JP2021016630A - Detection device and detection method - Google Patents

Abstract

To provide a minute evaluation result concerning periodic time-series data.SOLUTION: A periodic time-series data abnormal part detection system 1 divides whole data 44A being periodic time-series data so as to generate partial data 45A, calculates its partial data feature amount 45B, outputs a partial data abnormality detection result 45C by displaying, which is the detection result of an abnormality in the partial data 45A based on the partial data feature amount 45B. Thus the abnormality part detection system 1 detects an abnormality for each piece of partial data 45A obtained by dividing the whole data 44A, thereby to enable presentation of information that may identify which part of the periodic time-series data is abnormal.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing service technique. The present invention also relates to a technique for realizing a detection device and a detection method for detecting an abnormal portion of periodic time series data.

In the fields of healthcare, medical care, long-term care, etc., the number of systems that measure data for people is increasing. In these systems, analysis results are calculated from the obtained data and fed back to the user to provide valuable information to the user. The above data is often periodic time series data.

An example of such a system is a system (finger tap measurement analysis system) that simply evaluates cognitive function and motor function by measuring and analyzing a user's finger tap movement (for example, Patent Document 1).

Here, the finger tapping motion is an exercise of repeatedly opening and closing the thumb and index finger. Periodic time series data can be obtained by measuring the finger tap movement. It is known that the finger tapping movement differs depending on the presence or absence and severity of brain dysfunction such as dementia and Parkinson's disease. From the analysis results of the periodic time series data measured by the above system, it has been pointed out that it is possible to evaluate the user's early detection of brain dysfunction and estimation of severity.

Japanese Unexamined Patent Publication No. 2013-109540

If the evaluation result obtained by analyzing the periodic time series data of the finger tap movement is bad, it is desirable to obtain a more detailed result about the evaluation result.

An object of the present invention is to provide more detailed evaluation results for periodic time series data.

The above and other objects and novel features of the present invention will become apparent from the description and accompanying drawings herein.

As a means for solving the above-mentioned problems, the technique described in the claims is used.

To give an example, it is a detection device that detects anomalies using periodic information indicating the state of a living body, and is a periodic information acquisition unit that acquires periodic information and a period acquired by the periodic information acquisition unit. The partial information generation unit that generates partial information based on the period from the target information, the partial information feature amount calculation unit that calculates the feature amount of the partial information generated by the partial information generation unit, and the partial information feature amount calculation unit. A partial information abnormality detection unit that detects an abnormality in the partial information generated by the partial information generation unit based on the calculated feature amount, an output unit that outputs information based on the detection result by the partial information abnormality detection unit, and an output unit. To be equipped.

By using the technique of the present invention, it is possible to provide more detailed evaluation results for periodic time series data.

It is a block diagram of the person data measurement system including the periodic time series data abnormality part detection system of 1st Embodiment. It is a block diagram of the periodic time series data abnormality part detection system of 1st Embodiment. It is a block diagram of the measuring apparatus of 1st Embodiment. It is a block diagram of the terminal apparatus of 1st Embodiment. It is a figure which shows the state which the magnetic sensor which is the motion sensor is attached to the finger of a user. It is a figure which shows the detailed configuration example of the motion sensor control part of a measuring device. It is a flowchart which shows the procedure of the whole processing in the person data measurement system of 1st Embodiment. It is a figure which shows the example of the waveform signal of a feature amount. It is a figure which shows the whole data feature quantity list. It is a figure which shows the continuation of the whole data feature quantity list. It is a figure which shows the definition example of a partial data. It is a figure which shows the partial data feature quantity list. It is a figure which shows the example of the partial data which was detected anomaly. It is a figure which shows the practice menu list. It is a figure which shows the practice menu correspondence table. It is a figure which shows the example of the menu screen which is an initial screen of a service. It is a figure which shows the task measurement screen. It is a figure which shows the evaluation result screen. It is a figure which shows the abnormality part detection result screen. It is a figure which shows the periodic time series data abnormality part detection system of 2nd Embodiment. It is a figure which shows the configuration of a server. It is a figure which shows the data structure example of the user information which a server manages in a DB.

Hereinafter, examples of embodiments of the present invention will be described with reference to the drawings. In principle, the same parts are designated by the same reference numerals in all the drawings for explaining the embodiments, and the repeated description thereof will be omitted.

The embodiment will be described in detail with reference to the drawings. However, the present invention is not construed as being limited to the description of the embodiments shown below. It is easily understood by those skilled in the art that a specific configuration thereof can be changed without departing from the idea or purpose of the present invention.

When there are a plurality of elements having the same or similar functions, they may be described by adding different subscripts to the same code. However, if it is not necessary to distinguish between a plurality of elements, the subscript may be omitted for explanation.

Notations such as "first", "second", and "third" in the present specification and the like are attached to identify the components, and do not necessarily limit the number, order, or contents thereof. is not it. In addition, numbers for identifying components are used for each context, and numbers used in one context do not always indicate the same composition in other contexts. Further, it does not prevent the component identified by a certain number from having the function of the component identified by another number.

The position, size, shape, range, etc. of each configuration shown in the drawings and the like may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings and the like.

(First Embodiment)
The periodic time series data abnormality partial detection system (detection device) of the first embodiment will be described with reference to FIGS. 1 to 18. The periodic time-series data abnormal part detection system of the first embodiment has a function of detecting an abnormal part in the periodic time-series data (periodic information indicating the state of a living body) obtained by measuring a subject. .. With this function, abnormal parts can be detected with high accuracy.

[People data measurement system]
FIG. 1 is a configuration diagram of a human data measurement system including the periodic time series data abnormality partial detection system of the first embodiment. In the first embodiment, the person data measurement system is provided in a facility such as a hospital or a facility for the elderly, a user's home, or the like. The human data measurement system includes a periodic time-series data abnormality partial detection system 1 and a measurement system 2 which is a magnetic sensor type finger tap movement system, and they are connected through a communication line. The measuring system has a measuring device 3 and a terminal device 4, and they are connected to each other through a communication line. A plurality of measurement systems 2 may be provided in the facility.

The measurement system 2 is a system that measures finger movements using a magnetic sensor type motion sensor. A motion sensor is connected to the measuring device 3. The motion sensor is attached to the user's finger. The measuring device 3 measures the finger movement through the motion sensor and obtains measurement data including a time-series waveform signal. The terminal device 4 displays various information including the partial data abnormality detection result on the display screen, and accepts the operation input by the user. In the first embodiment, the terminal device 4 is a PC.

The periodic time-series data abnormality part detection system 1 has a function of providing an abnormality part detection service as a service by information processing. The periodic time-series data abnormality partial detection system 1 has a partial data abnormality detection function as its function. The partial data abnormality detection function is a function of detecting an abnormal part in the periodic time series data measured by the measurement system 2.

The periodic time-series data abnormality part detection system 1 inputs, for example, periodic time-series data or the like as input data from the measurement system 2. The periodic time series data abnormality partial detection system 1 outputs, for example, a partial data abnormality detection result or the like as output data to the measurement system 2. The partial data abnormality detection result includes the partial data abnormality degree and the partial data abnormality feature amount in addition to the partial data abnormality determination result.

The human data measurement system of the first embodiment can be applied not only to facilities such as hospitals and facilities for the elderly and their subjects, but also to a wide range of general facilities and people. The measuring device 3 and the terminal device 4 may be configured as an integrated measuring system. The measurement system 2 and the periodic time series data abnormality partial detection system 1 may be configured as an integrated device. The terminal device 4 and the periodic time series data abnormality partial detection system 1 may be configured as an integrated device. The measuring device 3 and the periodic time series data abnormality partial detection system 1 may be configured as an integrated device.

[Periodic time series data abnormality part detection system]
FIG. 2 is a configuration diagram of the periodic time series data abnormality partial detection system 1 of the first embodiment. The periodic time-series data abnormality part detection system 1 has a control unit 101, a storage unit 102, an input unit 103, an output unit 104, a communication unit 105, and the like, and they are connected via a bus. The input unit 103 is a part for performing operation input by the administrator or the like of the periodic time series data abnormality part detection system 1. The output unit 104 is a part that displays a screen or the like for the administrator or the like of the periodic time series data abnormality part detection system 1. The communication unit 105 has a communication interface and is a part that performs communication processing with the measuring device 3 and the terminal device 4.

The control unit 101 controls the entire periodic time-series data abnormality part detection system 1, and is composed of a Central Processing Unit (CPU), a Read Only Memory (ROM), a Random Access Memory (RAM), and the like, and is used for software program processing. Based on this, a data processing unit that performs partial data abnormality detection and the like is realized. The data processing unit of the control unit 101 includes a user information management unit 11, a task processing unit 12, an overall data evaluation unit 13, a partial data abnormality evaluation unit 14, a practice menu determination unit 15, and a result output unit 16. The control unit 101 has a function of inputting measurement data from the measuring device 3, a function of processing and analyzing the measurement data, a function of outputting control instructions to the measuring device 3 and the terminal device 4, and data for display to the terminal device 4. Realize the function to output.

The user information management unit 11 performs a process of registering and managing the user information input by the user in the user information 41 of the DB 40, a process of confirming the user information 41 of the DB 40 when the user uses the service, and the like. The user information 41 includes attribute values for each individual user, usage history information, user setting information, and the like. Attribute values include gender, age, and the like. The usage history information is information that manages the history of the user using the service provided by this system. The user setting information is the setting information set by the user regarding the function of this service.

The task processing unit 12 is a part that performs processing related to tasks for analysis and evaluation of motor function and the like. The task is, in other words, a predetermined finger movement. The task processing unit 12 outputs a task to the screen of the terminal device 4 based on the task data 42 of the DB 40. Further, the task processing unit 12 acquires the measurement data (periodic information indicating the state of the living body) of the task measured by the measuring device 3 and stores it in the DB 40 as the total data 44A. Here, the whole data means the whole of the periodic time series data measured at a predetermined time. In this way, the task processing unit 12 (periodic information acquisition unit) acquires periodic information indicating the state of the living body.

The overall data evaluation unit 13 has an overall data feature amount calculation unit 13A (periodic information feature amount calculation unit) and an overall data evaluation unit 13B (periodic information abnormality detection unit). The total data feature amount calculation unit 13A calculates a feature amount representing the property of the total data 44A (periodic time series data) based on the user's total data 44A, and stores it in the DB 40 as the total data feature amount 44B. The overall data evaluation unit 13B evaluates the entire data based on the overall data feature amount 44B with reference to the overall data DB 43, and stores the overall data evaluation result 44C in the DB 40.

The partial data abnormality evaluation unit 14 includes a partial data generation unit 14A (partial information generation unit), a partial data feature amount calculation unit 14B (partial information feature amount calculation unit), and a partial data abnormality detection unit 14C (partial information abnormality detection unit). Including. The partial data generation unit 14A divides the total data 44A to generate the partial data 45A, and stores the partial data 45A in the DB 40. The partial data feature amount calculation unit 14B calculates the feature amount for each partial data 45A and stores it in the DB 40 as the partial data feature amount 45B. The partial data abnormality detection unit 14C determines the abnormality of the partial data based on the partial data feature amount 45B while referring to the partial data obtained from the total data DB 43, and stores the partial data abnormality detection result 45C in the DB 40. The partial data abnormality detection result 45C includes a partial data abnormality degree 45Ca, a partial data abnormality presence / absence 45Cb, and a partial data abnormality feature amount 45Cc.

As described above, the partial data abnormality detection unit 14C indicates the degree of abnormality of the partial information generated by the partial data generation unit 14A and whether or not the partial information generated by the partial data generation unit 14A is abnormal. And, and the information indicating the abnormal feature amount which is the feature amount which is the source for detecting that the partial information generated by the partial data generation unit 14A is abnormal is generated. In this case, since the periodic time-series data abnormality partial detection system 1 generates detailed information regarding the abnormality of the partial information, it is possible to provide more detailed information than the information capable of identifying the portion where the abnormality has occurred.

The practice menu determination unit 15 determines the practice menu 46 from the partial data abnormality feature amount 45 Cc based on the practice menu list 50C and the practice menu correspondence table 50D, and stores the practice menu 46 in the DB 40. In this way, the practice menu determination unit 15 determines the practice menu for improving the abnormality feature amount calculated by the partial data abnormality detection unit 14C.

The result output unit 16 performs a process of outputting the overall data evaluation result 44C, the partial data abnormality detection result 45C, and the practice menu 46 to the screen of the terminal device 4. The overall data evaluation unit 13 and the partial data abnormality evaluation unit 14 perform screen output processing in cooperation with the practice menu determination unit 15 and the result output unit 16. In this way, the result output unit 16 further outputs the menu determined by the practice menu determination unit 15. In this case, since the periodic time series data abnormal portion detection system 1 presents a practice menu regarding the abnormal portion of the partial data, it is possible to present information useful for eliminating the abnormal portion.

Further, since the result output unit 16 outputs the overall data evaluation result 44C, it also outputs the overall result, so that it is possible to provide information from a plurality of viewpoints based on the periodic time series data.

As data and information stored in the DB 40 of the storage unit 102, user information 41, task data 42, total data DB 43, total data 44A, total data feature 44B, total data evaluation result 44C, partial data 45A, partial data feature It has 45B, partial data abnormality detection result 45C, and the like. The control unit 101 holds and manages the management table 50 in the storage unit 102.

The administrator can set the contents of the management table 50. The management table 50 shows an overall data feature amount list 50A for setting the feature amount of the entire data, a partial data feature amount list 50B for setting the feature amount of the partial data, a practice menu list 50C for setting the practice menu candidates, and a partial data abnormality. A practice menu correspondence table 50D and the like for setting the correspondence between the feature amount 45 Cc and the practice menu are stored.

[Measuring device]
FIG. 3 is a configuration diagram of the measuring device 3 of the first embodiment. The measuring device 3 includes a motion sensor 20, a housing unit 301, a measuring unit 302, a communication unit 303, and the like. The accommodating unit 301 has a motion sensor interface section 311 to which the motion sensor 20 is connected, and a motion sensor control unit 312 that controls the motion sensor 20. The measuring unit 302 measures the waveform signal through the motion sensor 20 and the accommodating unit 301 and outputs it as measurement data. The measurement unit 302 includes a task measurement unit 321 that obtains measurement data. The communication unit 303 has a communication interface and communicates with the peripheral time series data abnormality portion detection system 1 to transmit measurement data to the peripheral time series data abnormality portion detection system 1. The motion sensor interface unit 311 includes an analog-digital conversion circuit, and converts the analog waveform signal detected by the motion sensor 20 into a digital waveform signal by sampling. The digital waveform signal is input to the motion sensor control unit 312.

The measurement device 3 may hold each measurement data in the storage means, or the measurement device 3 may hold each measurement data only by the periodic time-series data abnormality part detection system 1 without holding the measurement data. Good.

[Terminal device]
FIG. 4 is a configuration diagram of the terminal device 4 of the first embodiment. The terminal device 4 includes a control unit 401, a storage unit 402, a communication unit 403, an input device 404, and a display device 405. The control unit 401 displays the overall data evaluation result, the partial data abnormality detection result display, and the like as control processing based on the software program processing. The storage unit 402 stores user information, task data, overall data (periodic time series data), overall data evaluation result, partial data abnormality detection result, and the like obtained from the periodic time series data abnormality partial detection system 1. The communication unit 403 has a communication interface, communicates with the periodic time series data abnormality part detection system 1, receives various data from the periodic time series data abnormality part detection system 1, and detects the periodic time series data abnormality part. User instruction input information and the like are transmitted to the system 1. The input device 404 includes a keyboard, a mouse, and the like. The display device 405 displays various information on the display screen 406. The display device 405 may be used as a touch panel.

[Finger, motion sensor, finger tap measurement]
FIG. 5 is a diagram showing a state in which a magnetic sensor, which is a motion sensor 20, is attached to a user's finger. The motion sensor 20 has a transmitting coil unit 21 and a receiving coil unit 22, which are paired coil units, through a signal line 23 connected to the measuring device 3. The transmitting coil unit 21 generates a magnetic field, and the receiving coil unit 22 detects the magnetic field. In the example of FIG. 5, in the right hand of the user, the transmitting coil portion 21 is mounted near the fingernail of the thumb, and the receiving coil portion 22 is mounted near the nail of the index finger. The finger to be worn can be changed to another finger. The place to attach is not limited to the vicinity of the nail.

As shown in FIG. 5, it is assumed that the motion sensor 20 is attached to the target finger of the user, for example, the thumb and index finger of the left hand. In that state, the user performs a finger tap, which is a repeated movement of opening and closing two fingers. In the finger tap, a transition movement is performed between a state in which the two fingers are closed, that is, a state in which the fingertips of the two fingers are in contact, and a state in which the two fingers are open, that is, a state in which the fingertips of the two fingers are open. Along with the movement, the distance between the coil portions of the transmitting coil portion 21 and the receiving coil portion 22 changes, which corresponds to the distance between the fingertips of the two fingers. The measuring device 3 measures the waveform signal according to the change in the magnetic field between the transmitting coil unit 21 and the receiving coil unit 22 of the motion sensor 20.

The motion sensor 20 may be a sensor other than the magnetic sensor as long as the distance between the two fingers can be measured. For example, a two-finger distance waveform may be obtained by repeatedly opening and closing a tablet terminal or a touch panel PC with two fingers. Further, the shape of the hand and the position of the fingertip may be detected by an infrared sensor to obtain a two-finger distance waveform.

Finger taps include the following types of tasks in detail. Examples of the exercise include a one-handed free run, a one-handed metronome, a two-handed simultaneous free run, a two-handed alternating free run, a two-handed simultaneous metronome, and a two-handed alternating metronome. One-handed free run refers to tapping the fingers as quickly and repeatedly as possible with the two fingers of one hand. A one-handed metronome refers to tapping with two fingers of one hand in response to a constant pace of stimulation. Simultaneous free run with both hands refers to tapping the two fingers of the left hand and the two fingers of the right hand at the same timing. Alternate two-handed free run refers to tapping the two fingers of the left hand and the two fingers of the right hand at alternating timings. There is also a finger tap that follows the marker.

[Motion sensor control unit and finger tap measurement]
FIG. 6 is a diagram showing a detailed configuration example of the motion sensor control unit 312 and the like of the measuring device 3. In the motion sensor 20, the distance D between the transmission coil unit 21 and the reception coil unit 22 is shown. The motion sensor control unit 312 includes an AC generation circuit 312a, a current generation amplifier circuit 312b, a preamplifier circuit 312c, a detection circuit 312d, an LPF circuit 312e, a phase adjustment circuit 312f, an amplifier circuit 312g, and an output signal terminal 312h.

The current generation amplifier circuit 312b and the phase adjustment circuit 312f are connected to the AC generation circuit 312a. The transmission coil unit 21 is connected to the current generation amplifier circuit 312b through the signal line 23. The receiving coil unit 22 is connected to the preamplifier circuit 312c through the signal line 23. A detection circuit 312d, an LPF circuit 312e, an amplifier circuit 312g, and an output signal terminal 312h are connected in this order to the subsequent stage of the preamplifier circuit 312c. A detection circuit 312d is connected to the phase adjustment circuit 312f.

The AC generation circuit 312a generates an AC voltage signal having a predetermined frequency. The current generation amplifier circuit 312b converts an AC voltage signal into an AC current having a predetermined frequency and outputs the AC voltage signal to the transmission coil unit 21. The transmitting coil unit 21 generates a magnetic field by an alternating current. The magnetic field generates an induced electromotive force in the receiving coil unit 22. The receiving coil unit 22 outputs an alternating current generated by the induced electromotive force. The alternating current has the same frequency as a predetermined frequency of the alternating voltage signal generated by the alternating current generation circuit 312a.

The preamplifier circuit 312c amplifies the detected alternating current. The detection circuit 312d detects the amplified signal based on the reference signal 312i from the phase adjustment circuit 312f. The phase adjusting circuit 312f adjusts the phase of the AC voltage signal having a predetermined frequency or a double frequency from the AC generating circuit 312a, and outputs the reference signal 312i. The LPF circuit 312e band-limiteds and outputs the detected signal, and the amplifier circuit 312g amplifies the signal to a predetermined voltage. Then, an output signal corresponding to the measured waveform signal is output from the output signal terminal 312h.

The waveform signal, which is an output signal, is a signal having a voltage value representing the distance D of two fingers. The distance D and the voltage value can be converted based on a predetermined calculation formula. The calculation formula can also be obtained by calibration. In the calibration, for example, the measurement is performed in a state where the user holds a block of a predetermined length with two fingers of the target hand. From the data set of the voltage value and the distance value in the measured value, a predetermined calculation formula is obtained as an approximate curve that minimizes the error. In addition, the size of the user's hand may be grasped by calibration and used for normalization of feature quantities and the like. In the first embodiment, the magnetic sensor is used as the motion sensor 20, and a measuring means corresponding to the magnetic sensor is used. Not limited to this, other detecting means and measuring means such as an acceleration sensor, a strain gauge, and a high-speed camera may be applied.

[Processing flow]
FIG. 7 is a flowchart showing a procedure of the entire process mainly performed by the periodic time series data abnormality partial detection system 1 in the human data measurement system of the first embodiment. FIG. 7 has steps S1 to S10. Hereinafter, the steps will be described in order.

(Step S1) First, the user operates the measurement system 2. Specifically, the terminal device 4 displays the initial screen on the display screen in response to the operation of the user. The user selects a desired operation item on the initial screen. For example, an operation item for detecting / processing abnormal data is selected. The terminal device 4 transmits the instruction input information corresponding to the selection to the periodic time series data abnormality partial detection system 1. In addition, the user can also input and register user information such as gender and age by operating the initial screen. In that case, the terminal device 4 transmits the input user information to the periodic time series data abnormality partial detection system 1. The user information management unit 11 of the periodic time series data abnormality partial detection system 1 registers the user information in the user information 41.

(Step S2) The task processing unit 12 of the periodic time-series data abnormality part detection system 1 transmits task data for the user to the terminal device 4 based on the instruction input information of S1 and the task data 42 of the finger tap. The task data includes information on one or more types of tasks related to finger movement, such as one-handed free run, two-handed simultaneous free run, and two-handed alternating free run. The terminal device 4 displays the task information of the finger movement on the display screen based on the received task data. The user performs a finger movement task according to the task information on the display screen. The measuring device 3 measures the task and transmits it as measurement data to the periodic time series data abnormality part detection system 1. The periodic time series data abnormality part detection system 1 stores the measurement data in the measurement data 42B.

(Step S3) The total data feature amount calculation unit 13A of the periodic time series data abnormality partial detection system 1 calculates the total data feature amount 44B from the total data 44A based on the total data feature amount list 50A. Then, the overall data evaluation unit 13B applies a statistical method such as multivariate analysis or machine learning to the overall data feature amount 44B to obtain the overall data evaluation result 44C. In this way, the overall data evaluation unit 13B detects an abnormality in the periodic information based on the feature amount calculated by the overall data feature amount calculation unit 13A.

(Step S4) The result output unit 16 of the periodic time series data abnormality partial detection system 1 sends the overall data evaluation result 44C to the terminal device 4 and displays it on the screen. In this way, the result output unit 16 outputs information based on the detection result by the overall data evaluation unit 13B. The user can check the evaluation result of his / her periodic time series data on the screen.

(Step S5) The partial data generation unit 14A of the periodic time series data abnormality partial detection system 1 generates partial data 45A from the total data 44A. The partial data generation unit 14A generates partial information based on the cycle (for example, information for one cycle) from the total data 44A. Then, the partial data feature amount calculation unit 14B calculates the partial data feature amount 45B from the partial data 45A based on the partial data feature amount list 50B. Then, the partial data abnormality detection unit 14C applies a statistical method such as multivariate analysis or machine learning to the partial data feature amount 45B to obtain the partial data abnormality detection result 45C. As described above, the partial data abnormality detection unit 14C detects the abnormality of the partial data 45A based on the feature amount calculated by the partial data feature amount calculation unit 14B.

(Step S6) The result output unit 16 of the periodic time series data abnormality partial detection system 1 sends the partial data abnormality detection result 45C to the terminal device 4 and displays it on the screen. The user can check the abnormal part of his / her periodic time series data on the screen.

(Step S7) The practice menu determination unit 15 of the periodic time series data abnormality partial detection system 1 sets the practice menu 46 based on the partial data abnormality feature amount 45 Cc while referring to the practice menu list 50C and the practice menu correspondence table 50D. Generate.

(Step S8) The result output unit 16 of the periodic time series data abnormality part detection system 1 sends the practice menu 46 to the terminal device 4 and displays it on the screen. In this way, the result output unit 16 outputs information based on the detection result. As a result, the user can confirm the practice menu to be performed on the screen.

[Calculation of overall data features]
FIG. 8 shows an example of a waveform signal of a feature amount. FIG. 8A shows a waveform signal of a distance D of two fingers, FIG. 8B shows a waveform signal of a velocity of two fingers, and FIG. 8C shows a waveform signal of acceleration of two fingers. Shown. The velocity in FIG. 8 (b) is obtained by time differentiation of the waveform signal at the distance in FIG. 8 (a). The acceleration of FIG. 8 (c) is obtained by time differentiation of the waveform signal of the velocity of FIG. 8 (b). The total data feature amount calculation unit 13A obtains a waveform signal having a predetermined feature amount as in this example from the waveform signal of the total data 44A based on calculations such as differentiation and integration. Further, the overall data feature amount calculation unit 13A obtains a value by a predetermined calculation from the feature amount.

FIG. 8 (d) is an enlargement of FIG. 8 (a) and shows an example of the feature amount. The maximum value Dmax of the finger tap distance D, the tap interval TI, and the like are shown. The horizontal broken line indicates the average value Dav of the distance D over the entire measurement time. The maximum value Dmax indicates the maximum value of the distance D in the total measurement time. The tap interval TI is the time corresponding to the cycle TC of one finger tap, and particularly indicates the time from the minimum point Pmin to the next minimum point Pmin. In addition, the maximum point Pmax and the minimum point Pmin within one cycle of the distance D, the time T1 of the opening operation and the time T2 of the closing operation described later are shown.

In the following, detailed examples of feature quantities will be further shown. In the first embodiment, a plurality of feature quantities obtained from the waveforms of the distance, velocity, and acceleration are used. In other embodiments, only some of the plurality of features may be used, other features may be used, and details of the definition of the features may be used. Not limited.

FIG. 9 is a diagram showing an overall data feature amount list 50A. The setting of this association is an example and can be changed. In the overall data feature amount list 50A of FIG. 9, the feature amount classification, the identification number, and the feature amount parameter are provided as columns. The feature classification has [distance], [velocity], [acceleration], [tap interval], [phase difference], and [marker tracking].

For example, in the feature amount [distance], it has a plurality of feature amount parameters identified by the identification numbers (A1) to (A11). The unit is shown in parentheses [] of the feature parameter. (A1) “Maximum amplitude of distance” [mm] is the difference between the maximum value and the minimum value of the amplitude in the waveform of the distance ((a) in FIG. 8). (A2) “Total movement distance” [mm] is the total sum of the absolute values of the amount of change in distance in the total measurement time of one measurement.

(A3) “Average of maximum distances” [mm] is the average of maximum amplitudes of each period. (A4) “Standard deviation of the maximum value of the distance” [mm] is the standard deviation with respect to the above value. (A5) “Slope of the approximate curve of the maximum point of distance (attenuation rate)” [mm / sec] is the slope of the curve that approximates the maximum point of amplitude. This parameter mainly represents the amplitude change due to fatigue during the measurement time. (A6) The "coefficient of variation of the maximum value of the distance" is the coefficient of variation of the maximum value of the amplitude, and the unit is a dimensionless quantity (indicated by [-]). This parameter is a mean normalized standard deviation, thus eliminating individual differences in finger length. (A7) “Standard deviation of local maximum value of distance” [mm] is the standard deviation of the maximum value of the amplitudes of three adjacent locations.

This parameter is a parameter for evaluating the degree of local amplitude variation in a short time. (A8) “Average of the minimum values of the distance” [mm] is the average of the minimum values of the amplitude of each period. (A9) “Standard deviation of the minimum value of the distance” [mm] is the standard deviation of the above value. (A10) The "coefficient of variation of the minimum value of the distance" is the coefficient of variation of the minimum value of the amplitude, and the unit is a dimensionless quantity (indicated by [-]). This parameter is a mean normalized standard deviation, thus eliminating individual differences in finger length. (A11) “Standard deviation of local minimum value of distance” [mm] is the standard deviation of the minimum value of the amplitude of three adjacent locations. This parameter is a parameter for evaluating the degree of local amplitude variation in a short time.

The feature amount [velocity] has the feature amount parameters indicated by the following identification numbers (A12) to (A26). (A12) The “maximum velocity amplitude” [m / sec] is the difference between the maximum value and the minimum value of the velocity in the velocity waveform ((b) of FIG. 8). (A13) “Average of maximum values of opening speed” [m / sec] is the average of maximum values of speed at the time of opening operation of each finger tap waveform. The opening operation is an operation of changing the two fingers from the closed state to the maximum open state ((d) in FIG. 8). (A14) “Average of minimum values of closing speed” [m / sec] is the average of minimum values of speed during closing operation. The closing operation is an operation of moving two fingers from the maximum open state to the closed state. (A15) “Standard deviation of the maximum value of the opening speed” [m / sec] is the standard deviation of the maximum value of the speed during the opening operation.

(A16) “Average of minimum points of closing speed” [m / sec] is the standard deviation of the minimum value of the speed during the closing operation. (A17) “Energy balance” [-] is the ratio of the sum of squares of the speeds during the opening operation and the sum of squares of the speeds during the closing operation. (A18) “Total energy” [m2 / sec 2] is the sum of squares of speeds during the total measurement time. (A19) The “coefficient of variation of the maximum value of the opening speed” [-] is the coefficient of variation of the maximum value of the speed during the opening operation, and is a value obtained by normalizing the standard deviation on average. (A20) “Average of minimum values of closing speed” [m / sec] is a coefficient of variation of the minimum value of speed during closing operation. (A21) “Number of shakings” [-] is a number obtained by subtracting the number of large opening / closing finger taps from the number of round trips in which the positive / negative of the velocity waveform changes. (A22) "Average of distance ratio at peak opening speed" [-] is the average value of the ratio when the amplitude of the finger tap is 1.0 with respect to the distance at the maximum value of the speed during the opening operation. is there. (A23) “Average distance ratio at peak closing speed” [-] is an average for the same ratio with respect to the distance at the minimum value of the speed during the closing operation. (A24) “Ratio of distance ratio at peak speed” [-] is the ratio of the value of (A22) to the value of (A23). (A25) "Standard deviation of the distance ratio at the peak of the opening speed" [-] is the standard deviation of the ratio when the amplitude of the finger tap is 1.0 with respect to the distance at the maximum value of the speed during the opening operation. Is. (A26) “Standard deviation of the distance ratio at the peak of the closing speed” [-] is the standard deviation of the same ratio with respect to the distance at the time of the minimum value of the speed during the closing operation.

The feature amount [acceleration] has the feature amount parameters indicated by the following identification numbers (A27) to (A36). (A27) “Maximum acceleration amplitude” [m / sec 2] is the difference between the maximum value and the minimum value of acceleration in the acceleration waveform ((c) in FIG. 8). (A28) “Average of maximum values of opening acceleration” [m / sec 2] is the average of maximum values of acceleration during the opening operation, and is the first of four types of extreme values that appear in one cycle of finger tapping. It is a single value. (A29) “Average of the minimum values of the opening acceleration” [m / sec 2] is the average of the minimum values of the acceleration during the opening operation, and is the second value among the four types of extreme values.

(A30) “Average of maximum values of closing acceleration” [m / sec 2] is the average of maximum values of acceleration during closing operation, and is the third value among four types of extreme values. (A31) “Average of the minimum values of the closing acceleration” [m / sec 2] is the average of the minimum values of the acceleration during the closing operation, and is the fourth value among the four types of extreme values. (A32) “Average contact time” [seconds] is the average contact time in the closed state of two fingers. (A33) “Standard deviation of contact time” [seconds] is the standard deviation of the contact time. (A34) The “coefficient of variation of contact time” [-] is the coefficient of variation of the contact time. (A35) “Number of zero intersections of acceleration” [-] is the average number of times the positive and negative accelerations change during one cycle of finger tapping. This value is ideally twice. (A36) “Number of freezing” [-] is a value obtained by subtracting the number of large opening / closing finger taps from the number of round trips in which the positive / negative of acceleration changes during one cycle of finger taps.

Subsequently, FIG. 10 is a diagram showing a continuation of the overall data feature amount list 50A. [Tap interval] has the feature amount parameters indicated by the following identification numbers (A37) to (A45). (A37) “Number of taps” [-] is the number of finger taps during the total measurement time of one measurement. (A38) “Tap interval average” [seconds] is an average for the above-mentioned tap interval ((d) in FIG. 8) in the waveform of the distance. (A39) “Tap frequency” [Hz] is a frequency at which the spectrum is maximized when the waveform of the distance is Fourier transformed. (A40) “Tap interval standard deviation” [seconds] is a standard deviation with respect to the tap interval. (A41) “Tap interval coefficient of variation” [-] is a coefficient of variation related to the tap interval, and is a value obtained by normalizing the standard deviation with an average value. (A42) “Tap interval fluctuation” [mm2] is an integrated value having a frequency of 0.2 to 2.0 Hz when the tap interval is spectrally analyzed.

(A43) “Skewness of tap interval distribution” [-] is the skewness of the frequency distribution of tap intervals, and indicates the degree to which the frequency distribution is distorted as compared with the normal distribution. (A44) “Local tap interval standard deviation” [seconds] is the standard deviation of three adjacent tap intervals. (A45) “Slope of the approximate curve of the tap interval (damping factor)” [-] is the slope of the curve that approximates the tap interval. This slope mainly represents the change in the tap interval due to fatigue during the measurement time.

The feature amount [phase difference] has the feature amount parameters indicated by the following identification numbers (A46) to (A49). (A46) “Average of phase difference” [degree] is the average of phase difference in the waveforms of both hands. The phase difference is an index value expressing the deviation of the finger tap of the left hand with respect to the right hand as an angle when one cycle of the finger tap of the right hand is 360 degrees. The case where there is no deviation is defined as 0 degree. (A47) “Standard deviation of phase difference” [degree] is the standard deviation of the phase difference. The larger the values of (A46) and (A47), the larger the deviation of both hands and the more unstable. (A48) “Similarity of both hands” [-] is a value representing the correlation when the time lag is 0 when the cross-correlation function is applied to the waveforms of the left hand and the right hand. (A49) “Time lag that maximizes the similarity between both hands” [seconds] is a value that represents the time lag that maximizes the correlation of (A48).

The feature amount [marker tracking] has the feature amount parameters indicated by the following identification numbers (A50) to (A51). (A50) “Average delay time from marker” [seconds] is an average regarding the delay time of the finger tap with respect to the time indicated by the periodic marker. Markers correspond to stimuli such as visual stimuli, auditory stimuli, and tactile stimuli. This parameter value is based on the time when the two fingers are closed. (A51) “Standard deviation of delay time from marker” [seconds] is the standard deviation of the delay time.

[Overall data evaluation]
The overall data evaluation unit 13B obtains an overall data evaluation result 44C indicating the quality of the overall data based on the overall data feature amount 44B calculated by the overall data feature amount calculation unit 13A. For example, using the total data DB 43, a plurality of feature quantities in the total data feature quantity 44B are used as explanatory variables, and multiple regression analysis is applied with the degree of abnormality as the objective variable to obtain an estimation formula for estimating the degree of abnormality. The degree of abnormality is defined as an index that decreases as it is normal and increases as it is abnormal. Examples of the degree of abnormality include the Mini-Mental State Examination (MMSE), which indicates the severity of dementia, and the Universal Parkinson's Disease Rating Scale (UPDRS), which indicates the severity of Parkinson's disease. Can be mentioned. However, these severities tend to increase as they are normal and decrease as they become abnormal. For example, the MMSE has the highest cognitive function on a scale of 30 points, and the cognitive function declines as it approaches 0 points. Therefore, after inverting the positive and negative of MMSE and UPDRS as pretreatment, it is used as the degree of abnormality. Here, in order to estimate the severity score, a similar method may be used instead of the multiple regression analysis. For example, discriminant regression analysis in which discrimination and regression are performed simultaneously by a linear model may be used.

In addition, other regression methods such as support vector machine regression and neural network may be used. Further, as a simpler method, one feature amount in the total data feature amount 44B may be selected, and the quality of the total data may be determined by the size thereof.

[Partial data generation]
The partial data generation unit 14A cuts out a finger tap waveform every cycle to obtain partial data 45A. In order to cut out the partial data 45A, as shown in FIG. 11, one cycle of the finger tap is defined as from the time when the average value of the total data 44A is crossed from top to bottom to the time when the next crossing from top to bottom. In this way, by defining one cycle based on the average value of the entire data 44A, the distance value (maximum value) when the two fingers are fully opened or the distance value when the two fingers are closed is too small. When (minimum value) is too large, it is possible to eliminate halfway up and down movements that cannot be called finger tap movements. The definition of one cycle may be another method, from the minimum point to the next minimum point, or from the maximum point to the next maximum point. As a method of cutting out the partial data 45A, it may be cut out by dividing it into a plurality of cycles instead of every one cycle.

The partial data feature amount calculation unit 14B, which will be described later, also calculates the feature amounts (P19) to (P20) using the waveforms of both hands, but in order to calculate these feature amounts, both hands in the same time zone are calculated. Waveforms are needed. For that purpose, one cycle may be extracted from the waveform of the right hand, and the waveform of the same time zone may be extracted from the waveform of the left hand. You may ask for it by reversing your right and left hands.

[Calculation of partial data features]
FIG. 12 is a diagram showing a partial data feature amount list 50B. Based on this list, the partial data feature amount calculation unit 14B calculates the partial data feature amount 45B. As a column, it has a feature classification, an identification number, and a feature parameter. The feature classification has [distance], [velocity], [acceleration], [tap interval], [phase difference], and [marker tracking]. The partial data feature amount calculation unit 14B may calculate all the feature amounts in the partial data feature amount list 50B, or may select and calculate some feature amounts.

For example, in the feature amount [distance], it has a plurality of feature amount parameters identified by the identification numbers (P1) to (P3). The unit is shown in parentheses [] of the feature parameter. (P1) “Minimum distance” [mm] is the minimum amplitude of partial data. (P2) The “maximum value of distance” [mm] is the maximum value of the amplitude of the partial data. (P3) “Total movement distance” [mm] is the sum of the absolute values of the amount of change in distance during the total measurement time of the partial data.

The feature amount [velocity] has the feature amount parameters indicated by the following identification numbers (P4) to (P11). (P4) “Maximum opening speed” [m / sec] is the maximum speed at the time of opening operation of partial data. The opening operation is an operation of changing the two fingers from the closed state to the maximum open state. (P5) “Minimum closing speed” [m / sec] is the minimum speed during closing operation. The closing operation is an operation of moving two fingers from the maximum open state to the closed state. (P6) “Energy balance” [-] is the ratio of the sum of squares of the speeds during the opening operation and the sum of squares of the speeds during the closing operation. (P7) “Total energy” [m2 / sec 2] is the sum of squares of the velocities of the partial data during the total measurement time. (P8) “Number of shakings” [-] is a number obtained by subtracting 1, which is the number of finger taps, from the number of round trips in which the positive / negative of the velocity waveform changes. (P9) “Distance ratio at peak opening speed” [-] is the distance at the maximum value of the speed during the opening operation when the amplitude of the finger tap is 1. (P10) “Distance ratio at peak closing speed” [-] is the distance at the minimum value of the speed during the closing operation when the amplitude of the finger tap is 1. (P11) “Ratio of distance ratio at peak speed” [-] is the ratio of the value of (9) to the value of (10).

The feature amount [acceleration] has the feature amount parameters shown by the following identification numbers (P12) to (P17). (P12) “Maximum value of opening acceleration” [m / sec 2] is the maximum value of acceleration during the opening operation, and is the first value among four types of extreme values appearing in one cycle of finger tapping. .. (P13) “Minimum value of opening acceleration” [m / sec 2] is the minimum value of acceleration during the opening operation, and is the second value among the four types of extreme values. (P14) The “maximum value of closing acceleration” [m / sec 2] is the maximum value of acceleration during the closing operation, and is the third value among the four types of extreme values. (P15) The “minimum value of closing acceleration” [m / sec 2] is the minimum value of acceleration during the closing operation, and is the fourth value among the four types of extreme values. (P16) “Contact time” [seconds] is the contact time in the closed state of two fingers. (P17) The “freezing number” [-] is a value obtained by subtracting 1 which is the number of large opening / closing finger taps from the number of round trips in which the positive / negative of acceleration changes during one cycle of finger taps.

The feature amount [tap interval] has a feature amount parameter indicated by the following identification number (P18). (P18) “Tap interval” [second] is the time of one cycle of finger tapping.

The feature amount [phase difference] has the feature amount parameters shown by the following identification numbers (P19) to (P20). (P19) “Phase difference” [degree] is the phase difference in the waveforms of both hands. The phase difference is an index value expressing the deviation of the finger tap of the left hand with respect to the right hand as an angle when one cycle of the finger tap of the right hand is 360 degrees. The case where there is no deviation is defined as 0 degree. (P20) “Similarity of both hands” [-] is a value representing the correlation when the time lag is 0 when the cross-correlation function is applied to the waveforms of the left hand and the right hand.

The feature amount [marker tracking] has a feature amount parameter indicated by the following identification number (P21). This feature amount is calculated by the task of following the marker and moving it. (P21) “Delay time from marker” [second] is the delay time of the finger tap with respect to the time indicated by the periodic marker. Markers correspond to stimuli such as visual stimuli, auditory stimuli, and tactile stimuli. This parameter value is based on the time when the two fingers are closed.

[Partial data anomaly detection]
In order for the partial data abnormality detection unit 14C to detect the abnormality of the partial data 45A, a 1-class Support Vector Machine (SVM), which is a kind of machine learning, is adopted. SVM, which is the premise of this method, is a method of defining a classification boundary so as to maximize the margin between the classification boundary (hyperplane represented by a linear equation) and the data of each class in the classification of two classes. However, if the classification boundary is a hyperplane, it cannot be separated when the classification boundary of the two groups has a complicated shape. Therefore, SVM has been devised so that it can handle the classification boundary of a complicated shape by introducing a kernel function. ing. 1-class SVM has the same concept as the two-class classification problem of SVM, but is a method of classifying a certain percentage of abnormal data and other normal data in one class.

Before applying 1-class SVM to the feature data in the previous section, the features are standardized so that the average is 0 and the standard deviation is 1 as preprocessing. By standardizing, it is possible to prevent the weights with respect to the feature amount from becoming non-uniform due to the difference in the range for each feature amount in the calculation of 1-class SVM. The RBF kernel, which is often used for kernel functions, was used. The margin optimization algorithm uses a sequential minimum problem optimization method (SMO: Mathematical Minimal Optimization). The percentage of outliers is 8%.

An abnormality detection of partial data may be performed by a method other than 1-class SVM. For example, a normal distribution centered on the average of the feature distributions of the partial data 46A may be assumed, and data having a large distance from the center of the normal distribution may be detected as abnormal.

[Partial data anomaly detection result]
In 1-class SVM, the classification score y is calculated, and when the classification score y is a negative value, it is determined to be abnormal. The result detected by this determination is defined as the presence / absence of partial data abnormality 45Cb.

It is considered that the degree of abnormality increases as the classification score y decreases away from 0. Therefore, this classification score y is converted by a function that asymptotically approaches z = 0% at y = 0 and z = 100% at y = −∞, and z is defined as a partial data abnormality degree of 45Ca.

In addition, for the periodic finger tap waveforms judged to be abnormal by 1-class SVM, the standard deviation SD = from the mean value in order to investigate which feature among all the features contributed to the abnormality judgment. The feature amount deviated by 2.0 or more is defined as the partial data abnormal feature amount 45 Cc.

[Execution example of partial data abnormality evaluation unit]
When the above-mentioned partial data abnormality generation unit was executed on the total data DB 43 storing the total data of the right finger taps of 228 healthy persons, 12898 partial data 45A were obtained. Then, for each partial data 45A, the partial data feature amount 45B was obtained by the partial data feature amount calculation unit 14B. Here, 15 feature quantities P1-P8 and P12-P18 were selected and calculated from the partial data feature quantity list 50B. Then, when the partial data abnormality detection unit 14C was executed using the obtained partial data feature amount 45B, the partial data abnormality detection result 45C was obtained. As a result, the number of partial data 45A detected abnormally was 1032 out of 12898.

FIG. 13 is a diagram showing an example of partial data 45A in which an abnormality is detected. The top waveform is the total data 44A in which no abnormally detected partial data 45A was found. The four waveforms below are the total data 44A in which one or more partial data 45A detected abnormally were present. The partial data 45A in which the abnormality is detected is overwritten with a thick line on the distance waveform of the finger tap movement. Above that, the partial data anomalous features are shown.

[Practice menu decided]
FIG. 14 is a practice menu list 50C showing an index item showing the nature of the finger tapping motion and a practice menu for improving the index item. Index items include [momentum], [endurance], [rhythmicity], [bilateral coordination], [marker followability], [exercise magnitude], [waveform balance], and [amplitude control]. The settings of this index item and practice menu are examples and can be changed.

FIG. 15 is a diagram showing a practice menu correspondence table 50D regarding setting information of the association between the feature amount and the practice menu item. The setting of this association is an example and can be changed. In this table, as columns, there are feature classification, identification number, feature parameter, and index item. The feature classification has [distance], [velocity], [acceleration], [tap interval], [phase difference], and [marker tracking]. The features of this list correspond to the partial data feature list 50B and are associated with at least one or more of the index items set in the practice menu list 50C.

[Display screen (1) -Menu]
FIG. 16 is a diagram showing an example of a menu screen which is an initial screen of a service as an example of a display screen of the terminal device 4. This menu screen has a user information field 1501, an operation menu field 1502, a setting field 1503, and the like.

In the user information field 1501, the user can input and register the user information. If the entered user information exists in the electronic medical record or the like, it may be linked with the user information. Examples of user information that can be input include user ID, name, date of birth or age, gender, dominant hand, disease / symptom, memo, and the like. The dominant hand can be selected and input from right hand, left hand, both hands, unknown, and the like. The disease / symptom may be selected and input from the options in the list box, for example, or may be input by arbitrary text. When using this system in a hospital or the like, a doctor or the like may input on behalf of the user instead of the user. This peripheral time series data abnormality partial detection system 1 can be applied even when the user information is not registered.

In the operation menu column 1502, the operation items of the functions provided by the service are displayed. The operation items include "calibration", "measurement of finger movement", "abnormal data detection / processing", "end" and the like. In the case of selecting "calibration", the above-mentioned calibration, that is, the process related to the adjustment of the motion sensor 20 or the like with respect to the user's finger is performed. The status of whether or not it has been adjusted is also displayed. When "Measure finger movement" is selected, the screen transitions to the task measurement screen for measuring the task of finger movement such as finger tapping. When "abnormal data detection / processing" is selected, an abnormality is detected for the measured data, the abnormal data detection result is displayed, and the screen transitions to the screen for processing the detected abnormal data. If you select "Exit", this service will be terminated.

In the setting field 1503, user setting is possible. For example, if there is a type of abnormality detection item that the user, the measurer, or the administrator wants to detect, the abnormality detection item can be selected and set from the options. In addition, the process corresponding to each abnormality detection item can be selected. It is also possible to set a threshold value for detecting abnormal data. These settings are sent to the periodic time series data abnormal part detection system 1 through the communication unit 105, and the periodic time series data abnormal part detection system 1 detects and processes abnormal data with reference to the settings specified here. To do.

[Display screen (2) -Task measurement]
FIG. 17 is a diagram showing a task measurement screen as another example. On this screen, task information is displayed. For example, for each of the left and right hands, a graph 1600 is displayed with time on the horizontal axis and distance between two fingers on the vertical axis. Other teaching information for explaining the task content may be output to the screen. For example, a video area may be provided to explain the task contents by video and audio. The screen has operation buttons such as "start measurement", "redo measurement", "end measurement", and "save (register)", which can be selected by the user. The user selects "start measurement" according to the task information on the screen and exercises the task. The measuring device 3 measures the movement of the task and obtains a waveform signal. The terminal device 4 displays the measurement waveform 1602 corresponding to the waveform signal being measured on the graph 1600 in real time. After exercising, the user selects "end measurement", and selects "save (register)" when confirming. The measuring device 3 transmits the measured data to the peripheral time series data abnormality part detection system 1.

[Display screen (3) -Evaluation result]
FIG. 18 is a diagram showing an evaluation result screen as another example. On this screen, task analysis evaluation result information is displayed. This screen is automatically displayed after the task analysis and evaluation. In this example, the case where the feature quantities of the five finger tap movements A to E are displayed in a radar chart format graph is shown. The solid border 1701 shows the analysis evaluation result after the task measurement this time. The estimated severity score of the overall data evaluation result 44C calculated by the overall data evaluation unit 13B is displayed. In addition, a plurality of feature quantities are displayed on a radar chart. In addition, an evaluation comment or the like regarding the analysis evaluation result may be displayed. The overall data evaluation unit 13 creates the evaluation comment. For example, a message such as "(B) and (E) are good" is displayed. The screen has operation buttons such as "confirm an abnormal part of the finger tap waveform" and "end". The periodic time-series data abnormal part detection system 1 transitions to the abnormal part detection result screen when "confirm the abnormal part of the finger tap waveform" is selected, and returns to the initial screen when "end" is selected. Make a transition.

[Display screen (4) -Abnormal part detection result]
FIG. 19 is a diagram showing an abnormal portion detection result screen as another example. On this screen, the partial data abnormality detection result 45C calculated by the partial data abnormality detection unit 14C is presented to the user. The waveform of the entire data 44A is displayed as a thin line. Then, the partial data 45A having an abnormality in the partial data abnormality presence / absence 45Cb is displayed with a thick line on the waveform. A partial data abnormality feature amount of 45 Cc and a partial data abnormality degree of 45 Ca are displayed above the partial data abnormality feature amount 45 Cc. The partial data abnormal feature amount 45Cc is provided with an upward arrow when the feature amount value is too large, and a downward arrow when the feature amount value is too small. The partial data abnormality degree 45Ca is displayed as an abnormality degree. Then, the evaluation comment regarding the partial data abnormal feature amount 45 Cc is also displayed. Further, a practice menu 46 for improving it is presented.

The screen display of the partial data evaluation result shown in FIG. 19 is not limited to the graph of time and distance, and may be a graph of time and speed, time and acceleration, and the like. Further, the display is not limited to the graph display, and may be a display of numerical data or a moving image of a finger tap movement. In the case of moving image display, a warning sound may be generated at the abnormal part or the background of the moving image of the abnormal part may be changed so that the abnormal part can be recognized.

Further, it is more preferable to display the screen display of the overall data evaluation result shown in FIG. 18 and the abnormal portion detection result screen shown in FIG. 19 in parallel on one screen. As a result, the cause of the score of the overall data evaluation result can be inferred from the abnormal part detection result screen, and the effect that the subject can easily understand or accept the score and the cause can be obtained.

The overall data evaluation result in the parallel display of the content of the overall data evaluation result and the content of the abnormal part detection result may be only the score, only the radar chart, both the score and the radar chart, or another display. The method may be used. Similarly, the display of the abnormal portion detection result is not limited to the graph display shown in FIG. 19, and is another display method as long as the abnormal portion is visually recognized in the entire data. Can be used.

[Effects, etc.]
The periodic time series data abnormality partial detection system 1 of the first embodiment divides the entire data 44A which is the periodic time series data to generate partial data 45A, calculates the partial data feature amount 45B, and obtains the said partial data feature amount 45B. Based on the partial data feature amount 45B, the partial data abnormality detection result 45C, which is the result of detecting the abnormality of the partial data 45A, is displayed and output. In this way, the abnormal part detection system 1 detects an abnormality for each partial data 45A obtained by dividing the total data 44A, and therefore presents information capable of identifying which part of the periodic time series data is abnormal. Can be done. That is, the periodic time series data abnormality partial detection system 1 can provide a more detailed evaluation result.

As a result, when the overall data evaluation result 44C is bad, the user can know which part specifically has the problem. Further, by presenting the practice menu 46 obtained by the practice menu determination unit 15, the user can know the practice method for improving the problem.

In the present embodiment, the abnormal portion detection for the time-series data of the finger tap movement has been described, but other data may be used as long as it is periodic time-series data. For example, time-series data obtained by measuring electrocardiographic signals, magnetocardiographic signals, pulse waves, respiration, brain waves, walking, blinking eyes, mastication, and the like can be mentioned.

(Second Embodiment)
The periodic time series data abnormality partial detection system of the second embodiment will be described with reference to FIGS. 20 to 22. The basic configuration of the second embodiment is the same as that of the first embodiment, and the parts of the configuration of the second embodiment that are different from the configuration of the first embodiment will be described below.

[system]
FIG. 20 is a diagram showing a periodic time series data abnormality partial detection system of the second embodiment. The periodic time-series data abnormality partial detection system has a server 6 of a service provider and a system 7 of a plurality of facilities, and they are connected via a communication network 8. The communication network 8 and the server 6 may include a cloud computing system.

Various facilities can be used, such as hospitals, health examination centers, public facilities, entertainment facilities, and user's homes. The facility is equipped with a system 7. As an example of the facility system 7, the hospital H1 system 7A, the hospital H2 system 7B, and the like are provided. For example, the systems 7A and 7B of each hospital have a measuring device 3 and a terminal device 4 that constitute a measuring system 2 similar to the first embodiment. The configuration of each system 7 may be the same or different. The facility system 7 may include a hospital electronic medical record management system and the like. The measuring device of the system 7 may be a dedicated terminal.

The server 6 is a device under the jurisdiction of the service provider. The server 6 has a function of providing the facility and the user with a partial data abnormality detection service similar to the periodic time series data abnormality partial detection system 1 of the first embodiment as a service by information processing. The server 6 provides a service process to the measurement system in a client-server manner. The server 6 has a user management function and the like in addition to such a function. The user management function is a function of registering, accumulating, and managing user information, measurement data, analysis evaluation data, and the like of a user group obtained through systems 7 of a plurality of facilities in a DB.

[server]
FIG. 21 is a diagram showing the configuration of the server 6. The server 6 has a control unit 601, a storage unit 602, an input unit 603, an output unit 604, and a communication unit 605, which are connected via a bus. The input unit 603 is a part for performing operation input by the administrator or the like of the server 6. The output unit 604 is a part that displays a screen or the like for the administrator or the like of the server 6. The communication unit 605 has a communication interface and is a part that performs communication processing with the communication network 8. DB640 is stored in the storage unit 602. The DB 640 may be managed by a DB server or the like different from the server 6.

The control unit 601 controls the entire server 6 and is composed of a CPU, ROM, RAM, etc., and realizes a data processing unit 600 that detects abnormal data, determines abnormal data processing, and the like based on software program processing. The data processing unit 600 includes a user information management unit 11, a task processing unit 12, an overall data evaluation unit 13, a partial data abnormality evaluation unit 14, a practice menu determination unit 15, and a result output unit 16.

The user information management unit 11 registers and manages user information regarding a user group of systems 7 of a plurality of facilities in DB 640 as user information 41. The user information 41 includes attribute values, usage history information, user setting information, and the like for each individual user. The usage history information includes the actual information that each user has used the abnormal part detection service in the past.

[Server management information]
FIG. 22 is a diagram showing a data configuration example of user information 41 managed by the server 6 in the DB 640. In the table of the user information 41, the user ID, the facility ID, the in-facility user ID, the gender, the age, the disease, the severity score, the symptom, the history information, and the like are included. The user ID is unique identification information of the user in this system. The facility ID is identification information of the facility in which the system 7 is provided. Separately, the communication address and the like of the measuring device of each system 7 are also managed. The in-facility user ID is the user identification information when the user identification information managed in the facility or the system 7 exists. That is, the user ID and the user ID in the facility are associated and managed. The disease item or symptom item stores a value representing the disease or symptom selected and input by the user, or a value diagnosed by a doctor or the like at a hospital. The severity score is a value indicating the degree of disease.

The history information item is information that manages the user's record of using the abnormal part detection service, and information such as the date and time of each use is stored in chronological order. In addition, the history information item stores each data when the practice is performed at that time, that is, data such as the above-mentioned measurement data, analysis evaluation data, abnormality data detection result, and abnormality data processing content. In the history information item, the information of the address where each data is stored may be stored.

[Effects, etc.]
According to the peripheral time-series data abnormality partial detection system 1 of the second embodiment, as in the first embodiment, the entire data 44A which is the periodic time-series data is divided to generate the partial data 45A, and the portion thereof. By calculating the data feature amount 45B and obtaining the partial data abnormality detection result 45C, it is possible to detect the abnormal portion in the total data 44A and present it to the user. As a result, the user can know which part has a problem when the overall data evaluation result 44C is bad. Further, by presenting the practice menu 46 obtained by the practice menu determination unit 15, the user can know the practice method for improving the problem.

Although the present invention has been specifically described above based on the embodiment, the present invention is not limited to the embodiment and can be variously modified without departing from the gist thereof.

The present invention is not limited to the above-described embodiment, and includes various modifications. For example, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, it is possible to add / delete / replace the configurations of other examples with respect to a part of the configurations of each embodiment.

1 ... Periodic time series data abnormality part detection system, 2 ... Measurement system, 3 ... Measurement device, 4 ... Terminal device.

Claims (5)

A detection device that detects abnormalities using periodic information that indicates the state of the living body.
The periodic information acquisition unit that acquires the periodic information,
A partial information generation unit that generates partial information based on the period from the periodic information acquired by the periodic information acquisition unit,
The partial information feature amount calculation unit that calculates the feature amount of the partial information generated by the partial information generation unit, and
A partial information abnormality detection unit that detects an abnormality in the partial information generated by the partial information generation unit based on the feature amount calculated by the partial information feature amount calculation unit.
An output unit that outputs information based on the detection result by the partial information abnormality detection unit, and an output unit.
A detection device comprising. The detection device according to claim 1.
The periodic information feature amount calculation unit that calculates the feature amount of the periodic information acquired by the periodic information acquisition unit, and the periodic information feature amount calculation unit.
A periodic information abnormality detection unit for detecting an abnormality in periodic information based on the feature amount calculated by the periodic information feature amount calculation unit is further provided.
The output unit is a detection device that further outputs information based on the detection result by the periodic information abnormality detection unit. The detection device according to claim 1 or 2.
The partial information abnormality detection unit includes information indicating the degree of abnormality of the partial information generated by the partial information generation unit, whether or not the partial information generated by the partial information generation unit is abnormal, and the above. A detection device that generates information indicating an abnormal feature amount, which is a feature amount that is a source for detecting that the partial information generated by the partial information generation unit is abnormal. The detection device according to claim 3.
A menu determination unit that determines a practice menu for improving the abnormality feature amount calculated by the partial information abnormality detection unit, and a menu determination unit.
The output unit is a detection device that further outputs a menu determined by the menu determination unit. It is a detection method executed by a detection device that detects abnormalities using periodic information indicating the state of the living body.
The periodic information acquisition step for acquiring the periodic information and
A partial information generation step that generates partial information based on a period from the periodic information acquired in the periodic information acquisition step, and a partial information generation step.
The partial information feature amount calculation step for calculating the feature amount of the partial information generated in the partial information generation step, and the partial information feature amount calculation step.
A partial information abnormality detection step that detects an abnormality in the partial information generated in the partial information generation step based on the feature amount calculated in the partial information feature amount calculation step, and a partial information abnormality detection step.
A detection method including an output step for outputting information based on a detection result detected in the partial information abnormality detection step.