Measurement device, program, and model generation device

Abstract

The present invention provides a measurement device including: an acquisition unit that acquires a reference signal outputted to a measurement space and a response signal indicating a response by an examinee to measurement information that is outputted to the measurement space in conjunction with the output of the reference signal; and a measurement unit that measures the responsiveness of the examinee on the basis of the response signal and the reference signal.

Description

Measurement device, program and model generation device

[0001] The present disclosure relates to a measurement device, a program, and a model generation device.

[0002] Conventionally, devices have been proposed that detect and diagnose the movement of a subject's gaze in order to diagnose, for example, cognitive dysfunction. For example, Patent Literature 1 discloses a cognitive dysfunction diagnostic device that simply diagnoses cognitive dysfunction. This cognitive dysfunction diagnostic device detects the subject's gaze point in time on a display screen that displays diagnostic images, and diagnoses the subject's cognitive dysfunction based on the distribution of the gaze points.

[0003] International Publication No. 2019 / 098173

[0004] However, the device of Patent Document 1 has difficulty in accurately measuring and recording the movement of the subject's viewpoint relative to the diagnostic image.

[0005] The present disclosure aims to provide a measurement device, a program, and a model generation device that accurately measure the reactivity of a subject.

[0006] The measurement device according to the present disclosure has an acquisition unit that acquires a reference signal output to a measurement space and a reaction signal that indicates the subject's reaction to the measurement information output to the measurement space in conjunction with the output of the reference signal, and a measurement unit that measures the subject's reactivity based on the reaction signal and the reference signal.

[0007] The program of the present disclosure causes a computer to perform the steps of acquiring a reference signal output to a measurement space and a response signal indicating the subject's response to measurement information output to the measurement space in conjunction with the output of the reference signal, and measuring the subject's reactivity based on the response signal and the reference signal.

[0008] The model generation device according to the present disclosure includes an acquisition unit that acquires a reference signal output to a measurement space, reaction signals indicating the reactions of multiple subjects to measurement information output to the measurement space in conjunction with the output of the reference signal, evaluation contents that evaluate the conditions of the multiple subjects, and judgment results that are determined, and a training unit that associates the reaction signals of the multiple subjects based on the reference signal, and generates a machine learning model based on a data set that combines the associated reaction signals and judgment results.

[0009] The program of the present disclosure causes a computer to execute the steps of acquiring a reference signal output to a measurement space, response signals indicating the responses of multiple subjects to measurement information output to the measurement space in conjunction with the output of the reference signal, evaluation contents evaluating the conditions of the multiple subjects, and judgment results; and matching the response signals of the multiple subjects based on the reference signal, and generating a machine learning model based on a data set combining the matched response signals and judgment results.

[0010] According to the present disclosure, it is possible to accurately measure a subject's responsiveness to measurement information.

[0011] 1 is a diagram showing the configuration of a measurement system including a measurement device according to a first embodiment of the present disclosure. FIG. 1 is a diagram showing the configuration of a terminal device. FIG. 1 is a diagram showing the configuration of a measurement device. FIG. 2 is a diagram showing changes in eye response signals and reference signals. FIG. 2 is a diagram showing an overview of a first embodiment. FIG. 3 is a flowchart showing the operation of the first embodiment. FIG. 4 is a diagram showing a subject watching a measurement video. FIG. 5 is a diagram showing a screen for aligning the gaze in the measurement video. FIG. 6 is a diagram showing a circular mark moving in the measurement video. FIG. 7 is a diagram showing a state in which a machine learning model is used to evaluate the possibility that a subject has a specific disease. FIG. 8 is a diagram showing the configuration of a detection unit in the second embodiment. FIG. 9 is a diagram showing changes in scenes in the measurement video according to the second embodiment. FIG. 10 is a diagram showing changes in hand response signals and reference signals. FIG. 11 is a diagram showing an overview of a modification of the second embodiment. FIG. 12 is a diagram showing changes in eye and hand response signals and reference signals. FIG. 13 is a diagram showing the configuration of a model generation device according to a fifth embodiment. FIG. 14 is a flowchart showing the operation of the fifth embodiment. FIG. 15 is a diagram showing how optical signals are output in the modifications of the first to fourth embodiments.

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0013] 1 shows the configuration of a measurement system including a measurement device according to a first embodiment of the present disclosure. The measurement system 1 includes a plurality of terminal devices 2, a communication system 3, and a measurement device 4. The terminal devices 2, the communication system 3, and the measurement devices 4 are connected to each other.

[0014] The terminal device 2 is placed in a measurement space to detect the reactivity of the subject. Here, the measurement space refers to a space in which the subject is positioned and the response of the subject is measured.

[0015] The communication system 3 includes multiple BSs (Base Stations) 5, a core network 8, and the Internet 9. The BSs 5 are connected to the core network 8. The core network 8 is connected to the measurement device 4 via the Internet 9. The communication system 3 may be configured, for example, from a fifth-generation mobile communication system (5G). However, the communication system according to the present disclosure is not limited to this, and may be other cellular communication systems such as 4G and 6G, wireless communication systems such as Wi-Fi (registered trademark) and wireless LAN (Local Area Network), or fixed telephone lines.

[0016] The BS 5 wirelessly communicates with the terminal device 2 present in its wireless communication area, and transmits data including the detection data transmitted from the terminal device 2 to the core network 8. Here, the data may include information such as the age and sex of the subject, for example.

[0017] The core network 8 is a network line that connects the terminal device 2 and the measurement device 4 via the BS 5 and the Internet 9. The core network 8 may include, for example, a program that controls the communication function in the communication system 3, and upon receiving data transmitted from the BS 5, may transmit the data to the measurement device 4 via the Internet 9 based on the program.

[0018] The measurement device 4 acquires data including the detection data from the terminal device 2 via the communication system 3, and measures the reactivity of the subject, for example, the reactivity of the subject's eyes, based on the detection data. However, the measurement system 1 according to the present disclosure is not limited to the above-described system configuration, and may have other system configurations.

[0019] FIG. 2 shows the detailed configuration of the terminal device 2.

[0020] The terminal device 2 has a hardware configuration including a storage unit 10, a processor 11, a user interface (UI) 12, and a communication unit 13, which are interconnected via a bus B. The terminal device 2 may be realized by a computing device such as a smartphone, a tablet, a personal computer, or a gaze measurement system. Programs or instructions for realizing various functions and processes in the terminal device 2, which will be described later, may be downloaded from an external device via a network or the like, or may be provided from a removable storage medium such as a flash memory.

[0021] The storage unit 10 is realized by one or more non-transitory storage media, such as a random access memory, a flash memory, or a hard disk drive, and stores installed programs or instructions as well as files, data, etc. used to execute the programs or instructions. The storage unit 10 according to this embodiment may also store measurement information, a reference signal, etc.

[0022] Here, the measurement information is information used for measurements, examinations, etc. provided to the subject, and is output to a measurement space where the subject's reaction is measured. The measurement information is, for example, information for causing the subject to react to a specific part, and may be composed of a measurement video in which the image changes so that the subject's eyes react.

[0023] The reference signal may also be output into the measurement space in conjunction with the measurement information, and may be, for example, a signal indicating the temporal position of the sequentially output measurement information. For example, the reference signal may be configured so that its amplitude changes in a specific pattern to indicate the temporal position of the measurement information. The reference signal may also be configured so that its frequency changes in a specific pattern. The reference signal may also be configured so that its ON and OFF states change in a specific pattern. The reference signal may also be composed of one or more of the above signals. In one embodiment, the reference signal may be composed of one or more high-frequency sound signals higher than a predetermined frequency. Specifically, the sound signal may be composed of one or more sounds of frequencies above a frequency band of approximately 14 kHz that are difficult for the average person to perceive, or sounds in the inaudible frequency range. For example, the sound signal may be composed of multiple frequencies between 16 kHz and 18 kHz, and each sound signal may be configured to change in a specific pattern.

[0024] The user interface (UI) 12 may be composed of input devices such as a keyboard, a mouse, a camera, a microphone, a wireless controller, etc., output devices such as a display, an infrared LED, a speaker, a headset, a printer, etc., and input / output devices such as a touch panel, and realizes an interface between the user and the terminal device 2. For example, the user may operate the terminal device 2 by operating a GUI (Graphical User Interface) displayed on a display or a touch panel using a keyboard, a mouse, etc.

[0025] Here, the display displays measurement information such as a measurement video in the measurement space under the control of the processor 11. The speaker outputs a sound wave signal of a reference signal in the measurement space under the control of the processor 11.

[0026] The camera captures captured images by capturing the subject's reaction to the measurement video, for example, a captured video including the subject's continuous reaction signals to the measurement video. In this way, the camera also functions as a detection unit that detects the subject's reaction. The camera captures, for example, an area including the subject's eyes to detect the subject's eye reaction to the measurement video, i.e., movement of the viewpoint. Note that the method for detecting movement of the viewpoint may be a method combining a camera with an infrared LED point light source or a method using only a camera. Furthermore, the captured images are not limited to captured videos, and may include, for example, multiple images captured at a predetermined time interval. The microphone captures received sound including a sound wave signal output from the speaker to the measurement space as a reference signal.

[0027] The processor 11 may be realized by one or more central processing units (CPUs), graphics processing units (GPUs), processing circuitry, etc., each of which may be configured with one or more processor cores. The processor 11 executes various functions and processes of the terminal device 2, which will be described later, in accordance with programs, instructions, data such as parameters required to execute the programs or instructions, etc., stored in the storage unit 10. The processor 11 according to this embodiment realizes an output control unit 11a and a data processing unit 11b.

[0028] The output control unit 11a controls the display, speaker, etc. of the UI 12 to output the measurement video and sound wave signal stored in the storage unit 10. At this time, the output control unit 11a controls the UI 12 to output the measurement video into the measurement space in conjunction with the output of the sound wave signal. That is, the output control unit 11a causes the UI 12 to output the sound wave signal and the measurement video into the measurement space so that the amplitude of the sound wave signal changes sequentially in accordance with the progress of the measurement video.

[0029] The output control unit 11a is not limited to outputting the measurement video and sound wave signals stored in the storage unit 10. For example, the output control unit 11a may output the measurement video and sound wave signals distributed from the measurement device 4 via the communication system 3.

[0030] The data processing unit 11b receives the video captured by the camera of the UI 12 and the received sound captured by the microphone of the UI 12, and temporarily stores the video and the received sound in sequential association with each other. That is, the data processing unit 11b may sequentially associate the video and the received sound based on the input time and store them as a video file. This allows the subject's reaction contained in the video to be stored together with the sound signal contained in the received sound. In this way, detection data is obtained in which the subject's reaction to the measurement video is associated with the sound signal.

[0031] The communication unit 13 is realized by various communication circuits that execute communication processing with the BS 5, and outputs the detection data obtained by the data processing unit 11b to the BS 5.

[0032] It should be noted that the above-described hardware configuration is merely an example, and the terminal device 2 according to the present disclosure may be realized by any other appropriate hardware configuration.

[0033] FIG. 3 shows the configuration of the measuring device 4 in detail.

[0034] The measuring device 4 has, as its hardware configuration, a communication unit 14, a processor 15, a storage unit 16, and a user interface (UI) 17, which are interconnected via a bus B.

[0035] The measuring device 4 may be realized by a computing device such as a server, a smartphone, a tablet, a personal computer, etc. The programs or instructions that realize the various functions and processes of the measuring device 4 described below may be downloaded from an external device via a network or the like, or may be provided from a removable storage medium such as a flash memory.

[0036] The communication unit 14 is realized by various communication circuits that execute communication processing with the Internet 9 , and receives detection data output from a plurality of terminal devices 2 via the communication system 3 .

[0037] The processor 15 may be realized by one or more central processing units (CPUs), graphics processing units (GPUs), processing circuitry, etc., each of which may be configured with one or more processor cores. The processor 15 executes various functions and processes of the measuring device 4, which will be described later, in accordance with programs, instructions, data such as parameters required to execute the programs or instructions, etc., stored in the storage unit 16. The processor 15 according to this embodiment realizes an acquisition unit 15a and a measurement unit 15b.

[0038] Based on the detection data received by the communication unit 14, the acquisition unit 15a acquires a reference signal (e.g., an acoustic signal) output to the measurement space and a response signal indicating the subject's response to measurement information (e.g., a measurement video) output to the measurement space in conjunction with the output of the reference signal.

[0039] For example, as shown in FIG. 4 , the acquisition unit 15a acquires continuous response signals Sa1 and Sa2 indicating the response of the subject's eyes from the captured video of the detection data, and acquires a sound signal Sb associated with the response signals Sa1 and Sa2 from the received sound of the detection data. Here, the response signals Sa1 and Sa2 indicate time on the horizontal axis and the direction of eye movement of the subject S (Sa1 indicates the left-right direction, and Sa2 indicates the up-down direction) on the vertical axis. Furthermore, the sound signal Sb indicates time on the horizontal axis and amplitude on the vertical axis. The time of the response signals Sa1 and Sa2 and the sound signal Sb indicates the playback time (the time from start to end) of the measurement video. The response signals Sa1 and Sa2 are data indicating the direction of movement of the subject S's right eye, and data indicating the direction of movement of the left eye is omitted.

[0040] The measurement unit 15b measures the reactivity of the subject based on the response signal and the sound wave signal acquired by the acquisition unit 15a. For example, it is said that signs of Parkinson's disease can be detected based on the reactivity of the eyes. Therefore, in assessing the possibility of Parkinson's disease, the measurement unit 15b may measure the reactivity of the subject's eyes based on the sound wave signal Sb by comparing the response signals Sa1 and Sa2 with a response signal model generated by classifying subjects into healthy individuals, Parkinson's disease patients, etc. The measurement unit 15b may also assess the possibility of a specific disease by comparing the response signals Sa1 and Sa2 with the model.

[0041] The memory unit 16 stores the subject's response signals Sa1 and Sa2 and the sonic signal Sb obtained from the processor 15, the evaluation of the subject's eye reactivity, and the results of an expert's assessment of the subject's condition and specific diseases. The memory unit 16 is realized by one or more non-transitory storage media, such as a random access memory, a flash memory, or a hard disk drive, and stores installed programs or instructions, as well as files, data, etc. used to execute the programs or instructions. The memory unit 16 in this embodiment stores multiple subject's response signals in association with the sonic signals.

[0042] The user interface (UI) 17 may be composed of input devices such as a keyboard, mouse, camera, microphone, etc., output devices such as a display, speaker, headset, printer, etc., and input / output devices such as a touch panel, and provides an interface between the user and the measuring device 4.

[0043] It should be noted that the above-described hardware configuration is merely an example, and the measurement device 4 according to the present disclosure may be realized by any other appropriate hardware configuration.

[0044] Next, the operation of this embodiment will be described. FIG. 5 shows an overview of this embodiment. In this embodiment, the terminal device 2 outputs a reference signal and measurement information in conjunction with each other to the measurement space R, and acquires a captured video of the subject S's reaction to the measurement information together with received sound including the reference signal. In this case, the terminal device 2 that outputs the reference signal and measurement information in the measurement space R and the terminal device 2 that acquires the captured video of the subject S's reaction together with received sound including the reference signal may be different devices. Next, the measurement device 4 acquires a reaction signal indicative of the subject S's reaction to the measurement information from the captured video, and acquires a reference signal from the received sound. The measurement device 4 then measures the reactivity of the subject S based on the reaction signal and the reference signal, and outputs the evaluation results to the terminal device 2.

[0045] Next, the operation of this embodiment will be described in detail with reference to the flowchart shown in Fig. 6. First, the output control unit 11a of the terminal device 2 shown in Fig. 2 controls the display and speaker of the UI 12 to output the measurement video (measurement information) and sound wave signal (reference signal) Sb stored in the storage unit 10. As a result, in step S1, the measurement video is displayed on the display, and the sound wave signal Sb is output from the speaker to the measurement space R in which the measurement video is displayed.

[0046] At this time, the output control unit 11a of the processor 11 displays the sound wave signal Sb and the measurement video on the display in a linked manner. For example, the output control unit 11a may output the sound wave signal Sb and the measurement video based on saved data in which the sound wave signal Sb and the measurement video are associated and saved so that they are linked to each other. In other words, the saved data is data that is associated and saved so that when either the sound wave signal Sb or the measurement video is output, the other is also output in a linked manner. For example, the saved data may be data that includes the sound wave signal Sb and the measurement video in a single file, such as a video file in which the sound wave signal Sb and the measurement video are associated and saved. The saved video file may be, for example, MP4 (Moving Picture Experts Group Audio Layer-4), AVI (Audio Video Interleave), MOV (QuickTime File Format), or FLV (Flash Video). The output control unit 11a may save the sonic signal Sb and the measurement video separately and start displaying the measurement video simultaneously with the start of output of the sonic signal Sb. The output control unit 11a may also start displaying the measurement video at a predetermined timing while outputting the sonic signal Sb.

[0047] 7, in the measurement space R in which the subject S is measured, the subject S watches a measurement video displayed on the display 12b while a sound wave signal Sb is output from the speaker 12a of the UI 12. At this time, the eyes of the subject S react to changes in the scene in the measurement video.

[0048] An example of the measurement video will now be described. For example, as shown in FIG. 8 , when the measurement video M starts, a screen for aligning the subject S's gaze with respect to the display 12b is displayed as preprocessing. On this alignment screen, for example, four reference points P are displayed sequentially at predetermined positions, and audio and text instructions for the subject S to sequentially view the four reference points P may be output along with the sound wave signal Sb. This aligns the subject S's gaze with respect to the display 12b, allowing the position of the subject S's gaze to be accurately measured. At the same time, it is also possible to confirm that the sound wave signal Sb is being properly reproduced in the measurement space R in conjunction with the measurement video M.

[0049] 9A, a circular mark is displayed at the position of Ma in the measurement video M. Then, an instruction to "follow the moving circular mark with your eyes" is output to the subject S, for example, by voice.

[0050] In the measurement video M, a circular mark is displayed at position Ma for a predetermined time, and then moves to position Mb shown in Fig. 9B. Then, in the measurement video M, a circular mark is displayed at position Mb for a predetermined time, and then moves to position Mc shown in Fig. 9C. At this time, the subject S's line of sight moves along the path of the circular mark displayed on display 12b.

[0051] 7 is composed of frequencies higher than a predetermined frequency, for example, frequencies exceeding a frequency band that is difficult for the average person to perceive, so that the subject S can watch the measurement video M without being distracted by the sound wave signal Sb.

[0052] In this way, while the subject S is watching the measurement video M, the camera 12c of the UI 12 captures the subject S's eye reaction to the measurement video M, and the microphone 12d of the UI 12 collects the sound wave signal Sb output into the measurement space R.

[0053] Conventionally, when displaying a video on a terminal device and evaluating cognitive function, motor function, etc. based on the gaze movement of a subject S, an expert or assistant had to be present during the measurement to confirm that the video was correctly displayed on the screen of the terminal device during the measurement. Furthermore, conventionally, there was no method for accurately linking the temporal position of the video displayed during the measurement with the gaze movement of the subject S.

[0054] Therefore, in the present disclosure, the sound wave signal Sb is output to the measurement space R in conjunction with the measurement video M, the eye response of the subject S to the measurement video M is captured by the camera 12c, and the sound wave signal Sb output to the measurement space R is received by the microphone 12d. This allows the captured video of the eye response of the subject S and the sound wave signal Sb output to the measurement space R to be synchronously acquired. Therefore, even with detection data acquired from multiple terminal devices 2, for example, the eye response of each subject S and the temporal position of the measurement video M being played at that time can be accurately determined based on the acquired captured video and sound wave signal Sb. Furthermore, when aligning the gaze as shown in FIG. 8 , if the subject S's gaze does not move in the acquired captured video or if the sound wave signal Sb is not present, it can be determined that the measurement is invalid because the reference signal and measurement information are not correctly output from the terminal device 2 or the subject S cannot visually observe the measurement information. This ensures that measurement information is output correctly even if an expert or assistant is not present during the measurement.

[0055] Next, the captured video captured by the camera 12c and the received sound captured by the microphone 12d are input to the data processing unit 11b of the processor 11. The data processing unit 11b sequentially associates the captured video and received sound input from the camera 12c and the microphone 12d and temporarily stores them. Examples of a storage format include a file format in which the captured video and received sound are associated and included in a single video file. Here, the captured video is a capture of the subject S's eye response to the measurement video M, and the received sound includes a sound wave signal Sb linked to the measurement video M. Therefore, by synchronously acquiring the captured video and received sound, the subject S's eye response (movement) can be associated with the measurement video M via the sound wave signal Sb.

[0056] Here, consider a case where the eye response of the subject S to the measurement video M is recorded in association with the time of the clock built into the terminal device 2, rather than the sound wave signal Sb. In this case, the start time between pressing the play button for the measurement video M and the start of playback of the measurement video M may differ among the terminal devices 2 in multiple different measurement spaces R due to factors such as the processing speed and available cache capacity of the terminal device 2. Furthermore, there is a risk that playback of the measurement video M may temporarily freeze during playback. As a result, the eye response of the subject S and the measurement video M will be associated with different times. In this case, even if the logs of each terminal device 2 are examined, it is impossible to determine the exact time when the measurement video M was actually displayed on the display 12b.

[0057] Therefore, in the present disclosure, the eye response of the subject S is associated with the measurement video M via the sound wave signal Sb acquired from the measurement space R. As a result, even if there is a delay in starting playback of the measurement video M from the terminal device 2, the progress of the sound wave signal Sb also progresses with a delay along with the measurement video M, so that the eye response of the subject S and the measurement video M can be associated and recorded at the same time position via the sound wave signal Sb, regardless of the specifications of the terminal device 2.

[0058] Next, the data processing unit 11b determines that the reaction measurement has ended, for example, when the measurement video M has ended. Thereafter, the terminal device 2 outputs the detection data including the captured video and the received sound to the measurement device 4 via the communication system 3.

[0059] 3 is received by the acquiring unit 15a of the processor 15 via the communication unit 14. Then, in step S2, the acquiring unit 15a extracts response signals Sa1 and Sa2 indicating the eye movements of the subject S and a sound wave signal Sb from the detection data.

[0060] Specifically, the acquisition unit 15a detects the subject's eye region from the captured video included in the detection data and tracks the eye movement (movement of the line of sight) as the captured video progresses. This eye movement of the subject S indicates a reaction to the measurement video M. Therefore, by tracking the eye movement, the acquisition unit 15a can acquire continuous response signals Sa1 and Sa2 indicating the eye reaction of the subject S to the measurement video M. The acquisition unit 15a also performs a process to extract a sound wave signal Sb from the received sound included in the detection data. At this time, the acquisition unit 15a may detect the subject's facial direction in addition to the eye region from the captured image, and combine these to calculate and track the position of the subject's gaze. This allows the eye response signals Sa1 and Sa2 to be measured more accurately.

[0061] Here, since the captured video and the received sound are associated with each other in the terminal device 2, the acquisition unit 15a can extract data from the acquired or recorded detection data in a form in which the sound wave signal Sb and the response signals Sa1 and Sa2 are associated with each other, as shown in Figure 4.

[0062] The acquisition unit 15a may also record the detection data, or the data of the sound wave signal Sb and the response signals Sa1 and Sa2 extracted from the detection data, in the storage unit 16 for each subject.

[0063] Subsequently, in step S3, the measurement unit 15b of the processor 15 measures the reactivity of the eye of the subject S based on the response signals Sa1 and Sa2 and the sound wave signal Sb.

[0064] For example, when the response signals Sa1 and Sa2 of multiple subjects S are acquired, the response signals Sa1 and Sa2 are acquired and recorded in a manner correlated with the sound wave signal Sb in the same manner for each subject S. That is, the measurement unit 15b can accurately measure the eye reactivity of the subject S by comparing the response signals Sa1 and Sa2 of the subject S with the response signals of other subjects at the same time position based on the sound wave signal Sb.

[0065] For example, the measurement unit 15b may compare the response signals Sa1 and Sa2 of the subject S with the response signals of a healthy person, a Parkinson's disease patient, etc., based on the sound wave signal Sb. Generally, in diseases such as Parkinson's disease, symptoms such as an inability to focus the gaze and a delayed response to gaze movement appear in the eye reactivity, unlike in healthy people. Therefore, the measurement unit 15b may evaluate whether the eye reactivity of the subject S is normal by comparing the response signals Sa1 and Sa2 of the subject S with the response signals of a healthy person, a Parkinson's disease patient, etc.

[0066] The measurement unit 15b may also measure the eye reactivity of the subject S using AI (artificial intelligence). For example, as shown in FIG. 10 , the measurement unit 15b generates a trained machine learning model MD that learns the characteristics of both subjects based on response signal data obtained by classifying response signals Sa1 and Sa2 of multiple subjects into healthy subjects and Parkinson's disease patients. At this time, the trained machine learning model MD may be trained by assigning labels to each data item indicating the progression of Parkinson's disease and the age and gender of each subject. The measurement unit 15b may then input the sound wave signal Sb and the response signals Sa1 and Sa2 detected from a specific subject S into the trained machine learning model Ma, thereby evaluating the possibility that the subject S has Parkinson's disease based on the eye reactivity of the subject S. The measurement unit 15b may also evaluate the eye reactivity of the subject S in stages.

[0067] In this way, the measurement unit 15b measures the reactivity of the subject S based on the sound wave signal Sb output to the measurement space R and the response signals Sa1 and Sa2 indicating the response of the subject S to the measurement video M output to the measurement space R in conjunction with the output of the sound wave signal Sb. This response signal Sa is accurately correlated with the temporal position of the measurement video M via the sound wave signal Sb acquired from the measurement space R in synchronization with the measurement video M. Therefore, the reactivity of the subject S's eyes to the display content of the measurement video M can be accurately measured.

[0068] Furthermore, the measurement unit 15b measures the reactivity of the subject by associating the response signals Sa1 and Sa2 acquired at different times in different measurement spaces R based on the sound wave signal Sb. This allows the measurement unit 15b to easily and accurately measure the reactivity of multiple subjects measured in different measurement spaces R.

[0069] Furthermore, when the measurement unit 15b measures the reactivity of the subject S, using the continuously changing measurement video M results in a large difference in the amount of information of the response signal Sa indicating the subject's response that can be acquired within the same time period compared to using an unchanging still image. In this way, the measurement unit 15b can measure the reactivity of the subject S's eyes by acquiring a large amount of information by linking the time-series changes in the response signal Sa with the measurement video M.

[0070] Furthermore, the sound wave signal Sb may be configured to change in amplitude or frequency at intervals of, for example, several milliseconds. This allows the temporal position of the measurement video M to be associated with the response signals Sa1 and Sa2 of the subject S with higher accuracy, and therefore the movement of the subject S's line of sight can also be tracked with higher accuracy. This allows the reactivity of the subject S's eyes to be measured more accurately.

[0071] Furthermore, the measurement unit 15b compares the response signals Sa1 and Sa2 of the subject S with the response signals of other subjects based on the sound wave signal Sb. At this time, the response signals of multiple subjects can be compared in association with the same time position, so that the reactivity of the subject S's eyes can be measured more accurately.

[0072] In this way, the measurement unit 15b measures the reactivity of the eyes of the subject S, and then evaluates the state of the motor function of the subject S based on the measured reactivity. Then, in step S4, the measurement unit 15b outputs the evaluation of the reactivity and the possibility of the subject S suffering from a specific disease to the terminal device 2 of the subject S via the communication unit 14.

[0073] By periodically performing such measurements, it becomes possible to scientifically record and evaluate the decline in motor function by comparing the recorded past and present response signals Sa1 and Sa2 of the subject S based on the sound wave signal Sb. Furthermore, if the subject S develops Parkinson's disease, it becomes possible to accurately compare the recorded data of the subject S with the data of other healthy individuals measured using the same method.

[0074] Second Embodiment Hereinafter, a second embodiment of the present disclosure will be described. Here, differences from the first embodiment will be mainly described, and common reference numerals will be used to designate common features with the first embodiment, and detailed descriptions thereof will be omitted.

[0075] In the first embodiment described above, the measurement unit 15b measures the reactivity of the eyes of the subject S, but the present invention is not limited to this and may be adapted to measure the reactivity of the subject S. For example, the measurement unit 15b may measure the reactivity of the hands of the subject S. Furthermore, the measurement unit 15b may evaluate the possibility of the subject S suffering from Parkinson's disease or the like based on the reactivity of the hands.

[0076] An overview of this embodiment is shown in Figure 11. In this embodiment, the terminal device 2 outputs a reference signal and measurement information in conjunction with each other to the measurement space R, and acquires a response signal indicating the response of the subject S's hand to the measurement information along with received sound containing the reference signal. Next, the measurement device 4 extracts the hand response signal and reference signal contained in the received sound. The measurement device 4 then measures the hand reactivity of the subject S based on the hand response signal and reference signal, and outputs the evaluation results to the terminal device 2.

[0077] 12 , the UI 12 of the terminal device 2 may include a detection unit 21. The detection unit 21 has a grip portion 22, five detection buttons 23, and a speaker 24.

[0078] The grip portion 22 is a portion to be held by the subject S with the hand, and is formed to extend in a rod-like shape. The detection buttons 23 are to be pressed by the subject S with their fingers, and are arranged corresponding to the positions of the fingers. When the five detection buttons 23 are pressed by the subject S with their fingers, each of them controls the speaker 24 to output a reaction sound into the measurement space R. This reaction sound may be configured to output a sound of a different frequency for each detection button 23, for example. Furthermore, the reaction sound of the detection buttons 23 may be output into the measurement space R as a reaction signal Sc of a different frequency from the sound wave signal Sb.

[0079] Furthermore, the detection unit 21 incorporates a parallelism sensor and a vibration sensor (not shown). The parallelism sensor is a sensor that detects the parallelism and inclination of the hand. The vibration sensor is a sensor that detects hand tremors. Detection signals detected by the parallelism sensor and the vibration sensor are output to the data processing unit 11b of the processor 11 via an electric signal cable. The detection unit 21 may be physically connected to the terminal device 2 via an electric signal cable, or may be connected via wireless communication.

[0080] Next, the operation of this embodiment will be described.

[0081] First, as in embodiment 1, under the control of the output control unit 11a, a sound wave signal Sb is output from the speaker 12a of the UI 12 to the measurement space R, and a measurement video is displayed on the display 12b in conjunction with the output of this sound wave signal Sb.

[0082] An example of a measurement video will now be described. For example, as shown in FIG. 13 , a plurality of rectangular change portions M1a are displayed in the measurement video M1. An instruction such as "Press the detection button 23 when the change portions M1a turn red" is output to the subject S, for example, by voice and text. The plurality of change portions M1a then change to red in sequence at irregular intervals, thereby inducing a hand reaction from the subject S. The measurement video M1 and instructions may be configured so that measurements are taken separately for the left and right hands. The measurement video M1 and instructions may also be configured so that measurements are taken simultaneously for both the left and right hands.

[0083] As shown in FIG. 12 , the subject S holds the grip portion 22 of the detection unit 21 and presses the detection button 23 with his / her finger in response to a change in the display of the change portion M1a. Then, a response signal Sc is output from the speaker 24 in response to the pressing of the detection button 23. Furthermore, a vibration sensor or the like built into the detection unit 21 constantly detects the tremors of the subject S's hand and outputs the detection signal to the data processing unit 11b. The response signal Sc may be composed of, for example, a sound wave signal having a frequency lower than that of the sound wave signal Sb, for example, a frequency band included in the human audible range. Then, as in the first embodiment, the microphone 12d receives a received sound including the sound wave signal Sb and the response signal Sc output into the measurement space R and outputs the received sound to the data processing unit 11b.

[0084] The data processing unit 11b generates detection data by associating the detection signal detected by the detection unit 21 with the received sound input from the microphone 12d. Before performing the measurement, for example, four reference points P as shown in FIG. 8 may be sequentially flashed in red to prompt the subject S to press the detection button 23, thereby confirming whether there is any error between the detection signal and the response signal Sc output by the detection unit 21. By associating the detection signal with the received sound in this manner, the response signal Sc and the detection signal of the subject S's hand can be associated with the measurement video M1 via the sound wave signal Sb. The detection data is then output from the communication unit 13 via the data processing unit 11b to the measurement device 4 via the communication system 3. The detection data is then input to the acquisition unit 15a of the processor 15 via the communication unit 14.

[0085] The acquisition unit 15a extracts a sound wave signal Sb and a response signal Sc from the received sound included in the detection data. As a result, data is acquired in which the response signal Sc of the subject S's hand is associated with the sound wave signal Sb, as shown in FIG. 14 . Here, only the response signal Sc of one finger (sound of one frequency) is shown. The measurement unit 15b then measures the reactivity of the subject S's hand based on the response signal Sc and sound wave signal Sb acquired by the acquisition unit 15a. For example, the measurement unit 15b measures the reactivity of the subject S's hand by comparing the response signal Sc of the subject S with the response signals of other subjects based on the sound wave signal Sb.

[0086] At this time, the measurement unit 15b measures the reactivity of the subject S based on the sound wave signal Sb output to the measurement space R and the reaction signal Sc indicating the reaction of the subject S to the measurement video M output to the measurement space R in conjunction with the output of the sound wave signal Sb. This reaction signal Sc is accurately associated with the temporal position of the measurement video M1 via the sound wave signal Sb acquired from the measurement space R in synchronization with the measurement video M1. Therefore, by comparing the reaction signals of other subjects, the reactivity of the hand of the subject S can be measured more accurately.

[0087] Furthermore, the measurement unit 15b may evaluate the possibility that the subject S is suffering from Parkinson's disease or the like based on the reactivity of the hand of the subject S. Generally, diseases such as Parkinson's disease exhibit symptoms such as delayed reaction to opening and closing the hand compared to healthy individuals. Therefore, the measurement unit 15b may evaluate the possibility that the subject S is suffering from Parkinson's disease or the like based on the reactivity of the hand of the subject S.

[0088] Furthermore, diseases such as Parkinson's disease generally exhibit symptoms such as movement disorders and tremors, etc. Therefore, the measurement unit 15b may evaluate the possibility of the subject S suffering from Parkinson's disease or the like by measuring the tremors of the subject's hands based on detection signals obtained from a vibration sensor or the like built into the detection unit 21 in addition to the hand responsiveness.

[0089] As shown in FIG. 15 , the terminal device 2 may acquire a response signal indicating the subject S's hand response to the measurement information, a video capturing the eye response, and a detection signal detected by the detection unit 21, along with a received sound including a reference signal. This allows the measurement device 4 to acquire the reference signal, the hand response signal, the eye response signal, and the detection signal. The measurement device 4 then measures the reactivity of the subject S based on the hand and eye response signals, the reference signal, and the detection signal. At this time, the acquisition unit 15a acquires data associating the subject S's eye response signals Sa1 and Sa2 and hand response signal Sc with the sound signal Sb, as shown in FIG. 16 . For example, the measurement unit 15b may measure the reactivity of the subject S's eyes and hands by comparing the response signals Sa1 and Sa2 and the response signal Sc with the eye response signals and hand response signals of another subject at the same time based on the sound signal Sb. Furthermore, the measurement unit 15b may evaluate a particular disease of the subject S, such as Parkinson's disease, or the degree of progression of the disease, based on the reactivity of the eyes and hands and the tremors of the hands.

[0090] The detection unit 21 may also be configured, like a foot switch, to be placed under each foot and output a foot response signal Sc from a speaker 24 when a detection button 23 is pressed. This allows the acquisition unit 15a of the measurement device 4 to acquire the foot response signal and the sonic signal Sb. The measurement unit 15b may then evaluate the foot response of the subject S by comparing the foot response signal of the subject S with the foot response signals of other subjects based on the sonic signal Sb. The measurement unit 15b may also evaluate a specific disease or the degree of progression of the disease in the subject S, such as Parkinson's disease, based on the reactivity of the eyes, hands, and feet and the hand tremor.

[0091] In the above description, the reactivity of the subject S is measured using the detection unit 21. However, the detection unit 21 may be integrally arranged on the display 12b of the terminal device 2 (e.g., a touch panel) and the reactivity of the subject S may be directly measured without using the detection unit 21 by touching the display 12b with a hand. For example, when a change portion M1a in the measurement video M1 is displayed in red, the terminal device 2 instructs the subject S to touch the red portion with a finger. This allows the terminal device 2 to acquire a response signal Sc of the subject S's hand from the display 12b. Furthermore, the terminal device 2 may acquire detection signals from a parallel sensor and a vibration sensor provided in the terminal device 2 during this measurement. The data processing unit 11b may then generate detection data by associating the detection signal and response signal Sc detected by the terminal device 2 with the video captured by the camera 12c and the received sound input from the microphone 12d.

[0092] According to this embodiment, the measurement unit 15b measures the reactivity of the hand of the subject S based on the sound wave signal Sb output to the measurement space R and the reaction signal Sc indicating the reaction of the hand of the subject S to the measurement video M1 output to the measurement space R in conjunction with the output of the sound wave signal Sb. This reaction signal Sc can be correlated with the measurement video M1 with high accuracy via the sound wave signal Sb acquired from the measurement space R synchronously with the measurement video M1, so that the reactivity of the hand of the subject S can be accurately measured.

[0093] Third Embodiment Hereinafter, a third embodiment of the present disclosure will be described. Here, the differences from the first and second embodiments will be mainly described, and common reference numerals will be used for common features with the first and second embodiments, and detailed description thereof will be omitted.

[0094] In the above-mentioned embodiments 1 and 2, the measurement unit 15b evaluated the possibility that the subject S is suffering from a specific disease, such as Parkinson's disease, but this is not limited to this as long as the evaluation can be made based on the reactivity of the subject S.

[0095] For example, the measurement unit 15b may evaluate the improvement of reflexes based on the responsiveness of the subject S. For example, for a baseball player, reflexes (e.g., physical reflexes, dynamic visual acuity, etc.) are considered important when hitting a ball at bat. However, although various methods for improving reflexes (e.g., methods for practicing good quality sleep, training methods, etc.) have been proposed, a player's condition changes daily, and it has been difficult to accurately measure and evaluate the effects and changes of such methods in a short period of time, such as during a game.

[0096] Therefore, as in the first and second embodiments, the terminal device 2 detects the player's eye and hand responses before and after trying a method to improve reflexes, for example, before and after taking a drug. Next, the acquisition unit 15a of the measurement device 4 acquires and records the player's response signals Sa1, Sa2, and Sc associated with the sound wave signal Sb, as shown in FIG. 16 . The measurement unit 15b then compares the recorded response signals Sa1, Sa2, and Sc based on the sound wave signal Sb to evaluate the improvement in reflexes before and after the training. This measurement allows the effectiveness of the training for the player to be evaluated.

[0097] In this embodiment, the measurement unit 15b determines the improvement of reflexes based on the reaction signals before and after trying a method for improving reflexes, but the present invention is not limited to this. For example, the measurement unit 15b may evaluate the state of reflexes of each player on a team by comparing the reaction signals Sa1, Sa2, and Sc of multiple baseball players based on the sound wave signal Sb. This evaluation can be used to identify players to use in a game or as pinch hitters.

[0098] According to this embodiment, the measuring unit 15b can accurately indicate the state of the reflexes of the subject S (athlete) in numerical values ​​based on the reactivity of the subject S.

[0099] (Embodiment 4) Hereinafter, embodiment 4 of the present disclosure will be described. Here, the differences from the above-mentioned embodiments 1 to 3 will be mainly described, and common reference numerals will be used for common features with the above-mentioned embodiments 1 to 3, and detailed description thereof will be omitted.

[0100] In the above embodiments 1 to 3, a machine learning model was used to measure the response of the subject S from the sound signal Sb and the response signal, but such a machine learning model can be generated as follows.

[0101] For example, a machine learning model may be generated by a model generation device 61 shown in Fig. 18. The model generation device 61 has, as its hardware configuration, a communication unit 14, a processor 62, a storage unit 16, and a user interface (UI) 17, which are interconnected via a bus B. Note that the communication unit 14, the storage unit 16, and the UI 17 have the same configuration as those of the measurement device 4, and therefore their description will be omitted.

[0102] The processor 62 may be realized by one or more central processing units (CPUs), graphics processing units (GPUs), processing circuitry, etc., each of which may be configured with one or more processor cores. The processor 62 executes various functions and processes of the model generation device 61 (described below) in accordance with programs, instructions, data such as parameters required to execute the programs or instructions, and the like, stored in the storage unit 16. The processor 62 according to this embodiment realizes an acquisition unit 62a and a training unit 62b.

[0103] The acquisition unit 62a acquires training data for training the machine learning model. This training data includes a reference signal (e.g., a sound wave signal Sb) output to the measurement space R, reaction signals indicating reactions of multiple subjects S to measurement information (e.g., a measurement video) output to the measurement space R in conjunction with the output of the reference signal, and evaluation results in which an expert evaluates the conditions of the multiple subjects as “normal” or “abnormal,” for example.

[0104] Here, the expert would be a doctor when diagnosing a disease such as Parkinson's disease. For example, if a doctor suspects that a subject has a tendency toward Parkinson's disease, the doctor may conduct tests such as diagnostic imaging, blood tests, urine tests, and drug reaction tests to determine whether or not each subject has Parkinson's disease.

[0105] The training unit 62b associates the response signals of the multiple subjects S based on the reference signals acquired by the acquisition unit 62a, for example, by matching their temporal positions. Then, the training unit 62b performs machine learning on a machine learning model based on a data set combining the associated response signals and the determination results, thereby generating a trained machine learning model.

[0106] Next, the operation of this embodiment will be described with reference to the flowchart shown in FIG.

[0107] First, as in the first embodiment, in step S1, the terminal device 2 displays a measurement video M and outputs a sound wave signal Sb to the measurement space R in conjunction with the display of the measurement video M. The terminal device 2 synchronously acquires the captured video of the eye reaction of the subject S and the sound wave signal Sb, and generates detection data that associates these.

[0108] The detection data of the multiple subjects S generated in this manner is input to the acquisition unit 62a of the model generation device 61 via the communication unit 14 or the UI 17. In step S21, the acquisition unit 62a extracts a response signal Sa indicating the eye movement of the subject S and a sound wave signal Sb from the detection data to acquire training data. At this time, the acquisition unit 62a receives, as training data, input information such as the age and sex of the subject and the doctor's assessment of whether the subject S is "normal" or "abnormal." Furthermore, the assessment result may include other information such as the type of disease and the degree of progression.

[0109] Next, the training unit 62b associates the multiple response signals Sa with the same temporal position based on the sound signal Sb. Then, in step S22, the training unit 62b generates a machine learning model based on a data set combining the associated response signals Sa with the judgment results of each subject S. At this time, the response signals Sa of the multiple subjects S can be compared with high accuracy based on the sound signal Sb acquired synchronously with the measurement video M from the measurement space R. That is, the response signals Sa of the multiple subjects S all coincide in temporal position relative to the measurement video M based on the sound signal Sb, so that features can be compared with very high accuracy and extracted. Therefore, the training unit 62b can generate a machine learning model that outputs an accurate judgment result (normal, abnormal, etc.).

[0110] The model generation device 61 may be built into the measurement device 4. This allows the model generation device 61 to generate a machine learning model based on the reaction signal Sa and the sound wave signal Sb transmitted from the terminal device 2 to the measurement device 4. The model generation device 61 and the measurement device 4 may also be built into the terminal device 2. This allows the model generation device 61 to generate a machine learning model based on the reaction signal Sa and the sound wave signal Sb acquired by the terminal device 2.

[0111] According to this embodiment, the training unit 62b trains a machine learning model using as input the sound wave signal Sb output to the measurement space R, the response signal Sa indicating the subject S's reaction to the measurement video M output to the measurement space R in conjunction with the output of the sound wave signal Sb, the subject's age and sex, and the expert's diagnosis of the disease name and progression stage for the subject S. At this time, the response signal Sa can be compared with a large amount of training data with high accuracy based on the sound wave signal Sb acquired from the measurement space R synchronously with the measurement video M, so that a machine learning model that outputs accurate diagnosis results can be generated.

[0112] In the above-described first to third embodiments, the reference signal is composed of a sound wave signal Sb. However, the present invention is not limited to this and may be implemented as a signal output to the measurement space R. For example, the reference signal may be composed of an optical signal displayed on the display 12b, which can be captured by the camera 12c. For example, as shown in FIG. 20, a reflector 12e may be disposed on the display 12b of the first embodiment, and the optical signal Sd displayed together with the measurement video M may be projected onto the camera 12c by the reflector 12e. The reflector 12e may be a reflector that reflects light on its surface or inside, such as a mirror or glass, and projects the optical signal Sd onto the camera 12c. The reflector 12e may also be designed so that the projected optical signal Sd is focused on the camera 12c.

[0113] Specifically, the optical signal Sd may be configured to flash a portion or multiple portions of the measurement video M. The flashing colors may be colors that can be displayed on the display 12b, with each portion or multiple portions of the measurement video M flashing in their respective colors. The flashing pattern of the optical signal Sd is configured to change in a specific pattern to indicate a temporal position in the measurement video M, and is output to the measurement space R in conjunction with the measurement video M. The optical signal Sd is then projected by the reflector 12e and captured by the camera 12c along with the subject S's reaction. That is, the optical signal Sd is projected by the reflector 12e into the imaging range of the camera 12c, which captures the subject S's reaction. The optical signal Sd does not need to flash the entire measurement video M; it is sufficient to flash a portion or multiple portions of the measurement video M. This allows the data processing unit 11b to acquire a reference signal (optical signal Sd) from the captured video capturing the subject S's reaction.

[0114] Furthermore, the terminal device 2 may output a reference signal composed of the sound wave signal Sb and the light signal Sd to the measurement space R. The terminal device 4 can accurately associate the response signal of the detection data with the reference signal by extracting the reference signal of the sound wave signal Sb and the light signal Sd from the detection data acquired from the terminal device 2. For example, if the sound wave signal Sb is partially missing due to noise in the captured video, the terminal device 4 can accurately associate the response signal of the subject S with the temporal position of the measurement video M based on the light signal Sd included in the captured video.

[0115] Furthermore, in the above-described embodiments 1 to 4, the measuring device 4 may distribute the reference signal and the measurement video M to the terminal device 2 via the communication unit 14. For example, the measuring device 4 may stream the reference signal and the measurement video M, i.e., while distributing the reference signal and the measurement video M, the terminal device 2 may sequentially output the received measurement information from the speaker 12a and the display 12b. Furthermore, in the above-described embodiments 1 to 4, the terminal device 2 may distribute detection data that associates the captured video and the received sound to the measuring device 4 via the communication unit 13. For example, the terminal device 2 may stream the detection data, i.e., while acquiring the captured video and the received sound from the camera 12c and the microphone 12d, distribute the detection data to the measuring device 4.

[0116] Generally, when streaming measurement information or detection data, phenomena such as temporary interruptions or halts in the distribution and playback of the measurement information or detection data may occur due to factors such as the internet environment or device processing speed. Here, in the present disclosure, a reference signal is linked to the measurement information and output to the measurement space R. As a result, even if a temporary interruption or halt occurs in the distribution and playback of the measurement information or detection data, the time position at which the problem occurred in the measurement information or detection data can be accurately determined based on the reference signal.

[0117] Furthermore, in the above-described first to fourth embodiments, the terminal device 2 and the measurement device 4 are connected via the communication system 3, but this is not limiting as long as the response signal and the reference signal can be acquired by the acquisition unit 15a of the measurement device 4. For example, the terminal device 2 and the measurement device 4 may be directly connected.

[0118] Furthermore, in the above-described first to fourth embodiments, the measurement device 4 is configured as a device separate from the terminal device 2, but it may be built into the terminal device 2. For example, the processor 11 of the terminal device 2 may be configured to include an acquisition unit 15a and a measurement unit 15b. The acquisition unit 15a and the measurement unit 15b constitute the measurement device 4 of the present disclosure.

[0119] For example, as in the first embodiment, a sound wave signal Sb is output from the UI 12 to the measurement space R, and a measurement video M is output from the UI 12 to the measurement space R in conjunction with the output of the sound wave signal Sb. Subsequently, the UI 12 captures an image of the subject S's reaction to the measurement video M and receives a received sound including the sound wave signal Sb. The data processing unit 11b then generates detection data by associating the captured video with the received sound and outputs the detection data to the acquisition unit 15a. Upon receiving the detection data, the acquisition unit 15a acquires reaction signals Sa1 and Sa2 indicating the subject's reaction from the captured video, and acquires a sound wave signal Sb associated with the reaction signals Sa1 and Sa2 from the received sound. The measurement unit 15b then measures the reactivity of the subject S based on the reaction signals Sa1 and Sa2 and the sound wave signal Sb.

[0120] Furthermore, in the above-described embodiments 1 to 4, the measurement video M and the sound wave signal Sb are output from the terminal device 2. However, this is not limited to this, as long as they are output to the measurement space R in conjunction with each other. For example, the measurement video M and the sound wave signal Sb may be transmitted from a broadcasting station via television broadcasting radio waves and output to the measurement space R by a television that receives the radio waves. Furthermore, the measurement video M and the sound wave signal Sb may be stored on a recording medium such as a DVD (Digital Versatile Disc) and output from an output device located separately from the terminal device 2. The subject S's response to the output measurement video is then acquired by the terminal device 2, such as a smartphone, and transmitted to the measurement device 4 via the communication system 3. In this way, the response signal may be acquired by a device different from the device that output the sound wave signal Sb and the measurement video M to the measurement space R. Even if the response signal is acquired by a different device, the sound wave signal Sb and the measurement video M are output to the measurement space R in conjunction with each other, so that the reactivity of the subject S can be accurately measured.

[0121] Furthermore, in the above-described first to fourth embodiments, the measurement device 4 is connected to the terminal device 2 via the communication system 3, but the measurement device 4 may also be placed within the core network 8 of the communication system 3. For example, the measurement device 4 may be placed in a cloud server within the core network 8. Furthermore, if an edge server is provided between the BS 5 and the core network 8, the measurement device 4 may be placed in the edge server, or some functions of the measurement device 4 may be placed separately in the core network 8 and the edge server.

[0122] Although the embodiments according to the present disclosure have been described in detail above with reference to the drawings, the functions of the above-described devices can be realized by a computer program.

[0123] A computer that realizes the functions of each of the above-mentioned devices through a program includes input devices such as a keyboard, mouse, and touchpad; output devices such as a display, speaker, and infrared LED; a CPU (Central Processing Unit); a ROM (Read Only Memory); a RAM (Random Access Memory); storage devices such as a hard disk drive and an SSD (Solid State Drive); a reading device that reads information from recording media such as a DVD-ROM (Digital Versatile Disk Read Only Memory) and a USB (Universal Serial Bus) memory; and a network card that communicates via a network, with each part connected by a bus.

[0124] The reading device reads the program for realizing the functions of each of the above devices from a recording medium on which the program is recorded and stores the program in a storage device. Alternatively, the network card communicates with a server device connected to the network and downloads the program for realizing the functions of each of the above devices from the server device and stores the program in a storage device.

[0125] The CPU then copies the program stored in the storage device to the RAM, and sequentially reads out and executes the instructions contained in the program, thereby realizing the functions of each of the above devices.

[0126] Although specific examples of the present disclosure have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and alterations of the specific examples exemplified above.

[0127] The measurement device, program, and model generation device according to the present disclosure can be used in devices and programs that utilize response signals that indicate the response of a subject.

Claims

1. A measurement device having an acquisition unit that acquires a reference signal output to a measurement space and a response signal indicating a subject's response to measurement information output to the measurement space in conjunction with the output of the reference signal, and a measurement unit that measures the reactivity of the subject based on the reference signal and the response signal.

2. The measurement device of claim 1, wherein said reference signal varies in a specific pattern to indicate the time position of said measurement information.

3. The measurement device of claim 1, wherein the reference signal includes a sound wave signal composed of one or more high frequencies higher than a predetermined frequency.

4. The measurement device of claim 1, wherein the reference signal comprises an optical signal configured to cause a portion or portions of the measurement information to flash.

5. The measurement device of claim 1, wherein the reference signal includes a signal comprised of an acoustic signal and an optical signal.

6. The measurement device of claim 1, wherein the reference signal is configured to project a light signal onto a camera for display on a display.

7. The measuring device of claim 1, wherein the acquisition unit acquires a response signal indicating the response of the subject's eye to the measurement information, and the measurement unit measures the responsiveness of the subject's eye based on the response signal and the reference signal.

8. The measuring device of claim 1, wherein the acquisition unit acquires a response signal indicating the response of the subject's hand to the measurement information, and the measurement unit measures the responsiveness of the subject's hand based on the response signal and the reference signal.

9. The measuring device of claim 1, wherein the measurement information comprises a measurement video, the acquisition unit acquires the reference signal output in conjunction with the measurement video and a continuous response signal indicating the subject's response to the measurement video, and the measurement unit measures the subject's reactivity based on the reference signal and the continuous response signal.

10. The measuring device of claim 1, wherein the measurement unit measures the reactivity of the subject by comparing the response signal of the subject with response signals indicating the responses of other subjects to the measurement information based on the reference signal.

11. The measuring device according to claim 1, wherein the response signals acquired in different measurement spaces and at different times are correlated with the response signals of a plurality of subjects based on the reference signal to measure the reactivity of the subjects.

12. The measuring device of claim 1, wherein the measurement unit measures the responsiveness of the subject by inputting the response signal and the reference signal into a machine learning model trained based on response signals indicating the responses of other subjects to the measurement information.

13. The measuring device according to claim 1, wherein the measuring section evaluates the possibility that the subject suffers from a specific disease based on the subject's reactivity.

14. The measuring device according to claim 1, wherein the measuring section determines the state of the subject's reflexes based on the subject's responsiveness.

15. The measurement device according to claim 1, wherein the measurement unit is connected via a communication system to a terminal device that outputs the reference signal and the measurement information to the measurement space, and distributes the reference signal and the measurement information to the terminal device.

16. The measurement device according to claim 1, wherein the response signal is acquired by a device different from a device that outputs the reference signal and the measurement information to the measurement space.

17. A program that causes a computer to execute the steps of: acquiring a reference signal output to a measurement space and a response signal indicating the subject's response to measurement information output to the measurement space in conjunction with the output of the reference signal; and measuring the reactivity of the subject based on the response signal and the reference signal.

18. A model generation device having: an acquisition unit that acquires a reference signal output to a measurement space, response signals indicating responses of multiple subjects to measurement information output to the measurement space in conjunction with the output of the reference signal, and judgment results in which an expert judges the conditions of the multiple subjects; and a training unit that matches the response signals of the multiple subjects based on the reference signal, and generates a machine learning model based on a data set that combines the matched response signals and the judgment results.

19. A program that causes a computer to execute the steps of: acquiring a reference signal output to a measurement space, response signals indicating responses of multiple subjects to measurement information output to the measurement space in conjunction with the output of the reference signal, and judgment results determining the conditions of the multiple subjects; and matching the response signals of the multiple subjects based on the reference signal, and generating a machine learning model based on a data set combining the matched response signals and the judgment results.

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