JP6316063B2 - Information processing apparatus, information processing system, information processing method, and program - Google Patents

Description

  The present invention relates to an information processing apparatus, an information processing system, an information processing method, and a program.

  2. Description of the Related Art Conventionally, a method for measuring a heartbeat or the like using a so-called Doppler radar and wavelet transform (Wavelet Transform) is known (for example, Non-Patent Document 1 and Non-Patent Document 2).

Jingyu Wang et al., “1-D Microwave Imaging of Human Cardiac Motion: An Ab-Initiation Investigation”, IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUE. 61, NO. 5, MAY 2013 A. Tariq et al., “Vital signature detection using Doppler radar and Continuous Waveform Transform”, the 5th European Conference on Antennas and Propagation, 2011.

  However, in the conventional method, a heartbeat or the like is detected based on a peak appearing in the wavelet frequency spectrum, but a scale factor (hereinafter referred to as “Scale Factor”, hereinafter referred to as “SF”) used for obtaining the frequency spectrum is a waveform measured in advance. In some cases, the measurement accuracy deteriorates because it is not determined based on the above.

  One aspect of the present invention aims to improve the accuracy of measurement of heartbeats and the like.

  In one aspect, an information processing apparatus connected to a measurement apparatus that measures the movement of a human body, the acquisition means for acquiring measurement data from the measurement apparatus, and the movement of the human body when the subject to be measured is in a resting state Storage means for storing reference data that is measured data, determination means for determining a scale factor based on the reference data, and wavelet transform of the measurement data based on the scale factor determined by the determination means The movement of the human body is measured.

  Measurement accuracy such as heart rate can be improved.

It is a conceptual diagram explaining an example of the whole structure which concerns on one Embodiment of this invention. It is a conceptual diagram explaining an example of the Doppler radar which concerns on one Embodiment of this invention. It is a block diagram explaining an example of the hardware constitutions of the information processing apparatus which concerns on one Embodiment of this invention. It is a conceptual diagram explaining an example of the process regarding the mother wavelet which concerns on one Embodiment of this invention. It is a figure for demonstrating an example of calculation of the heart rate which concerns on one Embodiment of this invention. It is a figure for demonstrating an example of the evaluation result which concerns on one Embodiment of this invention. It is a figure for demonstrating an example of the evaluation result by RMSE which concerns on one Embodiment of this invention. It is a figure for demonstrating an example of the influence of the test subject's operation | movement which concerns on the measurement of this invention. It is a sequence diagram explaining an example of the whole process by the information processing system which concerns on the measurement of this invention. It is a flowchart explaining an example of the whole process by PC concerning the measurement of this invention. It is a figure explaining an example of SF selection processing by PC concerning measurement of the present invention. It is a figure explaining an example of the evaluation result in acquisition of the reference data concerning measurement of the present invention.

  Embodiments of the present invention will be described below.

<Overall configuration>
FIG. 1 is a conceptual diagram illustrating an example of the overall configuration according to an embodiment of the present invention.

  The information processing system is, for example, the information processing system 1. Hereinafter, the information processing system 1 will be described as an example. The information processing system 1 includes a PC (Personal Computer) 10, an amplifier 11, a Doppler radar 12, and a filter 13.

  The information processing apparatus is, for example, the PC 10. Hereinafter, the PC 10 will be described as an example.

  The measuring device is, for example, a Doppler radar 12. Hereinafter, the Doppler radar 12 will be described as an example.

  As shown in FIG. 1, the PC 10 is connected to the amplifier 11. The amplifier 11 is connected to the filter 13. The filter 13 is connected to the Doppler radar 12. The PC 10 acquires measurement data from the Doppler radar 12 via the amplifier 11 and the filter 13. The PC 10 analyzes body movements such as heartbeat, respiration, and body movement of the subject 2 based on the acquired measurement data, and measures the movement of the human body such as the heart rate. Hereinafter, a case where the movement of the human body is a heart rate will be described as an example.

  The amplifier 11 and the filter 13 preprocess the signal output from the Doppler radar 12.

  The preprocessing performed by the filter 13 is filter processing such as a high-pass filter, a low-pass filter, or a band-pass filter.

  The filter 13 is preferably set to extract 10 Hz larger than 0 Hz so that the heartbeat can be measured with high accuracy. Further, the filter 13 is desirably set to extract a frequency of 0.016 Hz to 7.0 Hz in order to be able to measure with higher accuracy. For example, the filter 13 has a high-pass filter cutoff frequency of 0.016 Hz and a low-pass filter cutoff frequency of 7.0 Hz.

  The amplifier 11 performs processing for amplifying the signal filtered by the filter 13.

  It is desirable that the amplifier 11 sets the amplification degree so that the output is about several volts in accordance with the Doppler radar 12 so that the heartbeat can be measured with higher accuracy.

  The filter 13 performs a process for reducing noise in order to make it easy to process the signal output from the Doppler radar 12 by the process performed by the PC 10 at the subsequent stage.

  The Doppler radar 12 is a measuring device that measures the position and speed of a measurement target based on a signal. The Doppler radar 12 transmits a signal. The transmitted signal is reflected by the subject 2 and received by the Doppler radar 12. The Doppler radar 12 compares the transmitted signal with the received signal. The transmitted signal and the received signal have different frequencies due to the so-called Doppler effect. The Doppler radar 12 outputs the position or speed of the subject 2 based on the frequency difference as a comparison result. The data is output as a signal, for example.

  The overall configuration is not limited to the configuration shown in FIG. For example, the overall configuration of the PC 10, the amplifier 11, the filter 13, and the Doppler radar 12 may be an integrated device.

<Doppler radar>
FIG. 2 is a conceptual diagram illustrating an example of a Doppler radar according to an embodiment of the present invention.

  The Doppler radar 12 is a device shown in FIG. 2, for example. The Doppler radar 12 includes a source 12S, a transmitter 12Tx, a receiver 12Rx, and a mixer 12M. The Doppler radar 12 includes an adjuster 12LNA (Low Noise Amplifier) that performs processing such as reducing noise of data received by the receiver 12Rx.

  The source 12S is a transmission source that generates a signal transmitted from the transmitter 12Tx.

  The transmitter 12Tx transmits a signal to the subject 2. The signal to be transmitted is represented by a function Tx (t) related to time t, and can be represented by the following equation (1), for example.

上述の（１）式で、ω_ｃは、発信する信号の角周波数である。

In the above equation (1), ω _c is an angular frequency of a signal to be transmitted.

  Here, the case where the subject 2, that is, the reflection surface of the transmitted signal has a displacement of x (t) at time t will be described as an example. The displacement x (t) of the subject 2 can be expressed by the following equation (2), for example.

上述の（２）式で、ｍは、変位の振幅を示す定数である。ωは、被験者２の動きによってシフトした角速度である。

In the above equation (2), m is a constant indicating the amplitude of displacement. ω is an angular velocity shifted by the movement of the subject 2.

  The receiver 12Rx receives the signal transmitted from the transmitter Tx and reflected by the subject 2. The signal to be received can be represented by a function Rx (t) related to time t, for example, the following equation (3).

上述の（３）式で、ｄ_０は、被験者２と、ドップラーレーダ１２と、の距離である。λは、信号の波長である。

In the above equation (3), d ₀ is the distance between the subject 2 and the Doppler radar 12. λ is the wavelength of the signal.

  The Doppler radar 12 generates a Doppler signal by mixing the function Tx (t) of the transmission signal expressed by the equation (1) and the function R (t) of the reception signal expressed by the equation (3). The Doppler signal can be expressed by the following equation (4) when expressed by a function B (t) related to time t.

ここで、ドップラー信号の角周波数をω_ｄとすると、ドップラー信号の角周波数ω_ｄは、下記（５）式で示せる。

Here, when the angular frequency of the Doppler signal is ω _d , the angular frequency ω _d of the Doppler signal can be expressed by the following equation (5).

上述の（４）、および（５）式の位相θは、下記（６）式で示せる。

The phase θ in the above equations (4) and (5) can be expressed by the following equation (6).

上述の（６）式のθ_０は、被験者２、すなわち反射面での位相変位である。

In the above equation (6), θ ₀ is the phase displacement at the subject 2, that is, the reflecting surface.

  The Doppler radar 12 outputs a signal indicating the position and speed of the subject 2 based on the calculation represented by the above-described equation for comparing the transmitted signal and the received signal.

<Hardware configuration of information processing device>
FIG. 3 is a block diagram illustrating an example of a hardware configuration of the information processing apparatus according to the embodiment of the present invention.

  The PC 10 includes a CPU (Central Processing Unit) 10H1, a storage device 10H2, an input device 10H3, an output device 10H4, and an input I / F (Interface) 10H5. Each element of the PC 10 is connected by a bus 10H6, and transmits and receives data and the like.

  The CPU 10H1 performs operations for controlling elements of the PC 10 and performing various processes.

  The storage device 10H2 is a main storage device and an auxiliary storage device. The main storage device is, for example, a memory. The auxiliary storage device is, for example, a hard disk. The storage device 10H2 stores data including intermediate data used by the PC 10, a program, and the like.

  The input device 10H3 is a device for inputting parameters and commands necessary for calculation to the PC 10 by a user operation. The input device 10H3 is, for example, a keyboard, a mouse, and a driver.

  The output device 10H4 is a device for outputting various processing results and calculation results by the PC 10 to a user or the like. The output device 10H4 is, for example, a display.

  Note that the hardware configuration of the PC 10 is not limited to the configuration of FIG. For example, the PC 10 may include elements (not shown) that perform the CPU 10H1 and the storage device 10H2 in parallel, distributed, or redundantly. The PC 10 may have a configuration in which another device (not shown) that performs computation, control, or storage in parallel, distributed, or redundantly is connected to the outside of the PC 10 or via a network.

<Wavelet transform and processing based on mother wavelet>
The PC 10 performs wavelet transform on data acquired from the Doppler radar 12 via the amplifier 11 of FIG.

  The wavelet transform is performed using a mother wavelet that is a predetermined waveform. The process based on the mother wavelet is a process of applying the mother wavelet to the target waveform and calculating the degree of coincidence. The fitting is performed while moving the mother wavelet to the waveform to be converted on the time axis. When fitting, the mother wavelet is enlarged or reduced.

  FIG. 4 is a conceptual diagram illustrating an example of processing related to a mother wavelet according to an embodiment of the present invention.

  FIG. 4A is a diagram illustrating an example of a mother wavelet according to an embodiment of the present invention.

  The mother wavelet is, for example, the mother wavelet 3A. The mother wavelet 3A is a so-called Morlet wavelet.

  The Morlet wavelet is, for example, a waveform that can be represented by the function Ψ (η) of the following equation (7) in the time domain.

上述の（７）式でｍは、ウェーブ数である。（７）式でηは、非次元パラメータである。

In the above equation (7), m is the wave number. In equation (7), η is a non-dimensional parameter.

  In addition, embodiment is not restricted to using a Morlet wavelet for a mother wavelet. For example, the embodiment may use Mexican hat, Daubechies, or Symlet for the mother wavelet.

  When measuring a heartbeat, the mother wavelet is preferably a Morlet wavelet that can accurately extract the timing of the heartbeat.

  Hereinafter, the mother wavelet 3A which is a Morlet wavelet will be described as an example.

  The PC 10 performs processing for enlarging or reducing the mother wavelet 3A in order to apply it to the waveform to be converted.

  FIG. 4B is a diagram for explaining an example of a reduced mother wavelet according to an embodiment of the present invention.

  The PC 10 performs a process of reducing the mother wavelet 3A in FIG. 4A, for example, a process of generating a reduced mother wavelet 3B.

  FIG. 4C is a diagram illustrating an example of an enlarged mother wavelet according to an embodiment of the present invention.

  The PC 10 performs a process of enlarging the mother wavelet 3A of FIG. 4A, for example, a process of generating an enlarged mother wavelet 3C.

  SF is an enlargement / reduction ratio at the time of enlargement / reduction processing.

  The reduced mother wavelet 3B is generated by being reduced in the time axis direction as illustrated in the mother wavelet 3A of FIG. In the case of reduction, SF is a value smaller than 1.0, such as SF = 0.5.

  The enlarged mother wavelet 3C is generated by being enlarged in the time axis direction as illustrated in the mother wavelet 3A of FIG. In the case of enlargement, SF becomes a value larger than 1.0, for example, SF = 2.0.

  FIG. 4D is a diagram illustrating an example of a waveform to be analyzed according to an embodiment of the present invention.

  The waveform shown in FIG. 4D is an example of waveform data acquired by the PC 10 from the Doppler radar 12 via the amplifier 11 and the filter 13 shown in FIG.

  The PC 10 performs the analysis of the waveform shown in FIG. 4D by applying the mother wavelet 3A shown in FIG.

  When the waveform shown in FIG. 4D is the function x (t) shown in the above equation (2), the degree of coincidence with the analysis result x (t) is the wavelet coefficient CWT in the following equation (8). Can be shown.

上述の（８）式で、ａは、０より大きい値であり、図４で説明したＳＦである。（８）式で、τは、時間遅延である。（８）式で、関数ｈは、インパルス応答である。（８）式で、＊は、複素共役を表す。

In the above equation (8), a is a value larger than 0 and is the SF described in FIG. In equation (8), τ is a time delay. In equation (8), the function h is an impulse response. In the formula (8), * represents a complex conjugate.

  PC10 determines SF by the process of prior learning.

  The pre-learning process is a process of acquiring the measurement data 4 in a state where the movement of the subject 2 in FIG. For example, the state where the movement is low is a state where the subject 2 in FIG. 2 is resting for 20 seconds.

  The reference data is measurement data measured by the Doppler radar 12 in a state where the heartbeat measurement noise is low. Heartbeat measurement noise is an action of a subject other than the heartbeat to be measured. The noise is, for example, movement of the subject's arm or breathing. That is, the reference data is the measurement data 4 acquired in a state where the subject's movement is small. Hereinafter, the measurement data 4 acquired in a state where the operation is small will be described as an example.

  The PC 10 acquires the measurement data 4 by the pre-learning process, and calculates the SF by the calculation described with reference to FIG. The PC 10 can easily detect a heartbeat or the like by determining the SF based on the measurement data 4 in a state where the body movement of the subject 2 in FIG. 2 is small.

<Heart rate calculation>
FIG. 5 is a diagram for explaining an example of heart rate calculation according to an embodiment of the present invention.

  The information processing system 1 measures the subject 2 in FIG. By the measurement, the PC 10 acquires the measurement data 4 when the work is being performed. The PC 10 performs wavelet transform on the measurement data 4 based on the SF determined in the pre-learning process, and performs analysis.

  Analysis FIG. 5 is an example of an analysis result obtained by analyzing the measurement data 4 by wavelet transform based on the SF determined by the pre-learning process.

  When the measurement data 4 of FIG. 5 is acquired, the PC 10 analyzes the local maximum values such as the vertex P1, the vertex P2, and the vertex P3 by analyzing based on the SF determined by the wavelet transform and the pre-learning process. Can be calculated.

  The PC 10 calculates the heart rate from the calculated time between vertices. For example, in the case of the vertex P1 and the vertex P2, the PC 10 calculates the time T1 between the vertex P1 and the vertex P2 from the time of the vertex P1 and the vertex P2. Similarly, in the case of the vertex P2 and the vertex P3, the PC 10 calculates the time T2 between the vertex P2 and the vertex P3 from the time of the vertex P2 and the vertex P3. The PC 10 can calculate the heart rate of the subject 2 in FIG. 2 in the case of the vertex P1 and the vertex P2 from the time T1. The PC 10 can calculate the heart rate of the subject 2 in FIG. 2 in the case of the vertex P2 and the vertex P3 from the time T2.

<Evaluation results>
FIG. 6 is a diagram for explaining an example of an evaluation result according to an embodiment of the present invention.

  Evaluation Results FIG. 6 is a diagram for comparing the results of measuring heartbeats in four ways. Evaluation Results FIG. 6 shows a first measurement result 61, a second measurement result 62, a third measurement result 63, and a fourth measurement result 64. Evaluation Results The vertical axis “RR” in FIG. 6 is a value indicating the interval between the maximum values of the heartbeat. That is, the vertical axis “RR” in FIG. 6 is a value indicating time T1, time T2, and the like in the case of FIG.

  The first measurement result 61 is a measurement result measured by the information processing system 1 of FIG.

  The second measurement result 62 is a measurement result measured by wavelet transform that does not take into account the movement of the human body.

  The third measurement result 63 is a measurement result obtained by removing unnecessary body movements from the measurement data by polynomial approximation.

  The fourth measurement result 64 is an electrocardiogram (ECG (Electrocardiogram)) measured by a so-called electrocardiograph.

  The evaluation result is more desirable as it is closer to the fourth measurement result 64.

  As illustrated, the first measurement result 61 measured by the information processing system 1 in FIG. 1 measures a value close to the fourth measurement result 64 with respect to the second measurement result 62 and the third measurement result 63. Can do.

  FIG. 7 is a diagram for explaining an example of an evaluation result by RMSE according to an embodiment of the present invention.

  FIG. 7 is a diagram showing the evaluation results in RMSE (Root Mean Square Error). RMSE is a value represented by the following formula (9).

上述の（９）式で、ｒ（ｔ）は、心電計で計測された計測結果である。ｒ（ｔ）は、図６の第四計測結果６４に相当する値である。（９）式で、Ｘ（ｔ）は、各種の計測方法で計測された計測結果である。Ｘ（ｔ）は、評価対象となる図６の第一計測結果６１乃至第四計測結果６４の計測結果である。ＲＭＳＥは、ｒ（ｔ）と、Ｘ（ｔ）と、の値が近いほど小さい値となる。すなわち、各計測方法の計測結果は、ＲＭＳＥが小さくなる、心電計の計測結果により近い計測結果が望ましい。

In the above equation (9), r (t) is a measurement result measured by an electrocardiograph. r (t) is a value corresponding to the fourth measurement result 64 of FIG. In Equation (9), X (t) is a measurement result measured by various measurement methods. X (t) is a measurement result of the first measurement result 61 to the fourth measurement result 64 of FIG. RMSE becomes smaller as the values of r (t) and X (t) are closer. That is, the measurement result of each measurement method is preferably a measurement result closer to the measurement result of the electrocardiograph, in which the RMSE is small.

  FIG. 7 is a diagram showing the results of evaluation under different conditions for each test subject's movement.

  Fig.7 (a) is a figure which shows the evaluation result by RMSE in case a test subject is a seating stationary state.

  FIG. 7B is a diagram showing an evaluation result by RMSE when the subject is in a typing game which is an example of deskwork.

  FIG. 7C is a diagram showing an evaluation result by RMSE when the subject is taking a note while looking at the PC screen as an example of desk work.

  FIG. 7D is a diagram illustrating an evaluation result by RMSE in a state where the subject is creating a document on a paper surface which is an example of desk work.

  In each figure of FIG. 7, the graph 7A is a result of evaluating the measurement result measured by polynomial approximation of the measurement data corresponding to the third measurement result 63 of FIG. 6 by RMSE.

  In each figure of FIG. 7, the graph 7B is the result of RMSE evaluating the measurement result measured by the wavelet transform that does not take into account the human motion corresponding to the second measurement result 62 of FIG.

  In each diagram of FIG. 7, a graph 7 </ b> C is a result of evaluating the measurement result measured by the information processing system 1 of FIG. 1 corresponding to the first measurement result 61 of FIG. 6 by RMSE.

  From FIG. 7, the measurement result measured by the information processing system 1 in FIG. 1 has a low RMSE value even when the subject is moving compared to other measurement methods. In other measurement methods such as the second measurement result 62 in FIG. 6 and the third measurement result 63 in FIG. 6, if there is an action of the subject similar to the heartbeat during the measurement, the action of the subject becomes noise, and the heart rate In some cases, the accuracy of the measurement deteriorated.

  FIG. 8 is a diagram for explaining an example of the influence of the motion of the subject related to the measurement of the present invention.

  Fig.8 (a) is a figure which shows an example of the measurement data in a state with a little test subject's motion.

  FIG. 8B is a diagram illustrating an example of a result of wavelet transform similar to the second measurement result 62 of FIG. 6 for the measurement data of FIG.

  FIG.8 (c) is a figure which shows an example of the measurement data in a state with a test subject's motion.

  FIG. 8D is a diagram showing an example of the result of wavelet transform similar to the second measurement result 62 of FIG. 6 on the measurement data of FIG.

  As shown in FIG. 8B, when the subject's movement is small, the PC 10 detects the heartbeat peak 8 by the same wavelet transform as the second measurement result 62 of FIG. 8B, even if it corresponds to the heartbeat peak 8 in FIG. 8B, as shown in FIG. 8D, the peak is affected by the influence of the subject's movement. Therefore, it is difficult to detect.

  The measurement result measured by the information processing system 1 in FIG. 1, that is, the measurement of the heart rate by wavelet transform based on the SF determined based on the reference data is the case as shown in FIG. As described with reference to FIG. 5, the peak 8 of the heartbeat can be detected with high accuracy.

  The measurement result measured by the information processing system 1 in FIG. 1 can reduce the influence of the subject's movement in the heart rate measurement. Therefore, the PC 10 can calculate the heart rate with high accuracy by performing wavelet transform on the measurement data with the SF determined based on the measurement data 4 acquired with little body movement.

  The movement of the human body is not limited to the heart rate. The movement of the human body may be a predetermined movement of the subject, for example. In the pre-learning process, the PC 10 determines SF for a predetermined operation instead of the heartbeat. That is, when measuring a predetermined motion of the subject, the reference data is used as data of the predetermined motion of the subject instead of the heart rate data. The PC 10 can detect the timing at which the subject has performed the predetermined operation by wavelet transforming the measurement data based on the SF determined for the predetermined operation.

<Overall processing>
FIG. 9 is a sequence diagram illustrating an example of overall processing by the information processing system according to the measurement of the present invention.

  The processes in steps S0901 to S0904 are prior learning processes. The pre-learning process is a process for determining SF based on the reference data.

  In step S0901, the Doppler radar 12 performs processing for generating reference data in a state where the movement of the subject 2 in FIG. 2 is small.

  In step S0902, the PC 10 performs processing for measuring received power. That is, the PC 10 performs processing for acquiring the reference data generated by the Doppler radar 12 in S0901.

  In step S0903, the PC 10 performs wavelet transform on the measurement data acquired in step S0902.

  In step S0904, the PC 10 performs processing for determining SF. Details of the process for determining the SF will be described later.

  The processes in steps S0905 to S0909 are processes for measuring the heart rate. The heart rate is a process of wavelet transforming measurement data based on the SF determined in the prior learning process.

  In step S0905, the Doppler radar 12 performs a process of measuring body movement and generating measurement data.

  In step S0906, the PC 10 performs processing for measuring received power. That is, the PC 10 performs processing for acquiring the measurement data 4 generated by the Doppler radar 12 in step S0905.

  In step S0907, the PC 10 performs a wavelet transform process on the measurement data 4 acquired in step S0906 based on the SF determined in the pre-learning process of steps S0901 to S0904. In the process of step S0907, the PC 10 generates the data described in FIG.

  In step S0908, the PC 10 performs processing for detecting vertices in the data generated by the wavelet transform based on the SF in step S0907. The processing result of step S0908 is a case where peaks are detected, for example, as vertex P1, vertex P2, and vertex P3 in FIG.

  In step S0909, the PC 10 calculates a heart rate. In step S0909, the PC 10 calculates the heart rate from the peak interval based on the vertices detected in step S0908.

<Processing by PC>
FIG. 10 is a flowchart for explaining an example of the entire processing by the PC according to the measurement of the present invention.

  The processes in steps S1001 to S1003 are pre-learning processes.

  In step S1001, the PC 10 performs processing for acquiring reference data. The process of step S1001 corresponds to the process of step S0902 of FIG.

  In step S1002, the PC 10 performs wavelet transform processing. The process in step S1002 corresponds to the process in step S0903 in FIG.

  In step S1003, the PC 10 performs SF selection processing. The process of step S1003 corresponds to the process of step S0904 in FIG.

  FIG. 11 is a diagram for explaining an example of SF selection processing by the PC according to the measurement of the present invention.

  FIG. 11A is a flowchart for explaining an example of SF selection processing by the PC according to the measurement of the present invention. The process described in FIG. 11A is an example of the process in step S1003 in FIG.

  In step S1101, the PC 10 performs a process of counting the number of wavelet coefficient peaks in each SF.

  In step S1102, the PC 10 performs a process of determining, for example, the peak number “stable” and the smallest SF.

  FIG. 11B is a diagram for explaining an example of SF selection processing by the PC according to the measurement of the present invention.

  The process in step S1101 is a process for generating the data in FIG. As shown in the figure, by counting the number of wavelet coefficient peaks in each SF, a “stable” value with the same number of peaks is calculated.

  From the calculated “stable” value by the processing in step S1101, for example, the value with the smallest SF is determined as the SF.

  The embodiment is not limited to the case where the number of SF peaks is determined to be the most “stable” and the smallest SF. For example, SF may be calculated by an average value.

  In step S1004, the PC 10 performs processing for acquiring measurement data. The process of step S1004 corresponds to the process of step S0906 in FIG.

  In step S1005, the PC 10 performs wavelet transform processing based on the SF determined in the prior learning processing. The process of step S1005 corresponds to the process of step S0907 in FIG.

  In step S1006, processing for detecting a vertex is performed. The process of step S1006 corresponds to the process of step S0908 of FIG.

  In step S1007, a process for calculating a heart rate is performed. The process of step S1007 corresponds to the process of step S0909 of FIG.

  Note that the pre-learning process, which is the process of steps S0901 to S0904, is preferably based on reference data acquired when there are few movements of the subject's arms and the like.

  FIG. 12 is a diagram illustrating an example of an evaluation result in obtaining reference data related to measurement according to the present invention.

  Fig.12 (a) is a figure explaining an example of the evaluation result which concerns on a test subject's action in acquisition of the reference data concerning measurement of the present invention.

  As shown in the figure, the SF is more likely to be in a “stable” state when the subject is in a resting state with little arm movement or the like. Therefore, it is desirable to measure the reference data when the subject's movement is small in order to determine the SF in a state of being easily “stable”.

  FIG. 12B is a diagram illustrating an example of an evaluation result related to the distance to the subject in the acquisition of the reference data according to the measurement of the present invention.

  As shown in the figure, the SF is in a “stable” state even when the distance to the subject is 1 meter and 2 meters. Therefore, the reference data can be acquired by a pre-learning process using a common setting without changing the setting of various parameters such as an amplifier regardless of the distance.

  The PC 10 determines the SF in the pre-learning process based on the reference data acquired from the Doppler radar 12. Since the pre-learning process is performed using data measured in a state where there is little noise other than the heartbeat, it is possible to determine an SF that can accurately detect the heartbeat in the wavelet transform. Since the PC 10 uses the reference data, it can accurately detect the heartbeat peak even when there is noise or the like when wavelet transforming the motion of the human body to be measured such as a heartbeat. Since the heartbeat peak can be detected with high accuracy, the PC 10 can accurately calculate the heart rate calculated based on the heartbeat peak.

  Therefore, the PC 10 can improve the accuracy of measurement such as heartbeat by performing wavelet transform processing on the measurement data 4 acquired from the Doppler radar 12 based on the SF determined by the prior learning processing.

  The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to such specific embodiments, and various modifications can be made within the scope of the gist of the present invention described in the claims. Can be changed.

1 Information processing system 10 PC
10H1 CPU
10H2 Storage device 10H3 Input device 10H4 Output device 10H5 Input I / F
10H6 Bus 11 Amplifier 12 Doppler radar 12S Source 12Tx Transmitter 12Rx Receiver 12LNA Adjuster 12M Mixer 13 Filter 2 Subject 3A Mother wavelet 3B Reduced mother wavelet 3C Expanded mother wavelet 4 Measurement data 5 Analysis diagram 6 Evaluation result diagram 61 1st measurement result 62 2nd measurement result 63 3rd measurement result 64 4th measurement result 71 1st RMSE calculation result 72 2nd RMSE calculation result 73 3rd RMSE calculation result 74 4th RMSE calculation result 7A Graph 7B Graph 7C Graph 8 Heartbeat peak

Claims (9)

An information processing device connected to a measurement device that measures the movement of a human body,
Obtaining means for obtaining measurement data from the measurement device;
Storage means for storing reference data, which is data obtained by measuring the movement of the human body while the subject to be measured is at rest;
Determining means for determining a scale factor based on the reference data;
An information processing apparatus that measures the movement of the human body by wavelet transforming the measurement data based on the scale factor determined by the determination unit.   The information processing apparatus according to claim 1, wherein the movement of the human body is a heart rate.   The information processing apparatus according to claim 1, wherein the movement of the human body is a movement of a subject's body.   The information processing apparatus according to claim 1, further comprising a filter unit that filters the measurement data and extracts measurement data having a frequency greater than 0 Hz and less than or equal to 10 Hz. An information processing method to be performed by an information processing device connected to a measurement device that measures the movement of a human body,
In the information processing apparatus,
An acquisition procedure for acquiring measurement data from the measurement device;
A storage procedure for storing reference data, which is data obtained by measuring the movement of the human body in a state where the subject to be measured is at rest,
A determination procedure for determining a scale factor based on the reference data;
An information processing method for measuring the movement of the human body by wavelet transforming the measurement data based on the scale factor determined in the determination procedure. A program to be executed by an information processing device connected to a measurement device that measures the movement of a human body,
In the information processing apparatus,
An acquisition procedure for acquiring measurement data from the measurement device;
A storage procedure for storing reference data, which is data obtained by measuring the movement of the human body in a state where the subject to be measured is at rest,
A determination procedure for determining a scale factor based on the reference data;
A program for executing measurement of the movement of the human body by wavelet transforming the measurement data based on the scale factor determined in the determination procedure. An information processing system having one or more computers and connected to a measuring device that measures the movement of a human body,
Obtaining means for obtaining measurement data from the measurement device;
Storage means for storing reference data, which is data obtained by measuring the movement of the human body while the subject to be measured is at rest;
Determining means for determining a scale factor based on the reference data;
An information processing system for measuring the movement of the human body by wavelet transforming the measurement data based on the scale factor determined by the determination means. An information processing system having a measurement device that measures the movement of a human body and an information processing device,
The measuring device is
Generating means for generating measurement data relating to the movement of the human body;
The information processing apparatus includes:
Obtaining means for obtaining measurement data from the measurement device;
Storage means for storing reference data, which is data obtained by measuring the movement of the human body while the subject to be measured is at rest;
Determining means for determining a scale factor based on the reference data;
An information processing system for measuring the movement of the human body by wavelet transforming the measurement data based on the scale factor determined by the determination means. An information processing system having a measurement device that measures the movement of a human body and a program that is executed by the information processing device,
The measuring device is
Generating means for generating measurement data relating to the movement of the human body;
In the information processing apparatus,
An acquisition procedure for acquiring measurement data from the measurement device;
A storage procedure for storing reference data, which is data obtained by measuring the movement of the human body in a state where the subject to be measured is at rest,
A determination procedure for determining a scale factor based on the reference data;
An information processing system having a program for causing the measurement data to be wavelet transformed based on the scale factor determined in the determination procedure to measure the movement of the human body.

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