WO2018230787A1 - Dispositif et procédé d'analyse de la démarche à l'aide d'un capteur d'accélération porté à la cheville - Google Patents

Dispositif et procédé d'analyse de la démarche à l'aide d'un capteur d'accélération porté à la cheville Download PDF

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WO2018230787A1
WO2018230787A1 PCT/KR2017/013842 KR2017013842W WO2018230787A1 WO 2018230787 A1 WO2018230787 A1 WO 2018230787A1 KR 2017013842 W KR2017013842 W KR 2017013842W WO 2018230787 A1 WO2018230787 A1 WO 2018230787A1
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Prior art keywords
gait
acceleration sensor
patient
feature
ankle
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PCT/KR2017/013842
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English (en)
Korean (ko)
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남윤영
이수환
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순천향대학교 산학협력단
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Publication of WO2018230787A1 publication Critical patent/WO2018230787A1/fr

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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
    • A61B5/112—Gait analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0024—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system for multiple sensor units attached to the patient, e.g. using a body or personal area network
    • 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
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6829—Foot or ankle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Definitions

  • the present invention relates to a gait analysis device and method using an acceleration sensor worn on the ankle, more specifically gait using an acceleration sensor worn on the ankle that can analyze the gait of a patient having difficulty gait.
  • An analysis device and method is provided.
  • the gait may be used as a measure to check basic body condition or to find signs of abnormality in the body. Therefore, by analyzing a person's gait, it is possible to examine the state of the body or detect an abnormality in the body early.
  • the gait analysis method uses image processing, flow sensors, and wearable sensors.
  • Patent Document 1 Korean Patent Publication No. 2012-0085064 (2012.07.31 publication)
  • the present invention has been proposed to solve the above problems, an ankle that is attached to the acceleration sensor to the ankle of the patient having difficulty walking, ankle that can analyze the gait of the patient using the signal obtained from the acceleration sensor It is an object of the present invention to provide a gait analysis device and method using an acceleration sensor worn on the.
  • Gait analysis device using an acceleration sensor worn on the ankle for achieving the above object, the signal according to the gait of the patient from the acceleration sensor included in the band worn on the patient's ankle Receiving unit for receiving; And preprocessing the signal according to the gait of the received patient, extracting a feature from the preprocessed signal, calculating the energy of the gait using the extracted feature, and calculating a correlation coefficient for the gait characteristics.
  • Receiving unit for receiving; And preprocessing the signal according to the gait of the received patient, extracting a feature from the preprocessed signal, calculating the energy of the gait using the extracted feature, and calculating a correlation coefficient for the gait characteristics.
  • an analysis unit for analyzing the gait of the for analyzing the gait of the.
  • the analysis unit a pre-processing unit for removing noise from the signal; And a feature extractor which extracts a feature on a time domain, a feature on a frequency domain, and an energy feature from the signal from which the noise is removed.
  • the preprocessor may remove noise from the signal using a digital low pass filter.
  • the feature extractor may extract a feature on a time domain by calculating a vertical component from the preprocessed signal.
  • the feature extractor may extract a feature in a frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform.
  • the feature extractor may calculate the energy value of the gait by Equation X, and calculate a correlation coefficient for the gait characteristic by Equation Y.
  • x i are values converted into a frequency band using an FFT
  • w is a window size
  • x and y are the values calculated by the energy formula for the walking of the left foot and the right foot
  • cov (x, y) is the covariance of x and y
  • P is a correlation coefficient value of x and y.
  • Step analysis method in the gait analysis device using an acceleration sensor worn on the ankle for achieving the above object, from the acceleration sensor included in the band worn on the patient's ankle
  • Receiving a signal according to a gait Preprocessing the signal according to the gait of the received patient; Extracting features from the preprocessed signal; And calculating the energy of the gait using the extracted features and calculating a correlation coefficient for the characteristics of the gait.
  • the digital low pass filter may be used to remove noise from the signal.
  • Extracting a feature from the preprocessed signal includes: extracting a feature in a time domain by calculating a vertical component in the preprocessed signal; And extracting a feature on a frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform.
  • the energy value of the gait is calculated by Equation X
  • the gait characteristic is calculated by Equation Y.
  • the correlation coefficient can be calculated.
  • x i are values converted into a frequency band using an FFT
  • w is a window size
  • x and y are the values calculated by the energy formula for the walking of the left foot and the right foot
  • cov (x, y) is the covariance of x and y
  • P is a correlation coefficient value of x and y.
  • the gait analysis algorithm can analyze the gait of the patient more accurately.
  • FIG. 1 is a view showing a schematic configuration of a gait analysis system according to an embodiment of the present invention
  • FIG. 2 is a view showing a stance and a swing according to an embodiment of the present invention
  • FIG. 3 is a view showing a schematic configuration of a gait analysis device according to an embodiment of the present invention
  • Figure 4 is a diagram showing the flow of the gait analysis method according to an embodiment of the present invention.
  • FIG. 1 is a view showing a schematic configuration of a gait analysis system according to an embodiment of the present invention
  • Figure 2 is a view showing a stance and a swing according to an embodiment of the present invention
  • Figure 3 is a view of the present invention It is a figure which shows schematic structure of the gait analyzer according to the embodiment.
  • the gait analysis system includes an acceleration sensor 100 and a gait analyzer 200 attached to an ankle of a patient.
  • the patient may be a patient suffering from a neurological disease such as Parkinson's disease.
  • a signal generated by the acceleration sensor 100 may be transmitted to the gait analyzer 200.
  • the network may be a wireless communication such as Bluetooth.
  • the acceleration sensor 100 is for acquiring a signal according to a patient's walking and may be included in a band. Accordingly, the patient may wear the band on the ankle, so that the acceleration sensor 100 may be attached to the ankle of the patient. In this case, as the band is worn on both ankles of the patient, the acceleration sensor 100 included in the band may acquire a signal according to the patient's walking.
  • the signal according to the walking of the patient may be a signal generated according to the walking of the patient.
  • the acceleration sensor 100 may be a very small Attitude Heading Reference System (AHRS) module having a three-axis acceleration sensor, a three-axis gyroscope, and a three-axis geomagnetic sensor.
  • AHRS Attitude Heading Reference System
  • the acceleration sensor 100 may support data update and output speeds up to 1000 Hz, and output a pure acceleration value from which gravity components are removed.
  • the acceleration sensor 100 according to the present embodiment may be EBIMU-9DOFV2 manufactured by E2BOX.
  • the acceleration sensor 100 may include three data output modes, such as an ASCII output mode, a hex (binary) output mode, and a polling output mode. In this embodiment, it is assumed that the ASCII output mode is used as the data output mode.
  • the operating power of the three-axis acceleration sensor 100 may use a 4.5V power supplied from a battery (not shown).
  • a digital low pass fitter designed inside the sensor may be used to remove noise included in the signal output from the acceleration sensor 100.
  • the acceleration sensor 100 since the acceleration sensor 100 is small, it takes up less space and can reduce discomfort for the patient wearing the band.
  • the gait analyzing apparatus 200 may analyze the gait of the patient using a signal according to the walking of the patient received from the acceleration sensor 100.
  • the gait analyzing apparatus 200 includes a receiver 210 and an analyzer 230.
  • the receiver 210 receives a signal according to the walking of the patient from the acceleration sensor 100.
  • the signal according to the walking of the patient may be a signal according to the walking of the patient.
  • the receiver 210 receives a signal according to the walking of the patient from the acceleration sensor 100 included in the band worn on the ankle of the patient.
  • the analysis unit 230 may analyze the gait of the patient using a signal according to the walking of the patient received by the receiver 210.
  • the analyzer 230 preprocesses the signal according to the walking of the patient, extracts a feature from the preprocessed signal, and uses the extracted feature to calculate a correlation coefficient for the energy of the gait and the characteristics of the gait. Calculate to analyze the patient's gait.
  • the analyzer 230 may include a preprocessor 231 and a feature extractor 233.
  • the preprocessor 231 may perform preprocessing by removing noise from a signal according to a patient's walking.
  • the preprocessor 231 may remove noise from the signal using a digital low pass filter.
  • the noise may be noise in a high-frequency noise band.
  • a moving average filter may be used to give an overall smoothing effect of the signal.
  • the signal may be a signal according to the gait of the patient measured by the acceleration sensor 100.
  • the signal can be measured by the acceleration sensor 100 included in the band worn on the left and right ankles of the patient, the signal is transmitted to the gait analysis device 200 through a network to be stored in a separate storage It may be.
  • the gait analysis device 200 may be a smart phone with a gait analysis application. In this case, when the gait analysis device 200 is a smartphone, a signal may be transmitted by Bluetooth communication.
  • the feature extractor 233 may extract a feature from a signal from which noise is removed by the preprocessor 231.
  • the feature extractor 233 may include a feature on the time domain, a feature on the frequency domain, and an energy feature on the signal from which the noise is removed by the preprocessor 231. Can be extracted.
  • the feature extractor 233 may extract a feature on a time domain by calculating a vertical component from a preprocessed signal.
  • the reason for extracting the feature on the time domain is to divide a human's gait into a stance and a swinging step.
  • a human gait consists of a stance and a swing. Stance refers to the foot touching the ground, and swinging refers to the foot being held up until one foot hits the ground again.
  • an event that occurs at the start of a stance phase is called a heel strike
  • an event that occurs at the start of a swing phase (or the end of a stance phase) is heeled off. off and / or toe off.
  • the feature extractor 233 may find a heel off and / or a toe off and a heel strike point, divide the stance and the swing, and the heel off And / or compute the vertical component from the signal obtained by the acceleration sensor 100 to find the toe off and heel strike points.
  • the vertical component may be calculated by Equation 1 below.
  • Mx ', my' and mz ' are the mean values for the intervals sampled on each axis.
  • x ', y', z ' is the acceleration signal value coming from the three-axis acceleration sensor. In this case, it is a vector value of a specific point in the sampled section, and N is the length of the sampled section.
  • the characteristics of the gait such as movement time, stride length, period, speed, etc. may be calculated based on heel off and / or toe off and heel strike.
  • the feature extractor 233 may extract a feature in the frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform (FFT).
  • the feature extractor 233 may convert a calculated vertical component into a frequency band by using a fast Fourier transform in order to check the value of the frequency band represented by gait between normal and abnormal persons. In this case, various frequency characteristics may appear according to the walking style of the person according to the converted result.
  • the feature extractor 233 may calculate an energy value of the gait to estimate a correlation coefficient of the gait of the left and right feet of the patient.
  • the energy value of the gait can be calculated by Equation 2 below.
  • x i is a value converted to the frequency band using the FFT
  • w is the window size
  • the overlapping setting so as not to miss the components of the gait according to the walking characteristics of the patient.
  • the correlation may be calculated by Equation 3 below.
  • x and y are the values calculated by the energy formula for the walking of the left foot and the right foot
  • cov (x, y) is the covariance of x and y
  • P is a correlation coefficient value of x and y.
  • Figure 4 is a diagram showing the flow of the gait analysis method according to an embodiment of the present invention.
  • the gait analyzing apparatus 200 receives a signal according to the gait of the patient from the acceleration sensor 100 included in the band worn on the ankle of the patient (S410).
  • the gait analyzing apparatus 200 preprocesses the signal by using the signal according to the gait of the patient (S420).
  • the gait analyzing apparatus 200 may perform preprocessing by removing noise from a signal according to a patient's walking. In this case, the gait analyzer 200 may remove noise from a signal using a digital low pass filter.
  • the gait analyzing apparatus 200 extracts a feature from a preprocessed signal (S430).
  • the gait analyzer 200 may extract a feature on a time domain and a feature on a frequency domain from a signal from which noise is removed.
  • the gait analyzer 200 may extract a feature on a time domain by calculating a vertical component from a preprocessed signal. In this case, the vertical component may be calculated by Equation 1 described above.
  • the gait analyzing apparatus 200 may extract a feature on the frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform (FFT).
  • FFT fast Fourier transform
  • the gait analyzing apparatus 200 calculates the energy of the gait using the extracted feature and calculates a correlation coefficient with respect to the gait of the gait (S440).
  • the energy value of the gait may be calculated by Equation 2 described above, and the correlation may be calculated by Equation 3 described above.
  • Methods according to an embodiment of the present invention may be implemented in the form of program instructions that may be implemented as an application or executed through various computer components, and may be recorded on a computer-readable recording medium.
  • the computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.
  • Program instructions recorded on the computer-readable recording medium may be those specially designed and constructed for the present invention, and may be known and available to those skilled in the computer software arts.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs, DVDs, and magneto-optical media such as floptical disks. media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.
  • Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.
  • the hardware device may be configured to operate as one or more software modules to perform the process according to the invention, and vice versa.

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Abstract

L'invention concerne un dispositif et un procédé d'analyse d'une démarche à l'aide d'un capteur d'accélération porté à la cheville. Selon un aspect de l'invention, un dispositif d'analyse d'une démarche à l'aide d'un capteur d'accélération porté à la cheville comprend: une unité de réception pour recevoir, en fonction de la démarche d'un patient, un signal d'un capteur d'accélération inclus dans une bande portée à cheville du patient; et une unité d'analyse pour prétraiter le signal reçu en fonction de la démarche du patient, extraire des caractéristiques du signal prétraité, et calculer l'énergie de la démarche et le coefficient de corrélation pour les caractéristiques de la démarche à l'aide des caractéristiques extraites, de façon à analyser la démarche du patient.
PCT/KR2017/013842 2017-06-15 2017-11-29 Dispositif et procédé d'analyse de la démarche à l'aide d'un capteur d'accélération porté à la cheville WO2018230787A1 (fr)

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Cited By (2)

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CN113133761A (zh) * 2020-01-17 2021-07-20 宝成工业股份有限公司 左右步态的判断方法及其分析装置
WO2023067694A1 (fr) * 2021-10-19 2023-04-27 日本電気株式会社 Dispositif de génération de données, système d'apprentissage, dispositif d'estimation, procédé de génération de données et support d'enregistrement

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KR102336580B1 (ko) * 2019-10-30 2021-12-10 한국생산기술연구원 좌우 걸음의 보행 균형도 분석방법
KR102421310B1 (ko) * 2020-07-21 2022-07-14 한국해양대학교 산학협력단 파킨슨 환자의 재활을 위한 스마트 걸음 보조 장치 및 방법

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JP2010005033A (ja) * 2008-06-25 2010-01-14 Panasonic Electric Works Co Ltd 歩行動作分析装置
KR101053491B1 (ko) * 2009-04-07 2011-08-08 (주)휴레브 모션 센서를 이용한 보행주기 검출시스템과 방법
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113133761A (zh) * 2020-01-17 2021-07-20 宝成工业股份有限公司 左右步态的判断方法及其分析装置
CN113133761B (zh) * 2020-01-17 2024-05-28 宝成工业股份有限公司 左右步态的判断方法及其分析装置
WO2023067694A1 (fr) * 2021-10-19 2023-04-27 日本電気株式会社 Dispositif de génération de données, système d'apprentissage, dispositif d'estimation, procédé de génération de données et support d'enregistrement

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