JP5808550B2 - Bicycle maneuverability evaluation method and bicycle maneuverability evaluation apparatus - Google Patents

Description

  The present invention relates to a bicycle maneuverability evaluation method and a bicycle maneuverability evaluation apparatus that evaluate a bicycle maneuverability using a signal that can be acquired from an occupant.

  Conventionally, in the development of products such as automobiles and motorcycles, tires, etc., items based on the user's sense, such as “steering stability” and “riding comfort”, for example, have accumulated a long driving experience. It is evaluated based on the subjectivity of testers such as test drivers and test riders (for example, Patent Document 1).

JP 2010-12605 A (paragraph [0062], Table 2)

  However, the subjectivity of a tester who is skilled in driving skills may be different from a general user's feeling that is not necessarily familiar with driving, and it is difficult to say that the result reflects the general user's feeling. For this reason, in recent years, an index that can objectively evaluate an evaluation item based on a sense is required.

  Accordingly, an object of the present invention is to provide a bicycle maneuverability evaluation method and a bicycle maneuverability evaluation apparatus for evaluating maneuverability such as “riding comfort” and “steering stability” of a bicycle based on objective indices. To do.

  Applicants can use brain waves that reflect brain activity associated with human emotional changes as an index to objectively evaluate items based on sensation, such as “riding comfort” and “steering stability” As a result of diligent investigation under the prediction, so-called Midα, Fastα, and β waves extracted from electrical signals acquired from electrodes placed on the human head (particularly the back of the head) are used during bicycle operation. It came to the knowledge that the passenger's emotional change was reflected in real time.

  According to the present invention, the spectrum of Midα, Fastα, and β waves extracted from the occupant during the operation of the bicycle is used as a new index such as a ride comfort during the operation of the bicycle.

  A feature of the present invention is a bicycle maneuverability evaluation method for evaluating the maneuverability of the bicycle using a signal that can be obtained from the occupant when the bicycle travels on the road surface. A signal acquisition process for acquiring an electrical signal from electrodes arranged based on the method, and an analysis for executing a signal analysis for extracting spectra of frequency band components corresponding to Midα, Fastα, and β waves from the acquired electrical signal A process, an electrical signal obtained from an electrode arranged in accordance with the International Law 10-20 method on the back of a subject who controls a reference bicycle traveling on a road surface, and a maneuverability of the reference bicycle input by the subject. Classifying the extracted spectrum into one of a plurality of categories for assessing the maneuverability of the bicycle based on a criterion associated with an assessment value to be assessed; A classification step that is summarized in that with.

  According to the features of the present invention, a human “feel” such as operability of a bicycle can be quantitatively represented by a spectrum extracted from an electrical signal acquired from the back of the occupant. Thereby, it is possible to objectively evaluate the maneuverability such as the “riding comfort” of the bicycle, which has been conventionally evaluated as a tester.

  In the characteristics of the present invention described above, in the signal acquisition step, an electrical signal may be acquired from electrodes arranged at the O3 and O4 positions based on the International Law 10-20.

  According to the present invention, a bicycle maneuverability evaluation method and a bicycle maneuverability evaluation device for evaluating maneuverability based on a sense such as “riding comfort” and “steering stability” of a bicycle based on an objective index are provided. Can be provided.

FIG. 1 is a schematic diagram for explaining a tire performance evaluation test performed using a bicycle maneuverability evaluation apparatus. FIG. 2 is a configuration diagram illustrating the bicycle maneuverability evaluation apparatus 1. FIG. 3 is a conceptual diagram for explaining derivation of a mathematical expression indicating a boundary line. FIG. 4 is a flowchart for explaining an evaluation method for evaluating the maneuverability of a bicycle. FIGS. 5 (a) to 5 (d) show an electroencephalogram during normal running in which a predetermined section of an experimental course, which is a flat straight line, is run on a bicycle without load, and during running with a bicycle loaded with a load of 20 kg. It is a figure which shows the result of the significant difference calculated | required by comparing with an electroencephalogram. FIG. 6 is a diagram illustrating a result of principal component analysis. FIG. 7 is a diagram showing the result of principal component analysis. FIG. 8A is a diagram showing the result of the principal component load amount of the subject 1, and FIG. 8B is a diagram showing the result of the principal component load amount of the subject 2.

  Next, a bicycle maneuverability evaluation method and a bicycle maneuverability evaluation apparatus according to the present invention will be described with reference to the drawings. Specifically, (1) Configuration of bicycle maneuverability evaluation device, (2) Description of evaluation method, (3) Action / effect, (4) Test for identifying frequency component that can be an index, (5) Other implementations A form is demonstrated.

(1) Configuration of Bicycle Maneuverability Evaluation Device With reference to FIG. 1, the bicycle maneuverability evaluation device 1 will be described. Specifically, (1-1) a test for evaluating the maneuverability of a bicycle, (1-2) the configuration of the bicycle maneuverability evaluation device, and (1-3) derivation of a mathematical expression indicating a boundary line will be described.

(1-1) Test for Evaluating Bicycle Maneuverability FIG. 1 is a schematic diagram for explaining a test for evaluating the maneuverability of a bicycle performed using the bicycle maneuverability evaluation apparatus 1.

  The bicycle maneuverability evaluation apparatus 1 includes an evaluation apparatus body 10 and a signal acquisition unit 20. The bicycle maneuverability evaluation apparatus 1 acquires an electrical signal that can be acquired from the occupant 3 when the occupant 3 controls the bicycle 100 to be evaluated, extracts a characteristic frequency component from the acquired signal, and analyzes the feature amount. To do. Details of the evaluation apparatus body 10 will be described later.

  The signal acquisition unit 20 acquires an electrical signal from an electrode disposed on the back of the occupant 3. In the bicycle maneuverability evaluation apparatus 1 according to the embodiment, the signal acquisition unit 20 includes an electrode 21 and an electrode 22. The electrode 21 is disposed at the O3 position based on the international law 10-20. The electrode 22 is disposed at the O4 position based on the International Law 10-20. Although not shown, for example, a reference electrode disposed on the earlobe or the like is provided. The difference between the potential at the electrodes 21 and 22 and the potential at the reference electrode is amplified by a differential amplifier. Thereby, noise can be removed and a weak electroencephalogram signal can be accurately extracted.

  The occupant 3 gets on the bicycle 100 with the signal acquisition unit 20 mounted at the O3 and O4 positions. The bicycle maneuverability evaluation apparatus 1 acquires electrical signals that can be acquired from the O3 and O4 positions of the occupant 3 via the signal acquisition unit 20 while the occupant 3 controls the bicycle 100 on a predetermined test course.

(1-2) Configuration of Bicycle Maneuverability Evaluation Device FIG. 2 is a configuration diagram illustrating the bicycle maneuverability evaluation device 1. The bicycle maneuverability evaluation apparatus 1 includes an evaluation apparatus body 10 and a signal acquisition unit 20. The signal acquisition unit 20 includes the electrodes 21 and 22 described above, and is attached to the O3 and O4 positions on the back of the occupant 3. The signal acquisition unit 20 acquires electrical signals from the electrodes 21 and 22 attached to the O3 and O4 positions.

  The evaluation apparatus main body 10 includes a signal input unit 11 to which the electrical signal acquired by the signal acquisition unit 20 is supplied, an analysis unit 12 that analyzes the electrical signal supplied to the signal input unit 11, and an electrical signal generated by the analysis unit 12. And a display unit 13 for displaying the result of the analysis.

  The evaluation apparatus main body 10 also includes an input unit 14 such as a keyboard and a mouse that receives input from the user, and a storage unit 15 including a hard disk drive, a semiconductor memory, and the like.

  The storage unit 15 extracts a reference spectrum of frequency band components corresponding to Midα, Fastα, and β waves from an electrical signal acquired from an electrode arranged on the back of the subject based on the international method 10-20. The reference spectrum is classified into a plurality of classes according to the evaluation value for evaluating the maneuverability of the reference bicycle input by the subject, and a mathematical expression indicating a boundary line for dividing each class among the plurality of classes is stored. Yes.

  The analysis unit 12 performs time frequency analysis of the electric signal according to the elapsed time from the time when the operation is started, and performs multivariate analysis on the frequency component of the electric signal obtained by the time frequency analysis.

  In the present embodiment, the analysis unit 12 includes a frequency analysis calculation unit 121 and a classification analysis unit 122.

  The frequency analysis calculation unit 121 performs time frequency analysis of the electrical signal. The electroencephalogram is roughly classified into four types, δ wave, θ wave, α wave, and β wave, depending on the frequency band. For example, the δ wave is an electroencephalogram having a frequency band of less than 4 Hz. The δ wave does not appear when awake, but frequently appears during deep sleep. The θ wave is an electroencephalogram having a frequency band of 4 Hz or more and less than 8 Hz. Appears frequently during light sleep. The α wave is an electroencephalogram having a frequency band of 8 Hz or more and less than 13 Hz. Appears conspicuously on the occipital side when the eyes are resting, and the appearance is suppressed when the eyes are opened. It is also an index for evaluating relaxation. The β wave is an electroencephalogram having a frequency band of 13 Hz or higher, is dominant in the frontal region and the temporal region, and has little regularity.

  In the present embodiment, frequency analysis is performed to extract the spectrum of frequency components corresponding to so-called Midα, Fastα, and β waves from electrical signals acquired from the O3 and O4 positions. As the frequency analysis, fast Fourier transform, wavelet transform, or the like can be used. In this embodiment, fast Fourier transform is used.

  The classification analysis unit 122 includes an electrical signal acquired from an electrode arranged based on the International Law 10-20 on the back of the subject who controls the reference bicycle traveling on the road surface, and the control of the reference bicycle input by the subject. Based on the evaluation value for evaluating the sex, the extracted spectrum is classified into one of a plurality of categories for evaluating the maneuverability of the bicycle. In the embodiment, the extracted spectrum is classified into a plurality of categories according to the value calculated by the feature value of the extracted spectrum and the mathematical expression indicating the boundary line. This operation is preferably obtained using SVM.

  The display unit 13 displays the category into which the extracted spectrum is classified as an evaluation value of the maneuverability of the bicycle by the occupant.

(1-3) Derivation of a mathematical expression indicating a boundary line An electrical signal acquired from an electrode arranged based on the International Law 10-20 method on the back of a subject who controls a reference bicycle traveling on a road surface, and input by the subject The reference associated with the evaluation value for evaluating the maneuverability of the reference bicycle is an index for maneuverability evaluation based on the sense of the occupant. In the present embodiment, an electrical signal acquired from an electrode arranged based on the International Law 10-20 method on the back of a subject who controls a reference bicycle traveling on a road surface, and the control of the reference bicycle input by the subject. A reference associated with an evaluation value for evaluating sex, that is, a mathematical expression indicating a boundary line, is derived by the following method. In addition, in order to derive | require the numerical formula which shows a boundary line, what has the structure similar to the above-mentioned bicycle maneuverability evaluation apparatus 1 can be used.

  The subject rides on a reference bicycle with an electrode attached to the back of the head and runs on a predetermined course. During this time, an electrical signal is acquired from the subject. For example, when evaluating steering stability, it is preferable to prepare a reference bicycle with high steering stability and a reference bicycle with low steering stability. Have a subject who has run a predetermined course evaluate the control performance of the reference bicycle. For example, the test subject scores the quality of “riding comfort” with five-level evaluation values, and the scoring result is associated with a reference spectrum described later. You may determine the average value of the result of having done the test of multiple times to a reference | standard spectrum. The passenger 3 and the subject may be the same. Moreover, you may use the electrical signal acquired from the some test subject.

  The time frequency analysis of the electrical signal supplied from the signal acquisition unit 20 is performed. Specifically, fast Fourier transform is performed on the acquired electrical signal. After a certain period of time has elapsed from the time when the travel is started, signal analysis is performed to extract spectrums of frequency band components corresponding to Midα, Fastα, and β waves from electrical signals acquired from the O3 and O4 positions.

  Let the extracted spectrum be a reference spectrum. Regarding the reference spectrum, it is preferable to divide the data space using SOM (self-organizing map) and apply the pattern classification method by SVM (support vector machine) to each divided space. It is preferable to apply PCA (principal component analysis) before pattern classification by SVM. Bicycle maneuvering involves complex operations such as steering, pedaling, and balancing, and the response is felt throughout the body. For this reason, since the electroencephalogram measurement accompanying operation | movement is performed, the signal obtained tends to contain a noise component. In addition, since the electrical signal is measured from a plurality of measurement positions, the detected signal becomes multidimensional. By using SOM, PCA, and SVM, dimensions and noise components can be reduced.

  When applying SVM, the extracted spectrum is classified into a plurality of classes. For example, a reference spectrum based on an electric signal obtained by riding a reference bicycle with high steering stability is classified into a class with “high steering stability”. The reference spectrum based on the electric signal acquired at the time of riding the reference bicycle having low steering stability is classified into the “low steering stability” class. For example, as shown in FIG. 3, the reference spectrum is classified into a reference spectrum (black circle) having high steering stability and a reference spectrum (white circle) having low steering stability. FIG. 3 is a conceptual diagram for explaining derivation of a mathematical expression indicating a boundary line.

  By applying SVM, a mathematical expression indicating a boundary line that divides each class among a plurality of classes is derived. As shown in FIG. 3, the mathematical formula indicating the boundary line is a mathematical formula (dotted line passing through a black circle in FIG. 3) passing through the reference spectrum having the smallest distance from the other class among the reference spectra belonging to one class, Among the reference spectra belonging to other classes, an equation passing through the reference spectrum having the smallest distance from one class (a dotted line passing through the white circle in FIG. 3) and an equation passing through the center thereof are defined as an equation indicating a boundary line. There may be one mathematical expression indicating the boundary line, or a plurality of mathematical expressions may be derived.

  For example, when classifying into the “high steering stability” class and the “low steering stability” class, the label of the reference spectrum classified as “high steering stability” is set to “+1”, and “steering stability” The label of the reference spectrum classified as “low in nature” is set to “−1”. Based on the reference spectrum and the label, a parameter representing a mathematical expression indicating the boundary line is calculated. Based on the calculated parameters, a mathematical expression indicating the boundary line is derived.

(2) Description of Evaluation Method for Evaluating Bicycle Ride Comfort Next, an evaluation method for evaluating bicycle ride comfort will be described. Specifically, (2-1) Overall description of the evaluation method, (2-2) Step S1: Processing for obtaining an electric signal from the occupant, (2-3) Step S2: Analysis processing, (2-4) Step S3 : Display processing will be described.

(2-1) Overall Description of Evaluation Method FIG. 4 is a flowchart illustrating the evaluation method. As shown in FIG. 4, the evaluation method for evaluating the riding comfort of a bicycle has steps S1, S2, and S3. In step S <b> 1, an electrical signal is acquired from an electrode disposed on the back of the occupant 3. In step S2, the electrical signal is analyzed, and the spectrum of frequency band components corresponding to Midα, Fastα, and β waves is extracted. Further, the extracted spectrum is classified into one of a plurality of categories based on a mathematical expression indicating a boundary line. In step 3, the classified category is displayed as an evaluation value of the maneuverability of the bicycle by the occupant.

(2-2) Step S1: Process for Acquiring an Electric Signal from an Occupant In step S1, a process for acquiring an electric signal from an occupant will be specifically described. The occupant travels a predetermined course on the bicycle to be evaluated with the electrodes attached to the back of the head (O3, O4). During this time, an electrical signal is acquired from the occupant. For example, while the occupant is driving the bicycle 100, the electric signal may be continuously acquired.

(2-3) Step S2: Analysis Processing In step S2, first, in step S21, time frequency analysis of the electrical signal supplied from the signal acquisition unit 20 is performed. Specifically, fast Fourier transform is performed on the acquired electrical signal. In step S22, after a certain period of time has elapsed from the start of traveling, a signal analysis is performed to extract spectrums of frequency band components corresponding to Midα, Fastα, and β waves from electrical signals acquired from the O3 and O4 positions. To do.

  In step S23, based on the criteria stored in the storage unit 15, the spectrum extracted in step S22 is classified into any of a plurality of categories, and the category into which the extracted spectrum is classified is used for the maneuverability of a bicycle by an occupant. The evaluation value of In the embodiment, using SVM, the extracted spectrum is classified into a plurality of categories based on a mathematical expression indicating a boundary line. Specifically, a value indicating the extracted spectrum is substituted into a mathematical expression indicating the boundary line. As a result, the extracted spectrum is classified into one of a plurality of categories according to the obtained numerical value. The range of a plurality of categories can be arbitrarily determined.

  For example, it is determined by setting the label of the reference spectrum classified as “high steering stability” to “+1” and the label of the reference spectrum classified as “low steering stability” to “−1”. Consider a case where a mathematical expression indicating a boundary line is used. When the result obtained by substituting the value indicating the extracted spectrum into the mathematical expression indicating the boundary line is 0 or more, it is classified into the category “high steering stability”. If the result obtained by substituting the value indicating the extracted spectrum into the mathematical expression indicating the boundary line is less than 0, it is classified into the category of “low steering stability”. In this case, the range of one category is 0 or more, and the range of the other category is less than 0.

  The number of classes into which the reference spectrum is classified and the categories into which the result obtained by the mathematical expression indicating the boundary line is classified are not necessarily the same. For example, in the above case, when the obtained result is −1 to −0.8, “steering stability: 1”, and when the obtained result is −0.8 to −0.6, , “Steering stability: 2”, and when the obtained result is 0.8 to +1, “steering stability: 10” may be used. In this case, the number of categories is 10, and the range of each category is a range that divides −1 to +1 by 0.2.

  When a plurality of mathematical expressions indicating the boundary lines are derived, a value indicating the extracted spectrum is substituted into the mathematical expressions indicating the boundary lines. Based on the mathematical formula indicating the boundary line where the absolute value of the obtained numerical value is the largest, the extracted spectrum is classified into one of a plurality of categories.

  With respect to the extracted spectrum, it is preferable to divide the data space using SOM (self-organizing map) and to apply the pattern classification method by SVM (support vector machine) to each divided space. It is preferable to apply PCA (principal component analysis) before pattern classification by SVM. Bicycle maneuvering involves complex operations such as steering, pedaling, and balancing, and the response is felt throughout the body. For this reason, since the electroencephalogram measurement accompanying operation | movement is performed, a noise component is easy to be contained in the obtained signal. In addition, since the electrical signal is measured from a plurality of measurement positions, the detected signal becomes multidimensional. By using SOM, PCA, and SVM, dimensions and noise components can be reduced, and maneuverability can be evaluated more accurately.

(2-4) Step S3
The evaluation value of the maneuverability is displayed on the display unit 13 with the category into which the extracted spectrum is classified as the evaluation value of the maneuverability of the bicycle by the occupant.

(3) Action / Effect The bicycle maneuverability evaluation apparatus 1 according to the embodiment is based on the occupant's senses such as “riding comfort” and “steering stability” based on the spectrum extracted from the electrical signal acquired from the occupant's back head. It can represent maneuverability. Therefore, the maneuverability based on the occupant's sense can be expressed quantitatively, and the maneuverability of the bicycle can be objectively evaluated.

  Bicycle maneuverability can be objectively evaluated more accurately by acquiring electrical signals from electrodes arranged at the O3 and O4 positions based on the International Law 10-20.

(4) Tests that identify frequency components that can serve as indicators (4-1) Test descriptions Find frequency components of electrical signals that can be used to evaluate maneuverability such as “riding comfort” and “steering stability” of a bicycle Therefore, the following tests were conducted. Hereinafter, the evaluation test will be described.

  Electrical signals that can be detected from the head of the occupant were measured at each measurement position based on the International Method 10-20, and the characteristics of the spectrum obtained by analyzing the electrical signal detected at each position were analyzed.

  The applicant considered that the factor affecting the “stability” felt by the occupant (subject) traveling on the bicycle was the rolling of the bicycle. Furthermore, in addition to rolling, the presence or absence of a load was considered to affect the “stability”. Therefore, the applicant installed an accelerometer in the front part of the bicycle, and detected the acceleration peak in the lateral direction with respect to the traveling direction. In addition, biological information before and after the peak value was acquired and analyzed.

  If the subject's bicycle driver is traveling on a straight road at a low speed, an attempt to maintain the “stability” will help to bring the actual driving state closer to the ideal driving state.

Among the information input to the subject's sensory organs in the process of trying to bring the actual running state closer to the ideal running state while the subject is driving a bicycle, It is thought that.
(1) Visual information for grasping the actual running state (2) Information on the movement of the arm trying to move the steering wheel in the ideal direction (3) Information on the movement of the foot trying to adjust the speed of the bicycle (1 )-(3) information related to the movement of the body, visual information, etc. are processed in the primary motor cortex (parietal lobe) and primary visual cortex (occipital lobe), then other information (for example, saddle Stiffness, running sound, etc.), and finally a feeling of “riding comfort” in the frontal lobe of the subject.

  Therefore, the applicant measured the change in the subject's brain wave before and after the time when the subject's “stability” changed during the bicycle run, and it is considered that “the difference in stability is caused by the brain wave data distribution. The difference was extracted, and it was verified which part of the head had a great influence on the electroencephalogram acquired from the head.

(4-2) Test content (evaluation items)
・ Detection of data distribution of biometric information (electroencephalogram) associated with differences in “stability” ・ Identification of variables (electroencephalogram measurement site, characteristic frequency) estimated as factors of difference in “stability” ・ “Riding comfort” by questionnaire And matching between the result of hearing from the subject and the specified variable (measurement position)
Based on the viewpoints of (1) to (3) described above, the following measurement positions were selected as sites where the electroencephalogram is considered to vary due to the difference in “stability”.
Fp1, Fp2, F3, F4, C3, C4, Cz, O3, O4, T3, T4 based on international law 10-20
(Detection method)
・ Traveling road: A straight road that is wide and can run at a low speed for a sufficiently long time ・ Driving method: Low speed driving from the beginning on a straight road ・ Bicycle condition: Difference in "stability" (weight of pedal) , With or without load)
・ Accelerometer: Installed in the waist of the subject, under the handlebar, and under the saddle (test content)
The subjects were 3 adult men (average age 28 years)
The test subject wore the bicycle with the electroencephalogram measurement electrode mounted at the measurement position. The electroencephalogram was measured while the subject traveled through a predetermined section of the experimental course, which was a flat straight line. After the tenth run, the brain waves were measured while the subject was running in the same section with the load (20 kg) mounted on the front of the bicycle. Similarly, it drove 10 times.

(4-3) Results Short-time Fourier transform (window size: 1 second, shift amount: 0.2 seconds) was applied to the detected brain waves, and the amplitude spectrum of the brain wave signal was calculated. θ band (4-6 [Hz]), slow α band (7-8 [Hz]), mid α band (9-11 [Hz]), fast α band (12-14 [Hz]), β band (14-26) The amplitude spectrum at [Hz]) was used for the analysis.

(4-3-1) Verification of influence of load on electric signal The influence of “the presence or absence of load” on the waveform (especially high frequency band) of the electric signal (electroencephalogram) was verified. In this verification, the brain wave during normal running in which a subject runs on a bicycle without load on a predetermined section of an experimental course that is a flat straight line and the brain wave during running with a bicycle loaded with a load of 20 kg are compared. did. The results are shown in FIG.

  FIGS. 5A to 5C show the results for each subject, and FIG. 5D shows the results of a significant difference test common to the subjects. When there was a difference of 5% or more between the electrical signal detected during normal traveling and the electrical signal detected during load traveling, it was determined that there was a significant difference. A band that was recognized as “significantly different” was marked with a circle.

  From the result of the significant difference test common to the three persons shown in FIG. 5D, the parietal lobe (C3 · C4 · Cz), the temporal lobe (T3 · T4), and the left occipital lobe (O3) Differences common to the subjects were confirmed. Therefore, the electrical signals (electroencephalograms) detected in the parietal lobe (C3, C4, Cz), temporal lobe (T3, T4), and left occipital lobe (O3) are related to “stability”. Is considered to be included.

(4-3-2) Verification of measurement sites important for detection of “stability” by principal component analysis In the analysis using principal component analysis, attention was paid particularly to “rolling” in the operation while riding a bicycle. In each accelerometer, a peak of acceleration transverse to the traveling direction was detected, and an electroencephalogram for 1 second before and after the peak value was analyzed.

  In the analysis, there was a difference common to subjects in the parietal lobe (C3 · C4 · Cz), temporal lobe (T3 · T4), left occipital lobe (O3), which was suggested to be effective in the previous section. confirmed. Therefore, 15-30 [Hz belonging to a high frequency band of electrical signals (electroencephalogram) detected in the parietal lobe (C3, C4, Cz), temporal lobe (T3, T4), and left occipital lobe (O3). The band-pass filter and all the measured EEG channels were used.

  The absolute value of the amplitude of the electroencephalogram was calculated at each sampling point, and an average value for 0.50 seconds in one second was used as the feature amount. FIGS. 6 and 7 show the results of principal component analysis performed on the electroencephalogram when a lateral peak is confirmed in the accelerometer installed in the front of the bicycle. 6 and 7 show the results of two subjects. The round marks in FIGS. 6 and 7 are for normal traveling, and the triangular marks are for load traveling. As a result, it was confirmed that there was a difference in the distribution of electroencephalogram data common to the two subjects.

  In identifying the measurement site that affected the distribution of the components of the electroencephalogram, the principal component loading in the principal component analysis was calculated. Each component of the principal component load amount corresponds to a correlation coefficient between the principal component and the measurement site. From the main component having a high contribution rate, components (7 from PC1 to PC7) having a cumulative contribution rate of 95% were extracted. The principal component load amount is shown in FIGS. 8A and 8B for each subject.

  In FIGS. 8A and 8B, “**” is given to the component having the largest absolute value of correlation, and “*” is given to the component having the second largest absolute value of correlation. In addition, the component for which the correlation between the main component and the measurement site could not be confirmed was expressed in italics.

  From the results shown in FIGS. 8 (a) and (b), it was confirmed that the correlation between the principal component and the measurement site was not confirmed in common with the subject at the location close to the parietal lobe of F3, C3, and Cz. . The occipital lobe portions of O3 and O4 were confirmed to have a common correlation with the upper principal component.

  From the above test, the electrical signal (electroencephalogram) contains components including information related to maneuverability based on the sense, such as “riding comfort” and “steering stability” of the bicycle. It was found that the spectrum can be indexed using the analyzed spectrum. From the results shown in FIG. 5, it was found that the difference in “riding comfort” appears in the intensity of frequency band components corresponding to so-called Midα, Fastα, and β waves.

(5) Other Embodiments As described above, the contents of the present invention have been disclosed through the embodiments of the present invention. However, it is understood that the description and drawings constituting a part of this disclosure limit the present invention. Should not. From this disclosure, various alternative embodiments, examples and operational techniques will be apparent to those skilled in the art.

  In the present embodiment, it has been described that the spectrum obtained by analyzing an occupant's brain wave is matched by pattern recognition. However, multivariate analysis may be used. Multivariate analysis includes multiple regression analysis and discriminant analysis. In addition, discriminant analysis includes factor analysis and principal component analysis. Any of these analysis methods can be applied.

  In the present embodiment, only the electrodes arranged at the O3 and O4 positions are used, but electrodes other than those arranged at the O3 and O4 positions may be used. You may use an electrode different from the electrode arrange | positioned at O3 and O4 position. For example, electrodes arranged at positions T5, T3, PZ, P4, and T6 may be used.

  It goes without saying that the present invention includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specifying matters according to the scope of claims reasonable from the above description.

  DESCRIPTION OF SYMBOLS 1 ... Bicycle maneuverability evaluation apparatus, 3 ... Crew, 10 ... Evaluation apparatus main body, 11 ... Signal input part, 12 ... Analysis part, 13 ... Display part, 14 ... Input part, 15 ... Memory | storage part, 20 ... Signal acquisition part, DESCRIPTION OF SYMBOLS 21 ... Electrode, 22 ... Electrode, 100 ... Bicycle, 121 ... Frequency analysis calculation part, 122 ... Analysis analysis part

Claims (6)

A bicycle maneuverability evaluation method for evaluating the maneuverability of the bicycle using a signal that can be obtained from a passenger when the bicycle travels on a road surface,
A signal acquisition step of acquiring an electrical signal from an electrode arranged on the back of the occupant based on the International Law 10-20,
It detects the peak value of the lateral acceleration with respect to the traveling direction of the bicycle from the acceleration data acquired by the accelerometer placed on the front of the bicycle, from the electrical signal acquired by the signal acquiring unit, the acceleration An analysis step of performing signal analysis for extracting spectra of frequency band components corresponding to Midα, Fastα, and β waves before and after the peak value ;
Evaluation to evaluate the maneuverability of the reference bicycle inputted by the subject and the electrical signal acquired from the electrode arranged based on the International Act 10-20 on the back of the subject who controls the reference bicycle traveling on the road surface And a classification step of classifying the extracted spectrum into any of a plurality of categories for evaluating the maneuverability of the bicycle based on a criterion associated with the value.   2. The bicycle maneuverability evaluation method according to claim 1, wherein in the signal acquisition step, an electrical signal is acquired from electrodes arranged at the O3 and O4 positions based on the International Law 10-20. In the classification step,
Extracting a reference spectrum of frequency band components corresponding to Midα, Fastα, and β waves from an electrical signal acquired from an electrode arranged based on the International Method 10-20 method on the back of the subject,
Classifying the reference spectrum into a plurality of classes according to an evaluation value for evaluating the maneuverability of the reference bicycle input by the subject;
Of the plurality of classes, a mathematical expression indicating a boundary line that divides each class is the reference, and a calculated value calculated by a mathematical expression indicating the boundary line and a value obtained from the extracted spectrum. The bicycle maneuverability evaluation method according to claim 1 or 2, wherein the extracted spectrum is classified into one of a plurality of categories in response. A bicycle maneuverability evaluation apparatus that evaluates the maneuverability of the bicycle using a signal that can be obtained from a passenger when the bicycle travels on a road surface,
An accelerometer installed at the front of the bicycle;
A signal acquisition unit for acquiring an electrical signal from an electrode disposed on the back of the occupant based on the International Law 10-20;
The peak value of the acceleration in the lateral direction with respect to the traveling direction of the bicycle is detected from the acceleration data obtained by the accelerometer , and Midα and Fastα before and after the peak value of the acceleration are detected from the electrical signal acquired by the signal acquisition unit. , And a signal analysis unit that performs signal analysis to extract a spectrum of a frequency band component corresponding to a β wave,
Evaluation to evaluate the maneuverability of the reference bicycle inputted by the subject and the electrical signal acquired from the electrode arranged based on the International Act 10-20 on the back of the subject who controls the reference bicycle traveling on the road surface A classification analysis unit that classifies the extracted spectrum into one of a plurality of categories for evaluating the handling of the bicycle based on a criterion associated with a value;
Bicycle maneuverability evaluation apparatus having   5. The bicycle maneuverability evaluation apparatus according to claim 4, wherein the signal acquisition unit acquires an electrical signal from electrodes arranged at O3 and O4 positions based on the International Law 10-20. In the classification unit, a reference spectrum of frequency band components corresponding to Mid α, Fast α, and β waves is extracted from an electrical signal acquired from an electrode arranged on the back of the subject based on the international method 10-20. The reference spectrum is classified into a plurality of classes according to an evaluation value that evaluates the maneuverability of the reference bicycle input by the subject, and a mathematical expression that indicates a boundary line that divides each class out of the plurality of classes Is the reference, and classifies the extracted spectrum into one of a plurality of categories according to a calculation value calculated by a mathematical expression indicating the boundary line and a value obtained from the extracted spectrum The bicycle maneuverability evaluation apparatus according to claim 4 or 5.

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