WO2017065241A1 - Dispositif de diagnostic automatisé - Google Patents

Dispositif de diagnostic automatisé Download PDF

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WO2017065241A1
WO2017065241A1 PCT/JP2016/080447 JP2016080447W WO2017065241A1 WO 2017065241 A1 WO2017065241 A1 WO 2017065241A1 JP 2016080447 W JP2016080447 W JP 2016080447W WO 2017065241 A1 WO2017065241 A1 WO 2017065241A1
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patient
feature
motion
posture
automatic
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PCT/JP2016/080447
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English (en)
Japanese (ja)
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三宅　美博
祐樹 廣部
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国立大学法人東京工業大学
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Priority to JP2017545473A priority Critical patent/JP6951750B2/ja
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements

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  • the present invention relates to a pathological diagnosis apparatus for cranial nerve diseases such as Parkinson's disease.
  • Parkinson's disease is a progressive disease that exhibits extrapyramidal signs and is characterized by a lack of dopamine in the brain and a relative increase in acetylcholine. It is one of neurodegenerative diseases and is designated as an intractable disease (specific disease) in Japan. There are more than 150,000 PD patients in the country with only severe cases, and the number is increasing rapidly.
  • ⁇ Symptoms are broadly divided into motor and non-motor, but there is a major feature where significant movement disorders appear. This can be divided into resting tremors (trembling), postural reflex disturbances (postural abnormalities), and gait disturbances such as freezing legs, which are important clues for doctors to diagnose the stage.
  • the Hoehn-Yahr classification is known as an index widely used for diagnosis of PD stage. If a system for automatically diagnosing the stage of PD can be constructed by simply measuring and evaluating such movement disorders in PD patients, it will be a gospel for doctors and patients involved in treatment. Such an automatic diagnosis system is useful not only for PD but also for various cranial nerve diseases with other movement disorders such as stroke and dementia.
  • the present invention has been made in such a situation, and one of the exemplary purposes of an embodiment thereof is an automatic diagnostic apparatus for a cranial nerve disease that can easily and quantitatively or objectively measure a stage, a degree of progression, and the like. Is in the provision of.
  • an apparatus for automatically diagnosing a cranial nerve disease is provided.
  • This automatic diagnostic device focuses on posture, vibration, and walking among physical movements of patients to easily diagnose movement disorders that appear in at least one of them in order to diagnose the stage and progression of PD patients with cranial neurological disorders. And measure quantitatively, and diagnose the disease and progression based on it.
  • FIGS. 5A and 5B are diagrams showing the inclination ⁇ in the left-right direction and the inclination ⁇ in the front-rear direction actually obtained for each young healthy person and PD patient.
  • FIG. 6A is a diagram illustrating another example of sensor arrangement
  • FIG. 6B is a diagram illustrating a forearm and an upper arm.
  • FIGS. 7A to 7D are diagrams in which mean, time, variance, and kurtosis are plotted on a dimension plane as feature vectors of gradients ⁇ and ⁇ .
  • FIGS. 8A to 8D are diagrams showing the trajectories for 30 seconds of the horizontal inclination ⁇ and the front-rear inclination ⁇ obtained for each of the four groups. It is a figure which shows the result of a Kruskal-Wallis test.
  • FIGS. 10 (a) to 10 (c) are graphs showing the values of the average and variance of the back and forth inclinations of the respective groups when sitting, standing and walking.
  • FIG. 11A is a diagram illustrating an example of a classifier for young healthy individuals and PD patients
  • FIG. 11B is a diagram illustrating an example of a mild and severe classifier for PD patients.
  • 12A to 12E are diagrams for explaining vibration measurement.
  • FIGS. 13A and 13B are diagrams showing the results of vibration measurement of PD patients and healthy individuals.
  • FIGS. 14A and 14B are diagrams showing the power spectrum of a resting tremor and its time waveform measured for a healthy person and a PD patient, respectively. It is the figure which plotted the power of 4-6Hz of the seismic war at posture and the resting tremor on a two-dimensional plane. It is a time waveform figure of the acceleration norm of the right index finger measured about PD patient.
  • FIGS. 17A and 17B are diagrams showing power spectra of a resting tremor measured for healthy subjects and PD patients.
  • FIG. 18A is a diagram illustrating an example of a classifier for young healthy individuals and PD patients, and FIG.
  • FIG. 18B is a diagram illustrating an example of a mild and severe classifier for PD patients. It is a figure which shows the angular velocity data of a Z-axis. It is a flowchart of orbit estimation.
  • FIG. 21A is a diagram for explaining the correction of the X-axis angle
  • FIG. 21B is a three-dimensional view of the ankle obtained from the acceleration and angular velocity data during walking by the estimation method according to the embodiment. It is a figure which shows an orbit (for 1 period). It is a figure which shows the track
  • FIG. 1 is a block diagram of an automatic diagnosis apparatus according to an embodiment.
  • This automatic diagnosis apparatus 1 diagnoses a patient with a cranial nerve disease accompanied by a movement disorder, and generates diagnostic data S4 indicating the diagnosis result.
  • the diagnosis data S4 may be an index indicating a stage, a degree of progression, a sign, and the like.
  • the automatic diagnosis apparatus 1 for PD will be described, and the diagnosis data S4 indicates the Hoehn-Yahr classification (1 to 6) related to the PD disease. Shall.
  • FIG. 2 is a diagram for explaining the Hoehn-Yahr classification.
  • 2.5-4 degrees with posture reflex disorder may be classified as severe, and 1-2 degrees without attitude may be classified as mild.
  • diagnostic data S4 indicating mildness may be generated.
  • the automatic diagnosis apparatus 1 includes a motion measurement unit 10, a feature extraction unit 20, and an interpreter 30.
  • the motion measurement unit 10 measures the motion S1 of the patient 2. Specifically, the motion measurement unit 10 measures at least one of (i) posture and (ii) vibration (tremor) among (i) posture, (ii) vibration (tremor), and (iii) walking of the patient 2. To do.
  • the movement S1 can be measured, for example, by one or more sensors 12 attached to the patient 2.
  • the motion measurement unit 10 generates measurement data S2 indicating the motion S1 by converting the output of the sensor 12 into digital data.
  • Various sensors such as an acceleration sensor, a speed sensor, a gyro sensor, and a geomagnetic sensor can be used as the sensor.
  • the measurement data S2 shows a time waveform of the motion S1.
  • the sensor 12 may be wireless or wired.
  • the marker 14 may be attached to one or more specific parts of the patient 2 and observed using a video camera, and the motion S1 may be measured based on the movement of the marker 14. Good.
  • the feature extraction unit 20 extracts a feature value S3 based on the measurement data S2.
  • the feature amount S3 include an average, a variance, a skewness, a kurtosis, and a spectrum with respect to a time waveform of certain measurement data. Or when several measurement data S2 are obtained, it is good also considering those difference, the sum, a product, correlation, etc. as feature-value S3.
  • the type of exercise and the feature amount may be determined according to the disease to be diagnosed. This will be described later.
  • a combination of them can be understood as a vector (hereinafter referred to as a feature vector).
  • the single feature quantity S3 can also be interpreted as a one-dimensional feature vector. Therefore, hereinafter, it is also referred to as a feature vector S3 regardless of the number of feature quantities S3.
  • the interpreter (semantic understanding unit) 30 generates diagnostic data S4 by comparing the calculated feature vector S3 with the database 32.
  • the database 32 can be generated by prior machine learning. That is, measurement data (also referred to as a learning sample) S2 of a young healthy person, a healthy elderly person, and an affected person with a different Hoehn-Yahr classification frequency are collected in advance.
  • the interpreter 30 finds the correlation between the feature vector S3 obtained from the learning sample and the frequency of the Hoehn-Yahr classification. Specifically, in the feature vector space, a hyperplane serving as a boundary of the frequency of Hoehn-Yahr classification is learned, and a discriminator is constructed.
  • the interpreter 30 can determine the frequency of Hoehn-Yahr classification by referring to the database 32 using a pattern classifier such as a support vector machine or principal component analysis.
  • the database 32 may be updated every time in order to reflect the newly measured feature vector S3 in the database 32.
  • the database 32 may be stored in a part of the automatic diagnosis apparatus 1 or a hard disk of a computer associated therewith.
  • the database 32 may be stored on a server connected to the automatic diagnosis apparatus 1 via a network.
  • the automatic diagnosis apparatus 1 may be mounted using a cloud computing architecture. For example, part or all of the processing of the feature extraction unit 20 and the interpreter 30 may be executed by a server on the cloud.
  • the feature extraction unit 20 and the interpreter 30 can be configured by a computer, that is, they can be a combination of hardware such as CPU and memory and software.
  • FIG. 3 is a block diagram of the automatic diagnosis apparatus 1.
  • the patient 2 is shown inside the motion measurement unit 10, but this is for convenience of explanation, and it goes without saying that the patient 2 is not a component of the automatic diagnostic apparatus 1.
  • the motion measurement unit 10 measures (i) posture, (ii) vibration, and (iii) walking as the motion of the patient 2, and generates measurement data S2A to S2C indicating each of them.
  • the measurement data S2 is measured by the wearable sensor 12.
  • a small 6-axis sensor acceleration 3 axes and angular velocity 3 axes
  • the time resolution is 100Hz or more
  • the acceleration range is ⁇ 2g / 16bit or more
  • the angular velocity range is ⁇ 250dps / 16bit or more.
  • the body translational motion and the inclination with respect to the direction of gravitational acceleration are evaluated from the acceleration information, and the rotational motion is evaluated from the angular velocity information.
  • the measurement data S2 obtained in the motion measurement unit 10 is input to a computer (that is, the feature extraction unit 20 and the interpreter 30) by wire or wireless, and data analysis is performed online or offline. Note that the sensor mounting position and the feature value extraction method differ between posture measurement, vibration measurement, and walking measurement, and will be described in detail later.
  • the feature extraction unit 20 generates feature amounts S3A to S3C for each of the measurement data S2A to S2C. Specifically, the feature extraction unit 20 extracts feature amounts S3A to S3C from motions such as posture, vibration, and walking estimated by an acceleration integration system, an angular velocity integration system, a correction algorithm, and the like. These feature amounts S3A to S3C are input to the interpreter 30 as a feature vector S3.
  • the interpreter 30 refers to the database 32, determines a hyperplane (frequency threshold) in the vector space based on machine learning, and determines the stage based on the hyperplane and the feature vector S3 from the feature extraction unit 20. Classify.
  • Posture evaluation focuses on postural reflex disorder in PD patients. This is a symptom that the vertical body axis is tilted back and forth or left and right when standing, and mainly uses angle information about the axis of gravity acceleration obtained from a group of acceleration sensors mounted in the body axis direction. It is possible to use the inclination (gradient) in the front-rear direction, the inclination in the left-right direction, their spatial correlation, temporal variation, and the like as feature quantities.
  • FIGS. 4A to 4D are diagrams for explaining posture measurement.
  • 4A and 4B show an example of sensor arrangement.
  • sensors 12_1 and 12_2 are mounted 10 cm below C7 from the vertebra and L4, respectively.
  • C7 is the portion that protrudes most behind the neck when the head is lowered, and mainly detects the state of the back.
  • L4 is the intersection of the Jacobi line and the lumbar vertebra that connects the upper ends of the hip bones, and mainly detects the state of the waist.
  • these combinations also have an advantage that it is easy to determine the attachment position when the sensor 12 is attached to the patient 2.
  • the posture can be evaluated by a combination of the front-rear direction inclination ⁇ and the left-right direction inclination ⁇ .
  • the forward / backward inclination ⁇ is expressed by the following equation.
  • the X-axis represents the right hand direction of the patient
  • the Y-axis represents the forward direction
  • the Z-axis represents the vertical direction
  • the subscripts x, y, and z represent components in the respective directions.
  • FIG. 6A is a diagram illustrating another example of sensor arrangement.
  • at least 11 sensors are required for the upper body only, and at least 17 sensors are required for the lower body.
  • FIG. 6B shows the forearm and the upper arm.
  • the X axis is taken from the shoulder to the elbow, the elbow to the hand, and the Y axis is taken from the elbow head as shown in the figure.
  • rolls, pitches, and yaws with rotation angles in the counterclockwise directions of the upper arms X, Y, and Z are ⁇ u , ⁇ u , and ⁇ u , respectively, and ⁇ f , ⁇ f , and ⁇ f are the front arms, respectively. .
  • Pitch angle theta facc determined from the acceleration is expressed by equation (2).
  • a x g x0 cos ⁇ u + e x
  • the posture feature amount will be described.
  • Various features can be considered, but as an example here, as noted above, when focusing on the body axis gradient ⁇ in the front-rear direction and the body axis gradient ⁇ in the left-right direction, the first order statistics (Average), second order statistics (variance), third order statistics (distortion), and fourth order statistics (kurtosis) can be used as feature quantities.
  • 7A to 7D are diagrams in which mean, time, variance, and kurtosis are plotted on a two-dimensional plane as feature vectors of gradients ⁇ and ⁇ .
  • time series information For example, by looking at the change in the inclination of the body axis during walking, the influence due to fatigue can be seen.
  • a feature vector may be formed using any combination of the average value of gradient ⁇ , variance, kurtosis, average value of gradient ⁇ , variance, and kurtosis, and machine learning may be performed.
  • each of the above measurements is an example of a statistical feature quantity based on gradient measurement at one point, but further feature quantities can be defined by extending to spatial correlation or temporal correlation. Is possible.
  • spatial characteristics can be taken into account if sensors are attached to a plurality of locations and motion is measured.
  • PD patients are known to exhibit unilateral parkinsonism at Hoehn-Yahr classification once and bilateral parkinsonism at twice. Therefore, it is possible to distinguish between the right half and the left half by spatially characterizing the difference.
  • time is measured continuously, it is possible to take into account characteristics related to time variation. For example, when the body is tilted, feedback is applied so that the body is returned to its original position in a short time in order to correct it, so that a periodic movement can be found on the time axis.
  • PD patients are poor in such a response, and it is known that the time correlation in the fluctuation of movement is low when it becomes severe. Therefore, evaluation of an autocorrelation function, cross-correlation function, or self-similarity is also effective as a feature quantity.
  • the posture was measured under the following three conditions. ⁇ Sitting ⁇ Standing ⁇ Walking
  • a feature amount that has confirmed a significant difference between groups can be an index for quantitative evaluation of posture abnormalities in PD.
  • FIGS. 8A and 8B are diagrams showing the trajectories for 30 seconds of the horizontal inclination ⁇ and the forward / backward inclination ⁇ obtained for each of the four groups.
  • FIGS. 8A and 8B it can be seen that a healthy person regardless of age has an inclination angle close to 0 degrees in the vicinity of the origin, that is, in the front-rear direction and the left-right direction.
  • the mild PD patient in FIG. 8C is located in a region away from the origin, and it can be confirmed that the body axis is greatly inclined in the front-rear direction and the left-right direction. The tendency for PD patients is even stronger. Further, it can be seen that the fluctuations (range) of the gradients ⁇ and ⁇ are very small and stable in normal subjects, but are greatly fluctuating in PD patients.
  • FIG. 9 is a diagram showing the results of the Kruskal-Wallis test. The p-value is plotted with 4 ranks. As is apparent from FIG. 9, among the 150 feature values, 41 features such as dispersion of the back and forth and right and left tilt angles when sitting, dispersion of the back when standing, and dispersion of the back and forth of the back when walking A significant difference between the groups in the amount could be confirmed.
  • FIGS. 10 (a) to 10 (c) are graphs showing the values of the average and variance of the back and forth inclinations of the respective groups when sitting, standing and walking.
  • FIG. 10A shows the average value and variance of the inclination ⁇ u (t) in the front-rear direction of the back when sitting.
  • FIG. 10B shows the average value and variance of the inclination ⁇ u (t) in the front-rear direction of the back when standing.
  • FIG. 10C shows the average value and the variance of the inclination ⁇ u (t) in the front-rear direction of the back during walking.
  • the above experimental results support that the automatic diagnosis apparatus 1 according to the embodiment is effective in quantitative evaluation of posture abnormalities in PD.
  • the configuration of the classifier that distinguishes the severe PD group from the mild PD group is limited due to the number of samples, but by increasing the number of samples and selecting the feature vector appropriately, the Hoehn-Yahr classification Diagnosing 1-5 degrees is realistic enough.
  • Experiment 2 has the same conditions as Experiment 1, but in Experiment 2, participants are classified into three groups. ⁇ Severe PD patients (13) ⁇ Mild PD patients (15) ⁇ Young healthy subjects (7)
  • FIG. 11A is a diagram showing an example of a classifier for young healthy individuals and PD patients
  • FIG. 11B is a diagram showing an example of a mild and severe classifier for PD patients
  • FIG. 11A is a graph plotting the relationship between the dispersion of the tilt of the back and forth of the back when standing and the difference between the average values of the tilts of the back and left and right obtained when standing and sitting as feature vectors. It is. According to the classifier constructed by SVM, it is possible to diagnose young healthy persons and PD patients with a certainty of 72.2%.
  • FIG. 11B is a graph plotting the relationship between the difference between the average values of the back and front inclinations obtained during standing and sitting and the dispersion ratio of the waist inclination when standing as a feature vector. It is. According to the classifier constructed by SVM, it is possible to diagnose severe PD patients and mild PD patients with a certainty of 71.4%. The certainty of diagnosis can be further increased by increasing the dimension of the feature vector.
  • Vibration evaluation we focus on the resting tremor of PD patients. This is a symptom in which hands and fingers spontaneously tremble when resting without voluntary movement or the like, and vibration information obtained from a plurality of acceleration sensors that are attached to the hand, fingers, and parts that easily vibrate may be mainly used. In addition to the vibrations that have been observed in the 4-6 Hz band, which has been attracting attention in the past, it is possible to pay attention to the lower and higher frequency bands, as well as their spatial and temporal correlations. .
  • the time variation may be measured.
  • the vibration may be spectrally analyzed and separated for each frequency band, and the respective characteristics may be examined.
  • FIGS. 12A and 12B show an example of the arrangement of the sensor 12B. Although various positions are conceivable as the mounting site of the sensor 12B, it may be fixed to the upper surface of the distal phalanx of the index finger as shown in FIG. Or you may fix to the back of a hand, as shown in FIG.12 (b).
  • FIGS. 12C to 12E illustrate postures at the time of measuring the earthquake.
  • FIG. 12 (c) shows a measurement of a resting tremor
  • FIG. 12 (d) shows a posture seismic measurement
  • FIG. 12 (e) shows an intentional tremor measurement.
  • a resting seismic battle the elbow and back of the hand are placed on the armrest, and the measurement is performed in a natural shape with the palm up.
  • the postural seismic battle measure with the palm down and the arm in front horizontally.
  • an intention tremor the tremor during the finger-nose test will be measured.
  • FIGS. 13A and 13B are diagrams showing the results of vibration measurement of PD patients and healthy individuals.
  • the sensor 12B is attached to the distal phalanx of the index finger, and the time variation of the acceleration norm is shown for 4 seconds.
  • the light gray is the index finger on the right hand
  • the black line is the index finger on the left hand.
  • significant fingertip shaking is observed in PD patients.
  • it is a typical symptom of unilateral parkinsonism that occurs only in the right hand and is likely to occur in Hoehn-Yahr classification once. On the other hand, such vibration does not occur in healthy persons. In other words, it was confirmed that vibration measurement is very useful for frequency classification of PD patients.
  • FIGS. 14A and 14B are diagrams showing the power spectrum of a resting tremor and its time waveform measured for a healthy person and a PD patient, respectively. As shown in FIG. 14B, it can be seen that the vibration power of the PD patient is strong in the 4 to 6 Hz band. Therefore, in this example, power of 4 to 6 Hz is used as a vibration feature.
  • FIG. 15 is a diagram in which the 4 to 6 Hz powers of the post-posture and resting tremors are plotted on a two-dimensional plane.
  • the 14 PD patients have increased power of resting and postural tremors, but 7 healthy individuals are distributed near the origin. This means that the vibration power in the 4 to 6 Hz band is effective in discriminating both groups.
  • FIG. 16 is a time waveform diagram of the acceleration norm of the right index finger measured for a PD patient. It is also clear that a burst phenomenon is specifically observed in the resting tremor of PD patients on the lower frequency side (period 1-5 seconds, that is, 0.2-1 Hz) than the previous 4-6 Hz. It was. Specifically, as shown in FIG. 16, a burst phenomenon having a period of several seconds is observed. This phenomenon is regarded as a periodic variation of the amplitude of vibration and is one of important feature quantities.
  • 17 (a) and 17 (b) are diagrams showing the power spectrum of a resting tremor measured for healthy subjects and PD patients.
  • Healthy individuals and PD patients have different degrees of power attenuation with respect to frequencies in the high-frequency region, and it is observed that the power spectrum decreases linearly in the 10-40 Hz band. It will be an important feature in distinguishing In other words, healthy individuals have high fractal characteristics, whereas PD patients have low fractal characteristics.
  • FIG. 17B there is a case where a peak is shown in the range of 30 to 40 Hz, and it is also useful to use the power in this band as the feature amount.
  • All of the above measurements are examples of feature values calculated from vibration measurements at one point of the fingertip. However, it is possible to define additional feature values by extending to spatial correlation and temporal correlation. become.
  • spatial characteristics can be taken into account by measuring finger vibrations at multiple points.
  • PD patients are known to exhibit unilateral parkinsonism at Hoehn-Yahr classification 1 and bilateral parkinsonism at 2 degrees. Therefore, it is possible to distinguish between the right half and the left half by spatially characterizing the difference.
  • the vibration of the seismic war is a dynamic motion that changes with time, it is possible to take into account the characteristics related to time fluctuation. It is known that when PD patients become severe, the temporal correlation in movement fluctuation is low. Therefore, evaluation of autocorrelation function, cross-correlation function, or self-similarity is also effective.
  • stationary tremor is known as a characteristic symptom of PD. For this reason, it is possible to determine whether the vibration is generated only when stationary or whether the vibration is generated regardless of the posture by looking at the ratio of the vibration power when the posture is stationary.
  • Fig. 18 (a) is a diagram showing an example of a classifier for young healthy individuals and PD patients
  • Fig. 18 (b) is a diagram showing an example of a mild and severe classifier for PD patients.
  • Fig. 18 (a) is a plot of the 4-6Hz band power and the 30-45Hz band power of the stationary tremor as feature vectors. According to the classifier constructed by SVM, it is possible to diagnose young healthy individuals and severe PD patients with a probability of 75.0%.
  • frequency bands (4 to 6 Hz, 30 to 45 Hz) are merely examples, and it can be seen that a discriminator can be configured by using powers of a plurality of different frequency bands as feature vectors.
  • Fig. 18 (b) is a plot of the feature vectors of the ratio of stationary and postural tremors in the 4-6 Hz band, and the ratio of stationary seismic and postseismic tremors in the entire area. is there. According to the classifier constructed by SVM, it is possible to diagnose young healthy individuals and mild PD patients with a certainty of 73.3%.
  • the frequency band (4 to 6 Hz) is merely an example, and it can be seen that the discriminator can be configured by using the power ratio of a predetermined frequency band and the power ratio of the entire band as feature vectors.
  • ankle trajectory information obtained from a group of acceleration and angular velocity sensors attached to the ankle, knee, waist, etc. is mainly used.
  • the method of estimating the trajectory can be divided into two stages: a stage in which continuous walking data is divided for each period, and a stage in which the trajectory is estimated in each period. Details of each are shown below.
  • FIG. 19 is a diagram showing the angular velocity data of the Z axis. Since walking is a periodic motion, the acceleration and angular velocity data acquired from the sensor group shows a periodic pattern. Therefore, the measured data is divided into one period. It is desirable that the dividing point be in a stable state where the foot is grounded and the angular velocity is close to zero. This is because it is easy to assume an initial value at the time of integration. By dividing the walking for each cycle, it is possible to reduce an accumulated error when integrating acceleration and angular velocity. Furthermore, the feature quantity of each cycle can be extracted efficiently.
  • the moving average section is a plurality of points before and after the start point of each cycle, and can be, for example, 5 before and after, for a total of 11 points.
  • the steady component that is, the gravity component can be taken out, and the initial angles of the Y axis and the Z axis can be estimated.
  • the initial angle is assumed to be zero and will be corrected later.
  • the attitude of the sensor is estimated by integrating the angular velocities ⁇ (i) of the respective axes as in equation (5) (S102).
  • the initial value of the angle at the time of integration is obtained by the equation (4).
  • the sensor posture T is estimated based on the angle of each axis (S104).
  • the posture T is represented by a three-column matrix with the x-axis, y-axis, and z-axis as columns.
  • the acceleration a at each time i is decomposed into a traveling direction ⁇ 1 , a vertical direction ⁇ 2 , and a side surface direction ⁇ 3 by matrix calculation using the estimated sensor posture T (S 106).
  • the initial value of the velocity in each direction is assumed to be zero, and the starting point of each cycle is set as the origin for the position.
  • the initial value of the velocity in each direction is assumed to be zero, and the starting point of each cycle is set as the origin for the position.
  • the stable state at the time of ground contact not only in the vertical and horizontal directions but also in the longitudinal direction, it is sufficiently smaller than the swinging motion of the foot, so it is approximated to zero.
  • double integration of equations (6) and (7) is performed for each direction to estimate the ankle trajectory during walking.
  • the weight corresponding to the distance from the start point / end point is calculated for the two waveforms obtained by integrating from both the start point and end point of each period (8) , (9), and a weighted average is taken according to equation (10).
  • i indicates the time in each period (that is, what sampling point), and indicates the total number of samples in each period (that is, how many times ⁇ t is the period).
  • the parameter m is preferably about 0.1.
  • the initial value must be set when integrating from the reverse direction (direction to return the time axis). Therefore, the velocity and position are similarly set to 0, and the initial value of the angle is the same as the angle at the start point of the next cycle.
  • w 1 1 ⁇ w 2 (8)
  • w 2 1 / ⁇ 1 + exp ⁇ m (i ⁇ T / 2) ⁇ (9)
  • V w1 ⁇ V fwrd + w2 ⁇ V back ... (10)
  • the integration in the direction in which the time is advanced from the start point of the cycle and the integration in the direction in which the time is returned from the end point of the cycle is the coefficient w 1. , W 2 and adding them, the influence of errors can be reduced.
  • FIG. 21A is a diagram for explaining correction of the X-axis angle. If ⁇ is inclined in the initial posture, a straight line connecting the origin and the end point is inclined from the traveling direction as shown in FIG. Accordingly, the position in the side surface direction is obtained without taking the above-described cumulative error countermeasure, and the trajectory is rotated and corrected so that the end point coincides with the traveling direction in the side surface-traveling direction plane (S110). The rotation of the trajectory can be performed by matrix calculation.
  • FIG. 21B is a diagram showing an ankle three-dimensional trajectory (for one cycle) obtained from acceleration and angular velocity data during walking by the estimation method according to the embodiment.
  • the front-rear direction is the stride
  • the left-right direction is the swing width
  • the up-down direction is the foot lift amount.
  • Paying attention to these mean values (primary statistics) and variance (secondary statistics) PD patients have the characteristics that the mean value is small in both stride and lift, and the variance (trajectory fluctuation) is large. It was done. In normal subjects, the opposite was true, and the average value was large and the variance was small for both stride and lift. This means that the feature amount is effective in separating both groups. This is an important finding for automatic diagnosis of the stage of PD patients.
  • spatial characteristics can be taken into account by measuring at multiple points instead of just one ankle.
  • PD patients are known to exhibit unilateral parkinsonism at Hoehn-Yahr classification once and bilateral parkinsonism at twice. Therefore, it is possible to distinguish between the right half and the left half by spatially characterizing the difference.
  • the hip and knee trajectories may be measured. It is particularly useful to combine an ankle track and a waist track.
  • FIG. 23 is a diagram for explaining a feature amount related to a walking trajectory.
  • the horizontal axis represents the front-rear direction
  • the vertical axis represents the height direction
  • one cycle of walking is shown.
  • Split points (Split Points) 1, 2 and 3 indicate the heel takeoff, the vertical maximum point, and the foot swing start point, respectively.
  • Feature quantity 1 Advancing direction displacement at division point 1
  • Feature quantity 2 Advancing direction displacement at division point 2
  • Feature quantity 3 Advancing direction displacement at dividing point 3
  • Feature quantity 4 Advancing direction displacement at dividing point 4
  • Feature quantity 5 Displacement in the traveling direction at the dividing point 5
  • Feature 6 Displacement in the traveling direction at the dividing point 6
  • 24 (a) and 24 (b) are diagrams showing factor loadings of the first principal component and the second principal component in the principal component analysis.
  • the contribution ratio of each main component was 48.8% for the first main component and 30.2% for the second main component.
  • the cumulative contribution rate is 79%.
  • the first principal component tended to have a large factor loading of feature amounts 4-6.
  • the second principal component is contributed by the feature quantities 1 to 3. It can be said that the first principal component is the amount of the traveling direction component of the walking trajectory, and the second principal component is the amount of the vertical direction component.
  • the cumulative contribution rate is 79.0%, it can be said that the six feature amounts obtained from the walking trajectory can be sufficiently reduced to the two-dimensional feature space.
  • FIG. 25 is a diagram showing a feature space in which the walking state of each participant is plotted.
  • the horizontal axis indicates the first main component, and the vertical axis indicates the second main component.
  • FIG. 26 (a) shows the results of applying SVM to the mild PD group and the healthy elderly group.
  • the black solid line is the classification boundary.
  • the accuracy of each classifier by 10-fold cross validation was 92.6%.
  • FIG. 26B is a diagram showing the results of performing SVM on the mild PD group and the severe PD group. The accuracy of each classifier by 10-fold cross validation was 76.8%.
  • the automatic diagnosis apparatus 1 is a simple system in which a sensor is simply attached to an ankle, and is a simple method of measuring walking.
  • the fact that the method was able to classify with high accuracy is evidence that the walking trajectory is effective for PD diagnosis support.
  • the classification accuracy of the mild PD group and the severe PD group was 76.8%. This is because the defined feature space is not appropriate for capturing the PD posture reflex disturbance, and there is room for improvement.
  • the automatic diagnosis apparatus 1 uses a small sensor to realize measurement that does not limit the environment. Therefore, it is expected to be used in everyday environments such as homes. At this time, it is assumed that the user himself / herself uses it in the absence of an expert, and the analysis result must be fed back in an easy-to-understand form. Therefore, it is considered that visual information such as figures is intuitive and easy to understand, not numerical values such as feature quantities and indices, so that the user can grasp his / her walking state.
  • feature vectors are constructed based on several feature quantities.
  • the more feature quantity the higher the classification accuracy, but the calculation cost becomes high. It can be considered that the higher the correlation between different feature amounts, the greater the amount of information when combining them. Therefore, feature quantities are selected and reconfigured as necessary using principal component analysis. This makes it possible to reduce the calculation cost without greatly reducing the classification accuracy. In addition, it is possible to improve performance such as diagnostic accuracy by constructing an appropriate feature vector according to the target disease or application problem.
  • the database 22 is constructed by collecting measurement data of young healthy persons, healthy elderly persons, and affected patients. Machine learning is performed based on the information. As a result, it is possible to construct a discriminator for separating the presence or absence and severity of a disease. By using this discriminator, it becomes possible to classify the measurement data of a target whose disease presence or severity is not clear, and automatic diagnosis can be realized.
  • FIG. 28 is a diagram illustrating an example of a classifier that classifies young healthy individuals and PD patients constructed using SVM based on fingertip vibration measurement data.
  • the automatic diagnosis apparatus 1 can distinguish a healthy person and a mild PD patient with high accuracy, it greatly contributes to the treatment of a PD disease patient.
  • the automatic diagnosis apparatus 1 it is possible to realize an automatic diagnosis system of severity with PD disease as an example.
  • This system can be applied not only to automatic diagnosis but also to other applications.
  • One of them is drug efficacy evaluation using quantitative evaluation of symptoms.
  • By measuring and analyzing using this system before and after taking medicine it is possible to confirm how effective the medicine is for which symptom.
  • this system on a daily basis, it becomes possible to grasp whether the patient has sustained drug efficacy, and it is possible to suggest the timing of medication by this system.
  • the present invention is also effective for early diagnosis of a disease that progresses gradually such as a neurodegenerative disease, and is applied to the diagnosis of dementia as described below. Is also expected. ⁇ Alzheimer type dementia ⁇ Lewy body type dementia ⁇ Cerebrovascular type dementia ⁇ Normal pressure hydrocephalus type dementia
  • the present invention can also be used for applications that evaluate the degree of improvement in the rehabilitation process. Specifically, the following are exemplified. Rehabilitation of movement disorders due to hemiplegia of stroke Rehabilitation of movement disorders due to orthopedic diseases such as osteoarthritis
  • the present invention can be used for pathological diagnosis of cranial nerve diseases.

Abstract

L'invention concerne un dispositif de diagnostic automatisé (1) pour des troubles des nerfs crâniens. Une unité de mesure de mouvement (10) comprend des capteurs (12) attachés à un patient (2) et mesure des mouvements, c'est-à-dire la posture et/ou les vibrations du patient (2), sur la base de sorties provenant des capteurs (12). Une unité d'extraction de caractéristiques (20) extrait des quantités caractéristiques de mouvements sur la base des données mesurées (S2) à partir de l'unité de mesure de mouvement (10). Sur la base des quantités caractéristiques, un interpréteur (30) génère des données de diagnostic indiquant les résultats d'un diagnostic de trouble des nerfs crâniens.
PCT/JP2016/080447 2015-10-14 2016-10-13 Dispositif de diagnostic automatisé WO2017065241A1 (fr)

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JP2022061625A (ja) * 2020-10-07 2022-04-19 国立大学法人宇都宮大学 運動障害に対する薬効評価システム
EP4046570A1 (fr) * 2021-01-08 2022-08-24 Fujitsu Limited Système de détermination d'état de la marche, procédé de détermination d'état de la marche, et programme de détermination d'état de la marche
JP7141610B1 (ja) 2022-01-31 2022-09-26 株式会社ワコール スキンモデルから骨格に基づく姿勢を統計的に分析するプログラム、装置及び方法
WO2022208700A1 (fr) * 2021-03-30 2022-10-06 株式会社Medicolab Système de prédiction de diagnostic utilisant un dispositif numérique, dispositif d'apprentissage, programme d'ordinateur, procédé de prédiction de diagnostic et procédé de génération/mise à jour de modèle de prédiction
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JP2019047952A (ja) * 2017-09-11 2019-03-28 国立研究開発法人国立精神・神経医療研究センター 臨床評価装置、臨床評価方法および臨床評価プログラム
WO2019181483A1 (fr) * 2018-03-23 2019-09-26 パナソニックＩｐマネジメント株式会社 Dispositif d'évaluation de fonction cognitive, système d'évaluation de fonction cognitive, procédé d'évaluation de fonction cognitive et programme
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JP7421703B2 (ja) 2020-02-21 2024-01-25 公立大学法人会津大学 分類プログラム、分類装置及び分類方法
JP2022061625A (ja) * 2020-10-07 2022-04-19 国立大学法人宇都宮大学 運動障害に対する薬効評価システム
EP4046570A1 (fr) * 2021-01-08 2022-08-24 Fujitsu Limited Système de détermination d'état de la marche, procédé de détermination d'état de la marche, et programme de détermination d'état de la marche
WO2022208700A1 (fr) * 2021-03-30 2022-10-06 株式会社Medicolab Système de prédiction de diagnostic utilisant un dispositif numérique, dispositif d'apprentissage, programme d'ordinateur, procédé de prédiction de diagnostic et procédé de génération/mise à jour de modèle de prédiction
WO2022244222A1 (fr) * 2021-05-21 2022-11-24 日本電気株式会社 Dispositif d'estimation, système d'estimation, procédé d'estimation et support d'enregistrement
WO2022269985A1 (fr) * 2021-06-22 2022-12-29 ソニーグループ株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et programme
JP2023111654A (ja) * 2022-01-31 2023-08-10 株式会社ワコール スキンモデルから骨格に基づく姿勢を統計的に分析するプログラム、装置及び方法
JP7141610B1 (ja) 2022-01-31 2022-09-26 株式会社ワコール スキンモデルから骨格に基づく姿勢を統計的に分析するプログラム、装置及び方法

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