Information processing device, program, and information processing method

Abstract

A signal processing device (100) comprises: a signal acquisition unit (131) that acquires a time-series signal indicating a plurality of waveforms; a signal-shaping unit (132) that identifies one waveform from the time-series signal; and a frequency analysis unit (133) that calculates a power parameter that is a parameter indicating the power of the one waveform.

Description

Information processing device, program, and information processing method

[0001] The present disclosure relates to an information processing device, a program, and an information processing method.

[0002] In recent years, techniques have been developed for analyzing signals that have components that repeatedly fluctuate up and down, such as pulse waves, each of which has a different waveform.

[0003] The pulse wave analysis device described in Patent Document 1 detects the minimum and maximum values ​​of the pulse wave, divides the waveform value into units equivalent to one beat, and plays back the waveform value at high speed multiple times in beat units to determine the spectrum of the pulse wave.

[0004] Japanese Patent Application Publication No. 7-148126

[0005] Pulse waves are influenced by hemodynamic biomechanics such as cardiac contractility, aortic valve closure, and systemic vascular resistance. It is known that the pulse wave period, pulse wave power, and pulse wave shape of a pulse wave signal not only indicate whether a pulse is normal or abnormal, such as arrhythmia or atrial fibrillation, but also indicate emotional states as a result of autonomic nervous function. For example, autonomic nervous function can be evaluated by analyzing pulse wave power.

[0006] However, while conventional techniques calculate the pulse wave spectrum through pulse wave signal processing, they do not disclose or suggest how to extract and use pulse wave power. As a result, conventional techniques fail to take into account fluctuations within a waveform due to hemodynamic biomechanics, making accurate analysis impossible.

[0007] Therefore, one or more aspects of the present disclosure aim to make it possible to calculate power parameters for each wave from a time-series signal.

[0008] An information processing device according to one aspect of the present disclosure is characterized by comprising a signal acquisition unit that acquires a time series signal indicating a plurality of waveforms, an identification unit that identifies one waveform from the time series signal, and a calculation unit that calculates a power parameter that is a parameter indicating the power of the one waveform.

[0009] A program according to one aspect of the present disclosure is characterized in that it causes a computer to function as a signal acquisition unit that acquires a time series signal indicating a plurality of waveforms, an identification unit that identifies one waveform from the time series signal, and a calculation unit that calculates a power parameter that is a parameter indicating the power of the one waveform.

[0010] An information processing method according to one aspect of the present disclosure is characterized in that it acquires a time series signal indicating a plurality of waveforms, identifies one waveform from the time series signal, and calculates a power parameter that is a parameter indicating the power of the one waveform.

[0011] According to one or more aspects of the present disclosure, power parameters can be calculated for each wave from a time-series signal.

[0012] FIG. 1 is a block diagram schematically showing the configuration of a signal processing device according to a first embodiment. FIG. 2 is a schematic diagram showing an example of a waveform represented by a time-series signal. FIG. 3 is a block diagram schematically showing the configuration of a computer. FIG. 4 is a flowchart showing frequency analysis processing. FIG. 5 is a diagram schematically showing decomposition of waveform shape and power. FIG. 6 is a block diagram schematically showing the configuration of an emotion determination system according to second and third embodiments. FIG. 7 is a block diagram schematically showing the configuration of a learning device 200 according to a second embodiment. FIG. 8 is a block diagram schematically showing the configuration of an inference device according to the second and third embodiments. FIG. 9 is a block diagram schematically showing the configuration of a learning device according to a third embodiment. FIG. 10 is a block diagram schematically showing the configuration of a signal reconstruction device according to a fourth embodiment.

[0013] Embodiment 1. Fig. 1 is a block diagram showing a schematic configuration of a signal processing device 100, which is an information processing device according to embodiment 1. The signal processing device 100 includes a detection unit 110, a connection I / F (Interface) unit 120, a control unit 130, and a parameter storage unit 140. Although Fig. 1 shows both the detection unit 110 and the connection I / F unit 120, the signal processing device 100 may include at least one of them.

[0014] The detection unit 110 detects a physical quantity related to the object, and provides the control unit 130 with a time-series signal that corresponds to the physical quantity and has a component that repeatedly fluctuates up and down, such as a pulse wave.

[0015] The connection I / F unit 120 is a functional unit that can connect to an external storage device (not shown) and exchange data with the external storage device. The external storage device may be an optical disk or a flash memory storage device. Here, it is assumed that the time-series signal is stored in the external storage device. The connection I / F unit 120 then reads out the time-series signal stored in the external storage device and provides the time-series signal to the control unit 130.

[0016] The control unit 130 controls the processing in the signal processing device 100. The control unit 130 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, and a parameter storage unit 134.

[0017] The signal acquisition unit 131 acquires a time-series signal from the detection unit 110 or from an external storage device via the connection I / F unit 120. The acquired time-series signal is provided to the signal shaping unit 132. The time-series signal is a signal that indicates a plurality of waveforms.

[0018] The signal shaping unit 132 is an identifying unit that identifies a waveform from the time-series signal from the signal acquiring unit 131 and sequentially generates a waveform signal that is a signal corresponding to the identified waveform. The generated waveform signal is provided to the frequency analyzing unit 133.

[0019] 2, the signal shaping unit 132 may sequentially capture one waveform by capturing the zero crossing position P3 at the rising edge of the waveform represented by the time-series signal. Note that the position at which one waveform is captured may be the peak P1, bottom P2, or zero crossing position P4 at the falling edge of the waveform, in addition to the zero crossing position P3 at the rising edge. Alternatively, one waveform may be defined and extracted using other methods.

[0020] Furthermore, if the time series signal contains long-term fluctuation components in addition to periodicity, and therefore characteristic points such as the zero crossing position P3 cannot be captured, the signal shaping unit 132 may perform processing by removing the long-term fluctuation components by calculating the difference using polynomial fitting or the difference using a moving average.

[0021] The frequency analysis unit 133 is a calculation unit that performs frequency analysis on the waveform signal from the signal shaping unit 132, thereby parameterizing the waveform represented by the waveform signal into three elements: period, power, and waveform shape. In this way, the frequency analysis unit 133 acquires period parameters, power parameters, and waveform shape parameters corresponding to the waveform. The acquired period parameters, power parameters, and waveform shape parameters are provided to the parameter storage unit 134.

[0022] For example, the frequency analysis unit 133 calculates the period of one waveform or the frequency of the fundamental wave of one waveform, and performs frequency analysis processing to calculate the power parameter. The frequency analysis processing includes complex Fourier series expansion. Specifically, the frequency analysis unit 133 calculates a frequency spectrum by complex Fourier series expansion, and can use the square root of the sum of squares calculated by normalizing the frequency spectrum using the square root of the sum of squares as the power parameter.

[0023] The parameter storage unit 134 stores the period parameters, power parameters, and waveform shape parameters from the frequency analysis unit 133 in the parameter storage unit 140. The parameter storage unit 140 stores the period parameters, power parameters, and waveform shape parameters.

[0024] The signal processing device 100 described above can be realized by a mobile terminal such as a smart watch or a PC (Personal Computer) that corresponds to the computer 10 shown in FIG. 3 .

[0025] The computer 10 includes a storage 11 such as a hard disk drive (HDD), a solid state drive (SSD), or a nonvolatile memory, a nonvolatile memory 12, a processor 13 such as a central processing unit (CPU), a sensor 14, and a connection I / F 15. The computer 10 may also include a communication I / F 16 such as a network interface card (NIC) that communicates with a network.

[0026] For example, the parameter storage unit 140 can be realized by the storage 11 or the memory 12. The control unit 130 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program.

[0027] The detection unit 110 can be realized by the sensor 14. The connection I / F unit 120 can be realized by the connection I / F 15 compatible with a USB (Universal Serial Bus) or the like. As described above, it is sufficient if at least one of the sensor 14 and the connection I / F 15 is provided. Although not shown, the computer 10 may also be provided with a display interface that displays processing results depending on the application. This allows the signal processing device 100 to be provided with a display unit (not shown) that displays processing results.

[0028] The program may be downloaded to the storage 11 from a recording medium (not shown) via a reader / writer (not shown) or from a network via a communication I / F (not shown), and then loaded into the memory 12 and executed by the processor 13. Alternatively, the program may be directly loaded into the memory 12 from a recording medium via a reader / writer or from a network via a communication I / F, and executed by the processor 13. In other words, the program may be provided by a computer program product such as a recording medium.

[0029] 4 is a flowchart showing the frequency analysis process in frequency analysis unit 133. First, frequency analysis unit 133 receives a waveform signal from signal shaping unit 132 (S10) and extracts a waveform represented by the waveform signal.

[0030] Next, the frequency analysis unit 133 calculates the waveform length from the waveform extracted in step S10 (S11).The frequency analysis unit 133 then acquires a period parameter from the calculated waveform length (S12).For example, the frequency analysis unit 133 may calculate the fundamental frequency by taking the reciprocal of the waveform length calculated in step S11, and use the fundamental frequency as the period parameter.It is also possible to use the waveform length itself as the period parameter.

[0031] Next, the frequency analysis unit 133 performs frequency transformation based on complex Fourier series expansion on the waveform extracted in step S10 (S13).The frequency analysis unit 133 then calculates a real Fourier spectrum (S14).The frequency analysis unit 133 also calculates an imaginary Fourier spectrum (S15).For example, in the complex Fourier series expansion, the frequency analysis unit 133 calculates a total of n frequency components from the fundamental frequency to frequencies that are n times the fundamental frequency (n is an integer equal to or greater than 2).

[0032] Next, the frequency analysis unit 133 calculates a frequency spectrum from the square root of the sum of the squares of the frequency components of the real Fourier spectrum calculated in step S14 and the imaginary Fourier spectrum calculated in step S15 (S16). The frequency analysis unit 133 also calculates a phase spectrum from the arctangent of the frequency components of the real Fourier spectrum calculated in step S14 and the imaginary Fourier spectrum calculated in step S15 (S17). For example, if calculations are performed up to five times the frequency, a frequency spectrum and a phase spectrum consisting of five frequency components are obtained.

[0033] Here, in step S17, the frequency analysis unit 133 obtains the phase spectrum as the initial phase value in each frequency spectrum in the range of -π to π. However, while -π and π have the same meaning, they are at the farthest positions numerically, which may limit their use as feature quantities. Therefore, it is desirable that the frequency analysis unit 133 calculates the cosine of the phase to calculate the real phase spectrum, and then calculates the sine of the phase to calculate the imaginary phase spectrum.

[0034] Next, the frequency analysis unit 133 normalizes the frequency spectrum obtained in step S16 by the square root of the sum of squares in the frequency direction (S18). As a result, the frequency analysis unit 133 calculates a normalized frequency spectrum (S19). Furthermore, the frequency analysis unit 133 calculates the square root of the sum of squares of the frequency spectrum normalized in step S17 (S20).

[0035] Then, the frequency analysis unit 133 acquires the normalized frequency spectrum calculated in step S19 and the phase spectrum calculated in step S17 as waveform shape parameters (S21). Note that, if the real phase spectrum and the imaginary phase spectrum have been calculated in step S17, it is desirable to acquire these as waveform shape parameters.

[0036] Furthermore, the frequency analysis unit 133 acquires the square root of the sum of squares calculated in step S20 as a power parameter (S22).

[0037] Here, FIG. 5 is a diagram showing a schematic diagram of the waveform shape and power decomposition.

[0038] Through the above processing, the frequency analysis unit 133 realizes parameterization into period, waveform shape, and power. As a result, by calculating the power of the waveform using the square root of the sum of squares of the spectrum from the fundamental wave to the harmonics, the frequency analysis unit 133 can realize calculation of power in an appropriate manner that takes into account the spectrum from the fundamental wave to the harmonics, rather than using the amplitude value of the waveform, in other words, the peak value, as power. Furthermore, the frequency analysis unit 133 can realize calculation of the waveform shape in a manner that is separate from the strength or weakness of the entire signal.

[0039] As described above, in the first embodiment, the frequency analysis unit 133 processes the time-series signal waveform by waveform, and the parameter storage unit 113 continuously stores the processing results, so that it is possible to obtain the changes over time in the parameters of the period, waveform shape, and power of the time-series signal.

[0040] Embodiment 2. Fig. 6 is a block diagram showing a schematic configuration of an emotion determination system 1 as an information processing system according to embodiment 2. The emotion determination system 1 includes a learning device 200 as an information processing device, and an inference device 300 as another information processing device. Here, the learning device 200 and the inference device 300 are capable of transmitting and receiving data via a network 20 such as the Internet.

[0041] Learning device 200 performs processing in a learning phase in which a learning model is learned. Inference device 300 acquires the trained learning model from learning device 200 and performs processing in an inference phase in which emotion is detected from a pulse wave signal, which is a time-series signal, using the trained learning model. The pulse wave signal is a signal indicating a pulse wave detected from a person.

[0042] 7 is a block diagram showing a schematic configuration of a learning device 200 according to embodiment 2. The learning device 200 includes a control unit 230, a parameter storage unit 140, a communication I / F unit 250, and a learning model storage unit 260.

[0043] The parameter storage unit 140 of the learning device 200 according to the second embodiment is similar to the parameter storage unit 140 of the signal processing device 100 according to the first embodiment.

[0044] The communication I / F unit 250 performs communication via the network 20 .

[0045] The control unit 230 controls the processing in the learning device 200. The control unit 230 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, a parameter storage unit 134, a data acquisition unit 235, and a learning unit 236.

[0046] The signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 230 in the second embodiment are similar to the signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 130 in the first embodiment. However, the signal acquisition unit 131 acquires a time-series signal from the data acquisition unit 235, as will be described later.

[0047] The data acquisition unit 235 acquires training data including a pulse wave signal as a time-series signal and a label indicating the emotion of the subject from whom the pulse wave signal was acquired. Here, the label indicates the emotion of the subject at each predetermined period in the pulse wave signal. Note that here, the subject from whom the pulse wave signal was acquired is assumed to be a person, and this person is also referred to as the learning subject.

[0048] The data acquisition unit 235 may acquire teacher data from another device (not shown), for example, via the communication I / F unit 250, or may acquire the teacher data by reading the teacher data from a storage unit (not shown). The pulse wave signal included in the acquired teacher data is provided to the signal acquisition unit 131, and the label is provided to the learning unit 236.

[0049] Learning unit 236 learns a learning model for inferring the emotion of the subject from whom the pulse wave signal was acquired, by using the period parameters, power parameters, and waveform shape parameters for each predetermined period stored in parameter storage unit 140, and the labels corresponding to those periods. Here, the power parameters, period parameters, and waveform shape parameters used in learning are also referred to as learning power parameters, learning period parameters, and learning waveform shape parameters, respectively.

[0050] For example, the learning unit 236 uses at least the power parameters as examples and learns the emotion of the person when a waveform corresponding to the power parameters is acquired as a correct answer, thereby generating a learning model for inferring the emotion of the person. Note that a waveform identified from the time-series signal used for learning is also referred to as a learning waveform.

[0051] Note that, as the machine learning method used by the learning unit 236, a neural network or a support vector machine can be selected as a good example, but other machine learning methods may also be used. The parameters used for learning may be at least one of a period parameter, a power parameter, and a waveform shape parameter. Here, it is assumed that at least the power parameter is used.

[0052] The learning model storage unit 260 stores the learning model learned by the learning unit 236. The communication I / F unit 250 transmits the learning model stored in the learning model storage unit 260 to the inference device 300.

[0053] The learning device 200 described above can also be realized by a computer 10 as shown in Fig. 3. For example, the parameter storage unit 140 and the learning model storage unit 260 can be realized by the storage 11 or the memory 12. The control unit 230 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program. The communication I / F unit 250 can be realized by the communication I / F 16.

[0054] 8 is a block diagram showing a schematic configuration of an inference device 300 according to embodiment 2. The inference device 300 includes a detection unit 110, a connection I / F unit 120, a control unit 330, a parameter storage unit 140, a communication I / F unit 350, and a learning model storage unit 360.

[0055] The detection unit 110, connection I / F unit 120 and parameter storage unit 140 of the inference device 300 in embodiment 2 are similar to the detection unit 110, connection I / F unit 120 and parameter storage unit 140 of the signal processing device 100 in embodiment 1.

[0056] The communication I / F unit 350 performs communication via the network 20. For example, the communication I / F unit 350 receives a learning model from the learning device 200 via the network 20. The learning model storage unit 360 stores the learning model received by the communication I / F unit 350.

[0057] The control unit 330 controls the processing in the inference device 300. The control unit 330 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, a parameter storage unit 134, and an inference unit 337. The signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 330 in the second embodiment are similar to the signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 130 in the first embodiment.

[0058] The inference unit 337 infers the emotion corresponding to the period parameters, power parameters, and waveform shape parameters for each predetermined period stored in the parameter storage unit 140 by inputting the parameters into the learning model stored in the learning model storage unit 360. Note that the parameters used by the inference unit 337 may be any parameters that the learning device 200 used for learning.

[0059] The inference device 300 described above can also be realized by a computer 10 such as that shown in Fig. 3. For example, the parameter storage unit 140 and the learning model storage unit 360 can be realized by the storage 11 or the memory 12. The control unit 330 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program. The communication I / F unit 350 can be realized by the communication I / F 16.

[0060] For example, the emotion determination system 1 according to the second embodiment may be applied when classifying emotions into pleasant and unpleasant, or awake and awake, using a dataset of pulse wave signals labeled with emotional states such as pleasant and unpleasant, or awake and awake.

[0061] It is known that emotions, which are internal states of a person, are expressed in the pulse waveform (waveform shape) that represents changes within a single waveform, and learning may be performed using only the parameters of the pulse waveform (waveform shape) for multiple pulses, respectively, using a convolutional neural network (CNN). This allows the power to be separated from the pulse waveform, separating the components related to the overall strength of the signal, and the pulse waveform components resulting from the biomechanics of the blood vessels can be properly learned in the time-frequency domain, enabling proper classification.

[0062] Embodiment 3. In the above-described embodiment 2, classification is performed by machine learning based on parameters, but waveform prediction may also be performed by regression. As shown in Figure 6, a signal prediction system 2 as an information processing system according to embodiment 3 includes a learning device 400 as an information processing device and an inference device 500 as an information processing device. Here, the learning device 400 and the inference device 500 are capable of transmitting and receiving data via a network 20 such as the Internet.

[0063] The learning device 400 performs processing in a learning phase in which a learning model is learned. The inference device 500 performs processing in an inference phase in which the learned learning model is acquired from the learning device 400 and a future time-series signal is predicted from a time-series signal using the learned learning model.

[0064] 9 is a block diagram showing a schematic configuration of a learning device 400 according to embodiment 3. Learning device 400 includes a detection unit 110, a connection I / F unit 120, a control unit 430, a parameter storage unit 440, a communication I / F unit 250, and a learning model storage unit 260.

[0065] The detection unit 110 and the connection I / F unit 120 of the learning device 400 in the fourth embodiment are similar to the detection unit 110 and the connection I / F unit 120 of the signal processing device 100 in the first embodiment. In addition, the communication I / F unit 250 and the learning model storage unit 260 of the learning device 400 in the fourth embodiment are similar to the communication I / F unit 250 and the learning model storage unit 260 of the learning device 200 in the second embodiment.

[0066] The control unit 430 controls the processing in the learning device 400. The control unit 430 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, a parameter storage unit 434, and a learning unit 436.

[0067] The signal acquisition unit 131, the signal shaping unit 132, and the frequency analysis unit 133 of the control unit 430 in the fourth embodiment are similar to the signal acquisition unit 131, the signal shaping unit 132, and the frequency analysis unit 133 of the control unit 130 in the first embodiment. However, the signal acquisition unit 131 also provides the acquired time-series signal to the parameter storage unit 434.

[0068] The parameter storage unit 434 stores the period parameters, power parameters, and waveform shape parameters from the frequency analysis unit 133 and the time-series signal in the parameter storage unit 440 .

[0069] The parameter storage unit 440 stores period parameters, power parameters, waveform shape parameters, and time-series signals.

[0070] The learning unit 436 uses the period parameters, power parameters, and waveform shape parameters for each predetermined period stored in the parameter storage unit 140 to learn a learning model for predicting future time series signals.

[0071] For example, the learning unit 436 generates a learning model for inferring future time-series signals by using at least power parameters corresponding to a time-series signal at a first time as an example and learning a time-series signal at a second time that is later than the first time as a correct answer. The power parameters, period parameters, and waveform shape parameters used for learning are also referred to as learning power parameters, learning period parameters, and learning waveform shape parameters, respectively.

[0072] The learning unit 436 may use a neural network as a suitable machine learning method, but other machine learning methods may also be used. The parameters used for learning may be at least one of a period parameter, a power parameter, and a waveform shape parameter. Here, it is assumed that at least the power parameter is used.

[0073] The learning model learned in the above manner is stored in the learning model storage unit 260 and transmitted to the inference device 500 via the communication I / F unit 250.

[0074] The learning device 400 described above can also be realized by a computer 10 such as that shown in Fig. 3. For example, the parameter storage unit 440 and the learning model storage unit 260 can be realized by the storage 11 or the memory 12. The control unit 430 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program. The communication I / F unit 250 can be realized by the communication I / F 16.

[0075] As shown in FIG. 8, the inference device 500 in embodiment 3 includes a detection unit 110, a connection I / F unit 120, a control unit 530, a parameter memory unit 140, a communication I / F unit 350, and a learning model memory unit 360.

[0076] The detection unit 110, the connection I / F unit 120, and the parameter storage unit 140 of the inference device 500 in embodiment 3 are similar to the detection unit 110, the connection I / F unit 120, and the parameter storage unit 140 of the signal processing device 100 in embodiment 1. The communication I / F unit 350 and the learning model storage unit 360 of the inference device 500 in embodiment 3 are similar to the communication I / F unit 350 and the learning model storage unit 360 of the inference device 300 in embodiment 2.

[0077] The control unit 530 controls the processing in the inference device 500. The control unit 530 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, a parameter storage unit 134, and an inference unit 537. The signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 530 in the third embodiment are similar to the signal acquisition unit 131, the signal shaping unit 132, the frequency analysis unit 133, and the parameter storage unit 134 of the control unit 130 in the first embodiment.

[0078] The inference unit 537 infers a future time-series signal from the period parameters, power parameters, and waveform shape parameters for each predetermined period stored in the parameter storage unit 140 by inputting the parameters into the learning model stored in the learning model storage unit 360. Note that the parameters used by the inference unit 537 may be the parameters used by the learning device 400 for learning.

[0079] The inference device 500 described above can also be realized by a computer 10 such as that shown in Fig. 3. For example, the parameter storage unit 140 and the learning model storage unit 360 can be realized by the storage 11 or the memory 12. The control unit 530 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program. The communication I / F unit 350 can be realized by the communication I / F 16.

[0080] For example, the signal processing described in embodiment 3 may be applied when predicting future changes in daily average temperatures in Tokyo from approximately 10 years of stored data on changes in daily average temperatures in Tokyo. In this case, the waveform shape represents the way the seasons change in Tokyo, the power represents the degree of change in the temperature difference throughout the year, and the period represents the cycle of the seasonal changes. Here, since the period does not change over a one-year period, period parameters are not required for learning. Therefore, by using two parameters, waveform shape and power, and performing learning using LSTM (Long Short Term Memory), respectively, more appropriate inference can be performed than with conventional parameterization and learning methods. In this case, a learning model can be trained to predict the time series signal for the following year from, for example, 10 years of parameters.

[0081] Embodiment 4 Fig. 10 is a block diagram showing a schematic configuration of a signal reconstruction device 600, which is an information processing device according to embodiment 4. The signal reconstruction device 600 includes a detection unit 110, a connection I / F unit 120, a control unit 630, and a parameter storage unit 140. Note that while Fig. 10 shows both the detection unit 110 and the connection I / F unit 120, it is sufficient for the signal reconstruction device 600 to include at least one of these units.

[0082] The detection unit 110, connection I / F unit 120 and parameter storage unit 140 of the signal reconstruction device 600 of embodiment 4 are similar to the detection unit 110, connection I / F unit 120 and parameter storage unit 140 of the signal processing device 100 of embodiment 1.

[0083] The control unit 630 controls the processing in the signal reconstruction device 600. The control unit 630 includes a signal acquisition unit 131, a signal shaping unit 132, a frequency analysis unit 133, a parameter storage unit 134, a signal filter unit 638, and an inverse transformation unit 639.

[0084] The signal acquisition unit 131, signal shaping unit 132, frequency analysis unit 133 and parameter storage unit 134 of the control unit 630 in embodiment 6 are similar to the signal acquisition unit 131, signal shaping unit 132, frequency analysis unit 133 and parameter storage unit 134 of the control unit 130 in embodiment 1.

[0085] The signal filter unit 638 performs filtering on at least one of the power parameters, period parameters, and waveform shape parameters. For example, the signal filter unit 638 executes processing to delete or replace with a constant an unnecessary component from the period parameters, power parameters, and waveform shape parameters stored in the parameter storage unit 140.

[0086] Specifically, to eliminate periodic fluctuations in the time-series signal, the signal filter unit 638 replaces the period parameter with a constant. Furthermore, to eliminate changes in the strength of the time-series signal over time, the signal filter unit 638 replaces the power parameter with a constant. Furthermore, to eliminate harmonic components in the time-series signal and express it as a simple sine wave, the signal filter unit 638 replaces harmonic components of the waveform shape parameter with 0. The parameters filtered in this manner are provided to the inverse transform unit 639.

[0087] The inverse transform unit 639 converts into time-series signals the power parameters, period parameters, and waveform shape parameters that have been filtered by the signal filter unit 638. For example, the inverse transform unit 639 calculates a reconstructed signal by performing an inverse complex Fourier series expansion for each waveform using the parameters that have been filtered by the signal filter unit 638.

[0088] For example, in a pulse wave signal obtained as a time-series signal by a pulse wave Doppler sensor, the distance from the signal provider affects the amplitude of the pulse wave signal, and the information on the strength of each waveform may be considered noise. In this case, by using the pulse period and pulse waveform parameters to replace the pulse wave power with a constant that does not change for each waveform and then performing an inverse transformation, a reconstructed signal can be output in which the components of the strength of the signal over time have been removed.

[0089] The signal reconstruction device 600 described above can also be realized by a computer 10 as shown in Fig. 3. For example, the control unit 630 can be realized by the processor 13 loading a program stored in the storage 11 into the memory 12 and executing the program.

[0090] 1 emotion determination system, 2 signal prediction system, 100 signal processing device, 200, 400 learning device, 300, 500 inference device, 600 signal reconstruction device, 110 detection unit, 120 connection I / F unit, 130, 230, 330, 430, 530, 630 control unit, 131 signal acquisition unit, 132 signal shaping unit, 133 frequency analysis unit, 134, 434 parameter storage unit, 235 data acquisition unit, 236, 436 learning unit, 337, 537 inference unit, 638 signal filter unit, 639 inverse transformation unit, 140, 440 parameter storage unit, 250, 350 communication I / F unit, 260, 360 learning model storage unit.

Claims

1. An information processing device comprising: a signal acquisition unit that acquires a time series signal indicating a plurality of waveforms; an identification unit that identifies one waveform from the time series signal; and a calculation unit that calculates a power parameter that is a parameter indicating the power of the one waveform.

2. The information processing device according to claim 1, wherein the calculation unit calculates the period of the waveform or the frequency of the fundamental wave of the waveform, and performs frequency analysis processing to calculate the power parameters.

3. The information processing device according to claim 2, wherein the frequency analysis processing includes complex Fourier series expansion.

4. The information processing device according to claim 3, wherein the calculation unit calculates a frequency spectrum by the complex Fourier series expansion, normalizes the frequency spectrum by the square root of the sum of squares, and sets the square root of the sum of squares calculated as the power parameter.

5. An information processing device according to any one of claims 1 to 4, wherein the calculation unit also calculates a period parameter which is a parameter indicating the period of the one waveform, and a waveform shape parameter which is a parameter indicating the waveform shape of the one waveform.

6. An information processing device according to any one of claims 1 to 5, characterized in that the time series signal is a signal indicating a pulse wave detected from a person, and further comprising a learning unit that generates a learning model for inferring the person's emotions by using the power parameters as examples and learning the person's emotions at the time the one waveform was acquired as a correct answer.

7. The information processing device according to any one of claims 1 to 5, further comprising an inference unit that infers the emotions of the person by inputting a learning power parameter, which is a parameter indicating the power of a learning waveform, which is a waveform identified from the signal indicating the pulse wave detected from the person being trained, as an example problem, into a learning model generated by learning the emotions of the person being trained when the learning waveform was acquired as a correct answer, wherein the time series signal is a signal indicating a pulse wave detected from the person being trained, 8. An information processing device according to any one of claims 1 to 5, further comprising a learning unit that generates a learning model for inferring future time series signals by using the power parameters corresponding to the time series signals at a first time as an example and learning a time series signal at a second time that is later than the first time as a correct answer.

9. An information processing device according to any one of claims 1 to 5, further comprising an inference unit that infers future time series signals by inputting a learning power parameter that indicates the power of one waveform identified from a time series signal that indicates multiple waveforms at a first time as an example and learning a time series signal that indicates a waveform at a second time that is later than the first time as a correct answer.

10. The information processing device according to claim 5, further comprising: a signal filter unit that applies filtering to at least one of the power parameter, the period parameter, and the waveform shape parameter; and an inverse conversion unit that converts the power parameter, the period parameter, and the waveform shape parameter after the filtering into a time-series signal.

11. A program causing a computer to function as: a signal acquisition unit that acquires time-series signals showing multiple waveforms; an identification unit that identifies one waveform from the time-series signals; and a calculation unit that calculates a power parameter that is a parameter indicating the power of the one waveform.

12. An information processing method comprising: acquiring a time-series signal showing multiple waveforms; identifying one waveform from said time-series signal; and calculating a power parameter which is a parameter indicating the power of said one waveform.

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