JP7328044B2 - Detection device and detection method - Google Patents

Description

The present invention relates to information processing service technology. 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 health care, medical care, nursing care, and the like, the number of systems for measuring human data is increasing. These systems provide users with valuable information by calculating analysis results from the obtained data and feeding them back to the users. The above data is often periodic time-series data.

As an example of such a system, there 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 motion (for example, Patent Document 1).

Here, the finger tap exercise is an exercise of repeatedly opening and closing the thumb and forefinger. Periodic time-series data can be obtained by measuring the finger tap motion. It is known that the performance of finger-tapping movement varies depending on the presence or 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 early detection and severity estimation of the brain dysfunction that the user has.

Japanese Unexamined Patent Application Publication No. 2013-109540

If the evaluation result obtained by analyzing the periodic time-series data of finger tap motion is poor, it is desirable to obtain more detailed results for 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 of the specification and the accompanying drawings.

As means for solving the above problems, the technology described in the claims is used.

For example, a detection device for detecting an abnormality using periodic information indicating the state of a living body, comprising: a periodic information acquisition unit that acquires the periodic information; A partial information generation unit that generates partial information based on a period from the target information, a 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 feature amount calculation unit. a partial information anomaly detection unit for detecting an anomaly in the partial information generated by the partial information generation unit based on the calculated feature amount; an output unit for outputting information based on the detection result of the partial information anomaly detection unit; Prepare.

By using the technique of the present invention, more detailed evaluation results can be provided for periodic time-series data.

1 is a configuration diagram of a human data measurement system including the periodic time-series data abnormal portion detection system of the first embodiment; FIG. 1 is a configuration diagram of a periodic time-series data abnormal portion detection system according to a first embodiment; FIG. It is a lineblock diagram of a measuring device of a 1st embodiment. 1 is a configuration diagram of a terminal device according to a first embodiment; FIG. It is a figure which shows the state by which the user's finger was mounted|worn with the magnetic sensor which is a motion sensor. It is a figure which shows the example of a detailed structure, such as a motion sensor control part of a measuring device. 4 is a flow chart showing the overall procedure of processing in the human data measurement system of the first embodiment; It is a figure which shows the example of the waveform signal of a feature-value. It is a figure which shows a whole data feature-value list. FIG. 10 is a diagram showing the continuation of the entire data feature quantity list; It is a figure which shows the example of a definition of partial data. It is a figure which shows a partial data feature-value list. It is a figure which shows the example of the partial data by which abnormality was detected. It is a figure which shows a practice menu list. It is a figure which shows a practice menu correspondence table. FIG. 4 is a diagram showing an example of a menu screen that is an initial screen of a service; It is a figure which shows a task measurement screen. It is a figure which shows an evaluation result screen. It is a figure which shows an abnormal part detection result screen. FIG. 10 is a diagram showing a periodic time-series data abnormal portion detection system according to a second embodiment; It is a figure which shows the structure of a server. It is a figure which shows the data structural example of the user information which a server manages in DB.

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

Embodiments will be described in detail with reference to the drawings. However, the present invention should not be construed as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed without departing from the idea or gist of the present invention.

When there are a plurality of elements having the same or similar functions, they may be described with the same reference numerals and different suffixes. However, if there is no need to distinguish between multiple elements, the subscripts may be omitted.

Notations such as “first”, “second”, “third” in this specification etc. are attached to identify the constituent elements, and do not necessarily limit the number, order, or content thereof isn't it. Also, numbers for identifying components are used for each context, and numbers used in one context do not necessarily indicate the same configuration in other contexts. Also, it does not preclude a component identified by a certain number from having the function of a component identified by another number.

The position, size, shape, range, etc. of each component shown in the drawings, etc. 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 positions, sizes, shapes, ranges, etc. disclosed in the drawings and the like.

(First embodiment)
A periodic time-series data abnormal portion detection system (detection device) according to the first embodiment will be described with reference to FIGS. 1 to 18. FIG. 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 the living body) obtained by measuring the subject. . With this function, an abnormal portion can be detected with high accuracy.

[People data measurement system]
FIG. 1 is a configuration diagram of a human data measurement system including a periodic time-series data abnormal portion detection system according to the first embodiment. In the first embodiment, facilities such as hospitals and facilities for the elderly, user's homes, and the like have human data measurement systems. The human data measurement system has a periodic time-series data abnormal part detection system 1 and a measurement system 2 which is a magnetic sensor type finger tap motion system, which are connected through a communication line. The measurement system has a measurement device 3 and a terminal device 4, which are connected through a communication line. A plurality of measurement systems 2 may be provided within the facility.

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

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

The periodic time-series data abnormal part detection system 1 receives, for example, periodic time-series data as input data from the measurement system 2 . The periodic time-series data anomaly detection system 1 outputs, for example, partial data anomaly detection results 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 quantity 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 subjects thereof, but also to 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 abnormal portion detection system 1 may be configured as an integrated device. The terminal device 4 and the periodic time-series data abnormal portion detection system 1 may be configured as an integrated device. The measuring device 3 and the periodic time-series data abnormal portion detection system 1 may be configured as an integrated device.

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

The control unit 101 controls the entire periodic time-series data anomaly detection system 1, is composed of a Central Processing Unit (CPU), a Read Only Memory (ROM), a Random Access Memory (RAM), etc., 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. A data processing section of the control section 101 includes a user information management section 11 , a task processing section 12 , a whole data evaluation section 13 , a partial data abnormality evaluation section 14 , a practice menu determination section 15 and a result output section 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 a function of transmitting display data to the terminal device 4. Realize functions such as output.

The user information management unit 11 performs a process of registering and managing user information input by a 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 a 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 sex, age, and the like. The usage history information is information for managing the history of the user's usage of the services provided by this system. The user setting information is setting information set by the user regarding the functions of this service.

The task processing unit 12 is a part that performs processing related to tasks for analysis and evaluation of motor functions and the like. A task is, in other words, a predetermined finger movement. The task processing unit 12 outputs tasks 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 task measurement data (periodic information indicating the state of the living body) measured by the measuring device 3, and stores it in the DB 40 as overall data 44A. Here, the total data means the entire periodical time-series data measured for a predetermined period of time. Thus, 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). Based on the user's overall data 44A, the overall data feature amount calculator 13A calculates a feature amount representing the properties of the overall data 44A (periodic time-series data), and stores it in the DB 40 as an overall data feature amount 44B. The overall data evaluation unit 13B evaluates the overall data based on the overall data feature amount 44B while referring to the overall data DB 43, and stores the result in the DB 40 as the overall data evaluation result 44C.

The partial data abnormality evaluation unit 14 includes a partial data generation unit 14A (partial information generation unit), a partial data feature value calculation unit 14B (partial information feature value calculation unit), and a partial data error detection unit 14C (partial information error detection unit). include. The partial data generation unit 14A divides the entire data 44A to generate partial data 45A, and stores the partial data 45A in the DB 40. FIG. The partial data feature quantity calculator 14B calculates a feature quantity for each piece of partial data 45A, and stores it in the DB 40 as a partial data feature quantity 45B. The partial data abnormality detection unit 14C determines an abnormality of the partial data based on the partial data feature amount 45B while referring to the partial data obtained from the entire data DB 43, and stores it in the DB 40 as a partial data abnormality detection result 45C. 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.

In this way, the partial data abnormality detection unit 14C detects the degree of abnormality of the partial information generated by the partial data generation unit 14A and information indicating whether or not the partial information generated by the partial data generation unit 14A is abnormal. and information indicating an abnormal feature quantity, which is a feature quantity based on which it is detected that the partial information generated by the partial data generation unit 14A is abnormal. In this case, the periodic time-series data abnormal portion detection system 1 generates detailed information regarding the abnormality of the partial information, so it is possible to provide more detailed information than the information that can identify the portion where the abnormality has occurred.

The practice menu determination unit 15 determines the practice menu 46 from the partial data abnormal feature amount 45Cc based on the practice menu list 50C and the practice menu correspondence table 50D, and stores it in the DB 40. FIG. In this way, the practice menu determination unit 15 determines a practice menu for improving the abnormal feature quantity 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. FIG. The entire data evaluation unit 13 and the partial data abnormality evaluation unit 14 cooperate with the practice menu determination unit 15 and the result output unit 16 to perform screen output processing. Thus, the result output unit 16 further outputs the menu determined by the practice menu determination unit 15. FIG. In this case, the periodic time-series data abnormal portion detection system 1 presents a practice menu for the abnormal portion of the partial data, so information useful for resolving the abnormal portion can be presented.

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

Data and information stored in the DB 40 of the storage unit 102 include user information 41, task data 42, overall data DB 43, overall data 44A, overall data feature amount 44B, overall data evaluation result 44C, partial data 45A, partial data feature amount. 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 includes a total data feature quantity list 50A for setting the feature quantity of the whole data, a partial data feature quantity list 50B for setting the feature quantity of the partial data, a practice menu list 50C for setting practice menu candidates, and a partial data abnormality list. A practice menu correspondence table 50D for setting the correspondence between the feature amount 45Cc and the practice menu is stored.

[Measuring device]
FIG. 3 is a configuration diagram of the measuring device 3 of the first embodiment. The measurement device 3 has a motion sensor 20, a housing section 301, a measurement section 302, a communication section 303, and the like. The housing section 301 has a motion sensor interface section 311 to which the motion sensor 20 is connected, and a motion sensor control section 312 that controls the motion sensor 20 . The measurement unit 302 measures waveform signals through the motion sensor 20 and the housing unit 301 and outputs them 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, communicates with the ambient time-series data abnormal portion detection system 1 , and transmits measurement data to the ambient time-series data abnormal portion detection system 1 . The motion sensor interface section 311 includes an analog-to-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 motion sensor control section 312 .

Note that the measurement device 3 may hold each measurement data in a storage means, or the measurement device 3 may hold only the periodic time-series data abnormal portion detection system 1 without holding each measurement data. good.

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

[Finger, motion sensor, finger tap measurement]
FIG. 5 is a diagram showing a state in which a magnetic sensor, which is the motion sensor 20, is attached to a user's finger. The motion sensor 20 has a transmission coil section 21 and a reception coil section 22 which are paired coil sections through a signal line 23 connected to the measuring device 3 . The transmission coil section 21 generates a magnetic field, and the reception coil section 22 detects the magnetic field. In the example of FIG. 5, on the user's right hand, the transmitting coil section 21 is attached near the nail of the thumb, and the receiving coil section 22 is attached near the nail of the index finger. The finger to be worn can be changed to another finger. The place to be attached 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 this state, the user performs finger tapping, which is a repeated movement of opening and closing the two fingers. In finger tapping, movement is performed to transition between a state in which 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 motion, the distance between the coil portions of the transmitting coil portion 21 and the receiving coil portion 22 corresponding to the distance between the fingertips of the two fingers changes. The measuring device 3 measures a waveform signal corresponding to a magnetic field change between the transmitting coil section 21 and the receiving coil section 22 of the motion sensor 20 .

As the motion sensor 20, any sensor other than the magnetic sensor may be used as long as the distance between two fingers can be measured. For example, a two-finger distance waveform may be obtained by repeatedly opening and closing two fingers on a tablet terminal or a touch panel type PC. Alternatively, an infrared sensor may be used to detect the shape of the hand and the positions of the fingertips to obtain a two-finger distance waveform.

Finger tapping includes the following types of tasks in detail. The exercise includes, for example, one-handed free run, one-handed metronome, two-handed free run, alternating two-handed free run, two-handed simultaneous metronome, two-handed alternate metronome, and the like. One-handed free run refers to performing finger taps as quickly and as many times as possible with two fingers of one hand. One-handed metronome refers to performing finger taps with two fingers of one hand in accordance with stimuli at a constant pace. Simultaneous free run with both hands refers to performing finger taps with two fingers of the left hand and two fingers of the right hand at the same timing. Alternating free run with both hands refers to performing finger tapping with two fingers of the left hand and two fingers of the right hand at alternate timings. In addition, 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. As shown in FIG. In the motion sensor 20, the distance D between the transmitter coil section 21 and the receiver coil section 22 is shown. The motion sensor control unit 312 has 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.

A current generation amplifier circuit 312b and a phase adjustment circuit 312f are connected to the AC generation circuit 312a. The transmission coil section 21 is connected through the signal line 23 to the current generating amplifier circuit 312b. The receiving coil section 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 rear stage of the preamplifier circuit 312c. A detection circuit 312d is connected to the phase adjustment circuit 312f.

The AC generator circuit 312a generates an AC voltage signal with a predetermined frequency. The current generation amplifier circuit 312 b converts the AC voltage signal into an AC current of a predetermined frequency and outputs the AC current to the transmitting coil section 21 . The transmission coil unit 21 generates a magnetic field by alternating current. The magnetic field causes the receiving coil section 22 to generate an induced electromotive force. The receiving coil unit 22 outputs alternating current generated by the induced electromotive force. The alternating current has the same frequency as the 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 adjustment circuit 312f adjusts the phase of the AC voltage signal of the predetermined frequency or double frequency from the AC generation circuit 312a and outputs it as the reference signal 312i. The LPF circuit 312e band-limits the detected signal and outputs it, and the amplifier circuit 312g amplifies the signal to a predetermined voltage. An output signal corresponding to the measured waveform signal is output from the output signal terminal 312h.

A waveform signal, which is an output signal, is a signal having a voltage value representing the distance D between two fingers. The distance D and voltage value can be converted based on a predetermined formula. The calculation formula can also be obtained by calibration. In calibration, for example, measurement is performed while the user holds a block of a predetermined length with two fingers of the target hand. A predetermined calculation formula is obtained as an approximation curve that minimizes the error from a data set of voltage values and distance values in the measured values. Also, the size of the user's hand may be determined by calibration, and used for normalizing the feature amount. In the first embodiment, the magnetic sensor described above is used as the motion sensor 20, and the measuring means corresponding to the magnetic sensor is used. Not limited to this, other detection means and measurement means such as acceleration sensors, strain gauges, and high-speed cameras may be applied.

[Processing flow]
FIG. 7 is a flow chart showing the procedure of the entire process mainly performed by the periodic time-series data abnormal portion detection system 1 in the human data measurement system of the first embodiment. FIG. 7 has steps S1 to S10. The steps will be described below.

(Step S<b>1 ) First, the user operates the measurement system 2 . Specifically, the terminal device 4 displays the initial screen on the display screen according to the user's operation. The user selects a desired operation item on the initial screen. For example, an operation item for detecting and processing abnormal data is selected. The terminal device 4 transmits instruction input information corresponding to the selection to the periodic time-series data abnormal portion detection system 1 . 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 abnormal portion detection system 1 . The user information management unit 11 of the periodic time series data abnormal part 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 abnormal portion detection system 1 transmits task data for the user to the terminal device 4 based on the instruction input information in S1 and the finger tap task data 42 . The task data includes information on one or more types of tasks related to finger movements, such as free run with one hand, free run with both hands simultaneously, free run with both hands alternately. 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 the finger exercise 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 abnormal part detection system 1 . The periodic time series data abnormal portion detection system 1 stores the measurement data in the measurement data 42B.

(Step S3) The overall data feature amount calculator 13A of the periodic time-series data abnormal portion detection system 1 calculates the overall data feature amount 44B from the overall data 44A based on the overall data feature amount list 50A. Then, the overall data evaluation unit 13B applies statistical methods such as multivariate analysis and machine learning to the overall data feature amount 44B to obtain overall data evaluation results 44C. In this manner, 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 abnormal part detection system 1 sends the overall data evaluation result 44C to the terminal device 4 and displays it on the screen. Thus, 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 own periodic time-series data on the screen.

(Step S5) The partial data generator 14A of the periodic time-series data abnormal portion detection system 1 generates partial data 45A from the overall data 44A. The partial data generator 14A generates partial information based on the period (for example, information for one period) from the overall data 44A. Then, the partial data feature amount calculator 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 obtains a partial data abnormality detection result 45C by applying a statistical method such as multivariate analysis or machine learning to the partial data feature amount 45B. As described above, the partial data abnormality detection unit 14C detects an abnormality in the partial data 45A based on the feature amount calculated by the partial data feature amount calculation unit 14B.

(Step S6) The result output section 16 of the periodic time-series data abnormal portion 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 abnormal portion detection system 1 selects the practice menu 46 based on the partial data abnormal feature amount 45Cc 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 abnormal portion detection system 1 sends the practice menu 46 to the terminal device 4 and displays it on the screen. Thus, the result output unit 16 outputs information based on the detection result. As a result, the user can check the practice menu that he/she should do on the screen.

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

FIG. 8(d) is an enlarged view of FIG. 8(a) and shows an example of the feature amount. It shows the maximum value Dmax of the finger tap distance D, the tap interval TI, and the like. A horizontal dashed 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 during the entire measurement time. The tap interval TI is the time corresponding to the period TC of one finger tap, and particularly indicates the time from one minimum point Pmin to the next minimum point Pmin. In addition, a maximum point Pmax and a minimum point Pmin within one period of the distance D, time T1 for an opening operation and time T2 for a closing operation, which will be described later, are also shown.

In the following, further detailed examples of the feature amount will be shown. In the first embodiment, a plurality of feature quantities obtained from the waveforms of distance, velocity, and acceleration are used. Note that in other embodiments, only some of the plurality of feature amounts may be used, or other feature amounts may be used. Not limited.

FIG. 9 is a diagram showing an overall data feature quantity list 50A. This association setting is an example and can be changed. The entire data feature amount list 50A in FIG. 9 has feature amount classification, identification number, and feature amount parameter as columns. The feature quantity classification has [distance], [speed], [acceleration], [tap interval], [phase difference], and [marker follow].

For example, the feature [distance] has a plurality of feature parameters identified by identification numbers (A1) to (A11). Units are 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 amplitude in the distance waveform ((a) in FIG. 8). (A2) “Total movement distance” [mm] is the sum of absolute values of distance change amounts in the entire measurement time of one measurement.

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

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

The feature amount [speed] has feature amount parameters indicated by the following identification numbers (A12) to (A26). (A12) “Maximum amplitude of velocity” [m/sec] is the difference between the maximum value and the minimum value of velocity in the velocity waveform ((b) of FIG. 8). (A13) “Average of maximum values of opening speed” [m/sec] is the average of the maximum values of speed during the opening operation of each finger tap waveform. The opening motion is a motion to open 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 an average of minimum values of speed during the closing operation. The closing motion is a motion to close the two fingers from the maximum open state. (A15) “Standard deviation of maximum value of opening speed” [m/sec] is the standard deviation of the maximum value of 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 between the sum of squares of speed during the opening motion and the sum of squares of speed during the closing motion. (A18) “Total Energy” [m2/s2] is the sum of the squares of the velocities during the entire measurement time. (A19) "Variation coefficient of maximum value of opening speed" [-] is the variation coefficient of the maximum value of speed during the opening operation, and is a value obtained by normalizing the standard deviation with the average. (A20) “Average of minimum value of closing speed” [m/sec] is a coefficient of variation of the minimum value of speed during the closing operation. (A21) "Number of tremors" [-] is the number obtained by subtracting the number of large opening/closing finger taps from the number of reciprocations in which the sign of the velocity waveform changes. (A22) "Average of distance ratio at peak opening speed" [-] is the average value of the ratio of the distance at the maximum speed during the opening motion when the amplitude of the finger tap is 1.0. be. (A23) "Average of distance ratio at peak closing speed" [-] is the average of similar ratios for distance at minimum speed during the closing motion. (A24) "Ratio of distance ratio at peak speed" [-] is the ratio between the value of (A22) and the value of (A23). (A25) "Standard deviation of distance ratio at peak opening speed" [-] is the standard deviation of the ratio of the distance at the maximum speed during the opening motion when the amplitude of the finger tap is 1.0 is. (A26) "Standard deviation of distance ratio at peak closing speed" [-] is the standard deviation for a similar ratio for distance at minimum speed during the closing motion.

The feature amount [acceleration] has feature amount parameters indicated by the following identification numbers (A27) to (A36). (A27) “Maximum amplitude of acceleration” [m/sec2] is the difference between the maximum and minimum acceleration values in the acceleration waveform ((c) in FIG. 8). (A28) “Average of maximum values of opening acceleration” [m/s 2] is the average of maximum values of acceleration during the opening motion, and is the first of the four types of extreme values that appear during one cycle of finger tapping. 1 value. (A29) “Average of minimum values of opening acceleration” [m/sec 2] is the average of minimum values of acceleration during the opening motion, 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 the closing motion, and is the third value among the four types of extreme values. (A31) “Average of minimum values of closing acceleration” [m/sec 2] is the average of minimum values of acceleration during the closing motion, and is the fourth value among the four types of extreme values. (A32) “Average contact time” [seconds] is the average contact time when two fingers are closed. (A33) “Standard deviation of contact time” [seconds] is the standard deviation of the contact time. (A34) "Coefficient of variation of contact time" [-] is the coefficient of variation of the contact time. (A35) "Number of zero crossings of acceleration" [-] is the average number of times the acceleration changes between positive and negative during one cycle of finger tapping. This value is ideally twice. (A36) The number of freezing times [-] is a value obtained by subtracting the number of large opening/closing finger taps from the number of reciprocations in which the sign of acceleration changes during one cycle of finger tapping.

Next, FIG. 10 is a diagram showing the continuation of the entire data feature quantity list 50A. [Tap Interval] has feature parameters indicated by the following identification numbers (A37) to (A45). (A37) "Number of taps" [-] is the number of finger taps during the entire measurement time of one measurement. (A38) “Tap interval average” [seconds] is the average of the aforementioned tap intervals ((d) in FIG. 8) in the distance waveform. (A39) “Tap frequency” [Hz] is the frequency at which the spectrum becomes maximum when the waveform of the distance is Fourier transformed. (A40) “Tap interval standard deviation” [seconds] is the standard deviation of the tap interval. (A41) "Tap interval variation coefficient" [-] is a variation coefficient relating to tap intervals, and is a value obtained by normalizing the standard deviation by the average value. (A42) “Tap interval variation” [mm2] is the integrated value of the frequency of 0.2 to 2.0 Hz when spectrum analysis is performed on the tap interval.

(A43) "Skewness of tap interval distribution" [-] is the skewness of the tap interval frequency distribution, and represents the degree to which the frequency distribution is distorted compared to the normal distribution. (A44) “Standard deviation of local tap intervals” [seconds] is the standard deviation of three adjacent tap intervals. (A45) “Slope of tap interval approximation curve (attenuation rate)” [-] is the slope of the curve approximating the tap interval. This slope mainly represents the change in tap interval due to fatigue during the measurement time.

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

The feature amount [marker tracking] has feature amount parameters indicated by the following identification numbers (A50) to (A51). (A50) “Average delay time from marker” [seconds] is the average of delay times of finger taps with respect to the time indicated by the periodic markers. The markers correspond to stimuli such as visual stimuli, auditory stimuli, and tactile stimuli. This parameter value is based on the time when 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 overall data evaluation results 44C representing the quality of the overall data based on the overall data feature amounts 44B calculated by the overall data feature amount calculation unit 13A. For example, using the overall data DB 43, multiple regression analysis is applied with multiple feature quantities in the overall data feature quantity 44B as explanatory variables and 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 normal and increases as abnormality. Examples of the degree of abnormality, such as a severity score of brain dysfunction, Mini Mental State Examination (MMSE) representing the severity of dementia and Unified Parkinson's Disease Rating Scale (UPDRS) representing the severity of Parkinson's disease mentioned. However, these severity values have the property that the more normal, the larger the value, and the more abnormal, the smaller the value. For example, the MMSE has the highest cognitive function on a 30-point scale, with cognitive decline decreasing as the score approaches 0. Therefore, after inverting the sign of MMSE and UPDRS as preprocessing, it is used as the degree of anomaly. Here, instead of multiple regression analysis, a method similar to it may be used to estimate the severity score. For example, discriminant regression analysis in which discrimination and regression are performed simultaneously using a linear model may be used.

Other regression techniques such as support vector machine regression and neural networks may also be used. Alternatively, as a simpler method, one feature amount may be selected from the total data feature amount 44B, and the quality of the overall data may be determined based on the size of the feature amount.

[Generate partial data]
The partial data generator 14A extracts the finger tap waveform for each cycle to obtain partial data 45A. In order to cut out the partial data 45A, as shown in FIG. 11, one period of finger tapping is defined from the time when the average value of the whole data 44A is crossed from top to bottom until the time when the average value of the whole data 44A is crossed from top to bottom. In this way, by defining one period based on the average value of the overall data 44A, the distance value (maximum value) when the two fingers are completely opened is too small, or the distance value when the two fingers are closed When (minimum value) is too large, it is possible to eliminate halfway up-and-down movements that cannot be said to be finger-tapping movements. One cycle may be defined by other methods, such as from a local minimum point to the next local minimum point, or from a local maximum point to the next local maximum point. As a method of extracting the partial data 45A, the partial data 45A may be segmented not for each cycle but for each multiple cycles.

Note that the partial data feature amount calculation unit 14B, which will be described later, also calculates feature amounts (P19) to (P20) using the waveforms of both hands. A waveform is required. For this purpose, one cycle is extracted from the waveform on the right hand, and the waveform of the same time period is extracted from the waveform on the left hand. You can also find it by reversing the right and left hands.

[Calculation of feature quantity of partial data]
FIG. 12 is a diagram showing a partial data feature quantity list 50B. Based on this list, the partial data feature quantity calculator 14B calculates the partial data feature quantity 45B. As columns, it has a feature amount classification, an identification number, and a feature amount parameter. The feature quantity classification has [distance], [speed], [acceleration], [tap interval], [phase difference], and [marker follow]. 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 a part of the feature amounts.

For example, the feature amount [distance] has a plurality of feature amount parameters identified by identification numbers (P1) to (P3). Units are shown in parentheses [] of the feature parameter. (P1) “minimum distance” [mm] is the minimum amplitude of the partial data. (P2) “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 absolute values of distance change amounts during the entire measurement time of the partial data.

The feature amount [speed] has feature amount parameters indicated by the following identification numbers (P4) to (P11). (P4) “Maximum value of opening speed” [m/sec] is the maximum value of speed during the opening operation of the partial data. The opening motion is a motion to bring the two fingers from the closed state to the maximum open state. (P5) "minimum value of closing speed" [m/sec] is the minimum value of the speed during the closing operation. The closing motion is a motion to close the two fingers from the maximum open state. (P6) "Energy balance" [-] is the ratio of the sum of the squares of the speed during the opening motion to the sum of the squares of the speed during the closing motion. (P7) "Total energy" [m2/sec2] is the sum of the squares of the velocities during the entire measurement time of the partial data. (P8) "Shake count" [-] is a number obtained by subtracting 1, which is the number of finger taps, from the number of reciprocations in which the sign of the velocity waveform changes. (P9) "Ratio of distance at peak opening speed" [-] is the distance at the maximum value of speed during the opening motion when the amplitude of the finger tap is set to 1. (P10) "Ratio of distance at peak closing speed" [-] is the distance at the minimum value of speed during the closing motion when the amplitude of the finger tap is set to 1. (P11) "Ratio of distance ratio at peak speed" [-] is the ratio between the value of (9) and the value of (10).

The feature amount [acceleration] has feature amount parameters indicated by the following identification numbers (P12) to (P17). (P12) “Maximum value of opening acceleration” [m/s2] is the maximum value of acceleration during the opening motion, and is the first value among four types of extreme values that appear during one cycle of finger tapping. . (P13) "Minimum value of opening acceleration" [m/sec 2] is the minimum value of acceleration during the opening motion, and is the second value among the four extreme values. (P14) "Maximum value of closing acceleration" [m/sec 2] is the maximum value of acceleration during the closing motion, and is the third value among the four types of extreme values. (P15) "Minimum value of closing acceleration" [m/sec 2] is the minimum value of acceleration during the closing motion, and is the fourth value among the four types of extreme values. (P16) “Contact time” [seconds] is the contact time when two fingers are closed. (P17) "Freezing number" [-] is a value obtained by subtracting 1, which is the number of large open/close finger taps, from the number of reciprocations in which the sign of acceleration changes during one finger tap cycle.

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

The feature amount [phase difference] has feature amount parameters indicated by the following identification numbers (P19) to (P20). (P19) “Phase difference” [degrees] is the phase difference between the waveforms of both hands. The phase difference is an index value representing the deviation of the finger tap of the left hand from that of the right hand as an angle when one cycle of the finger tap of the right hand is 360 degrees. 0 degrees when there is no deviation. (P20) "Similarity of both hands" [-] is a value representing the correlation when the time shift is 0 when the cross-correlation function is applied to the waveforms of the left and right hands.

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

[Partial data error detection]
A 1-class Support Vector Machine (SVM), which is a kind of machine learning, is used to detect anomalies in the partial data 45A by the partial data anomaly detector 14C. SVM, which is the premise of this method, is a method of determining a classification boundary so as to maximize the margin between the classification boundary (hyperplane represented by a linear expression) and the data of each class in two-class classification. However, if the classification boundary is a hyperplane, two groups cannot be separated if the classification boundary has a complicated shape. Therefore, in SVM, a kernel function is introduced so that it can handle classification boundaries with complicated shapes. ing. 1-class SVM has the same concept as the 2-class classification problem of SVM, but is a method of classifying a certain proportion of abnormal data and other normal data in one class.

Before applying 1-class SVM to the feature amount data in the previous section, as preprocessing, the feature amount is standardized so that the mean is 0 and the standard deviation is 1. By standardization, in the calculation of 1-class SVM, it is possible to prevent uneven weights for feature amounts due to different ranges for each feature amount. The RBF kernel, which is commonly used in many cases, was used as the kernel function. The margin optimization algorithm uses sequential minimal optimization (SMO). The percentage of outliers is assumed to be 8%.

A method other than the 1-class SVM may be used to detect anomalies in partial data. For example, a normal distribution centered on the average of the feature quantity distribution of the partial data 46A may be assumed, and data with a large distance from the center of the normal distribution may be detected as abnormal.

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

It is considered that the degree of abnormality increases as the classification score y becomes smaller 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 the partial data abnormality degree 45Ca.

In addition, in order to investigate which feature value among all the feature values contributed to the error determination for the cyclic finger tap waveform determined to be abnormal by the 1-class SVM, the standard deviation SD = A feature amount that deviates by 2.0 or more is defined as a partial data abnormal feature amount 45Cc.

[Execution example of partial data abnormality evaluation unit]
When the partial data anomaly generator described above was executed on the total data DB 43 storing the total data of right-hand finger taps of 228 healthy subjects, 12898 pieces of partial data 45A were obtained. Then, a partial data feature amount 45B is obtained for each partial data 45A by the partial data feature amount calculator 14B. Here, 15 feature values P1-P8 and P12-P18 are selected from the partial data feature value list 50B and calculated. Then, when the partial data abnormality detection unit 14C was executed using the obtained partial data feature amount 45B, a partial data abnormality detection result 45C was obtained. As a result, 1032 out of 12898 pieces of partial data 45A are detected to be abnormal.

FIG. 13 is a diagram showing an example of partial data 45A in which abnormality is detected. The top waveform is the entire data 44A in which no partial data 45A is detected as abnormal. The lower four waveforms are the entire data 44A in which one or more abnormal partial data 45A are present. The abnormally detected partial data 45A is superimposed with a thick line on the distance waveform of the finger tap motion. In the upper part thereof, the partial data abnormal feature quantity is shown.

[Practice menu decision]
FIG. 14 is a practice menu list 50C showing index items representing properties of the finger tap motion and practice menus for improving the index items. The index items include [momentum], [endurance], [rhythm], [bilateral coordination], [marker tracking], [movement amplitude], [waveform balance], and [amplitude control]. These index items and practice menu settings are examples and can be changed.

FIG. 15 is a diagram showing a practice menu correspondence table 50D regarding setting information for association between feature amounts and practice menu items. This association setting is an example and can be changed. In this table, columns include feature quantity classification, identification number, feature quantity parameter, and index item. The feature quantity classification has [distance], [speed], [acceleration], [tap interval], [phase difference], and [marker follow]. The feature amount of this list matches the partial data feature amount list 50B, and is 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 the service, as an example of the display screen of the terminal device 4. As shown in FIG. This menu screen has a user information column 1501, an operation menu column 1502, a setting column 1503, and the like.

In the user information column 1501, the user can input and register user information. If user information that has already been input exists in an electronic medical chart or the like, the user information may be linked. Examples of user information that can be entered include a user ID, name, date of birth or age, gender, handedness, disease/symptom, notes, and the like. The dominant hand can be selected and input from right hand, left hand, both hands, unknown, and the like. Diseases/symptoms may be selected and input from, for example, options in a list box, or may be input as arbitrary text. When this system is used in a hospital or the like, a doctor or the like may input instead of the user. This ambient time-series data abnormal part detection system 1 can be applied even when there is no registration of user information.

An operation menu column 1502 displays operation items for functions provided by the service. Operation items include "calibration", "finger motion measurement", "abnormal data detection/processing", and "end". When "calibration" is selected, the above-described calibration, that is, processing related to adjustment of the motion sensor 20 and the like with respect to the user's fingers is performed. The state of whether or not the adjustment has been completed is also displayed. In the case of selecting "measurement of finger movement", the screen transitions to a task measurement screen for measuring finger movement tasks such as finger tapping. When "abnormal data detection/processing" is selected, an abnormality is detected in the measured data, the abnormal data detection result is displayed, and the screen transitions to a screen for executing processing of the detected abnormal data. If you select "Terminate", this service will be terminated.

In the setting column 1503, user setting is possible. For example, if there is a type of anomaly detection item that the user, measurer, or administrator desires to detect, the anomaly detection item can be selected from options and set. Also, it is possible to select a process corresponding to each abnormality detection item. Also, a threshold value for detecting abnormal data can be set. These settings are sent to the periodic time-series data anomaly detection system 1 through the communication unit 105, and the periodic time-series data anomaly detection system 1 refers to the settings specified here to detect and process abnormal data. do.

[Display screen (2) - Task measurement]
FIG. 17 is a diagram showing a task measurement screen as another example. This screen displays task information. For example, a graph 1600 is displayed with the time on the horizontal axis and the distance between the two fingers on the vertical axis for each of the left and right hands. Other teaching information for explaining the task content may be output on the screen. For example, a video area may be provided in which the content of the task is explained by audio and video. The screen includes operation buttons such as "start measurement", "redo measurement", "end measurement", and "save (register)", which can be selected by the user. The user follows the task information on the screen, selects "start measurement", and performs exercise for the task. The measuring device 3 measures the movement of the task and obtains a waveform signal. The terminal device 4 displays a measured waveform 1602 corresponding to the waveform signal being measured on the graph 1600 in real time. After exercising, the user selects "finish measurement", and selects "save (register)" to confirm. The measuring device 3 transmits the measured data to the ambient time-series data abnormal 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, analysis evaluation result information of the task is displayed. After the analysis evaluation of the task, this screen is automatically displayed. In this example, the feature values of five finger tapping motions A to E are displayed in a radar chart format. A solid frame line 1701 indicates 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, multiple feature values are displayed in a radar chart. In addition, evaluation comments and the like regarding analysis evaluation results 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. In the screen, there are operation buttons such as "Check abnormal part of finger tap waveform" and "End". The periodic time-series data abnormal part detection system 1 makes a transition to the abnormal part detection result screen when "check the abnormal part of the finger tap waveform" is selected, and to the initial screen when "end" is selected. 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 error detection result 45C calculated by the partial data error detection unit 14C is presented to the user. The waveform of the overall data 44A is displayed with thin lines. Then, the partial data 45A having an abnormality among the partial data abnormality presence/absence 45Cb is displayed in a thick line on the waveform. A partial data abnormality feature amount 45Cc and a partial data abnormality degree 45Ca are displayed above it. The partial data abnormal feature amount 45Cc is marked with an upward arrow when the feature amount value is too large, and is marked with a downward arrow when the feature amount value is too small. The partial data anomaly degree 45Ca is displayed as an anomaly degree. An evaluation comment on the partial data abnormal feature quantity 45Cc is also displayed. Furthermore, 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 graphs 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 display of finger tap motion. In the case of moving image display, a warning sound may be generated at the abnormal portion, or the background of the moving image of the abnormal portion may be changed so that the abnormal portion can be recognized.

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

The overall data evaluation result in the parallel display of the contents of the overall data evaluation result and the contents of the abnormal portion detection result may be only the score, only the radar chart, both the score and the radar chart, or another display. method may be used. Similarly, the display of the abnormal portion detection result is not limited to the graph display shown in FIG. can be used.

[Effects, etc.]
The periodic time-series data abnormal portion detection system 1 of the first embodiment divides the entire data 44A, which is periodic time-series data, to generate partial data 45A, calculates the partial data feature amount 45B, A partial data abnormality detection result 45C, which is the result of detecting an abnormality in the partial data 45A, is displayed and output based on the partial data feature amount 45B. In this way, the abnormal part detection system 1 detects an abnormality for each partial data 45A obtained by dividing the entire data 44A, so that it is possible to present information that can specify which part of the periodic time-series data is abnormal. can be done. That is, the periodic time-series data anomaly detection system 1 can provide more detailed evaluation results.

As a result, when the overall data evaluation result 44C is bad, the user can know specifically which part had the problem. Furthermore, 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.

Note that in the present embodiment, detection of an abnormal portion targeted for time-series data of a finger tap motion 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, electrocardiographic signals, pulse waves, respiration, electroencephalograms, walking, eye blinks, mastication, and the like can be cited.

(Second embodiment)
A periodic time-series data abnormal portion detection system according to the second embodiment will be described with reference to FIGS. 20 to 22. FIG. The basic configuration of the second embodiment is similar to that of the first embodiment, and the portions of the configuration of the second embodiment that differ from the configuration of the first embodiment will be described below.

[system]
FIG. 20 is a diagram showing a periodic time-series data abnormal portion detection system according to the second embodiment. The periodic time-series data anomaly detection system has a server 6 of a service provider and systems 7 of a plurality of facilities, which are connected via a communication network 8 . Communication network 8 and server 6 may include a cloud computing system.

Various types of facilities are possible, such as hospitals, health check-up centers, public facilities, entertainment facilities, etc., or user's homes. A system 7 is provided at the facility. Examples of the facility system 7 include a system 7A of the hospital H1, a system 7B of the hospital H2, and the like. For example, each hospital system 7A and system 7B has a measuring device 3 and a terminal device 4 that constitute the same measuring system 2 as in the first embodiment. The configuration of each system 7 may be the same or different. The facility's system 7 may include a hospital's electronic medical chart management system or the like. The measuring device of system 7 may be a dedicated terminal.

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

[server]
FIG. 21 is a diagram showing the configuration of the server 6. As shown in FIG. The server 6 has a control section 601, a storage section 602, an input section 603, an output section 604, and a communication section 605, which are connected via a bus. The input unit 603 is a part for inputting an operation by an administrator of the server 6 or the like. The output unit 604 is a part that displays a screen or the like for the administrator of the server 6 or the like. The communication unit 605 has a communication interface and performs communication processing with the communication network 8 . A DB 640 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, is composed of a CPU, a ROM, a RAM, etc., and implements a data processing unit 600 that detects abnormal data, determines abnormal data processing, etc. based on software program processing. The data processing section 600 has a user information management section 11 , a task processing section 12 , a whole data evaluation section 13 , a partial data abnormality evaluation section 14 , a practice menu determination section 15 and a result output section 16 .

The user information management unit 11 registers and manages user information about a group of users of the system 7 of a plurality of facilities in the DB 640 as user information 41 . The user information 41 includes attribute values for each individual user, usage history information, user setting information, and the like. The usage history information includes track record information of past usage of the abnormal portion detection service by each user.

[Server management information]
FIG. 22 is a diagram showing a data configuration example of the user information 41 managed in the DB 640 by the server 6. As shown in FIG. The table of the user information 41 includes user ID, facility ID, in-facility user ID, sex, age, disease, severity score, symptoms, history information, and the like. A user ID is unique identification information of a user in this system. The facility ID is identification information of the facility where the system 7 is installed. Separately, the communication addresses and the like of the measurement devices of each system 7 are also managed. The intra-facility user ID is user identification information managed within the facility or system 7, if any. That is, the user ID and the in-facility user ID are associated and managed. The disease item and the symptom item store values representing diseases and symptoms selected and input by the user, or values diagnosed by a doctor or the like at a hospital. A severity score is a value that expresses the extent to which a disease is associated.

The history information item is information for managing the usage history of the abnormal portion detection service of the user, and information such as the date and time of each usage is stored in chronological order. In addition, the history information item stores each data in the case where the exercise was performed at that time, that is, data such as the aforementioned measurement data, analysis evaluation data, abnormal data detection results, abnormal data processing details, and the like. The history information item may store information on the address where each data is stored.

[Effects, etc.]
According to the ambient time-series data abnormal portion detection system 1 of the second embodiment, as in the first embodiment, the entire data 44A, which is periodic time-series data, is divided to generate the partial data 45A, and the partial data 45A is generated. By calculating the data feature amount 45B and obtaining the partial data abnormality detection result 45C, it is possible to detect an abnormal part in the entire data 44A and present it to the user. This allows the user to know specifically which part has a problem when the overall data evaluation result 44C is bad. Furthermore, 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 embodiments, the present invention is not limited to the above embodiments, and various modifications can be made without departing from the scope of the invention.

The present invention is not limited to the above-described embodiments, and includes various modifications. For example, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Moreover, it is possible to add, delete, or replace a part of the configuration of each embodiment with the configuration of another embodiment.

1... periodic time-series data abnormal part detection system, 2... measurement system, 3... measurement device, 4... terminal device.

Claims (5)

A detection device that detects an abnormality using periodic information indicating the state of a living body,
a periodic information acquisition unit that acquires the periodic information;
a partial information generation unit that generates partial information based on a period from the periodic information acquired by the periodic information acquisition unit;
a partial information feature amount calculation unit that calculates a feature amount of the partial information generated by the partial information generation unit;
a partial information abnormality detection unit that detects an abnormality of 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 of the partial information abnormality detection unit;
with
The partial information anomaly detection unit receives the degree of anomaly of the partial information generated by the partial information generation unit, information indicating whether or not the partial information generated by the partial information generation unit is abnormal, and generating information indicating an abnormal feature quantity, which is a feature quantity based on which the partial information generated by the partial information generation unit is detected to be abnormal;
detection device. The detection device according to claim 1,
a periodic information feature amount calculation unit that calculates the feature amount of the periodic information acquired by the periodic information acquisition unit;
a periodic information abnormality detection unit that detects an abnormality in the periodic information based on the feature amount calculated by the periodic information feature amount calculation unit;
The detection device, wherein the output unit outputs information further based on a detection result by the periodic information anomaly detection unit. The detection device according to claim 1 or 2 ,
a menu determination unit that determines a practice menu for improving the abnormal feature amount calculated by the partial information abnormality detection unit;
The detection device, wherein the output unit further outputs the menu determined by the menu determination unit. A detection method executed by a detection device that detects an abnormality using periodic information indicating the condition of a living body,
a periodic information obtaining step of obtaining the periodic information;
a partial information generating step of generating partial information based on the period from the periodic information obtained in the periodic information obtaining step;
a partial information feature quantity calculation step of calculating a feature quantity of the partial information generated in the partial information generation step;
a partial information abnormality detection step of detecting an abnormality of the partial information generated in the partial information generation step based on the feature amount calculated in the partial information feature amount calculation step;
an output step of outputting information based on the detection result detected in the partial information anomaly detection step ;
The partial information anomaly detection step includes the degree of anomaly of the partial information generated by the partial information generation step, information indicating whether or not the partial information generated by the partial information generation step is abnormal, and generating information indicating an abnormal feature quantity, which is a feature quantity based on which the partial information generated by the partial information generating step is detected to be abnormal;
Detection method. The detection method according to claim 4,
A periodic information feature amount calculating step of calculating the feature amount of the periodic information acquired by the periodic information acquiring step;
a periodic information anomaly detection step of detecting an anomaly of the periodic information based on the feature quantity calculated by the periodic information feature quantity calculation step;
The detection method, wherein the output step outputs information further based on the detection result of the periodic information anomaly detection step.

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