to distinguish the white sclera part and the cornea.

[0185] The sensor unit of the wearable terminal device w2 may be an eye tracker with only the function of eye tracking. The wearable terminal device w2 may be of a contact lens type. Further, the sensor unit of the wearable terminal device w2 may include a sensor for measuring myoelectricity. This sensor unit may measure the myoelectricity around the eyes, calculate the direction of the eyeball, and obtain line-of-sight data. In addition, the wearable terminal device w2 may measure the pulse, blood pressure, body temperature, etc. from the temple part.

[0186] Note that as types of the wearable terminal devices w1 and w2, in addition to the glasses type and the wristband type shown in FIG. 2, a ring type, a shoe type, a pocket type, a jewelry type, a clothing type, etc. may also be used.

[0187] (2.3 Configuration and Functions of the In-Vehicle Terminal Device 30) Next, the configuration and functions of the in-vehicle terminal device 30 will be described with reference to FIG. 23.

[0188] FIG. 23 is a block diagram showing an example of the schematic configuration of the in-vehicle terminal device 30.

[0189] As shown in FIG. 23, the in-vehicle terminal device 30 includes an output unit 31, a storage unit 32, a communication unit 33, an input unit 34, a sensor unit 35, an input / output interface unit 36, and a control unit 37. The control unit 37 and the input / output interface unit 36 are electrically connected via a system bus 38. Each in-vehicle terminal device 30 is assigned a vehicle ID. The in-vehicle terminal device 30 has a clock function.

[0190] As shown in FIG. 2, the in-vehicle terminal device 30 is, for example, a navigation device mounted on the vehicle V.

[0191] The output unit 31 includes, for example, a liquid crystal display element or an EL element as a display function, and a speaker or the like that outputs sounds such as music.

[0192] The storage unit 32 is constituted by, for example, a hard disk drive, a solid state drive, or the like. The storage unit 32 stores an operating system and various programs such as applications for the in-vehicle terminal device 30. Note that the various programs may be acquired from another server device or the like via the network N, or may be recorded on a recording medium and read via a drive device. Further, the storage unit 32 may have information of a database such as the storage unit 12 of the information processing server device 10.

[0193] The storage unit 32 has map information for navigating the vehicle V.

[0194] Note that, regarding the target person T (which may be plural) who drives the vehicle V on which the in-vehicle terminal device 30 is mounted, a target person information DB, an operation amount DB, a behavior amount DB, a driving environment information DB, and a target person sensing DB may be constructed in the storage unit 32 in the same manner as the storage unit 12.

[0195] The communication unit 33 is electrically or electromagnetically connected to the network N to control the communication state with the information processing server device 10 and the like. Further, the communication unit 33 is electrically or electromagnetically connected to the information processing server device 10 to control the communication state with the information processing server device 10. The communication unit 33 controls communication with the mobile terminal device 20 by wireless communication. The communication unit 33 may communicate with the seat sensor ss and the wearable terminal devices w1, w2.

[0196] The communication unit 33 communicates with the drive mechanism of the vehicle V. For example, a control signal is transmitted to the drive mechanism of the vehicle V via the communication unit 33 of the in-vehicle terminal device 30 to stop the vehicle V, stop it at a predetermined location, or navigate it to a predetermined location such as a hospital.

[0197] The input unit 34 has, for example, a touch switch type display panel such as a touch panel. The input unit 34 acquires the position information of the output unit 31 where the user's finger touches or approaches. The input unit 34 has a microphone for inputting voice.

[0198] The sensor unit 35 has various sensors for measuring operation amounts such as an angle sensor for measuring the steering angle of the steering wheel sw, an accelerator stroke sensor for measuring the operation of the accelerator, and a brake stroke sensor for measuring the operation of the brake pedal.

[0199] The sensor unit 35 has various sensors for measuring the behavior amount of the vehicle V such as a GPS sensor, an azimuth sensor, an acceleration sensor, a gyro sensor, and a sensor for a millimeter-wave radar. The GPS sensor acquires the current position information of the vehicle V.

[0200] The sensor unit 35 has various sensors such as a pressure sensor, a temperature sensor, and a rain and shine sensor.

[0201] The sensor unit 35 has imaging elements such as a CCD image sensor and a CMOS image sensor of a digital camera. For example, as shown in FIG. 2, the sensor unit 35 has a camera 35a and a camera 35b.

[0202] The camera 35a captures the situation outside the vehicle V. The in-vehicle terminal device 30 measures the amount of movement of the vehicle V based on the image of the camera 35a. For example, based on the image data of a camera that captures the front, side, or rear of the vehicle V, jitter data, which is an example of the amount of movement data of the vehicle V, may be measured from the lane image or the scenery. The camera of the sensor unit 35 may measure the inter-vehicle distance, the stop position, or the lane departure. The camera of the sensor unit 35 may measure the road surface condition (such as the presence or absence of rain, snow, or pavement) or the presence or absence of people.

[0203] The camera 35b captures the subject T. The in-vehicle terminal device 30 authenticates the subject T by face recognition, measures the facial color of the subject T, or determines whether the subject T is dozing off based on the image of the camera 35b. Also, based on the image data of a camera that captures the inside of the vehicle V, the operation amount may be measured from the movement of the subject T.

[0204] Also, the camera 35b may be a camera for eye tracking. In this case, light rays such as far-infrared rays may be applied to the eyes, and the camera 35b may capture the reflected light.

[0205] The input / output interface unit 36 performs interface processing between the output unit 31, the storage unit 32, etc. and the control unit 37.

[0206] The control unit 37 is composed of a CPU 37a, a ROM 37b, a RAM 37c, etc. And the control unit 37 causes the CPU 37a to read and execute various programs stored in the ROM 37b and the storage unit 32.

[0207] [3. Operation Example of the Disease State Determination System S] An operation example of the disease state determination system S will be described with reference to the drawings.

[0208] (3.1 Data Collection) First, an operation example of data collection such as operation amount data, behavior amount data, and sensing data of the subject T will be described with reference to the drawings. FIG. 24 is a flowchart showing an operation example of data collection. FIG. 25 is a schematic diagram showing an example of a road on which the vehicle V has traveled.

[0209] As shown in FIG. 2, the subject T gets into the vehicle V, and the power of the in-vehicle terminal device 30 is turned on. The in-vehicle terminal device 30 identifies the driver of the vehicle V. For example, the in-vehicle terminal device 30 may image the subject T with the camera 35b and perform face recognition. The in-vehicle terminal device 30 may communicate with the mobile terminal device 20 or the wearable terminal devices w1, w2 of the subject T to identify the subject T. The in-vehicle terminal device 30 may identify the subject T by means of a fingerprint recognition sensor provided on the steering wheel sw of the vehicle V. The in-vehicle terminal device 30 may combine these methods of identifying the subject T to identify the driver. The subject T may be identified by the mobile terminal ID of the mobile terminal device 20 carried by the subject T.

[0210] Note that the subject T may be an actual vehicle or may drive using a driving simulator.

[0211] When the subject T drives the vehicle V, the in-vehicle terminal device 30 starts measuring the operation amount data and the behavior amount data. The mobile terminal device 20 measures the sensing data.

[0212] Next, as shown in FIG. 24, the disease state determination system S collects data from each sensor of the vehicle V (step S1). Specifically, the control unit 37 of the in-vehicle terminal device 30 acquires the data measured by each sensor of the sensor unit 35 from each sensor together with the measurement time of the clock function. For example, as the operation amount of the vehicle V, the control unit 37 acquires operation amount data such as steering angle data of the steering angle of the steering wheel sw, accelerator stroke data, and brake stroke data from each sensor of the sensor unit 35. Further, as the behavior amount of the vehicle V, the control unit 37 acquires from each sensor of the sensor unit 35 data on fluctuations, current position information of the vehicle V, traveling direction of the vehicle V, speed, acceleration, inter-vehicle distance, and the like.

[0213] Further, the control unit 37 may acquire an image outside the vehicle V as wobbling data by the camera 35a of the sensor unit 35. The control unit 37 acquires an image of the target person T by the camera 35b. Note that the measurement time measured by each sensor of the sensor unit 35 may be measured by the clock function of the in-vehicle terminal device 30. Next, the disease state determination system S collects sensing data of the target person T from the sensors of the wearable terminal devices w1 and w2 and the seat sensor ss (step S2). Specifically, the control unit 27 of the portable terminal device 20 of the target person T driving the vehicle V acquires the sensing data measured by each sensor of the sensor units of the wearable terminal devices w1 and w2 and the seat sensor ss from the wearable terminal devices w1 and w2. The control unit 27 acquires the line-of-sight data of the target person T operating the vehicle V from the wearable terminal device w2. The control unit 27 acquires the rotation data of the rotation of the arms of the target person T operating the vehicle V from the wearable terminal devices w1 on both arms. The control unit 27 acquires the data of the seat pressure distribution of the seat surface on which the target person T operating the vehicle V sits from the seat sensor ss.

[0214] The measurement time may be measured by the clock function of the portable terminal device 20 or by the clock functions of the wearable terminal devices w1 and 2.

[0215] Next, the in-vehicle terminal device 30 acquires the sensing data via the portable terminal device 20. Note that the portable terminal device 20 may acquire the operation amount data and the behavior amount data via the in-vehicle terminal device 30.

[0216] The measured data may be stored in each terminal device. When storing the measured data in the storage unit 32, the in-vehicle terminal device 30 may store the measured data in the storage unit 32 in association with the target person ID, the operation amount ID, the behavior amount ID, and the sensor ID. Alternatively, the portable terminal device 20 may store the measured data in the storage unit 22 in association with the target person ID, the operation amount ID, the behavior amount ID, and the sensor ID.

[0217] The measured data may be stored in each terminal device. When storing the measured data in the storage unit 32, the in-vehicle terminal device 30 may store the measured data in the storage unit 32 in association with the target person ID, the operation amount ID, the behavior amount ID, and the sensor ID. Alternatively, the portable terminal device 20 may store the measured data in the storage unit 22 in association with the target person ID, the operation amount ID, the behavior amount ID, and the sensor ID.

[0218] Next, the disease state determination system S transmits the collected data to the information processing server device 10 (step S3). Specifically, the in-vehicle terminal device 30 transmits the acquired data to the information processing server device 10. Further specifically, the control unit 37 transmits the operation amount data of the vehicle V, the measurement time, the measurement position, the subject ID, and the operation amount ID to the information processing server device 10. The control unit 37 transmits the behavior amount data of the vehicle V, the measurement time, the measurement position, the subject ID, and the behavior amount ID to the information processing server device 10. The control unit 37 transmits the line-of-sight data of the subject T, the measurement time, the measurement position, the subject ID, and the sensor ID of the sensor for measuring the movement of the line of sight to the information processing server device 10. The control unit 37 transmits the data of the seat pressure distribution of the subject T, the measurement time, the measurement position, the subject ID, and the sensor ID of the data of the seat pressure distribution to the information processing server device 10.

[0219] The control unit 37 may transmit the vehicle ID instead of the subject ID. The portable terminal device 20 may transmit the sensing data to the information processing server device 10. The portable terminal device 20 may transmit the operation amount data and the behavior amount data.

[0220] The measured data may be sequentially transmitted to the information processing server device 10 or may be transmitted collectively. In the case of sequential transmission, the in-vehicle terminal device 30 may transmit predetermined data in packets, or may transmit the data collectively when the communication state deteriorates and the communication is interrupted, such as in a tunnel.

[0221] Also, in the case of collective transmission, the in-vehicle terminal device 30 may transmit predetermined data such as the measured data in a predetermined driving section or the measured data in a predetermined driving period. Alternatively, the in-vehicle terminal device 30 may transmit the data measured collectively after the driving is completed to the information processing server device 10.

[0222] Next, the disease state determination system S receives the collected data from the in-vehicle terminal device 30 (step S4). Specifically, the information processing server device 10 receives operation amount data of the vehicle V and behavior amount data of the vehicle V from the in-vehicle terminal device 30. The information processing server device 10 receives sensing data such as line-of-sight data and seat pressure distribution data from the in-vehicle terminal device 30.

[0223] In this way, the information processing server device 10 functions as an example of driving characteristic data acquisition means for acquiring driving characteristic data indicating the driving characteristics of the target person with respect to the vehicle. The information processing server device 10 functions as an example of line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of the target person measured when the target person is driving the vehicle. The information processing server device 10 functions as an example of seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the target person sits in the vehicle.

[0224] Next, the information processing server device 10 stores the received data in the storage unit 12 (step S5). Specifically, the control unit 16 of the information processing server device 10 stores the received operation amount data, measurement time, position information, etc. in the operation amount DB12b in association with the target person ID and the operation amount ID. The control unit 16 stores the received behavior amount data, measurement time, position information, etc. in the behavior amount DB12c in association with the target person ID and the behavior amount ID. The control unit 16 stores the received sensing data, measurement time, position information, etc. in the target person sensing DB12e in association with the target person ID and the sensor ID.

[0225] As shown in FIG. 25, the road on which the vehicle V has traveled is specified from the received position information of the vehicle V. Note that the road to be traveled may be set in advance by the navigation function of the in-vehicle terminal device 30.

[0226] Also, the information processing server device 10 acquires driving environment information from the road information providing server device, and the data in the driving environment information DB12d is updated.

[0227] (3.2 Example of operation for determining disease state) Next, an operation example of determining a disease state for a specific subject T will be described with reference to the drawings.

[0228] FIG. 26 is a flowchart showing an operation example of determining a disease state such as epilepsy. FIG. 27 is a schematic diagram showing an example of a road on which the vehicle V has traveled.

[0229] As shown in FIG. 26, the information processing server device 10 acquires vehicle driving characteristic data such as operation amount data of the subject T operating the vehicle V and behavior amount data of the behavior of the vehicle (step S10).

[0230] In the case of the operation amount data, the control unit 16 of the information processing server device 10 refers to the operation amount DB12b and acquires each operation amount data such as steering angle data and accelerator stroke data, the measurement time, and the position information of the vehicle V based on the subject ID of the subject T and each operation amount ID. For example, the control unit 16 acquires each operation amount data when traveling on a road as shown in FIG. 25.

[0231] In the case of the behavior amount data, the control unit 16 refers to the behavior amount DB12c and acquires each behavior amount data such as wobbling data, vehicle speed data, and lateral acceleration data, the measurement time, and the position information of the vehicle V based on the subject ID of the subject T and each behavior amount ID. For example, the control unit 16 acquires each behavior amount data when traveling on a road as shown in FIG. 25.

[0232] The information processing server device 10 functions as an example of driving characteristic data acquisition means for acquiring driving characteristic data indicating the driving characteristics of the subject's vehicle.

[0233] Next, the information processing server device 10 acquires subject sensing data (step S11). Specifically, the control unit 16 refers to the subject sensing DB12e and acquires each subject sensing data such as line-of-sight data and seat pressure distribution data, the measurement time, and the position information of the vehicle V based on the subject ID of the subject T and each sensor ID. For example, the control unit 16 acquires each subject sensing data when traveling on a road as shown in FIG. 25.

[0234] In this way, the information processing server device 10 functions as an example of a line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle. The information processing server device 10 functions as an example of a seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle.

[0235] Next, the information processing server device 10 acquires road environment information (step S12). Specifically, the control unit 16 refers to the driving environment information DB 12d and acquires the road environment information of the traveled road as shown in FIG. 25.

[0236] Next, the information processing server device 10 classifies the data according to the road (step S13). Specifically, the control unit 16 classifies the vehicle driving characteristic data such as each operation amount data and each behavior amount data based on the position information of the vehicle V when these data are measured and the acquired road environment information. More specifically, the control unit 16 divides the operation amount data and the behavior amount data at the position where the vehicle is traveling on a standard highway and the operation amount data and the behavior amount data at the position where the vehicle is traveling on a highway with relatively many curves such as the Metropolitan Expressway in Tokyo. As described above, the control unit 16 classifies the data according to the type of road on which the vehicle V is traveling.

[0237] Further, the control unit 16 may calculate the curvature of the road from each position information, classify the curves according to the curvature, and classify which section each operation amount data and each behavior amount data belong to. The control unit 16 may regard a road with a predetermined curvature or less as a straight line, and the others as a curve section, and classify each operation amount data and each behavior amount data according to the straight line section and the curve section. As shown in FIG. 27, the curves may be classified into a left curve and a right curve. As shown in FIG. 27, the curves may be classified for each curve with a predetermined curvature or more.

[0238] In this way, the information processing server device 10 classifies the operation amount data according to the degree of curvature of the road on which the vehicle travels.

[0239] Next, the control unit 16 refers to the driving environment information DB 12d to specify the road section ID of each classified data from the position information. Note that the control unit 16 may specify the road section ID of a road having a pattern of the degree of bending of a similar road from the curvature or the position information.

[0240] Regarding each subject sensing data such as the line-of-sight data and the data of the seat pressure distribution, it is also classified according to the road in the same manner as each operation amount data and each behavior amount data, and the road section ID is specified.

[0241] Next, the information processing server device 10 calculates the degree of deviation between the line-of-sight data and the vehicle driving characteristic data (step S14). Specifically, the control unit 16 normalizes the acquired time-series line-of-sight data and vehicle driving characteristic data so that the minimum value is 0 and the maximum value is 100. For example, the control unit 16 normalizes the line-of-sight data of the movement of the lateral line of sight, the steering angle data, the steering torque data, and the vehicle lateral acceleration so that the minimum value is 0 and the maximum value is 100.

[0242] The control unit 16 calculates the difference value (for example, the absolute value of the difference) between the normalized line-of-sight data and the vehicle driving characteristic data. The control unit 16 calculates the sum or average value, etc. of the difference values over a predetermined time length and uses it as the degree of deviation. For example, the control unit 16 calculates the degree of deviation between the line of sight and the steering angle from the difference value between the normalized line-of-sight data of the movement of the lateral line of sight and the steering angle data. The control unit 16 calculates the degree of deviation between the line of sight and the steering torque from the difference value between the normalized line-of-sight data of the movement of the lateral line of sight and the steering torque. The control unit 16 calculates the degree of deviation between the line of sight and the vehicle lateral acceleration from the difference value between the normalized line-of-sight data of the movement of the lateral line of sight and the vehicle lateral acceleration data.

[0243] Note that the control unit 16 may calculate the moving average or exponential smoothing of the time series of the difference values between the normalized line-of-sight data and the vehicle driving characteristic data.

[0244] Next, the control unit 16 calculates, from the time series of each deviation degree, the elapsed time after each deviation degree exceeds the threshold value D0th. For example, when a certain dissociation degree is set (e.g., 20 or 30 when the normalized maximum value is 100), when the set dissociation degree is exceeded, the control unit 16 measures the elapsed time from the time point when the set dissociation degree is exceeded.

[0245] Next, the information processing server device 10 calculates, from the acquired gaze data, the duration and average time during which the gaze position has continuously deviated from the center of the set visual field (e.g., for 100 ms or more). The information processing server device 10 calculates the ratio of the time during which the gaze position has continuously been located outside the set center from the acquired gaze data.

[0246] The information processing server device 10 may calculate the duration, average time, ratio of being located outside the set center, etc. only from the gaze data of the horizontal gaze movement while being located at the set center.

[0247] For example, regarding the duration, when the range of the center of the visual field when sitting in the driver's seat and looking straight ahead is set, and the time (e.g., 100 ms or more) during which the viewpoint continuously deviates from the set center is set, the information processing server device 10 calculates, as the duration, the elapsed time during which the viewpoint has continuously been located outside the set center exceeding the set continuous time.

[0248] Regarding the average time, after the viewpoint is located outside the center for a period exceeding the set continuous time, it may return to within the range of the center again, and then the state of being located outside the center again may be repeated. In this case, the information processing server device 10 calculates, as the "average time", the time obtained by averaging the "durations during which the viewpoint has continuously been located outside the center" in each section (such as the seizure section).

[0249] Regarding the ratio, the information processing server device 10 calculates the ratio of the time (number of frames) during which the viewpoint has continuously been located outside the center to the total time (number of frames) of each section (such as the seizure occurrence section).

[0250] Next, the information processing server device 10 calculates operation-related data. Specifically, the control unit 16 calculates operation-related data in components within a predetermined frequency range from the operation amount data. For example, the control unit 16 performs a discrete Fourier transform on the operation amount data to calculate the power spectral density at each frequency.

[0251] Next, the control unit 16 refers to the disease determination DB12f and specifies a predetermined frequency range based on the road section ID, disease ID, and operation amount ID. The control unit 16 extracts, as operation-related data, the components of the power spectrum corresponding to the components in the predetermined frequency range from the power spectral density.

[0252] When the operation amount data is steering angle data, the operation-related data may be the steering angular velocity or steering angular acceleration (steering torque) calculated from the time derivative of the steering angle data.

[0253] Note that the control unit 16 may calculate behavior-related data in components with a predetermined frequency value or a predetermined frequency range from the behavior amount data. For example, the control unit 16 may perform a discrete Fourier transform on the behavior amount data and calculate the power spectral density at each frequency as behavior-related data.

[0254] Next, the information processing server device 10 calculates an operation value and a behavior value (step S15). Specifically, the control unit 16 refers to the disease determination DB12f based on the road section ID, disease ID, and operation amount ID, and calculates a time-series operation-related data and a frequency analysis value obtained by quantifying the spectrum of the operation-related data that is a function of frequency. Also, the control unit 16 refers to the disease determination DB12f based on the road section ID, disease ID, and operation amount ID, and calculates a time-series behavior amount data and a frequency analysis value obtained by quantifying the spectrum of the behavior amount data that is a function of frequency.

[0255] For example, when the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the operation amount ID indicates the steering angle, the power spectral density at either of the frequency values f0, f1 as shown in FIG. 13A, or at a frequency that is a combination of these, is calculated. When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the operation amount ID indicates the operation torque, for example, the power spectral density at the frequency value f2 as shown in FIG. 13B is calculated. In the case of a predetermined frequency range instead of a predetermined frequency value, the control unit 16 calculates, as a frequency analysis value, the total value obtained by summing the power spectral densities in the predetermined frequency range.

[0256] Note that the operation value may also be the total steering amount calculated from the integral of the absolute value of the steering angular velocity. The operation value may also be the corrected steering amount that is the sum of the power spectral densities in a predetermined frequency band obtained by performing a discrete Fourier transform on the steering angle data. The operation value may also be the standard deviation of the steering angular velocity, the smoothness of the steering, the maximum value of the steering angular velocity, or the steering entropy calculated from the entropy of the steering angle data.

[0257] Also, the control unit 16 calculates, as a behavior value, the degree of wobbling (for example, SDLP) when driving on the road indicated by the road section ID from the wobbling data. Note that the degree of wobbling of the vehicle V may also be the number of deviations or the frequency from the road (the transmission frequency of the lane departure warning). The behavior amount data as the inter-vehicle distance may also be the value or variability of the inter-vehicle distance (or the inter-vehicle time), the number and frequency of approaches to the vehicle traveling ahead (for example, when the inter-vehicle time is within 3 seconds, within 1 second, etc.) (the transmission frequency of the forward collision warning). The distance from the temporary stop location when stopped may also be used as the behavior amount data. The degree of wobbling, which is a behavior value, may be calculated from these behavior amount data.

[0258] The control unit 16 calculates, as a behavior value, the average vehicle speed when driving on the road indicated by the road section ID from the vehicle speed data. The control unit 16 calculates, as a behavior value, the average lateral acceleration value when driving on the road indicated by the road section ID from the lateral acceleration data.

[0259] Note that the control unit 16 may calculate a behavior value from the behavior-related data. For example, the control unit 16 calculates the total value obtained by summing the power spectral densities in a predetermined frequency range as the behavior value.

[0260] Next, the information processing server device 10 calculates the time during which the viewpoint is continuously located outside the central portion of the visual field (step S16). Specifically, the control unit 16 determines whether or not the gaze data is outside a range of a predetermined central portion. Note that, as shown in FIGS. 16A and 16B and the like, the determination may be made within a plurality of ranges of predetermined central portions.

[0261] When it is outside the range of the central portion, the control unit 16 calculates the duration during which it is continuously outside the range of the predetermined central portion. If the duration is, for example, less than 100 ms, it is not counted and discarded.

[0262] The control unit 16 calculates the average value of the durations, that is, the average time, in a predetermined period of the gaze data. Also, in a predetermined period of the gaze data, the ratio of the duration during which the viewpoint is continuously located outside the central portion is calculated.

[0263] Next, the information processing server device 10 calculates the central position according to the seat pressure distribution. When the central position is the shape center position, the control unit 16 scans from the minimum value of x in the distribution map of the seat pressure distribution as shown in FIG. 19 in order, and the maximum value of y, that is, from the front line of the subject T. When it reaches the outer edge of the distribution shape by scanning, the control unit 16 stores the value of y together with the value of x as the distance from the front line to the outer edge of the distribution shape. The control unit 16 increments the value of x and scans from the front line. The control unit 16 repeats the scanning until the maximum value of x. After the scanning is completed, the control unit 16 calculates the position of x at which the distance from the front line to the outer edge of the distribution shape becomes maximum as the shape center position of the distribution shape. As shown in FIG. 25, when the maximum value is 2 or more, the averaged position becomes the shape center position. Note that the method for calculating the shape center position is not limited to the above method, and it is sufficient if the left and right of the seat pressure distribution can be separated and the constricted portion of the distribution shape can be calculated.

[0264] The outer edge of the distribution shape is where the value of the seat pressure is equal to or greater than a predetermined value in the distribution map of the seat pressure distribution. Note that the control unit 16 may change the predetermined value, calculate a plurality of x-direction positions of the constricted portion of the distribution shape, and average the positions as the shape center position.

[0265] When the center position is the seat pressure center position, the control unit 16 calculates the distribution shape centroid (Gx, Gy) as the seat pressure center position from the values and positions of the pixels of the distribution map of the seat pressure distribution.

[0266] Next, the information processing server device 10 calculates the skewness as the subject sensing value. Specifically, the control unit 16 calculates the difference between the shape center position and the seat pressure center position.

[0267] Next, the information processing server device 10 calculates the magnitude of the seat pressure distribution (step S17). Specifically, as shown in FIG. 19, the control unit 16 counts the pixels or unit sections where the seat pressure is equal to or greater than a predetermined value. The control unit 16 divides the count number by the number of pixels or unit sections of the entire seat surface to calculate the seat pressure area, which is the ratio of the portion of the entire seat surface where pressure is applied.

[0268] In this way, the information processing server device 10 functions as an example of a pressure distribution calculation means for calculating the magnitude of the seat pressure distribution.

[0269] Next, the information processing server device 10 determines the disease state such as epilepsy (step S18). Specifically, the control unit 16 refers to the disease determination DB12f based on the road section ID, disease ID, and operation amount ID, compares the reference operation value with the calculated operation value of the subject T, and determines the disease state of whether the subject T has the disease of the disease ID and the degree of the disease of the disease ID.

[0270] For example, when the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, the sensor ID is the line of sight, and the operation amount ID indicates the steering angle, the control unit 16 compares the degree of deviation calculated in step S14 and the elapsed time since the degree of deviation exceeded D0th with the threshold value Dth of the degree of deviation and the threshold value Tth of the elapsed time as shown in FIG. 12A to determine whether it is an epileptic state. When the threshold value Dth of the degree of deviation and the threshold value Tth of the elapsed time are exceeded, the control unit 16 determines that it is an epileptic state.

[0271] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, the sensor ID is the line of sight, and the operation amount ID indicates the steering torque, the control unit 16 compares the degree of deviation calculated in step S14 and the elapsed time since the degree of deviation exceeded D0th with the threshold value Dth of the degree of deviation and the threshold value Tth of the elapsed time as shown in FIG. 12B to determine whether it is an epileptic state.

[0272] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, the sensor ID is the line of sight, and the behavior amount ID indicates the lateral acceleration of the vehicle, the control unit 16 compares the degree of deviation calculated in step S14 and the elapsed time since the degree of deviation exceeded D0th with the threshold value Dth of the degree of deviation and the threshold value Tth of the elapsed time as shown in FIG. 12C to determine whether it is an epileptic state.

[0273] In this way, the information processing server device 10 functions as an example of a disease state determination means capable of determining the epileptic state of the subject according to the relationship between the line-of-sight data and the driving characteristic data. The information processing server device 10 functions as an example of a disease state determination means that determines that it is epilepsy when the value of the relationship is equal to or less than a predetermined value. The information processing server device 10 functions as an example of a disease state determination means that determines that it is epilepsy when the time during which the degree of deviation is equal to or greater than a predetermined value is equal to or greater than a predetermined time.

[0274] Next, the case of operation-related data obtained by Fourier-transforming the data will be described.

[0275] For example, when the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the operation amount ID indicates the steering angle, the control unit 16 determines whether it is an epileptic state by comparing the operation value of the subject T at the frequency values f0 and f1 as shown in FIG. 13A calculated in step S15 with the power spectral density p0 for the frequency f0 and the power spectral density p1 for the frequency f1. When each calculated operation value is lower than the power spectral densities p0 and p1, the control unit 16 determines that it is an epileptic state.

[0276] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the operation amount ID indicates the steering torque, the control unit 16 determines whether it is an epileptic state by comparing the operation value of the subject T at the frequency value f2 as shown in FIG. 13B calculated in step S15 with the power spectral density p2 for the frequency f2.

[0277] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the behavior amount ID indicates the lateral acceleration of the vehicle, the control unit 16 determines whether it is an epileptic state by comparing the operation value of the subject T at the frequency value f3 as shown in FIG. 13C calculated in step S15 with the power spectral density p3 for the frequency f3.

[0278] Note that the information processing server device 10 may determine the disease state based on one operation value or behavior value, or may determine the disease state based on a plurality of operation values.

[0279] For example, when determining based on a plurality of operation values or behavior values, in the determination of each operation value and the like, when the number of determinations determined to be a predetermined disease state exceeds a predetermined threshold, the information processing server device 10 may determine that it is a predetermined disease state. When the determination result is 1 when it is a predetermined disease state and 0 when it is not a predetermined disease state, the information processing server device 10 calculates the sum (number of determinations) of the determination results of each operation value. Further, the information processing server device 10 may calculate the sum of the determination results by providing weights to each operation value.

[0280] When making a determination by combining the operation value and the behavior value, in the determination of each operation value and each behavior value, if the number of determinations determined to be a predetermined disease state exceeds a predetermined threshold, the information processing server device 10 may determine that it is a predetermined disease state. Further, the information processing server device 10 may provide weights to each operation value and each behavior value and calculate the sum of the determination results.

[0281] In this way, the information processing server device 10 functions as an example of a disease state determination means capable of determining the epilepsy state of the subject according to the relationship between the gaze data and the driving characteristic data.

[0282] Next, the case of subject sensing data will be described.

[0283] Based on the road section ID, disease ID, and sensor ID, the control unit 16 refers to the disease determination DB 12f, compares the reference subject sensing value with the calculated subject sensing value of the subject T, and determines whether the subject T has a disease of the disease ID and the disease state such as the degree of the disease of the disease ID.

[0284] For example, the case of the determination of gaze data will be specifically described.

[0285] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the sensor ID indicates gaze data, the duration of continuously deviating from the range of a predetermined central part calculated in step S16 is compared with the threshold values Tth1 and Tth2 as shown in FIGS. 16A and 16B, and the control unit 16 determines whether it is an epileptic state. When the calculated duration exceeds the threshold value Tth1, or exceeds the threshold value Tth2, or in the case of these combinations, the control unit 16 determines that it is an epileptic state.

[0286] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the sensor ID indicates line-of-sight data, the control unit 16 determines whether it is an epileptic state by comparing the average value of the duration continuously deviating from the range of a predetermined central part calculated in step S16 with the threshold value Tth as shown in FIGS. 17A and 17B. When the calculated average value of the duration exceeds the threshold value Tth, the control unit 16 determines that it is in an epileptic state.

[0287] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the sensor ID indicates line-of-sight data, the control unit 16 determines whether it is an epileptic state by comparing the ratio of the duration during which the viewpoints are continuously located outside the central part calculated in step S16 with the threshold values R1 and R2 as shown in FIGS. 18A and 18B. When the calculated duration exceeds the threshold value R1, or exceeds the threshold value TR2, or in the case of these combinations, the control unit 16 determines that it is in an epileptic state.

[0288] In this way, the information processing server device 10 functions as an example of a disease state determination means capable of determining the epileptic state of the subject according to the time when the position of the line of sight indicated by the line-of-sight data deviates from the central part of the field of view of the subject in the traveling direction of the vehicle.

[0289] Next, the case of determining the data of the seat pressure distribution will be specifically described.

[0290] When the disease ID indicates epilepsy, the road section ID indicates the Metropolitan Expressway in Tokyo, and the sensor ID indicates line-of-sight data, the control unit 16 determines whether it is an epileptic state by comparing the seat pressure area calculated in step S17 with the threshold value Sth as shown in FIG. 21. When the calculated seat pressure area is equal to or less than the threshold value Sth, the control unit 16 determines that it is in an epileptic state.

[0291] Also, based on the skewness calculated from the data of the seat pressure distribution, the control unit 16 may determine whether it is left paralysis or right paralysis.

[0292] In this way, the information processing server device 10 functions as an example of a disease state determination means capable of determining the epileptic state of the subject according to changes in the magnitude of the seat pressure distribution.

[0293] Note that, as the degree of the disease, in the case of epilepsy, the information processing server device 10 may be a partial seizure or a general seizure.

[0294] The information processing server device 10 may determine the disease state by combining the operation value, the behavior value, and the subject sensing value. From among operation amount data, operation related data, operation values, behavior amount data, behavior related data, behavior values, subject sensing data, subject sensing related data, subject sensing values, etc., the information processing server device 10 may determine the disease state with an optimal combination of feature amounts for a predetermined disease.

[0295] Note that the information processing server device 10 may sequentially acquire data from the in-vehicle terminal device 30 or the like and determine the disease state. The portable terminal device 20 or the in-vehicle terminal device 30 may determine the disease state from the measured data as an example of the disease state determination device instead of the information processing server device 10. In this case, the control unit 27 of the portable terminal device 20 or the control unit 37 of the in-vehicle terminal device 30 determines the disease state from the measured data.

[0296] Also, the information processing server device 10 may determine the disease state based on the physiological data from the home terminal device 40 and the electronic medical record information of the medical institution server device 50. In particular, the information processing server device 10 may determine the disease state based on the physiological data from the home terminal device 40 and the electronic medical record information of the medical institution server device 50.

[0297] As described above, according to the present embodiment, by determining the disease state such as epilepsy of the subject T according to the relationship between the line-of-sight data indicating the position of the line of sight of the subject T measured when the subject T is driving the vehicle V and the driving characteristic data indicating the driving characteristics of the subject T with respect to the vehicle V, even without special equipment, the epileptic state of the subject T can be determined from easily measurable data such as line-of-sight data and driving characteristic data.

[0298] In addition, when the value of the relationship is equal to or less than a predetermined value and it is determined that the subject has epilepsy, the state of epilepsy of the subject T can be easily determined based on the value of the relationship.

[0299] In addition, when the relationship is the degree of deviation between the line-of-sight data and the driving characteristic data, the state of epilepsy of the subject T can be easily determined based on the degree of deviation.

[0300] In addition, when the time during which the degree of deviation is equal to or greater than a predetermined value is equal to or greater than a predetermined time and it is determined that the subject has epilepsy, the accuracy of determining the state of epilepsy of the subject T is improved by the combination of the degree of deviation and the time during which the degree of deviation is equal to or greater than the predetermined value.

[0301] In addition, when the driving characteristic data is at least one of the operation amount data of the subject T operating the vehicle V and the behavior amount data of the behavior of the vehicle V, the state of epilepsy of the subject T can be easily determined based on the operation amount data or the like.

[0302] In addition, when determining the epilepsy state of the subject T according to the time when the position of the line of sight indicated by the line-of-sight data deviates from the center of the field of view of the subject T in the traveling direction of the vehicle V, the epilepsy state of the subject T can be easily determined based on this time, the duration calculated from this time, the time average time, the ratio of the position outside the center part, etc.

[0303] In addition, according to the present embodiment, by determining the epilepsy state of the subject T according to the change in the magnitude of the seat pressure distribution on the seat surface where the subject T sits in the vehicle V, it is possible to determine the epilepsy state of the subject T from easily measurable data such as the seat pressure distribution without using special equipment.

[0304] In addition, according to the present embodiment, by determining the epilepsy state of the subject T according to the time when the position of the line of sight indicated by the line-of-sight data measured when the subject T is driving the vehicle V deviates from the center of the field of view of the subject T in the traveling direction of the vehicle V, it is possible to determine the epilepsy state of the subject T from easily measurable data such as the line-of-sight data without using special equipment.

[0305] (Modified Example) Next, a modified example of the determination of the disease state will be described.

[0306] The information processing server device 10 may apply an identifier to the measured data to determine the disease state. The identifier may be a linear identifier or a non-linear identifier. The identifier may also be an identifier of machine learning that machine-learns the parameters of the identifier. Machine learning includes neural networks, genetic algorithms, Bayesian networks, decision tree learning, logistic regression, and the like.

[0307] For example, the information processing server device 10 machine-learns in advance using subject sensing data such as gaze data, subject sensing related data, subject sensing values, operation amount data, operation related data, operation values, the degree of deviation between gaze data and operation amount data, etc., and stores the parameters of the machine learning model in the disease determination DB12f. Note that the data used for machine learning may be data classified by road section.

[0308] In step S18, the information processing server device 10 may apply an identifier with reference to the disease determination DB12f to the degree of deviation between the gaze data calculated in step S14 and the vehicle driving characteristic data to determine the disease state.

[0309] In step S18, the information processing server device 10 may apply an identifier with reference to the disease determination DB12f to the operation value and behavior value calculated in step S15 to determine the disease state.

[0310] In step S18, the information processing server device 10 may apply an identifier with reference to the disease determination DB12f to the time when the viewpoints calculated in step S16 are continuously located outside the central part to determine the disease state.

[0311] In step S18, the information processing server device 10 may refer to the disease determination DB 12f and apply a discriminator to determine the disease state with respect to the magnitude of the seat pressure distribution calculated in step S17.

[0312] In step S18, the information processing server device 10 may refer to the disease determination DB 12f and apply a discriminator to determine the disease state with respect to the calculated operation-related data and behavior amount-related data.

[0313] In step S18, the information processing server device 10 may refer to the disease determination DB 12f and apply a discriminator to determine the disease state with respect to the data sorted according to the road in step S13.

[0314] With respect to a plurality of data such as operation amount data, behavior amount data, and subject sensing data, the information processing server device 10 may refer to the disease determination DB 12f and apply a discriminator to determine the disease state.

[0315] When determining the disease state of the subject by machine learning on operation amount data, behavior amount data, subject sensing data, etc., the disease state can be determined based on subject sensing data, subject sensing values, waveforms of operation amount data, operation values, waveforms of behavior amount data, patterns of behavior values, etc.

[0316] The mobile terminal device 20 or the in-vehicle terminal device 30 may include the above discriminator.

[0317] Furthermore, the present invention is not limited to the above-described embodiments. The above-described embodiments are examples, and any configuration that has substantially the same configuration as the technical idea described in the claims of the present invention and exhibits the same operational effects is included in the technical scope of the present invention.

Description of Reference Numerals

[0318] 10: Information processing server device (disease state determination device) 12: Storage unit (storage means) 12f: Disease determination database (memory means) 20: Portable terminal device (disease state determination device, terminal device) 30: In-vehicle terminal device (disease state determination device, terminal device) S: Disease state determination system T: Subject V: Vehicle

Claims

1. A line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle; A driving characteristic data acquisition means for acquiring driving characteristic data which is at least one of the operation amount data of the subject operating the vehicle and the behavior amount data of the behavior of the vehicle; According to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration that the line of sight indicated by the line-of-sight data is continuously located outside the central part of the field of view of the subject in the traveling direction of the vehicle and the first threshold value of the duration during non-epileptic seizures and the second threshold value of the duration during epileptic seizures, a disease state determination means capable of determining the epileptic state of the subject; A seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle; A pressure distribution calculation means for calculating the seat pressure area as the magnitude of the seat pressure distribution and the central position of the seat pressure distribution; Comprising; The disease state determination means determines the epileptic state of the subject according to the change in the seat pressure area and the change in the central position, and is characterized in that it is a disease state determination device.

2. In the disease state determination device according to claim 1, The disease state determination means is characterized in that when the value of the relationship is equal to or less than a predetermined value, it is determined that the subject has epilepsy.

3. In the disease state determination device according to claim 1 or claim 2, The relationship is characterized in that it is the degree of deviation between the line-of-sight data and the driving characteristic data.

4. In the disease state determination device according to claim 3, The disease state determination means is characterized in that when the time during which the degree of deviation is equal to or greater than a predetermined value is equal to or greater than a predetermined time, it is determined that the subject has epilepsy.

5. A line-of-sight data acquisition step in which the line-of-sight data acquisition means acquires line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle; A driving characteristic data acquisition step in which the driving characteristic data acquisition means acquires driving characteristic data which is at least one of the operation amount data of the subject operating the vehicle and the behavior amount data of the behavior of the vehicle; A disease state determination step in which the disease state determination means can determine the epilepsy state of the subject according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration for which the line of sight indicated by the line-of-sight data is continuously located outside the central part of the subject's field of view in the traveling direction of the vehicle and the first threshold value of the duration during non-epileptic seizure and the second threshold value of the duration during epileptic seizure. A seat pressure distribution acquisition step in which the seat pressure distribution acquisition means acquires data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle. A pressure distribution calculation step in which the pressure distribution calculation means calculates the seat pressure area as the magnitude of the seat pressure distribution and the central position of the seat pressure distribution. including A disease state determination method characterized in that the disease state determination means determines the epilepsy state of the subject according to changes in the seat pressure area and changes in the central position.

6. A computer Line-of-sight data acquisition means for acquiring line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving a vehicle. Driving characteristic data acquisition means for acquiring driving characteristic data which is at least one of the operation amount data for the subject to operate the vehicle and the behavior amount data of the behavior of the vehicle. Disease state determination means capable of determining the epilepsy state of the subject according to the relationship between the line-of-sight data and the driving characteristic data, and according to the comparison between the duration for which the line of sight indicated by the line-of-sight data is continuously located outside the central part of the subject's field of view in the traveling direction of the vehicle and the first threshold value of the duration during non-epileptic seizure and the second threshold value of the duration during epileptic seizure. Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, and Function as pressure distribution calculation means for calculating the seat pressure area as the magnitude of the seat pressure distribution and the central position of the seat pressure distribution. A program for a disease state determination device, characterized in that the disease state determination means determines the epilepsy state of the subject according to changes in the seat pressure area and changes in the central position.

7. In a disease state determination system including a terminal device for collecting data on a subject driving a vehicle and a disease state determination device for determining the epilepsy state of the subject based on the data on the subject. The disease state determination device Line-of-sight data acquisition means for acquiring, from the terminal device, line-of-sight data indicating the position of the line of sight of the subject measured when the subject is driving the vehicle. Driving characteristic data acquisition means for acquiring, from the terminal device, at least one of operation amount data for the subject to operate the vehicle and behavior amount data for the behavior of the vehicle; Disease state determination means capable of determining the epileptic state of the subject according to the relationship between the line-of-sight data and the driving characteristic data, and according to the duration that the line-of-sight indicated by the line-of-sight data is continuously located outside the central part of the subject's field of view in the traveling direction of the vehicle, in comparison with a first threshold value for the duration during non-epileptic seizure and a second threshold value for the duration during epileptic seizure; Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle; Pressure distribution calculation means for calculating the seat pressure area as the size of the seat pressure distribution and the central position of the seat pressure distribution; Comprising; A disease state determination system, characterized in that the disease state determination means determines the epileptic state of the subject according to changes in the seat pressure area and changes in the central position.

8. Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle; Pressure distribution calculation means for calculating the seat pressure area as the size of the seat pressure distribution and the central position of the seat pressure distribution; Disease state determination means capable of determining the epileptic state of the subject according to changes in the seat pressure area and changes in the central position; A disease state determination device, characterized by comprising.

9. A seat pressure distribution acquisition step in which the seat pressure distribution acquisition means acquires data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle; A pressure distribution calculation step in which the pressure distribution calculation means calculates the seat pressure area as the size of the seat pressure distribution and the central position of the seat pressure distribution; A disease state determination step in which the disease state determination means can determine the epileptic state of the subject according to changes in the seat pressure area and changes in the central position; A disease state determination method, characterized by including.

10. A program for a disease state determination device, characterized by causing a computer to function as seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle, pressure distribution calculation means for calculating the seat pressure area as the size of the seat pressure distribution and the central position of the seat pressure distribution, and disease state determination means capable of determining the epileptic state of the subject according to changes in the seat pressure area and changes in the central position. Seat pressure distribution acquisition means for acquiring data on the seat pressure distribution of the seat surface on which the subject sits in the vehicle; Pressure distribution calculation means for calculating the seat pressure area as the size of the seat pressure distribution and the central position of the seat pressure distribution, and Disease state determination means capable of determining the epileptic state of the subject according to changes in the seat pressure area and changes in the central position;

11. In a disease state determination system including a terminal device that collects data regarding a target person who is driving a vehicle, and a disease state determination device that determines the epilepsy state of the target person based on the data regarding the target person, a seating pressure distribution acquisition means that acquires, from the terminal device, data of the seating pressure distribution of a seating surface on which the target person sits in the vehicle, a pressure distribution calculation means that calculates a seating pressure area as the magnitude of the seating pressure distribution and a center position of the seating pressure distribution, and a disease state determination means that can determine the epilepsy state of the target person according to changes in the seating pressure area and changes in the center position. A disease state determination system characterized by comprising the above.

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