WO2007129452A1

WO2007129452A1 - Sensor output signal evaluation system

Info

Publication number: WO2007129452A1
Application number: PCT/JP2007/000404
Authority: WO
Inventors: Tomoki Siozawa; Hiroki Takada; Masaru Miyao
Original assignee: Nihon University
Priority date: 2006-04-13
Filing date: 2007-04-12
Publication date: 2007-11-15
Also published as: JP4859035B2; JP2007282733A

Abstract

A sensor output signal evaluation system for objectively and quantitatively evaluating the output signal of a sensor. An evaluation system for evaluating the output signal of a sensor to measure the relaxation and contraction of a subject. The system comprises an index signal generating unit (10) for generating an index signal representing the index according to which the subject relaxes and contracts and an output signal evaluating unit (20) for evaluating the approximation between the output signal of the sensor and the index signal. The output signal evaluating unit (20) has a maximum amplitude analyzing section (21) for evaluating the maximum amplitude of the output signal during the contraction in response to the index signal, a relaxation time analyzing section (22) for evaluating the output signal under a predetermined threshold, and a contraction time analyzing section (23) for evaluating the output signal over the predetermined threshold. The output signal evaluating unit (20) evaluates the output signal by using at least one of the sections.

Description

Specification

Sensor output signal evaluation system

Technical field

[0001] The present invention relates to a sensor output signal evaluation system that evaluates an output signal of a sensor based on a relaxed and contracted motion of a measured person, and in particular, based on a relaxed and contracted motion performed by a measured person according to an index signal. The present invention relates to a sensor output signal evaluation system for comparing and evaluating a sensor output signal and an index signal.

Background art

2. Description of the Related Art Various types of sensors such as electromyographs and pressure gauges for measuring electrical signals generated from muscles during relaxation and contraction of the human body have existed in the past.

[0003] For example, when examining the movement of muscles using an electromyograph, the output signal from the electromyograph usually represents the voltage measured by the surface electromyograph or the needle electromyograph along the time axis. It is shown. In the past, the measured waveform of the output signal and the integrated waveform were visually perceived by doctors who were measurers, and subjective evaluations were made based on empirical rules.

[0004] In needle electromyography, in order to determine whether the disorder is neurogenic or myogenic, whether it is acute, or whether it is chronic, etc. Evaluation of electromyograph output has become important.

[0005] In addition, in surface electromyography, involuntary movements are diagnosed as types of tremor, dystonia, spasm, etc., differential diagnosis of these, involuntary contraction muscle region identification, etc. The scope of application of EMG output evaluation is diverse. Surface electromyography is also used to create evoked electromyograms that show the detected potential of neurotransmission tests. Furthermore, the integrated waveform of the output signal from the electromyograph may be used as a material for determining the relative degree of muscle contraction, or may be used as an index for evaluating the state of muscle training.

[0006] Further, in order to measure the perineal muscle group, a pressure gauge having a probe inserted into the vagina is used. This is because the electromyograph output and the vaginal pressure have a high correlation. [0007] As described above, as an evaluation method of an output signal from a sensor such as an electromyograph or a pressure gauge that indicates important information from the muscle, the evaluator can simply calculate the raw waveform of the sensor output signal. This is a method of subjective evaluation. In addition, various methods for evaluating the sensor output signal have been studied. For example, one that integrates and evaluates output signals from sensors (Patent Document 1_3, etc.), one that evaluates using frequency analysis such as FFT (Patent Documents 4, 5, etc.), detection value and target value There are some which perform feedback control using an error detection means for obtaining the error (Patent Document 6, etc.).

[0008] Patent Document 1: JP-A-2005-278706

Patent Document 2: JP 2005-074063

Patent Document 3: JP 2003-230545

Patent Document 4: JP-A-2004-2021 96

Patent Document 5: Japanese Patent Laid-Open No. 2003 _ 1 69782

Patent Document 6: JP-A-11-253502

Disclosure of the invention

Problems to be solved by the invention

However, the sensor that measures the relaxation and contraction movements of the measurement subject greatly changes the intensity of the output signal depending on the contact position or insertion position of the electrode or probe. The subjective evaluation by did not make much sense. In addition, when trying to measure the effect of training before and after strength training, the conventional various evaluation methods that perform integrated waveforms, FFT analysis, error detection, etc. There were no standards, and these could not be evaluated quantitatively. Furthermore, since the intensity of the output signal varies greatly depending on the physical condition of the subject during measurement, there was no method that can objectively and quantitatively evaluate the sensor output signal.

[0010] Further, in the case of a needle electromyograph or an induced electromyograph, a needle electrode is used as a probe to be used. By inserting this needle electrode into the muscle tissue percutaneously, Measure myoelectric potentials and give electrical stimulation to peripheral nerves. Such tests are invasive and painful. Therefore, even though it was very harsh for the subject to measure many times, the conventional method could not make a quantitative evaluation. I had to grab the inclination.

[001 1] In view of such circumstances, the present invention is intended to provide a sensor output signal evaluation system capable of objectively and quantitatively evaluating a sensor output signal. Means for solving the problem

[0012] In order to achieve the above-described object of the present invention, the sensor output signal evaluation system according to the present invention generates an index signal that serves as an index for the measurement subject to perform a relaxation and contraction operation in response thereto. An index signal generation unit; and an output signal evaluation unit that evaluates the approximation between the sensor output signal and the index signal. The output signal evaluation unit includes a maximum amplitude analysis unit that evaluates the maximum amplitude of the output signal per unit time according to the index signal, a relaxation time analysis unit that evaluates an output signal that is below a predetermined threshold, and a predetermined threshold. Evaluate the output signal using a systolic analyzer that evaluates the output signal above and at least one of.

[0013] Here, the output signal evaluation unit may evaluate the output signal by combining all of the maximum amplitude analysis unit, the relaxation time analysis unit, and the contraction time analysis unit.

[0014] Further, the output signal evaluation unit may evaluate the output signal by weighting the evaluation results of the maximum amplitude analysis unit, the relaxation analysis unit, and the contraction analysis unit.

[0015] Further, the output signal evaluation unit may include a frequency spectrum analysis unit that evaluates the power spectrum of the output signal.

[0016] Here, the frequency spectrum analysis unit may use a power spectrum in a low frequency band.

[0017] The relaxation time analysis unit may use an average value of output signals that are below a predetermined threshold.

[0018] In addition, the relaxation time analysis unit may use a variation in the output signal that falls below a predetermined threshold. [0019] Further, the relaxation time analysis unit may perform regression analysis of an exponential decay function at the time of relaxation using a maximum value sequence of output signals that are below a predetermined threshold.

[0020] Further, the systolic analysis unit may use an average value of output signals exceeding a predetermined threshold.

[0021] Further, the systolic analysis unit may use a variation in the output signal exceeding a predetermined threshold.

[0022] Further, the contraction analysis unit may analyze the contraction duration using a maximum value sequence of output signals exceeding a predetermined threshold.

[0023] Further, the systolic analysis unit may perform regression analysis of an exponential decay function during contraction using a maximum value sequence of output signals exceeding a predetermined threshold.

[0024] In addition, the maximum and minimum values of the index signal may be determined based on the output signals at the time of maximum contraction and maximum relaxation of the measurement subject.

[0025] Further, the index signal may be determined such that its maximum value is lower by a predetermined amount than the output signal at the time of maximum contraction.

[0026] Here, the indicator signal may be a pulse wave. It may also be a sound.

The invention's effect

[0027] The sensor output signal evaluation system of the present invention has an advantage that the output signal from the sensor can be objectively and quantitatively evaluated. For this reason, it is possible to always perform stable diagnosis regardless of the measurement timing and measurement environment. Therefore, it is no longer necessary to measure repeatedly, and the burden on the subject is reduced.

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, the best mode for carrying out the present invention will be described with reference to the drawings. FIG. 1 is a block diagram for explaining the overall outline of the sensor output signal evaluation system of the present invention. The sensor output signal evaluation system of the present invention is mainly divided into an index signal generation unit 10 and an output signal evaluation unit 20. The index signal generator 10 generates an index signal that serves as an index for the person to be measured. The subject performs the relaxation and contraction of the measurement site in accordance with the index signal. The person to be measured is a person who was provided with an electromyograph or pressure gauge electrode or probe at the measurement site. More specifically, for example, patients with symptoms such as dysphagia, urinary incontinence and dystonia.

The index signal output from the index signal generator 10 is, for example, a pulse wave sound having a predetermined period. If the indicator signal is a pulse wave, the subject should be operated to relax when the pulse wave is low and contract when it is high. Further, when the index signal is a sound, for example, it may be operated so as to contract when the sound is emitted and relax when the sound stops. In the present description, the following description will basically describe a case where a pulse wave is used as an index signal. However, the present invention is not limited to this, and an index is used for the subject to perform relaxation and contraction operations. Anything can be used, such as a sine wave or a triangular wave.

[0030] The maximum and minimum values of the index signal output from the index signal generator 10 may be determined based on the sensor output at the time of maximum contraction and maximum relaxation of the measurement subject. That is, after the sensor is mounted, the sensor output when the measurement subject contracts the muscle of the measurement site as much as possible and the sensor output at normal times are recorded in advance. The sensor output may be based on a raw waveform or an integrated waveform obtained by integrating the output. Then, based on this output, for example, the maximum value of the index signal is determined to be lower by a predetermined amount from the output signal at the time of maximum contraction. For example, the maximum value of the indicator signal is 80% lower than the output signal at maximum contraction. More specifically, for example, in the case of measurement using an electromyograph, when the output of the electromyograph shows 2.5 V at the maximum contraction and 1 V at the maximum relaxation, the indicator signal has the maximum value. Set to a pulse wave with a minimum value of 1 V at 2 V. The pulse wave may be set to have a predetermined period, for example, a period of 12 seconds. By setting the index signal in this way, it is possible to provide an index signal based on the output at the maximum contraction of the measured person, which is not affected by the measurement environment, such as the sensor contact position and insertion position. It becomes.

[0031] Next, the output signal from the sensor when the measurement subject performs the relaxation and contraction operations in accordance with the index signal determined as described above is, for example, 0. Integration is performed every second to generate an integrated waveform. This integrated waveform is divided by the splitter 12 every period, for example, every 12 seconds in the above example, based on the index signal of the index signal generator 10.

[0032] Here, each example of the raw waveform from the electromyograph and its integrated waveform when the measurement subject performs relaxation and contraction motions in response to the index signal, and further each of the index signal based on the integrated waveform Figure 2 shows. Figure 2 (a) shows a pulse wave that is an indicator signal. Fig. 2 (b) shows the raw waveform from the electromyograph when the relaxation and contraction motions are performed according to this index signal.Conventionally, whether the muscle is moving correctly by looking at the amplitude of this raw waveform. Was evaluated by the subjectivity of the measurer. Figure 2 (c) shows the waveform obtained by integrating the raw waveform of Figure 2 (b). As shown in Fig. 2 (d), the indicator signal and the output signal may be displayed in an overlapped manner and shown to the subject in real time. Thus, the integrated waveform of the electromyograph output signal shows a low voltage when relaxed and a high voltage when contracted. In the graph of FIG. 2, the horizontal axis is time and the vertical axis is voltage. However, each value has no particular meaning, and the amplitude, pulse width, etc. are not limited to those shown in the illustrated example.

[0033] The output signal as shown in Fig. 2 (c) obtained in this manner is distributed by the splitter 12 for each period and input to the output signal evaluation unit 20 of the present invention. The output signal evaluation unit 20 mainly includes a maximum amplitude analysis unit 21, a relaxation analysis unit 22, a contraction analysis unit 23, and a frequency spectrum analysis unit 24. Note that the output signal evaluation unit in the sensor output signal evaluation system of the present invention does not necessarily require all the above-described analysis units, and at least one of the above-described ones depends on the use and configuration of the evaluation system. If an analysis part is included, it functions as an evaluation system. Hereinafter, regarding a preferred embodiment of the sensor output signal evaluation system of the present invention, the function of each evaluation unit will be described in detail with reference to FIG. 3 which is an enlarged view of one pulse waveform of the integrated waveform of FIG. 2 (c). explain.

[0034] The maximum amplitude analysis unit 21 evaluates and detects the maximum amplitude of the output signal per unit time according to the index signal. As shown in Fig. 3, the highest peak voltage of each output signal divided by the unit time by the splitter 12 is the period. Recorded every time, that is, every pulse. The maximum amplitude analyzer 21 analyzes whether or not an output signal of the same level as the index signal high is obtained.

The relaxation analysis unit 22 analyzes the output signal of the sensor when the measurement subject is relaxed. In the illustrated example, the relaxation time analysis unit 2 2 uses the average value from the average value detection unit 1 3 that detects the average value of the output signal, and extracts an output signal that falls below this, and the relaxation time data extraction unit 3 1 The relaxation time data extraction unit 31 includes a relaxation time average value detection unit 32 that detects the average value of the output signal. As a result, it is analyzed whether or not an output signal of the same level as the low index signal is obtained when the measurement subject is relaxed.

[0036] The systolic analysis unit 23 analyzes the output signal of the sensor when the measurement subject contracts. In the example shown in the figure, the systolic analysis unit 2 3 uses the average value from the average value detection unit 1 3 that detects the average value of the output signal, and detects the maximum value sequence that detects the maximum value sequence of the output signal that exceeds this value. 3 5 and the maximum value sequence detection unit 3 5 Using the output signal of the maximum value sequence detection unit 3 5 and the maximum value sequence detection unit 3 5 And a regression analysis unit 37 that performs regression analysis of the exponential decay function of the column. The contraction duration detection unit 36 and the maximum amplitude analysis unit 21 analyze whether or not contraction is possible with the same output signal for the same duration as the high level of the indicator signal.

It should be noted that the average value detection unit 13 does not necessarily detect the average value of the output signal, and can provide a predetermined threshold value to separate the high side and the low side of the output signal. Any device may be used as long as it can distribute signals so that the relaxation analysis unit 22 can analyze the relaxation signal and the contraction analysis unit 23 can analyze the contraction signal.

The maximum value string detection unit 35 detects the voltage of the peak (maximum value) of the portion exceeding the average value of the integrated waveform until the average value falls below. The contraction duration detection unit 36 detects the time from the first maximum value to the last maximum value from the output signal of the maximum value string detection unit 35. In consideration of the error at the rise and fall of the pulse-shaped integrated waveform, the maximum value next to the first maximum is taken into account. You may make it detect the time to the maximum value before the last maximum value.

The regression analysis unit 37 detects the “slope” of the maximum value sequence obtained by the maximum value sequence detection unit 35. Normally, when the subject contracts the muscle so that it follows the index signal, it tends to react excessively when the index signal rises, and then adjusts the muscle strength to produce a right-downward output signal. In addition, when a subject with weak muscle strength performs a contraction operation, the contraction force gradually decreases with time, even during the contraction operation. Become a signal. However, in the case of a measured person capable of normal muscle movement, the output signal is almost horizontal according to the index signal. In addition, when this output signal falls to the right, the integrated waveform of the electromyogram will oscillate. In the regression analysis unit 37, only the maximum value sequence from which the vibration component is eliminated by the maximum value sequence detection unit 35 can be analyzed, and only the damping characteristic can be analyzed. Therefore, the regression analysis unit 37 performs the regression analysis using an exponential decay curve by using the maximum value sequence at the time of contraction, for example, by obtaining the shape factor corresponding to the slope, thereby continuing the same for a certain period of time. It is analyzed whether it can contract by force. Specifically, for example, the following attenuation curve is regressed, and) 8 is obtained as a shape factor. The smaller this value, the more the follow-up waveform can be analyzed.

C e X p [-β t]

Where C is the maximum amplitude regression value and t is time.

[0040] Incidentally, the form factor ^ means how far away from the flat follow-up as with the pulse wave of the index signal, and 8) corresponds to the slope of attenuation. Therefore, it can be analyzed that the smaller the (8) in the above equation, the smaller the gradient and the closer it is to flat, and the larger the (8), the steeper the slope and the unevenness.

[0041] The frequency spectrum analyzer 24 analyzes the frequency of the output signal. In the illustrated example, the frequency spectrum analysis unit 24 includes an FFT analysis unit 3 8 and a low-pass filter unit 39. In the frequency spectrum analysis unit 24, the FFT analysis unit 38 analyzes the power spectrum for each frequency and By specifically in filter unit 39 focuses on the average value of for example 3 H _Z following Pawasu Bae vector, whether stable relaxation and shrinkage movements are performed that Ru is analyzed.

[0042] The results analyzed by the above analysis units are stored in the data stock unit 42, respectively. Then, using the pre-training database unit 45 that accumulates and accumulates data before biofeedback training, for example, 卜 enables comparison between the state before training and the state after training. Diagnose with 46. The diagnosis unit may display the analysis result of each analysis unit, or may display a raw waveform or an integrated waveform as necessary. It is also possible to display a standard deviation (and) indicating the variation in data before and after training, and to diagnose that the training result appears as it approaches zero.

[0043] Hereinafter, the measurement results of each analysis unit when measured using a sensor, particularly an electromyograph, and the output results of each analysis unit, etc. are used for the subject who exercises following the index signal. Will be described in more detail. As the measurement method, a pelvic floor muscle group electromyograph (Fem i Scan probe made by Mega Electronics Ltd. was inserted into the vagina of the subject and the pelvic floor muscle group was matched to the index signal. Specifically, a pulse wave of 1 cycle 1 2 seconds set to an amplitude of 80% of the output signal at the time of maximum contraction of the measurement subject is generated as an indicator signal generator 1 0 This was repeated 6 times for 6 seconds for contraction and 6 seconds for relaxation, and this was performed for 6 sets.The output signal from the electromyograph was integrated by the moving integration unit 1 1, This integrated waveform was divided for each period of the pulse wave by splitters 12. These integrated waveforms were regarded as independent trials, analyzed by each analysis unit, and statistically compared before and after biofeedback training. Here, Wilcoxon's sign rank test is used. The significance level was 0.05.

[0044] And biofeedback training for patients with stress urinary incontinence using the Fem i Sean probe, vaginal contraction 6 seconds and relaxation 6 seconds 1 2 times, 3 sets 1 week by 1 2 I went for a week. In the present invention, the above measurement It is not limited to the method, the number of measurements, the biofeedback training method, etc. The above description is merely an example.

[0045] Now, the analysis results measured in this way will be described below with reference to the drawings showing the output results of the respective analysis units. In each figure, Pre represents output data before biofeedback training and Post represents output data after training.

FIG. 4 shows an example of the output result of the maximum amplitude analysis unit 21. In Fig. 4, the vertical axis represents the amplitude. The figure shows that the maximum amplitude is suppressed before and after training. In other words, before training, even if you try to perform contraction motion following the high of the pulse wave of the index signal, you could not control well and it caused the muscle to contract too much, and it was like a ringing waveform. It can be controlled to follow the pulse wave well and the ringing is suppressed.

FIG. 5 shows an example of the output result of the relaxation time analysis unit 22. In Fig. 5, the vertical axis represents the amplitude. From the figure, it can be seen that the average amplitude during relaxation decreases before and after training. In other words, the muscle contraction signal is not output at the time of relaxation, which indicates that the muscle control function of the subject is improved.

FIG. 6 shows an example of an output result of the contraction duration detection unit 36 of the contraction time analysis unit 23. In Fig. 6, the vertical axis represents time. From the figure, it can be seen that the duration of contraction increases before and after training, and that more continuous muscle movement is possible.

FIG. 7 shows an example of an output result of the regression analysis unit 37 of the contraction time analysis unit 23. In Fig. 7, the vertical axis represents the value of the form factor ^ corresponding to the slope of the local maximum sequence. The figure shows that the shape factor value decreases before and after training, so the slope of the output waveform that follows the high of the indicator signal is flattened, and the muscle control function of the subject is improved. .

FIG. 8 shows an example of the output result of the frequency spectrum analysis unit 24. In Fig. 8, the vertical axis represents the average value of the power spectrum. From the figure, low frequency In the wave region, it can be seen that the power spectrum of OHZ has risen before and after training, and has fallen at a higher frequency. In other words, the increase in the low-frequency component OH z means that an output waveform with less variation is obtained, which means that more stable relaxation and contraction movements can be achieved. .

[0051] As described above, the output signal evaluation unit 20 shown in FIG. 1 can perform various analyzes and comprehensively use them to evaluate how approximate the index signal and the output signal are. Become. Of course, it is possible to use them individually if necessary and focus on only the analysis results. For example, using the analysis results of the maximum amplitude analysis unit 2 1, relaxation analysis unit 2 2 and contraction analysis unit 2 3 regression analysis unit 3 7 and contraction analysis unit 2 3 contraction duration detection unit 3 6 When evaluating how the measured sensor output signal approximates the index signal, the results from each analysis unit are displayed as numerical values, which part is approximated and which part is approximated. It may be clarified whether the part is not approximate.

[0052] Further, the results from the respective analysis units may be collectively scored to display one numerical value. When outputting one numerical value collectively, the result from each analysis unit may be output after weighting the result from each analysis unit instead of equally dividing the result. For example, regarding the analysis result of the contraction duration detection unit 36, even if the contraction continues for a certain time longer than the index signal without following the index signal, the duration is stable. If you can be determined to be able to improve sustained muscle activity. However, since the operation does not actually follow the index signal, the analysis result of the contraction duration detection unit 36, for example, is used to reduce the influence of such misrecognition. It is possible to reduce the weighting compared to the analysis results from other analysis units.

In the illustrated example, the relaxation time analysis unit 22 detects the average value of the output signal at the time of relaxation by the relaxation average value detection unit 32. However, the present invention is not limited to this. It is not limited, and it may be one that detects variations in the output signal during relaxation. You That is, it may be possible to analyze whether the output signal at the time of relaxation is stable by using a difference from the average value of the output signal at the time of relaxation to the output signal. Further, as in the case of the contraction analysis unit 23, a regression analysis of the exponential decay function may be performed using the maximum value sequence of the output signal at the time of relaxation. Further, similarly to the contraction duration detection unit 36, the relaxation duration time may be detected. Furthermore, these various relaxation analysis techniques may be combined.

On the other hand, the contraction analysis unit 23 may also detect an average value of output signals during contraction, or may detect variations in the output signals. Also, similar to the above, various methods of analysis at the time of contraction may be combined.

[0055] Thus, in the sensor output signal evaluation system of the present invention, various measurements, analysis, diagnosis, and the like can be objectively and quantitatively performed by combining the above analysis units in various ways.

It should be noted that the sensor output signal evaluation system of the present invention is not limited to the illustrated example described above, and it is needless to say that various changes can be made without departing from the scope of the present invention.

Brief Description of Drawings

[0057] [FIG. 1] FIG. 1 is a block diagram for explaining an overall outline of a sensor output signal evaluation system of the present invention.

[Fig. 2] Fig. 2 is a graph showing each waveform signal. Fig. 2 (a) is an index signal, and Fig. 2 (b) is an electromyograph when relaxation and contraction are performed in response to the index signal. Fig. 2 (c) shows the integrated waveform obtained by integrating the raw waveform. Fig. 2 (d) shows the result of overlapping Fig. 2 (a) and Fig. 2 (c).

[FIG. 3] FIG. 3 is an enlarged view of one pulse waveform of the integrated waveform of FIG. 2 (c).

FIG. 4 is a graph showing an example of the output result of the maximum amplitude analysis unit.

FIG. 5 is a graph showing an example of the output result of the relaxation time analysis unit.

FIG. 6 is a graph showing an example of the output result of the contraction duration detection unit of the analysis unit during contraction. [FIG. 7] FIG. 7 is a graph showing an example of the output result of the regression analysis unit of the systolic analysis unit.

[FIG. 8] FIG. 8 is a graph showing an example of an output result of the frequency spectrum analysis unit.

1 0 Indicator signal generator

1 1 Moving integrator

1 2 Splitter

1 3 Average value detector

2 0 Output signal evaluation section

2 1 Maximum amplitude analyzer

2 2 Analysis section during relaxation

2 3 Analysis section during contraction

2 4 Frequency spectrum analyzer

3 1 Data extraction unit during relaxation

3 2 Relaxation average value detector

3 5 Maximum value string detector

3 6 Contraction duration detector

3 7 Regression analysis section

3 8 F F T analysis part

3 9 Low-pass filter

4 2 Data Stock Department

4 5

4 6 Diagnostic Department

Claims

The scope of the claims

[1] An evaluation system for evaluating an output signal of a sensor for measuring a subject's relaxation and contraction motion, the system comprising:

An index signal generator for generating an index signal that serves as an index for the measurement subject to perform relaxation and contraction in response thereto;

An output signal evaluation unit that evaluates the closeness between the sensor output signal and the index signal;

The output signal evaluation unit

A maximum amplitude analyzer for evaluating the maximum amplitude of the output signal per unit time according to the index signal;

A relaxation analysis unit that evaluates an output signal below a predetermined threshold;

A systolic analysis unit that evaluates an output signal exceeding a predetermined threshold;

A sensor output signal evaluation system characterized in that an output signal is evaluated using at least one of the above.

[2] The sensor output signal evaluation system according to claim 1, wherein the output signal evaluation unit outputs an output signal by combining all of the maximum amplitude analysis unit, the relaxation analysis unit, and the contraction analysis unit. A sensor output signal evaluation system characterized by evaluating.

[3] The sensor output signal evaluation system according to claim 2, wherein the output signal evaluation unit weights and outputs the evaluation results of the maximum amplitude analysis unit, the relaxation analysis unit, and the contraction analysis unit. A sensor output signal evaluation system characterized by evaluating a signal.

[4] In the sensor output signal evaluation system according to any one of claims 1 to 3, the output signal evaluation unit further includes a frequency spectrum analysis unit that evaluates a power spectrum of the output signal. Sensor output signal evaluation system characterized by the above.

[5] The sensor output signal evaluation system according to claim 4, wherein the frequency spectrum analysis unit uses a power spectrum in a low frequency band. Sensor output signal evaluation system.

[6] In the sensor output signal evaluation system according to any one of [1] to [5], the relaxation time analysis unit uses an average value of output signals that are less than a predetermined threshold value. Output signal evaluation system.

[7] The sensor output signal evaluation system according to any one of [1] to [6], wherein the relaxation time analysis unit uses a variation in output signal that falls below a predetermined threshold value. Output signal evaluation system.

[8] In the sensor output signal evaluation system according to any one of [1] to [7], the relaxation time analysis unit uses a maximum value sequence of output signals that are less than a predetermined threshold value to indicate a relaxation time index. A sensor output signal evaluation system characterized by performing regression analysis of an attenuation function.

[9] In the sensor output signal evaluation system according to any one of claims 1 to 8, the contraction analysis unit uses an average value of output signals exceeding a predetermined threshold value. Output signal evaluation system.

[10] The sensor output signal evaluation system according to any one of [1] to [9], wherein the contraction analysis unit uses a variation in output signal exceeding a predetermined threshold value. Output signal evaluation system.

[11] In the sensor output signal evaluation system according to any one of claims 1 to 10, the contraction time analysis unit uses a maximum value sequence of output signals exceeding a predetermined threshold value, and a contraction duration time Sensor output signal evaluation system characterized by analyzing

[12] In the sensor output signal evaluation system according to any one of claims 1 to 11, the contraction time analysis unit uses a maximum value sequence of output signals exceeding a predetermined threshold value at the time of contraction. Sensor output signal evaluation system characterized by performing regression analysis of exponential decay function.

[13] The sensor output signal evaluation system according to any one of claims 1 to 12, wherein the index signal has a maximum value and a minimum value at the time of maximum contraction and maximum relaxation of the measurement subject. Sensor output signal, which is determined based on the output signal of No. evaluation system.

[14] In the sensor output signal evaluation system according to any one of claims 1 to 13, the maximum value of the index signal is smaller than the output signal at the time of the maximum contraction by a predetermined amount. A sensor output signal evaluation system characterized by being determined.

15. The sensor output signal evaluation system according to any one of claims 1 to 14, wherein the index signal is a pulse wave.

16. The sensor output signal evaluation system according to any one of claims 1 to 14, wherein the index signal is a sound.