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WO2023108315A1 - Procédé d'analyse et de détection d'anomalie de démarche basé sur un capteur de pas portable - Google Patents

Procédé d'analyse et de détection d'anomalie de démarche basé sur un capteur de pas portable Download PDF

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Publication number
WO2023108315A1
WO2023108315A1 PCT/CN2021/137344 CN2021137344W WO2023108315A1 WO 2023108315 A1 WO2023108315 A1 WO 2023108315A1 CN 2021137344 W CN2021137344 W CN 2021137344W WO 2023108315 A1 WO2023108315 A1 WO 2023108315A1
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Prior art keywords
gait
detection method
analysis
method based
angular velocity
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Ceased
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PCT/CN2021/137344
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Chinese (zh)
Inventor
孙方敏
唐嘉辉
李烨
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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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—Measuring 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 or mobility of a limb

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  • the invention relates to the technical field of computer applications, and more particularly to a gait analysis and abnormal detection method based on a wearable gait sensor.
  • the existing gait abnormality evaluation method based on mobile wearable devices adopts the traditional linear calculation method when calculating gait characteristics: through Kalman filtering, the acceleration signal is integrated twice to obtain gait characteristic parameters such as velocity and displacement
  • gait characteristic parameters such as velocity and displacement
  • the noise in the signal will be amplified, resulting in large errors in the calculated velocity and displacement, which will affect the gait analysis results.
  • the present invention provides a gait analysis and anomaly detection method based on a wearable gait sensor. Identify and alarm based on the collected images.
  • a gait analysis and anomaly detection method based on a wearable sensor including the following steps:
  • the inertial sensor is the built-in accelerometer in the mobile wearable device, which acquires the gait acceleration signal, obtains the angular velocity information through the gyroscope, and places the sensor on both sides of the left and right ankles to obtain the raw data of the gait , set the sampling frequency to 120HZ and the sampling mode to offline mode during the collection process.
  • the measured values output by the sensors all use the sensor coordinate system as the reference system, and in the process of gait analysis, the data in the geographic coordinate system need to be used for calculation, and the quaternion method is used for calculation.
  • Coordinate system conversion find out the relative relationship between the two coordinate systems and realize the coordinate system conversion, in order to obtain the quantity with actual physical meaning in the geographic coordinate system, the main reason for the data difference between the coordinate systems is that it is very difficult to wear the sensor when wearing the sensor. It is difficult to ensure that the axis is consistent with the geographic coordinate system, and three-dimensional space conversion is required between the two coordinate systems, so the coordinate conversion can be performed by the quaternion method.
  • the detection of the gait cycle is the basis of gait analysis, and the accurate division of the gait cycle has a great influence on the subsequent gait analysis results, and the angular velocity data has a strong periodicity in the process of walking , the gait cycle can be extracted by segmenting the periodic angular velocity.
  • the Butterworth filtering process is first performed, and the raw data is filtered through a third-order low-pass Butterworth filter with a cutoff frequency of 5 Hz, and the sensor raw signal With large noise, multiple peaks may appear in a small range.
  • the Butterworth filtering process can be performed before the original data is used for period segmentation, which can reduce the noise.
  • the signal after the noise reduction process uses the local peak detection method to extract the gait cycle, selects a sample point arbitrarily, and compares it with the left and right adjacent sample points, if the acceleration value of the sample point Larger than the left and right ones, this sample point is the maximum value point. If there are multiple maximum value points in a gait cycle, use the threshold to filter out the only maximum value point, so that it can be used as an accurate standard to divide the gait cycle.
  • the gait cycle is divided into different gait phases according to the contact situation between the sole and the ground, and the gait cycle is divided into four stages, the four stages are heel impact, flat foot , heel-off, and swing phases, we only need to determine the flat-foot phase, and use the condition that the angular velocity and XY-axis acceleration are close to zero during the flat-foot phase to divide it.
  • the periodicity of walking allows us to repeat this cycle step by step. Using the zero-velocity update method during flatfoot corrects for sensor drift errors when the foot is stationary on the ground.
  • the angular velocity of the X-axis of the ankle is integrated to obtain the angle between the ankle and the knee joint.
  • the gait phase diagram is drawn by integrating the angle and angular velocity obtained, based on the gait complexity quantitative evaluation method of the elliptic Fourier harmonic order, using the elliptic Fourier to fit the phase diagram,
  • the number of harmonics in the fitting process is calculated by the point-by-point square error method, the complexity is quantified by the number of harmonics required to describe the shape of the phase diagram, and the gait symmetry is achieved by the similarity analysis of the left and right foot gait phase diagrams quantitative assessment.
  • an experiment of simulating abnormal gait is designed, normal people limit the ability of lower limbs to walk at a suitable speed, use IMU to record gait parameters, compare their normal gait phase diagrams, and normalize the eigenvectors Use the common classification methods in machine learning to classify them, and distinguish different abnormal gaits.
  • the present invention proposes a zero-speed interval detection method based on data fusion technology.
  • the traditional zero-speed interval detection method generally judges the zero-speed interval according to the acceleration amplitude or angular velocity energy. Due to the noise of the original signal, when determining the zero-speed interval The jump that may occur during the interval process can accurately find the zero-speed interval by fusing acceleration and angular velocity and adding a window;
  • the present invention reduces the error in the angular velocity discrete integration process by proposing a method for updating with zero velocity. Since the acceleration and angle of the calf are close to zero when the human body is in the standing phase during walking, the present invention detects the standing phase, and in the In the standing phase, the calf angle is zeroed, thereby reducing the accumulation of angle integral errors and making the calculated calf angle more accurate;
  • the present invention carries out the method for gait abnormality evaluation by proposing the phase diagram that utilizes elliptic Fourier analysis fitting as gait feature, compared with traditional method with speed or step length as evaluation basis, this non-linear
  • the method can better reflect the biomechanical relationship during exercise, and avoid the problem of inaccurate characteristic parameters caused by the interference of inertial gravity on acceleration;
  • the present invention proposes a method for identifying abnormal gait types based on a machine learning classification algorithm.
  • the phase diagram of the abnormal gait has a more rapid change , higher gait complexity, the harmonic order required to fit the abnormal phase diagram is much higher than the normal situation, and the abnormal gait can be distinguished by the phase diagram contour and the minimum harmonic order, using the common classification method in machine learning Classify them to achieve the distinction of different abnormal gaits.
  • Fig. 1 is a schematic diagram of the overall process of the present invention.
  • Fig. 2 is a schematic diagram of the original acceleration and angular velocity signals of the present invention.
  • Fig. 3 is a schematic diagram of comparison of acceleration waveforms before and after coordinate conversion in the present invention.
  • Fig. 4 is a schematic diagram of comparison of angular velocity waveforms before and after coordinate conversion in the present invention.
  • Fig. 5 is a schematic diagram of gait cycle division in the present invention.
  • Fig. 6 is a schematic diagram of division of the zero-speed interval in the present invention.
  • Fig. 7 is a schematic diagram of the angle and angular velocity of a gait cycle of the present invention.
  • Fig. 8 is a schematic diagram of angles and angular velocities of multiple periods of the present invention.
  • Fig. 9 is a schematic diagram of a phase diagram of a gait cycle of the lower leg of the present invention.
  • Fig. 10 is a schematic diagram of a phase diagram of a single period fitting in the present invention.
  • Fig. 11 is a schematic diagram of a phase diagram fitted with multiple cycles in the present invention.
  • Fig. 12 is a schematic diagram of a normal phase diagram of the harmonic 13th order of the present invention.
  • Fig. 13 is a schematic diagram of an abnormal phase diagram of the 26th harmonic order of the present invention.
  • the invention provides a gait analysis and abnormal detection method based on a wearable gait sensor, comprising the following steps:
  • the inertial sensor is the built-in accelerometer in the mobile wearable device, which obtains the gait acceleration signal, obtains the angular velocity information through the gyroscope, and places the sensor on both sides of the left and right ankles to obtain the original data of the gait.
  • the sampling frequency is set to 120HZ, and the sampling mode is offline mode. By setting the sampling frequency and setting it to offline mode, the sensor can follow the operation of the body and perform real-time detection. The collected data is more accurate and reliable, thereby improving the follow-up The precision with which the processing is performed.
  • the measured values output by the sensors all take the sensor coordinate system as the reference system, and in the process of gait analysis, it is necessary to use the data in the geographic coordinate system for calculation, and use the quaternion method to convert the coordinate system.
  • the methods of transferring the coordinate system to the reference system are: Euler angle method, direction cosine method, trigonometric function method, quaternion method, Euler angle method in the process of coordinate conversion, when the pitch angle of the carrier is 90 degrees, the There is a singularity, so this method cannot solve the full attitude, and there are certain limitations.
  • the gait cycle detection is the basis of gait analysis, and the accurate division of gait cycle has a great influence on the follow-up gait analysis results.
  • the angular velocity data has a strong periodicity in the process of walking. The angular velocity is segmented to extract the gait cycle.
  • the Butterworth filtering process is first performed, and the original data is filtered through a third-order low-pass Butterworth filter with a cutoff frequency of 5 Hz, and only one filtering may cause phase
  • the movement of the filtered signal will cause the phase shift of the filtered signal and affect the division of the gait cycle.
  • the local peak detection method is used to extract the gait cycle from the signal after noise reduction processing, a sample point is randomly selected, and its size is compared with the left and right adjacent sample points, if the acceleration value of the sample point is larger than that of the left and right , this sample point is the maximum value point, if there are multiple maximum value points in a gait cycle, use the threshold to filter out the only maximum value point, so that it can be used as an accurate standard to divide the gait cycle, the threshold value can be passed There are two ways to determine, the first is to observe graphically, select an array between the first peak and the second peak as the threshold, and then filter out the second maximum value and the maximum value lower than it, leaving The first maximum value in the cycle, the second method can use the traditional formula to calculate the mean and standard deviation of all maximum points, and then calculate the threshold through them.
  • the gait cycle is divided into different gait phases according to the contact situation between the sole of the foot and the ground, and the gait cycle is divided into four stages, the four stages are heel strike, flat foot, heel off the ground,
  • the swing phase it is only necessary to determine the flat-foot phase, and divide it by using the condition that the angular velocity and the XY-axis acceleration are close to zero during the flat-foot phase.
  • the accelerometer and gyroscope signals in the fixed window avoid the zero-velocity state confusion caused by local signal noise, and can accurately divide the zero-velocity interval by comparing the original acceleration and angular velocity data.
  • the angular velocity of the X-axis of the ankle is integrated to obtain the angle between the ankle and the knee joint.
  • the integration process is also a process of error accumulation. In order to reduce this error, based on the flat foot phase The fact that the angle between the ankle joint and the knee joint is zero and the velocity is zero, the joint angle is corrected in the flat foot phase, and the cumulative error brought about by the integration process is eliminated, so as to obtain the relationship between the angular velocity and the angle.
  • the gait phase diagram is drawn by integrating the obtained angle and angular velocity, and the gait complexity quantitative evaluation method based on the elliptic Fourier harmonic order uses the elliptic Fourier to fit the phase diagram.
  • the number of harmonics is calculated by the point-by-point square error method, the complexity is quantified by the number of harmonics required to describe the shape of the phase diagram, and the quantitative evaluation of gait symmetry is realized through the similarity analysis of the left and right foot gait phase diagrams.
  • the sexiness measure is defined as the smallest number of harmonics in the reduced-order fit capable of eliminating 99.9% of the error between the full-order fit and the zero-order fit of the phase map.
  • an experiment is designed to simulate abnormal gait.
  • Normal people limit the movement ability of the lower limbs, walk at an appropriate speed, record gait parameters with IMU, compare their normal gait phase diagrams, normalize the feature vectors, and use the machine
  • the common classification methods in learning classify them distinguish different abnormal gaits, use the characteristics of different gait phase diagram profiles between healthy people and abnormal gait, use the gait phase diagram as a feature, and then normalize the feature vector Processing, and then use the classification algorithm based on machine learning to realize the distinction of different abnormal gaits, and the normal gait and abnormal gait can be distinguished through the phase diagram outline.
  • connection should be understood in a broad sense, which can be mechanical connection Or electrical connection, it can also be the internal communication of two components, it can be directly connected, "up”, “down”, “left”, “right”, etc. are only used to indicate the relative positional relationship, when the absolute position of the object being described Change, the relative positional relationship may change;

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Abstract

L'invention concerne un procédé d'analyse et de détection d'anomalie de démarche basé sur un capteur de pas portable, qui se rapporte au domaine technique des applications informatiques. Le procédé comprend les étapes suivantes consistant à : S1, acquérir des données au moyen d'un capteur inertiel ; S2, effectuer une conversion de système de coordonnées sur une valeur de mesure délivrée par le capteur ; S3, diviser des cycles de démarche ; S4, effectuer une détection d'intervalle à vitesse nulle ; S5, effectuer une intégration discrète sur la vitesse angulaire ; S6, évaluer la complexité de démarche sur la base d'une analyse de Fourier elliptique ; S7, évaluer une symétrie de démarche sur la base d'un coefficient de corrélation de Pearson ; et S8, détecter des démarches anormales sur la base d'un diagramme de phase de démarche. L'invention concerne un procédé de détection d'intervalle à vitesse nulle basé sur des technologies de fusion de données pour éviter des sauts pendant la détermination d'un intervalle à vitesse nulle dans un procédé de détection classique. L'accélération et la vitesse angulaire sont fusionnées et une fenêtre est ajoutée pour trouver avec précision l'intervalle à vitesse nulle. Un diagramme de phase de démarche est ajusté au moyen d'une analyse de Fourier elliptique pour évaluer des démarche anormales, ce qui permet d'éviter l'interférence de la gravité inertielle sur l'accélération.
PCT/CN2021/137344 2021-12-13 2021-12-13 Procédé d'analyse et de détection d'anomalie de démarche basé sur un capteur de pas portable Ceased WO2023108315A1 (fr)

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CN116662729A (zh) * 2023-08-02 2023-08-29 山东鲁玻玻璃科技有限公司 一种低硼硅玻璃上料控制数据智能监测方法
CN117235573A (zh) * 2023-09-14 2023-12-15 华民康(成都)科技有限公司 一种矫正飞盘竞技选手运动姿态的方法
CN117298449A (zh) * 2023-10-31 2023-12-29 首都医科大学宣武医院 一种基于可穿戴设备的闭环dbs调控方法和系统
CN118216906A (zh) * 2024-03-27 2024-06-21 清华大学 一种基于智能手机的卒中患者步态评估方法
CN118830829A (zh) * 2024-06-27 2024-10-25 中国康复研究中心 一种基于运动监测的关节炎矫正过程监测系统
CN118948262A (zh) * 2024-10-16 2024-11-15 青岛理工大学 一种基于机器学习决策分类的异常步态检测方法及系统
CN118964883A (zh) * 2024-07-19 2024-11-15 中国科学院宁波材料技术与工程研究所 人体步态参数确定方法、装置、电子设备及存储介质
CN118948257A (zh) * 2024-07-24 2024-11-15 南方科技大学 人体步态预测方法、装置、电子设备及存储介质
CN119936926A (zh) * 2025-04-08 2025-05-06 江苏云圣智能科技有限责任公司 传感器跳点检测方法、装置、设备及介质
CN120114044A (zh) * 2025-05-09 2025-06-10 华中科技大学 异常步态实时检测系统及其训练方法、康复外骨骼设备
CN120114043A (zh) * 2025-04-08 2025-06-10 南京林业大学 一种基于人工智能的运动行为动态监测方法及系统
CN120521658A (zh) * 2025-05-14 2025-08-22 山东盛途互联网科技有限公司 一种基于人工智能的状态检测及设备故障预警方法

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Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116662729A (zh) * 2023-08-02 2023-08-29 山东鲁玻玻璃科技有限公司 一种低硼硅玻璃上料控制数据智能监测方法
CN116662729B (zh) * 2023-08-02 2023-10-31 山东鲁玻玻璃科技有限公司 一种低硼硅玻璃上料控制数据智能监测方法
CN117235573A (zh) * 2023-09-14 2023-12-15 华民康(成都)科技有限公司 一种矫正飞盘竞技选手运动姿态的方法
CN117298449A (zh) * 2023-10-31 2023-12-29 首都医科大学宣武医院 一种基于可穿戴设备的闭环dbs调控方法和系统
CN117298449B (zh) * 2023-10-31 2024-04-09 首都医科大学宣武医院 一种基于可穿戴设备的闭环dbs调控方法和系统
CN118216906A (zh) * 2024-03-27 2024-06-21 清华大学 一种基于智能手机的卒中患者步态评估方法
CN118830829A (zh) * 2024-06-27 2024-10-25 中国康复研究中心 一种基于运动监测的关节炎矫正过程监测系统
CN118964883A (zh) * 2024-07-19 2024-11-15 中国科学院宁波材料技术与工程研究所 人体步态参数确定方法、装置、电子设备及存储介质
CN118948257A (zh) * 2024-07-24 2024-11-15 南方科技大学 人体步态预测方法、装置、电子设备及存储介质
CN118948257B (zh) * 2024-07-24 2025-06-06 南方科技大学 人体步态预测方法、装置、电子设备及存储介质
CN118948262A (zh) * 2024-10-16 2024-11-15 青岛理工大学 一种基于机器学习决策分类的异常步态检测方法及系统
CN119936926A (zh) * 2025-04-08 2025-05-06 江苏云圣智能科技有限责任公司 传感器跳点检测方法、装置、设备及介质
CN120114043A (zh) * 2025-04-08 2025-06-10 南京林业大学 一种基于人工智能的运动行为动态监测方法及系统
CN120114044A (zh) * 2025-05-09 2025-06-10 华中科技大学 异常步态实时检测系统及其训练方法、康复外骨骼设备
CN120521658A (zh) * 2025-05-14 2025-08-22 山东盛途互联网科技有限公司 一种基于人工智能的状态检测及设备故障预警方法

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