JP2019013656A - Muscle activity estimation apparatus, muscle activity estimation method, and muscle activity estimation processing program - Google Patents

Abstract

To estimate muscle activity on the basis of biopotential signal measurement at a measurement area to which measurement electrodes for a subject can be constantly stuck.SOLUTION: A muscle activity estimation apparatus according to one embodiment includes: biopotential signal measurement means that measures a first biopotential signal as a biopotential signal of an estimation object side of the muscle activity in a subject, and a second biopotential signal as a biopotential signal of a site that is different from the estimation object site and whose body shape variation by exercise is smaller than that of the estimation object site; signal preprocessing means that calculates root-mean-squares of the first and second biopotential signals respectively and outputs them; extraction mans that extracts a parameter associating preprocessing results of the first and second biopotential signals; and estimation means that estimates the muscle activity of the estimation object site on the basis of the root-mean-square of the second biopotential signal and the parameter.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a muscle activity estimation device, a muscle activity estimation method, and a muscle activity estimation processing program.

  In each country, especially in Japan, a super-aged society is advancing. With aging, motor functions such as muscle strength and balance sensation decline. In order to realize a healthy life for people, it is important to observe exercise on a daily basis.

  As information effective for observation of movement, an electromyogram signal (Electric signal) which is a biopotential signal used for electromyography (EMG) is used. For example, by using a conductive fiber as disclosed in Non-Patent Document 1, it is becoming possible to measure a biopotential signal using a clothing-type measuring device.

NTT DOCOMO, Inc. Press release, “Development and practical application of functional material“ hitoe ”that enables continuous measurement of biological information by simply wearing it”, https://www.nttdocomo.co.jp/info/news_release/ 2014/01 / 30_00.html

  On the other hand, in order to continuously and stably measure the bioelectric potential signal, it is necessary to closely attach the electrode for measuring the bioelectric potential signal to the measurement site of the subject by severe tightening. However, it is difficult for the subject to use clothes with a feeling of pressure in daily life. In addition, there is a problem that the electrode may be separated from the measurement site due to a change in the body shape accompanying the exercise.

  An object of the present invention is to provide a muscle activity estimation device, a muscle activity estimation method, and a muscle activity estimation process capable of performing muscle activity estimation based on biopotential signal measurement in a measurement region in which measurement electrodes can be always adhered to a subject. Is to provide a program.

  In order to achieve the above object, a first aspect of the muscle activity estimation device according to one embodiment of the present invention includes a first biopotential signal that is a biopotential signal of an estimation target site of muscle activity in a subject, and A biopotential signal measuring means for measuring a second biopotential signal that is a biopotential signal of a part that is different from the estimation target part in the subject and has a smaller body shape variation due to movement of the subject than the estimation target part; Signal preprocessing means for calculating a root mean square of the first biopotential signal and a root mean square of the second biopotential signal and outputting the result as a preprocessing result; and preprocessing of the first biopotential signal Extraction means for extracting a parameter relating the result and the preprocessing result of the second biopotential signal as a biopotential signal propagation characteristic; and output from the signal preprocessing means. And an estimation unit for estimating muscle activity of the estimation target region based on the preprocessed result of the second biopotential signal and the biopotential signal propagation characteristic extracted by the extraction unit. .

  According to a second aspect of the muscle activity estimation apparatus having the above-described configuration, in the first aspect, information on the biopotential signal propagation characteristics extracted by the extraction unit is used to extract the biopotential signal propagation characteristics from the subject. The bioelectric potential signal stored in the storage means is further provided with storage means for storing together with body characteristic information, and the estimation means stores information on biopotential signal propagation characteristics associated with characteristic information approximate to the characteristic information of the subject. Based on the second biopotential signal selected from the propagation characteristic information and output from the signal preprocessing means to the subject different from the subject to be extracted and the selected biopotential signal propagation characteristics The apparatus which estimates the muscle activity of the said estimation object site | part concerning the said different test subject is provided.

  According to a third aspect of the muscle activity estimation apparatus having the above configuration, in the second aspect, the body characteristic information of the subject includes at least one of the age, sex, height, weight, and body fat percentage of the subject. I will provide a.

According to a fourth aspect of the muscle activity estimation device configured as described above, in the first aspect, the parameter includes a coefficient of the second biopotential signal and a bias term, and the extracting means sets time as t. The time constant is T, the second biopotential signal is L ₀ , the second biopotential signal is A, the coefficient of the second biopotential signal is M, and the bias term is b Occasionally, the coefficients and the bias term of the second biopotential signal extracting said biopotential signal propagation characteristic by calculated by the equation L _{0 =} MA T + ^b, said estimating means, streaks of the estimated target region activity 'is taken as the muscle activity of the putative target site wherein L' L provides an apparatus for estimating muscle activity of the putative target site by calculating by = MA T + ^b.

  An aspect of a muscle activity estimation method according to an embodiment of the present invention is a muscle activity estimation method performed by a muscle activity estimation apparatus, and includes a first biopotential signal that is a biopotential signal of an estimation target site of muscle activity in a subject, And measuring a second biopotential signal that is a part of the subject that is different from the part to be estimated and has a smaller body shape variation due to movement of the subject than the part to be estimated, The root mean square of the bioelectric potential signal and the root mean square of the second biopotential signal are calculated and output as preprocessing results, and the preprocessing result of the first biopotential signal and the second biopotential A parameter relating the signal preprocessing result is extracted as a biopotential signal propagation characteristic, and the preprocessing result of the second biopotential signal in the preprocessing result and the extraction are extracted. Based on said biopotential signal propagation characteristics, a method of estimating muscle activity of the putative target site.

  An aspect of the muscle activity estimation processing program according to an embodiment of the present invention provides a program that causes a processor to function as each of the means of the muscle activity estimation apparatus according to any one of the first to fourth aspects.

  According to the present invention, it is possible to perform muscle activity estimation based on biopotential signal measurement in a measurement region in which measurement electrodes can be always adhered to a subject.

The figure which shows the function structural example of the muscle activity estimation apparatus in one Embodiment of this invention. The flowchart which shows an example of the process sequence by the muscle activity estimation apparatus in one Embodiment of this invention. The figure which shows the example of mounting | wearing of the bioelectric potential signal measuring apparatus in connection with the muscle activity estimation apparatus in one Embodiment of this invention. The figure which shows an example of the pre-processing result of the bioelectric potential signal measured by the muscle activity estimation apparatus in one Embodiment of this invention. The figure which shows an example of the storage of the bioelectric potential signal propagation characteristic computed by the muscle activity estimation apparatus in one Embodiment of this invention. The figure which shows an example of the storage of the bioelectric potential signal propagation characteristic computed by the muscle activity estimation apparatus in one Embodiment of this invention.

An embodiment according to the present invention will be described below.
In one embodiment, the bioelectric potential signal of the subject (user) is not measured in the vicinity of the muscle belly where the body shape variation accompanying the exercise is large, but the subject has a small body shape variation accompanying the exercise, that is, for measurement of the subject. The activity of a specific muscle region of the subject is estimated using a bioelectric potential signal measured in a measurement region where the electrodes can be always adhered.

(Constitution)
FIG. 1 is a diagram illustrating a functional configuration example of a muscle activity estimation system according to an embodiment of the present invention.
As shown in FIG. 1, the muscle activity estimation system in the present embodiment includes a muscle activity estimation device 100, a bioelectric potential signal measurement device 200, and an information presentation device 300.

  The muscle activity estimation device 100 can be connected to a biopotential signal measurement device 200 and an information presentation device 300 as external devices.

  As an example, the muscle activity estimation system uses a biopotential signal measuring device 200 as a wearable device that can be worn by a subject, and the muscle activity estimation device 100 and the information presentation device 300 are computers such as smartphones, tablet terminals, and personal computers (PCs). This is realized by using a device as a device. For example, the computer device includes a processor such as a CPU (Central Processing Unit), a memory connected to the processor, and a communication interface for communicating with the biopotential signal measuring apparatus 200 (for example, wirelessly). In addition, the implementation | achievement form of a muscular activity estimation system is not limited to this example. For example, the muscle activity estimation system may be realized as one device. Moreover, the bioelectric potential signal measuring apparatus 200 may be provided outside the muscle activity estimation system. In other words, the muscle activity estimation system may acquire a result of measuring a subject's biopotential signal from an external biopotential signal measuring apparatus corresponding to the biopotential signal measuring apparatus 200.

  The muscle activity estimation apparatus 100 includes a biopotential signal storage unit 101, a biopotential signal preprocessing unit 102, a biopotential signal propagation characteristic extraction unit 103, a biopotential signal propagation characteristic DB (database) 104, and a muscle activity estimation unit 105.

  The functions of the biopotential signal storage unit 101, the biopotential signal preprocessing unit 102, the biopotential signal propagation characteristic extraction unit 103, the biopotential signal propagation characteristic DB 104, and the muscle activity estimation unit 105 are stored in, for example, a memory in the processor. This is realized by reading out and executing the existing program. Note that some or all of these functions may be realized by a circuit such as an application specific integrated circuit (ASIC).

FIG. 2 is a flowchart illustrating an example of a processing procedure performed by the muscle activity estimation apparatus according to the embodiment of the present invention.
FIG. 3 is a diagram illustrating a wearing example of the bioelectric potential signal measuring apparatus related to the muscle activity estimating apparatus according to the embodiment of the present invention.

  As shown in FIG. 2, the muscle activity estimation apparatus 100 operates in the order of biopotential signal measurement (S1), calibration (S2), and muscle activity estimation (S3).

  In S <b> 1, the muscle activity estimation device 100 is (1) a myoelectric potential signal generated from a muscle of an estimation target site of muscle activity in a subject (hereinafter, simply referred to as an estimation target site) accompanying exercise in the subject. A biopotential signal including a biopotential signal and (2) a distal biopotential signal that is a myopotential signal generated from a muscle at a site different from the estimation target site in the subject is input as sensor information.

  In S <b> 2, the muscle activity estimation device 100 calculates a muscle activity estimation parameter (a parameter for estimating the muscle activity of the estimation target region from the distal bioelectric potential signal).

  In S3, the muscle activity estimation apparatus 100 estimates the muscle activity of the estimation target region using the distal biopotential signal input in S1 and the muscle activity estimation parameter obtained in S2.

(Operation example)
In the following description, five electrodes (electrodes A ₀ , A ₁ , A ₂ , A ₃ , A ₄ (hereinafter, referred to as FIG. 3) attached to a region different from the estimation target region, in this case, near the ankle of the sock worn by the subject. Based on the distal biopotential signal measured by A ₀ -A _{4 (} which may be referred to as A ₀ -A ₄ ), the estimated target region, here the subject's Tibialis anterior muscle (here, the electrode L ₀ shown in FIG. 3). A description will be given of an example of estimating the muscular activity of the region where the) is worn. As long as the muscle activity estimation target region satisfies the conditions of appropriate accuracy of the estimation of the muscle activity of the estimation target region, and the body shape variation accompanying exercise by the subject is smaller than the estimation target region, the anterior tibial muscle Other than that.

As shown in FIG. 3, the biopotential signal measuring apparatus 200 can be connected to the electrodes L ₀ and A ₀ -A ₄ attached to the subject, and can measure the biopotential signal from each electrode at 2000 Hz. Suppose there is.

The bioelectric potential signal storage unit 101 of the muscle activity estimation apparatus 100 includes a storage device such as a nonvolatile memory. The biopotential signal storage unit 101 receives a biopotential signal sent from the biopotential signal measuring apparatus 200 and holds information on the biopotential signal for a predetermined time in a storage device. Here biopotential signals, the signal _{L 0} obtained from the electrodes _{L 0,} signal _{A 0} obtained from the electrodes _{A 0,} the signal _{A 1} was obtained from the electrodes _{A 1,} the signal _{A 2} obtained from the electrodes _{A 2,} electrode _{A 3} The signal A ₃ obtained from the signal A _{4 and} the signal A ₄ obtained from the electrode A ₄ (hereinafter also referred to as signals L ₀ , A ₀ -A ₄ ).

The signal L ₀ is a biopotential signal of a site to be estimated for muscle activity in the subject. Also, A ₀ -A ₄ described above are biopotential signal estimation target site different from the site of muscle activity in a subject.

The biopotential signal preprocessing unit 102 inputs the information of the biopotential signal (signals L ₀ , A ₀ -A ₄ ) measured during a predetermined time T stored in the biopotential signal storage unit 101, and inputs this information As a preprocessing, the root mean square (RMS) of the bioelectric potential signal is calculated according to the following equation (1). T = 0.1 [s].

In this embodiment, s (t) in equation (1) represents a biopotential signal at time t, and RMS [s (t)] represents the root mean square of s (t). The calculation of the root mean square is performed for the signals L ₀ and A ₀ -A ₄ , respectively.
In the equation (1), τ is the number of signals L ₀ (number of samples) measured during a certain time T, the number of signals A ₀ measured during a certain time T, and during a certain time T. The number of signals A ₁ measured, the number of signals A ₂ measured during a certain time T, the number of signals A ₃ measured during a certain time T, or measured during a certain time T It corresponds to the number of signal _{a 4.} As described above, when the bioelectric potential signal from each electrode is measured at 2000 Hz and T = 0.1 [s], the number of τ is 200.

FIG. 4 is a diagram illustrating an example of a preprocessing result of the bioelectric potential signal measured by the muscle activity estimation device according to the embodiment of the present invention.
As illustrated in FIG. 4, the biopotential signal preprocessing unit 102 obtains biopotential signals (preprocessing results of the biopotential signal) after the preprocessing of the original biopotential signals L ₀ and A ₀ -A ₄ .
Here, biopotential signal after preprocessing, the signal L ₀ after the pretreatment for the original biopotential signals L _0, signal A ₀ after pretreatment for the original biopotential signals A _0, based biopotential signal A signal a ₁ after pretreatment for _1, the signal a ₂ after the pretreatment for the original biopotential signals a _2, after the pretreatment with respect to the original biopotential signal a ₃ signal a _3, to the original biopotential signal a ₄ preprocessed signal _{a 4,} the signal _{a 5} after the pretreatment for the original biopotential signal _{a 5} (hereinafter, front may be referred to as bio-potential signal _{L 0,} a 0 -A ₄ after treatment).

The biopotential signal pre-processing unit 102 uses the biopotential signal L ₀ , A ₀ -A ₄ after pre-processing as the biopotential signal pre-processing result as a biopotential signal propagation characteristic extraction unit 103 and muscle activity estimation. Output to the unit 105.

The biopotential signal propagation characteristic extraction unit 103 receives the preprocessed biopotential signal (L ₀ , A ₀ -A ₄ ) output from the biopotential signal preprocessing unit 102. The biopotential signal propagation characteristic extraction unit 103 satisfies the following expression (2) using the input preprocessed biopotential signal (L ₀ , A ₀ -A ₄ ) and the time constant T described above. M and b are respectively calculated by the least square method. The calculated M and b are parameters relating the preprocessed biopotential signal L ₀ and the preprocessed biopotential signals A _{0 to} A _4, and are referred to as biopotential signal propagation characteristics.

L ₀ = MA ^{T +} b ... formula (2)
Further, M in the formula (2) is represented by the following formula (3), and A in the formula (2) is represented by the following formula (4). The right side of the equation (4) shows the biopotential signal A ₀ -A ₄ after preprocessing.

M = [M ₀ , M ₁ , M ₂ , M ₃ , M ₄ ] Formula (3)
A = [A ₀ , A ₁ , A ₂ , A ₃ , A ₄ ] ... Formula (4)
M ₀ , M ₁ , M ₂ , M ₃ , and M _{4 on} the right side of Equation (3) are pre-processed biopotential signals A ₀ , A ₁ , A ₂ , A ₃ , a one-to-one correspondence with coefficients a _4. “B” on the right side of Equation (2) is a bias term.
In the present embodiment, the calculation method of M and b in Expression (2) is not limited to the above least square method, and another method may be used.

  When the calculated error in the biopotential signal propagation characteristic exceeds the threshold value, the biopotential signal propagation characteristic extraction unit 103 notifies the user to change the wearing using the information presentation device 300.

  The biopotential signal propagation characteristic extraction unit 103 outputs M and b calculated as described above to the biopotential signal propagation characteristic DB 104 as biopotential signal propagation characteristics.

FIG. 5 is a diagram illustrating an example of storage of biopotential signal propagation characteristics calculated by the muscle activity estimation apparatus according to the embodiment of the present invention.
The biopotential signal propagation characteristic DB 104 is realized by a storage device such as a nonvolatile memory. The biopotential signal propagation characteristic DB 104 is stored in the storage device in the format shown in FIG.

FIG. 6 is a diagram illustrating an example of storage of biopotential signal propagation characteristics calculated by the muscle activity estimation apparatus according to the embodiment of the present invention.
Further, the biopotential signal propagation characteristic DB 104 associates M and b, which are biopotential signal propagation characteristics, with at least one type of information such as age, sex, height, weight, and body fat percentage, as shown in FIG. Thus, it may be held in the storage device as biopotential signal propagation characteristic information. As a result, at least one of age, sex, height, weight, and body fat percentage of a new subject (new subject to be estimated for muscle activity) different from the subject from which the bioelectric potential signal propagation characteristic information is calculated. It is possible to select biopotential signal propagation characteristic information having characteristics close to those from various biopotential signal propagation characteristic information stored in the biopotential signal propagation characteristic DB 104.

Thereby, the biopotential signal propagation characteristic information of the new subject can be obtained without measuring the biopotential signal (L ₀ ) of the muscle activity estimation target site for the new subject.

Next, obtaining the muscle activity estimation result L ′ of the muscle activity estimation target part (here, the anterior tibial muscle) will be described.
After the bioelectric potential signal propagation characteristic information is obtained, the muscle activity estimation unit 105 performs preprocessing other than the muscle activity estimation target portion input from the biopotential signal preprocessing unit 102 according to the following equation (5). M and b indicated by the subsequent biopotential signal (here, signals A ₀ -A ₄ (corresponding to A on the right side of Expression (5)) and biopotential signal propagation characteristic information held by the biopotential signal propagation characteristic DB 104. L ′ is calculated using

L '= MA ^{T +} b ... (5)
The muscle activity estimation unit 105 uses the calculated L ′ as the estimation result of the muscle activity of the muscle activity estimation target part (here, the anterior tibial muscle).

  As described above, the muscle activity estimation system according to the embodiment of the present invention measures the biopotential signal of the estimation target part of the muscle activity in the subject and the biopotential signal of the part different from the estimation target part. The root mean square of the signal is calculated and output as a preprocessing result. This muscle activity estimation system inputs the pre-processing result, extracts the biopotential signal of the estimation target part and the parameter relating the biopotential signal of the part different from the estimation target part as biopotential signal propagation characteristics, and estimates Based on the root mean square of the bioelectric potential signal of the part different from the target part and the bioelectric potential signal propagation characteristic, the muscle activity of the estimation target part is estimated.

  Therefore, it is possible to estimate the muscle activity of the target part of muscle activity estimation using the bioelectric potential signal from the part where the body shape fluctuation due to the subject's movement is small, so that the measurement electrode can be kept in close contact with the subject at all times Muscle activity estimation based on bioelectric potential signal measurement in a simple measurement region can be performed.

  In addition, this invention is not limited to the said embodiment, In the implementation stage, it can change variously in the range which does not deviate from the summary. Further, the embodiments may be implemented in combination as appropriate, and in that case, the combined effect can be obtained. Furthermore, the present invention includes various inventions, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if several constituent requirements are deleted from all the constituent requirements shown in the embodiment, if the problem can be solved and an effect can be obtained, the configuration from which the constituent requirements are deleted can be extracted as an invention.

  In addition, the method described in each embodiment is, for example, a magnetic disk (floppy (registered trademark) disk, hard disk, etc.), optical disk (CD-ROM, etc.) as a program (software means) that can be executed by a computer (computer). It can be stored in a recording medium such as a DVD, MO, etc., semiconductor memory (ROM, RAM, flash memory, etc.), or transmitted and distributed by a communication medium. The program stored on the medium side includes a setting program that configures software means (including not only the execution program but also a table and data structure) in the computer. A computer that implements this apparatus reads a program recorded on a recording medium, constructs software means by a setting program as the case may be, and executes the above-described processing by controlling the operation by this software means. The recording medium referred to in this specification is not limited to distribution, but includes a storage medium such as a magnetic disk or a semiconductor memory provided in a computer or a device connected via a network.

  DESCRIPTION OF SYMBOLS 100 ... Muscle activity estimation apparatus, 101 ... Biopotential signal storage part, 102 ... Biopotential signal pre-processing part, 103 ... Biopotential signal propagation characteristic extraction part, 104 ... Biopotential signal propagation characteristic DB, 105 ... Muscle activity estimation part, 200 ... Bioelectric potential signal measuring device, 300 ... Information presentation device.

Claims (6)

A first biopotential signal that is a biopotential signal of an estimation target site of muscle activity in a subject, and a body shape variation due to exercise of the subject from the estimation target site that is different from the estimation target site in the test subject. Biopotential signal measuring means for measuring a second biopotential signal that is a biopotential signal of a small part;
Signal preprocessing means for calculating a root mean square of the first biopotential signal and a root mean square of the second biopotential signal and outputting the result as a preprocessing result;
Extraction means for extracting, as biopotential signal propagation characteristics, a parameter relating the preprocessing result of the first biopotential signal and the preprocessing result of the second biopotential signal;
Estimation means for estimating muscle activity of the estimation target region based on a preprocessing result of the second biopotential signal output from the signal preprocessing means and the biopotential signal propagation characteristic extracted by the extraction means A muscle activity estimation device comprising: A storage means for storing the biopotential signal propagation characteristic information extracted by the extraction means together with the body characteristic information of the subject from which the biopotential signal propagation characteristics are extracted;
The estimation means includes
Information on biopotential signal propagation characteristics associated with feature information approximating the subject's feature information is selected from information on biopotential signal propagation characteristics stored in the storage means, and output from the signal preprocessing means. The muscle activity of the estimation target portion related to the different subject is estimated based on the second biopotential signal applied to the subject different from the subject of the extraction and the selected biopotential signal propagation characteristics. The muscle activity estimation apparatus according to 1. The characteristic information of the subject's body is:
The muscle activity estimation apparatus according to claim 2, comprising at least one of the age, sex, height, weight, and body fat percentage of the subject. The parameter is
A coefficient of the second biopotential signal and a bias term;
The extraction means includes
The time is t, the time constant is T, the second biopotential signal is L ₀ , the second biopotential signal is A, the coefficient of the second biopotential signal is M, and the bias term the when is b, the coefficients and the bias term of the second biopotential signal wherein L 0 ₌ MA T ⁺ b
The biopotential signal propagation characteristics are extracted by calculating
The estimation means includes
Muscle activity of the putative target site 'when the equation muscle activity of the putative target site L' L = MA T ⁺ b
The muscle activity estimation apparatus according to claim 1, wherein the muscle activity of the estimation target part is estimated by calculating by the following. A muscle activity estimation method performed by a muscle activity estimation device,
A first biopotential signal that is a biopotential signal of an estimation target site of muscle activity in a subject, and a body shape variation due to exercise of the subject from the estimation target site that is different from the estimation target site in the test subject. Measure a second biopotential signal that is a biopotential signal of a small part,
Calculating a root mean square of the first biopotential signal and a root mean square of the second biopotential signal, respectively, and outputting the result as a preprocessing result,
Extracting a parameter relating the preprocessing result of the first biopotential signal and the preprocessing result of the second biopotential signal as a biopotential signal propagation characteristic;
A muscle activity estimation method for estimating muscle activity of the estimation target region based on a preprocessing result of the second biopotential signal in the preprocessing result and the extracted biopotential signal propagation characteristics.   The muscle activity estimation processing program which makes a processor function as each said means of the muscle activity estimation apparatus of any one of Claims 1 thru | or 4.