Vital sign subject identification system, subject identification method, and subject identification program

Abstract

To provide a measured person determination system on vital signs that enables identification of a measured person from vital sign data, a measured person determination method, and a measured person determination program.SOLUTION: A measured person determination system is composed of a measuring device 1 for measuring vital signs and a data processing device 2 for processing the measured vital sign data. The data processing device 2 acquires one or a plurality of feature amounts used for the identification of an object person using the vital sign data at rest for each object person from one or two or more pieces of the vital sign data that are input, and generates data for collation used for the identification of the measured person from the acquired feature amounts. Determination means 24 determines a measured person for new input data using the data for collation for the input of the new data of the same kind as the feature amount of the data for collation.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a vital sign measured person determination system, a measured person determination method, and a measured person determination program, and more particularly to a technique for identifying a measured person from input vital sign data.

Background Art

[0002] In recent years, measuring devices that can easily measure various vital signs (hereinafter referred to as "vital signs") useful for grasping an individual's health condition have been proposed. For example, so-called wearable measuring devices that are worn on the body such as the wrist or finger to measure a subject's pulse, heart rate, blood pressure, blood oxygen saturation, body temperature, sweating amount, body movement, etc., and stationary measuring devices that are placed under a futon or mat to measure a bedridden subject's pulse, heart rate, respiratory rate, body movement, etc. are known (see Patent Document 1 and Patent Document 2).

[0003] While this type of measuring device can easily measure vital signs, it can also extract the measurement results as electronic data, making it easy to store and process data and suitable for data analysis and interpretation. Therefore, it is not only used for individuals to grasp their health condition, but is also widely spreading recently, such as being used for the follow-up observation of patients and care recipients in hospitals and care facilities. Among them, wearable measuring devices, combined with the recent increase in health awareness, etc., smartwatch-type devices that also function as information terminal devices are rapidly spreading, and measuring devices owned by individuals have become more familiar than ever.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] Incidentally, when using vital sign data to assess and monitor health status, it is crucial to determine whose data the vital sign data being analyzed or interpreted belongs to. However, this verification is limited to identifying which measuring device was used to measure the vital signs, using the serial number of the measuring device included in the data. Therefore, for example, if a third party uses the same measuring device to measure the vital signs of a specific subject, there is a risk that the newly measured vital sign data of the third party may be treated as the vital sign data of the specific subject. In other words, there was absolutely no verification of whose data the measured vital sign data belonged to. Moreover, such data mix-ups can occur accidentally or intentionally, and the risk of data mix-ups always existed.

[0006] The present invention has been made in view of these problems, and its objective is to provide a vital signs subject identification system, subject identification method, and subject identification program that enable the identification of a subject from vital signs data. [Means for solving the problem]

[0007] To achieve the above objective, the vital sign measurement subject determination system according to the present invention is A measuring device that continuously measures the vital signs of the subject, It consists of a data processing device that processes vital sign data measured by the above measuring device, The above data processing device is An input means for inputting vital sign data of a subject measured by the above measuring device, A storage means for storing the above vital sign data for each of the above subjects, Input to the above input means , stored in the above storage meansA feature acquisition means that, from the vital sign data of one or more subjects, acquires one or more features used to identify each subject using the resting vital sign data for each subject, A matching data generation means that generates matching data for determination from the features obtained by the feature acquisition means described above, The system includes a determination means that, for new data of the same type as the above-mentioned features, compares the new data with the above-mentioned matching data to determine the person being measured. And so, The feature acquisition means uses information that identifies the measuring device and information that associates the measuring device with the subject to identify the subject from the information that identifies the measuring device contained in the vital sign data measured by the measuring device, and acquires the feature. It is characterized by the following:

[0008] And, as a preferred embodiment, it has the following configuration. (1) The vital signs described above are characterized in that at least one of the following is used: heart rate, pulse rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, and sweating rate.

[0009] (2) The above feature quantities are characterized by being processed data obtained by processing the above vital sign data.

[0010] (3) The processed data described above is characterized in that it includes at least one of the following: the mean, standard deviation, highest value, lowest value, median, or a calculated value based on these values ​​within a predetermined period.

[0011] (4) The matching data generation means uses the features acquired by the feature acquisition means as training data to pre-learn the characteristics of vital signs for each subject by machine learning and generate a trained model, and the determination means determines the subject of the input new data based on the trained model when new data of the same type as the features of the training data is input.

[0012] (5) The feature acquisition means described above is characterized by acquiring new features at predetermined intervals from continuously input vital sign data.

[0013] (6) The above characteristic quantity is the average heart rate during a predetermined period, the standard deviation of the heart rate during a predetermined period, the highest heart rate within a predetermined period, and the lowest heart rate within a predetermined period 、 It is characterized by including at least one of the average heart rate - standard deviation during a predetermined period, the average heart rate + standard deviation during a predetermined period, the median of the average heart rate for a predetermined period for each predetermined time, the median of the standard deviation of the heart rate for a predetermined period for each predetermined time, the median of the highest heart rate for a predetermined period for each predetermined time, and the median of the lowest heart rate for a predetermined period for each predetermined time.

[0014] (7) The above measurement device is characterized by being composed of a wearable device capable of measuring the heart rate of a subject.

[0015] (8) The above measurement device is characterized by being composed of a non-contact body motion sensor capable of measuring the heart rate of a subject.

[0016] (9) The above input means is characterized by acquiring data related to the heart rate from the above measurement device via a network.

[0017] Also, the method for determining the subject of vital signs according to the present invention is From the data of the vital signs of one or more subjects input from a measurement device that continuously measures the vital signs of a subject, for each of the above subjects, a feature quantity acquisition step of acquiring one or more feature quantities used for identifying the subject using the data of the vital signs at rest, A reference data generation step of generating reference data for determination from the acquired feature quantity, For new data of the same type as the above feature quantity, a determination vinegar step of collating the new data with the above reference data to determine the subject of the new data, and is provided with 、 The above feature quantity acquisition step is Using information that identifies the above measuring device and information that associates it with the above subject, the subject is identified from the information that identifies the above measuring device contained in the vital sign data measured by the above measuring device, and the above feature quantities are obtained. It is characterized by the above.

[0018] Also, the program for determining the subject of vital signs according to the present invention is A program for causing a computer to determine a subject of new vital sign data, the computer is caused to from data of vital signs of one or more subjects input from a measuring device that continuously measures the vital signs of the subject, for each subject, a feature quantity acquisition step of acquiring one or more feature quantities used for identifying the subject using the data of the vital signs at rest, a reference data generation step of generating reference data for determination from the acquired feature quantities, a determination step of collating the new data of the same type as the feature quantity with the reference data to determine the subject of the new data, Yes, In the feature acquisition step described above, using information that identifies the measuring device and information that associates the subject, the process of identifying the subject from the information that identifies the measuring device contained in the vital sign data measured by the measuring device and acquiring the features described above is performed. is executed characterized by

Effect of the Invention

[0019] In the present invention, one or more feature quantities used for identifying a subject are acquired using data of vital signs at rest, and reference data for determining the subject to be measured is generated from the acquired feature quantities. Then, when new data of the same type as the generated reference data is input, the new data is collated with the reference data to determine the subject of the new data. Therefore, it is possible to determine whether the input new data can be evaluated as the vital signs of a subject for whom reference data exists, and further, which subject's vital signs can be evaluated, and the subject of the new data can be identified.

Brief Description of the Drawings

[0020] [Figure 1] It is a system configuration diagram showing an example of the hardware configuration of a vital sign subject determination system according to the present invention. [Figure 2] It is a block diagram showing the functional configuration of the subject determination system. <00,00119>It is a system configuration diagram showing another example of the hardware configuration of the subject determination system. [Figure 4] This is a system configuration diagram showing another example of the hardware configuration of the subject determination system. [Figure 5] This is an explanatory diagram showing an example of the judgment criteria used for the judgment processing of new data in the first embodiment of the subject judgment system. [Figure 6] Figure 6(a) shows an example of the changes in the pulse rate during sleep of a subject used in the determination process of new data in the second embodiment of the subject determination system. Figure 6(b) shows the changes in pulse rate on the first day, and Figure 6(b) shows the changes in pulse rate on the second day. [Figure 7] Figure 6 shows a magnified view of the characteristic pulse rate fluctuations, with Figure 7(a) showing the pulse rate fluctuations at the onset of sleep and Figure 7(b) showing the periodic pulse rate fluctuations that appear after sleep. [Modes for carrying out the invention]

[0021] Embodiments of the present invention will be described below with reference to the drawings. However, the embodiments described below are merely illustrative and can be modified and implemented without departing from the spirit of the invention. In this specification, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions are omitted.

[0022] Figure 1 shows an example of the hardware configuration of the vital sign subject identification system according to the present invention. The vital sign subject identification system according to the present invention is a system that determines whether or not the input vital sign data is the vital sign data of a subject that has been registered in the system in advance, and mainly comprises a measuring device 1 and a data processing device 2.

[0023] The measuring device 1 consists of a device that measures the vital signs of subject T. Here, vital signs refer to various life indicators that are useful for understanding a person's health status, and examples include pulse rate, heart rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, sweating amount, and body movement. In this embodiment, heart rate is used as a vital sign, and the measuring device 1 is a device capable of measuring heart rate.

[0024] The measuring device 1 may consist of either a contact-type or non-contact-type device. Examples of a contact-type measuring device 1 include a measuring device that is worn on the body (wearable device). For example, examples include devices that are worn on the wrist, such as a so-called smartwatch, devices that are worn on the fingers, such as a smart ring, and devices that are attached to the body, consisting of a sheet with a built-in sensor. On the other hand, examples of a non-contact-type measuring device 1 include devices that measure heart rate using microwaves or infrared rays, and devices that measure heart rate using pressure sensors.

[0025] Figure 1 shows a case where a body movement sensor, placed under the mat, is used as the measuring device 1 to measure heart rate non-contact. This type of body movement sensor is equipped with a pressure sensor, which measures the heart rate and whether or not there is body movement of the subject T lying on the mat.

[0026] Furthermore, the measuring device 1 is a device that continuously measures the vital signs of subject T. In this embodiment, the measuring device 1 is a device that continuously measures the heart rate of subject T at a constant interval (for example, a period of several seconds to several tens of seconds).

[0027] As shown in Figure 2, the measuring device 1 includes at least a measuring unit 11 that measures the heart rate of subject T, and an interface unit 12 that outputs the measured heart rate data of subject T to the outside. The measuring unit 11 converts the measured heart rate into digital data. The interface unit 12 outputs the heart rate data to the outside via wired or wireless connection. Figure 1 shows the case where the interface unit 12 transmits heart rate data wirelessly. When using wireless, the interface unit 12 can be a short-range wireless communication device such as Bluetooth®, or a communication device compatible with wireless LAN (Local Area Network). Data such as heart rate and body movement, which are measured at regular intervals, are output from the measuring device 1 each time a measurement is taken or at predetermined intervals (for example, every few minutes).

[0028] The data processing device 2 is a device that performs analysis of vital sign data measured by the measuring device 1, and is composed of an electronic computer such as a personal computer or a server computer. Figure 1 shows a case where the data processing device 2 uses a notebook-type personal computer equipped with a display unit 201, an operation unit 202, an input / output interface unit 203, a storage unit 204, an arithmetic unit 205, etc. In the illustrated example, a printer 3 for printing judgment results, etc., which will be described later, is connected to the data processing device 2 via a wired connection.

[0029] The data processing device 2 primarily comprises, in terms of its functional configuration: an input means 21 for inputting vital sign data of a subject; a feature acquisition means 22 for acquiring one or more feature quantities for identifying a subject using resting vital sign data from the vital sign data of one or more subjects input to the input means 21; a matching data generation means 23 for generating matching data for determination from the feature quantities acquired by the feature acquisition means 22; and a determination means 24 for determining the subject by comparing new data of the same type as the feature quantities with the matching data.

[0030] The input means 21 consists of an interface (input / output interface unit 203) for inputting vital sign data measured by the measuring device 1, that is, data such as heart rate and body movement in this embodiment. Specifically, the input means 21 consists of a communication device for short-range wireless communication when heart rate data is input via short-range wireless communication, and a communication device compatible with wireless LAN when it is input via wireless LAN, and is configured as a wired or wireless interface depending on how the data such as heart rate is input. The vital sign data continuously transmitted from the measuring device 1 is received sequentially by the input means 21, and the vital sign data for each subject T whose vital signs are measured is stored in the storage unit 204.

[0031] The feature acquisition means 22 performs a process to extract one or more features used to identify subject T from the vital sign data stored in the memory unit 204. Specifically, it reads the vital sign data for each subject T from the memory unit 204 and performs feature extraction for each subject T. This process is executed by the computer's arithmetic unit according to a program installed on the computer.

[0032] Here, resting vital sign data is used to extract features for identifying subject T. Medically, "rest" means "minimizing physical activity, calming heart rate and respiratory rate, and taking a rest. After surgery or during treatment for an illness, patients are instructed to rest as needed. To rest, patients should lie in bed, remain as still as possible, breathe quietly, and try to relax and rest." (From the Japan Medical Association's "Medical Fee Manual"). However, "rest" here means a state in which vital signs have calmed (or would have calmed) to a degree suitable for feature extraction. For example, this could involve suppressing physical activity and calming heart rate and respiratory rate for a certain period of time (e.g., 1 minute). The specific definition can be appropriately set depending on the type of vital sign being measured, the age, sex, physique, and health status of the subject. Generally, to stabilize vital signs, sitting is preferable to standing, and lying down is preferable to sitting, and longer periods of stillness are preferable. Furthermore, actions that relax the mind and body, such as urinating, closing the eyes, and regulating breathing through deep breathing, are also effective. Moreover, sleep is preferable to wakefulness, and within sleep, deep non-REM sleep is preferable to light REM sleep. Feature extraction is performed using vital sign data that meets the definition of "rest" which is selectively set based on these conditions. For example, if rest is defined as remaining still in a lying position without moving the limbs for two minutes or more, the feature acquisition means 22 monitors the heart rate and body movement data received by the input means 21 or the vital sign data stored in the memory unit 204, and extracts features using the heart rate data when the person is lying down and has been in a state of no movement (or minimal movement that can be evaluated as rest) for two minutes.According to the applicant's experience, in order to obtain "rest" suitable for measuring vital signs, it is important to at least keep the limbs still, maintain a constant posture, and regulate breathing through deep breathing, etc. Therefore, it is preferable to define rest in a way that satisfies this condition. For example, rest can be defined as remaining still for one minute while sitting with eyes closed, breathing regulated, and limbs still.By defining the resting state in this way, it becomes possible to extract features at short intervals, providing a highly versatile system.

[0033] Feature extraction is performed at predetermined intervals based on the program settings. For example, when using daily processing data such as the average heart rate per day as a feature, the feature extraction period can be set to "1 day" to match the data aggregation period for processing. Similarly, when using the average heart rate every few tens of minutes as a feature, the system can be set to extract features at intervals of several tens of minutes. Feature extraction can also be set independently of such data aggregation periods for processing. For example, when using heart rate per minute as a feature, the system may be configured to extract features at intervals of less than one minute (e.g., 10-second intervals). In this way, the feature acquisition means 22 is configured to acquire new features at predetermined intervals, and the acquired features are stored in the storage unit 204 for each subject T. By setting the feature extraction period shorter than the data aggregation period for processing, it becomes possible to extract more features within a certain period.

[0034] Furthermore, feature extraction is performed for each subject T. While the subject T from which features are extracted is usually at least one, in this embodiment, feature extraction is performed for multiple subject T. Therefore, the feature acquisition means 22 identifies which subject T the input heart rate data belongs to and acquires features for each subject. Here, the subject T is identified, for example, by using information that has been pre-associated with subject T and information that identifies the measuring device 1 included in the header of the heart rate data (for example, the serial number of the measuring device 1), or by using information of subject T that is manually entered using the operation unit 202. For each subject T identified in this way, the feature acquisition means 22 acquires features from the heart rate data.

[0035] Furthermore, the features extracted are those that tend to show individual differences. These features are those identified from an academic perspective or empirically, and one or more of these items are selected to be used as features. For example, the average heart rate over a specified period (e.g., one day), the standard deviation of heart rate over a specified period, the highest heart rate during a specified period, and the lowest heart rate during a specified period. 、 Examples of features include the average heart rate minus the standard deviation over a specified period, the average heart rate plus the standard deviation over a specified period, the median of the average heart rate for each time interval over a specified period, the median of the standard deviation of the heart rate for each time interval over a specified period, the median of the maximum heart rate for each time interval over a specified period, the median of the minimum heart rate for each time interval over a specified period, and the median of the heart rate over a specified period. At least one of these items (for example, 10 of these items) is used as a feature.

[0036] In addition, other features such as heart rate during sleep are also cited as examples. For example, examples of heart rate during sleep include the heart rate when falling asleep (the initial stage of sleep) and the heart rate during non-REM sleep, when the brain is thought to be at rest. According to the applicant's experience, these are particularly prone to individual differences.

[0037] While the feature acquisition means is configured to perform feature calculations using a dedicated program, for features that can be calculated using a general-purpose spreadsheet program, such as average heart rate and standard deviation, a general-purpose spreadsheet program may be installed on the data processing device 2 and the calculations may be performed using this spreadsheet program.

[0038] The matching data generation means 23 generates matching data used for determination by the determination means 24 from the feature quantities acquired by the feature quantity acquisition means 22. This process is executed by the computer's arithmetic unit according to a program installed on the computer.

[0039] In generating the matching data, for example, for each subject T, multiple sets of feature values ​​are obtained for each feature (for example, if the feature is "average heart rate per day," then several sets of feature values ​​are obtained). The median of these multiple sets of feature values ​​is calculated, and this value (median) is used as the matching data. By generating matching data from multiple sets of feature values ​​in this way, the accuracy of the matching data is improved. The matching data is stored in the storage unit 204 of the data processing device 2 and used in the determination process by the determination means 24, which will be described later.

[0040] The determination means 24, upon input of new data of the same type as the features of the matching data, performs a process (determination process) to determine the subject of the input new data (the subject to whom the new data was measured) based on the matching data. This process is executed by the computer's arithmetic unit according to a program installed on the computer.

[0041] Here, the determination of the subject involves having the computer's arithmetic unit determine whether or not there is matching data corresponding to (approximating) the newly entered data, and if such matching data exists, it identifies which subject's matching data it approximates. In other words, if no matching data exists, the subject remains unknown, while if matching data exists, the subject T is identified as the subject with the most similar matching data. This determination result can be printed using printer 3.

[0042] Thus, according to the present invention, one or more feature quantities used to identify each subject T are obtained from the vital sign data of one or more subjects T, and matching data for determining the subject is generated from the obtained feature quantities. At the same time, when new data of the same type as the generated matching data is input, the subject of the new data is determined by comparing the new data with the matching data, thus preventing the vital sign data of a third party for whom no matching data exists from being treated as the vital sign of subject T. Moreover, if matching data corresponding to the input new data exists, it is also possible to identify which subject T the input new data belongs to. Therefore, when new data is determined to be the data of subject T according to the present invention, the subject of the measurement is clarified, the authenticity of the data can be guaranteed, and data mix-ups can be prevented.

[0043] Figure 3 shows an example of a modified hardware configuration. In Figure 3, the measuring device 1 is a device worn on the wrist of subject T. The data processing device 2 is a server computer. In this case, the measuring device 1 and the data processing device 2 are connected via a network N. Examples of network N include a LAN (Local Area Network) and the Internet. In the example shown in Figure 2, instead of the printer mentioned above, a mobile terminal 4 (such as a smartphone or tablet computer) connected to the network N is used as the output device for the judgment results, and the judgment results from the data processing device 2 are displayed on the display unit of the mobile terminal 4.

[0044] Figure 4 shows other examples of hardware configuration modifications. The configuration shown in Figure 4 is a modified version of the configuration shown in Figure 3, and uses a measuring device 1 that is worn on the finger of the subject T. The measuring device 1 and the mobile terminal 4 are connected wirelessly, such as by short-range wireless communication, and the measurement results from the measuring device 1 are transmitted to the mobile terminal 4. A dedicated application program is installed on the mobile terminal 4, and according to this program, the heart rate data acquired from the measuring device 1 is transmitted to the data processing device 2 via the network N. The results of the determination by the data processing device 2 are displayed on the display unit of the mobile terminal 4, as in Figure 3.

[0045] As shown in these modified examples, the vital sign subject determination system according to the present invention may be configured to allow the data processing device 2 to acquire heart rate data measured by the measuring device 1 via the network N.

[0046] Next, an embodiment of the vital sign measurement subject determination system according to the present invention will be described with reference to experimental results conducted by the applicant.

[0047] [Example 1] In this embodiment, a total of 10 items were used as training data features: average daily heart rate, standard deviation of daily heart rate, highest daily heart rate, lowest daily heart rate, average daily heart rate minus standard deviation, average daily heart rate plus standard deviation, median of average heart rate every 10 minutes over multiple days, median of standard deviation of heart rate every 10 minutes over multiple days, median of highest daily heart rate every 10 minutes over multiple days, and median of lowest daily heart rate every 10 minutes over multiple days.

[0048] The experiment involved recruiting 15 subjects (T) and collecting heart rate data from them over a period of 5 to 13 days (average 8.73 days). Features were extracted from the first half of the collected data, and matching data was generated from these extracted features. Specifically, 10 matching data items were generated for each of the 15 subjects (T) from the first half of the data.

[0049] In contrast, the new data used for determining the subjects was the second half of the data from each of the 15 subjects. Specifically, for the second half of the data from each subject, the average daily heart rate, the standard deviation of the daily heart rate, the highest daily heart rate, the lowest daily heart rate, the average daily heart rate minus the standard deviation, the average daily heart rate plus the standard deviation, the median daily average heart rate every 10 minutes, the median daily standard deviation of the heart rate every 10 minutes, the median daily highest heart rate every 10 minutes, and the median daily lowest heart rate every 10 minutes were calculated and used as the new data for determining the subjects. When creating the new data, if there were missing values, two types of new data were prepared: new data in which features were extracted using only the raw values ​​without interpolating the missing values, and new data in which features were extracted after interpolating the missing values.

[0050] Figure 5 shows the criteria for determining whether a subject is accurate. These criteria show the criteria for both raw values ​​(without interpolation of missing values) and interpolated values ​​(without interpolation) for new data. The upper section ("Width Setting") shows the judgment values ​​for the acceptable deviation between the matching data and the new data. For example, for "Average daily heart rate," the judgment values ​​for the deviation are set to 4.8 for the raw value and 5.2 for the interpolated value. For "Standard deviation per day," the judgment values ​​for the deviation are set to 2.4 for both the raw and interpolated values. Furthermore, for "Average daily heart rate," which uses a sum added every 10 minutes in the judgment process, the judgment values ​​for the deviation are set to 0.200 for the raw value and 0.260 for the interpolated value, while for "Standard deviation every 10 minutes," the judgment values ​​for the deviation are set to 0.055 for the raw value and 0.027 for the interpolated value.

[0051] On the other hand, the lower section ("Setting the score") shows the evaluation points to be added if the discrepancy between the new data and the matching data is within the judgment value for the discrepancy range. For example, if the discrepancy range for "Average daily heart rate" is within the judgment value, 1 point is added as an evaluation point. Similarly, if the discrepancy range for "Standard deviation per day" is within the judgment value, 1 point is added as an evaluation point. Furthermore, for the median of "Average daily heart rate," which uses a value added every 10 minutes in the judgment process, 0.2 points are added as an evaluation point if the discrepancy range is within the judgment value. Likewise, if the discrepancy range for "Standard deviation every 10 minutes" is within the judgment value, 0.2 points are added as an evaluation point.

[0052] The determination using these criteria is based on the sum of evaluation scores obtained by comparing the new data with the matching data. Specifically, it is determined whether the sum exceeds a predetermined threshold X. If the sum is less than threshold X, it is determined that there is no subject T corresponding to the new data. On the other hand, if the sum of evaluation scores is equal to or greater than threshold X, it is determined that the new data is the data of subject T in the matching data. If there are multiple subject T whose scores exceed threshold X, it is determined that the new data is the data of subject T in the matching data with the highest evaluation score.

[0053] As a result of performing this determination process for each of the 15 subjects, we succeeded in identifying subject T with a near 100% accuracy rate for all new data, regardless of whether we used raw values ​​or interpolated values. In other words, when we performed the above determination process using the second half of the new data for each of the 15 subjects, we were able to correctly identify which subject the new data belonged to for all 15 subjects. In particular, when we used interpolated values ​​obtained by interpolating missing values, we succeeded in identifying the subject with a higher accuracy rate (100% accuracy rate in this test) compared to when we used raw values ​​(93% accuracy rate in this test).

[0054] [Example 2] Next, a second embodiment of the present invention will be described. The second embodiment uses data obtained from pulse rate during sleep as the feature vector for training data. Figure 6 shows an example of the changes in pulse rate during sleep (lying down) for one subject, with Figure 6(a) showing the measurement results on the first day and Figure 6(b) showing the measurement results on the second day.

[0055] The applicant's research indicates that there are individual differences in the changes in pulse rate during sleep. For example, in the case of the subject shown in Figure 6, as can be seen by comparing the changes in pulse rate on the first and second days, the subject's characteristics are evident in the fluctuations in pulse rate at the beginning of sleep (see section indicated on P1) and the periodic fluctuations in pulse rate that appear about 200 minutes after lying down (see section indicated on P2).

[0056] Specifically, the fluctuation in pulse rate P1 at the onset of sleep is characterized by a rapid decrease in pulse rate starting a few minutes after lying down, followed by a rapid increase in pulse rate. Figure 7(a) shows a magnified view of this fluctuation in pulse rate at the onset of sleep. In this embodiment, to capture this characteristic as a feature, the approximately V-shaped interval from the lowest pulse rate to the peaks of the two peaks that occur before and after it is considered as a single unit. Examples of feature quantities include the number of occurrences of the approximately V-shaped portion (1 in the illustrated example), the time period in which it occurred (15 to 30 minutes after lying down in the illustrated example), height L1, width L2, and pulse rates of the peaks and valleys.

[0057] On the other hand, the periodic fluctuations in pulse rate P2 are characterized by an increase in pulse rate during light REM sleep and a decrease in pulse rate during deep non-REM sleep. Figure 7(b) shows a magnified view of these periodic pulse rate fluctuations. Here, when considering this characteristic as a feature, similar to the pulse rate fluctuations at the onset of sleep, the approximately U-shaped interval from the lowest pulse rate to the peaks of the two peaks that occur before and after it is considered as a single unit. Examples of feature quantities include the duration of the approximately U-shaped portion that occurs periodically, the number of occurrences (3 in the illustrated example), the time of occurrence (around 220 minutes, 280 minutes, and 310 minutes after lying down in the illustrated example), height L3, width L4, and pulse rates of the peaks and valleys.

[0058] In this embodiment, one or more features used to identify the subject are obtained from the changes in pulse rate during sleep, a trained model is generated for each subject T, and this model is used for the subject determination process. Thus, in this embodiment, the subject determination process is performed using the pulse rate when vital signs are stable while lying down, more specifically, during sleep, so that the subject can be identified with high determination accuracy.

[0059] The embodiments described above are merely preferred embodiments of the present invention, and the present invention is not limited to these, with various design modifications possible within its scope.

[0060] For example, in the embodiment described above, heart rate was used as the vital sign of subject T, but other vital signs such as pulse, blood pressure, blood oxygen saturation, body temperature, sweating, and body movement may be used instead of or in conjunction with heart rate. In other words, vital signs other than heart rate can be used as the vital signs from which features to be used as training data, or multiple types of vital signs can be used in combination.

[0061] Here, the choice of which vital sign to use as the vital sign from which to extract features is preferably determined in relation to the purpose and application in which the present invention is used. For example, if the system of the present invention requires a high accuracy rate in predicting positive results, such as when the input new data is vital sign data of a specific subject T, then the vital sign that yields a high accuracy rate in positive predictions should be identified through experiments or other means, and that vital sign should be used for feature extraction. Similarly, if the system requires a high accuracy rate in predicting negative results, such as when the input new data is not vital sign data of a specific subject T, then the vital sign that yields a high accuracy rate in negative predictions should be identified through experiments or other means, and that vital sign should be used for feature extraction. In other words, the vital sign used for feature extraction in the present invention is selected to be the most appropriate one according to the purpose and application of the system of the present invention.

[0062] Furthermore, in relation to the selection of vital signs, it is preferable that the characteristics to be extracted from the selected vital signs be determined in relation to the purpose and application for which the present invention is used. That is, as above, if the system of the present invention is a system that seeks a high accuracy rate in predicting positive results, the characteristics that yield a high accuracy rate in predicting positive results should be determined by experimentation or other means, and the characteristics that yield a high accuracy rate should be used. Similarly, if the system of the present invention is a system that seeks a high accuracy rate in predicting negative results, the characteristics that yield a high accuracy rate in predicting negative results should be determined by experimentation or other means, and the characteristics that yield a high accuracy rate should be used. In other words, the characteristics used in the present invention are selected to be optimal according to the purpose and application for which the system of the present invention is used.

[0063] Furthermore, in the embodiments described above, the explanation of the interpolation method when missing values ​​exist in the extraction of features such as average heart rate was omitted. However, missing values ​​can be interpolated using appropriate methods such as single interpolation or multiple interpolation.

[0064] Furthermore, while the above-described embodiment explained the process of determining the subject of the newly input data, it is also possible to configure the system so that if the subject is one of the subjects T for whom data exists in the memory unit 204, the newly input data is added to the training data of that subject T, and a trained model is generated. By doing so, the accuracy of the subject determination process can be improved using the newly input data.

[0065] Furthermore, although the above-described embodiment showed that the memory unit 204 stores vital sign data for 15 subjects, the number of subjects whose vital sign data is stored can be changed as appropriate. For example, it is possible to configure the system to store data for tens of thousands, hundreds of thousands, or even more subjects. In this way, the present invention can also be used as a biometric authentication technology.

[0066] Furthermore, in the embodiment described above, we showed a case where multiple sets of feature quantities were obtained for each feature quantity and the median was used as the matching data for matching with new data. However, it is also possible to use numerical values ​​other than the median as the matching data. For example, instead of the median of the feature quantities, the feature quantities obtained by the feature quantity acquisition means 22 can be used as training data, and machine learning can be used to pre-train the characteristics of vital signs for each subject T to generate a trained model. The determination means 24 can then be configured to determine the subject of the input new data based on this trained model, given new data of the same type as the feature quantities. By utilizing machine learning in this way, the accuracy of determining the subject can be improved. [Explanation of Symbols]

[0067] 1. Measuring device 2 Data Processing Devices 3. Printer 4 Mobile devices 11 Measuring part 12 Interface section 21 Input means 22 Feature acquisition method 23. Data generation means for verification 24 Judgment means N Network T target audience

Claims

1. A measuring device that continuously measures the vital signs of the subject, It consists of a data processing device that processes vital sign data measured by the aforementioned measuring device, The aforementioned data processing device is An input means for inputting vital sign data of a subject measured by the aforementioned measuring device, A storage means for storing the vital sign data for each subject, A feature acquisition means that, from the vital sign data of one or more subjects input to the input means and stored in the storage means, acquires one or more feature quantities for use in identifying each subject using the resting vital sign data for each subject. A matching data generation means that generates matching data for determination from the features acquired by the feature acquisition means, The system includes a determination means for determining the subject by comparing new data of the same type as the aforementioned feature quantities with the aforementioned matching data. The feature acquisition means uses information that identifies the measuring device and information that associates the measuring device with the subject to identify the subject from the information that identifies the measuring device included in the vital sign data measured by the measuring device, and acquires the feature. A system for determining the subject of vital sign measurement, characterized by the following features.

2. The vital signs used include at least one of the following: heart rate, pulse rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, and sweating rate. The vital signs measurement subject determination system according to feature 1.

3. The subject determination system according to claim 2, characterized in that the aforementioned feature quantities are processed data obtained by processing the vital sign data.

4. The processed data includes at least one of the following: mean, standard deviation, highest value, lowest value, median, or calculated value based on these values, within a predetermined period. The subject determination system according to feature 3.

5. The aforementioned matching data generation means uses the features acquired by the feature acquisition means as training data to pre-train the characteristics of vital signs for each subject using machine learning, and generates a trained model. The determination means determines the subject of the input new data based on the trained model, in response to input new data of the same type as the features of the training data. A system for determining the subject of vital sign measurement according to any one of features 1 to 4.

6. The feature acquisition means acquires new features at predetermined intervals from continuously input vital sign data. A system for determining the subject of vital sign measurement according to any one of features 1 to 4.

7. The aforementioned feature includes at least one of the following: the average heart rate over a predetermined period, the standard deviation of the heart rate over the predetermined period, the maximum heart rate during the predetermined period, the minimum heart rate during the predetermined period, the average heart rate minus the standard deviation over the predetermined period, the average heart rate plus the standard deviation over the predetermined period, the median of the average heart rate for each predetermined time interval over a predetermined period, the median of the standard deviation of the heart rate for each predetermined time interval over a predetermined period, the median of the maximum heart rate for each predetermined time interval over a predetermined period, and the median of the minimum heart rate for each predetermined time interval over a predetermined period. A system for determining the subject of vital sign measurement according to claim 1 or 2.

8. The aforementioned measuring device consists of a wearable device capable of measuring the heart rate of a subject. A system for determining the subject of vital sign measurement according to any one of features 1 to 4.

9. The aforementioned measuring device consists of a non-contact motion sensor capable of measuring the heart rate of a subject. A system for determining the subject of vital sign measurement according to any one of features 1 to 4.

10. The input means acquires heart rate data from the measuring device via the network. A system for determining the subject of vital sign measurement according to any one of features 1 to 4.

11. A feature acquisition step involves obtaining one or more features for each subject, using resting vital sign data, from vital sign data of one or more subjects input from a measuring device that continuously measures the vital signs of the subjects; A matching data generation step that generates matching data for determination from acquired features, The system includes a determination step of comparing new data of the same type as the aforementioned feature quantities with the aforementioned matching data to determine the person being measured in the new data. The feature acquisition step involves using information that identifies the measuring device and information that associates the measuring device with the subject, to identify the subject from the information that identifies the measuring device included in the vital sign data measured by the measuring device, and then acquiring the feature. A method for determining the subject's vital signs, characterized by the following features.

12. A program that allows a computer to determine the subject of new vital sign data, To the aforementioned computer, A feature acquisition step involves obtaining one or more features for each subject, using resting vital sign data, from vital sign data of one or more subjects input from a measuring device that continuously measures the vital signs of the subjects; A matching data generation step that generates matching data for determination from acquired features, The system includes a determination step of comparing new data of the same type as the aforementioned feature quantities with the aforementioned matching data to determine the person being measured in the new data. In the feature acquisition step, using information that associates the information identifying the measuring device with the subject, the process of identifying the subject from the information identifying the measuring device included in the vital sign data measured by the measuring device is executed to acquire the feature. A program for determining the subject of vital sign measurement, characterized by the following features.

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