CN115969355A - Wearable intelligent knee joint and gait monitoring method, device, system and storage medium - Google Patents

Wearable intelligent knee joint and gait monitoring method, device, system and storage medium Download PDF

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CN115969355A
CN115969355A CN202211575559.8A CN202211575559A CN115969355A CN 115969355 A CN115969355 A CN 115969355A CN 202211575559 A CN202211575559 A CN 202211575559A CN 115969355 A CN115969355 A CN 115969355A
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knee joint
measurement unit
inertial measurement
data
flexion
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娄楠
刁亚楠
赵国如
宁运琨
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Shenzhen Institute of Advanced Technology of CAS
Shenzhen Hospital University of Hong Kong
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Shenzhen Institute of Advanced Technology of CAS
Shenzhen Hospital University of Hong Kong
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Abstract

The present application relates to wearable smart knee joint and gait monitoring methods, devices, systems, and computer-readable storage media. The method comprises the following steps: s1, collecting motion data of a person during movement by adopting a first IMU (inertial measurement Unit) worn and fixed on the front of a thigh and a second IMU worn and fixed on the front of a shank; s2, preprocessing the acquired data; s3, calculating a first knee joint flexion and extension angle by using accelerometer data of the first IMU and the second IMU, calculating a second knee joint flexion and extension angle by using gyroscope data of the first IMU and the second IMU, and fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle; and S4, calculating horizontal and vertical movement of the second IMU during the activity period by using gyroscope data of the second IMU, estimating the stride and the minimum foot gap of each stride period, and calculating the gait speed and the stride frequency according to the stride. The monitoring method and the device are accurate, easy to use and low in cost.

Description

Wearable intelligent knee joint and gait monitoring method, device, system and storage medium
Technical Field
The present application relates to medical monitoring technology, and more particularly, to a wearable smart knee joint and gait monitoring method, apparatus, system and computer-readable storage medium.
Background
Osteoarthritis (OA) is a common degenerative joint disease that causes pain and discomfort when the joint is used normally. It is the most common chronic disease of the joint, caused by degeneration of articular cartilage. In China, europe and America and other countries, a large number of arthritis patients face the problems that the patients cannot be listed in hospitals and cannot be treated in time, and can only be recovered at home and in communities by adopting a rehabilitation training method.
Currently, there are four main methods for monitoring knee joints and gait of patients clinically: 1) Goniometer-based methods, which typically use resistance potentiometers or strain gauges for joint monitoring, have the major disadvantage that such systems are not flexible enough and have low accuracy; 2) Based on the analysis of video/imaging systems, such systems require complex image processing algorithms and machine learning techniques to track joints and analyze mobility, and furthermore, these systems require pre-equipped laboratory environments that are not only expensive, but also limit the user to a limited space within the field of view of the camera, and are therefore not suitable for continuous and long-term monitoring of the knee joint during everyday activities; 3) Textile-based sensors, such as flexible wire sensors, bending sensors, which are usually sewn to flexible, tight-fitting clothing at the joint, can measure joint angle, etc., but are not suitable for monitoring joint motion or direction, since most can only measure single-axis motion; 4) Based on the analysis of an Inertial Measurement Unit (IMU), the joint motion and the direction can be quantified by measuring the three-dimensional linear acceleration and the angular velocity magnetic field vector during the joint motion, the joint angle and other important gait parameters can be measured, and the method is low in price and simple to operate, so that the method is more suitable for the long-term domestic rehabilitation of knee joint inflammation patients. However, the current wearable monitoring method based on the inertial measurement unit generally has the problems of low monitoring accuracy, high algorithm complexity and the like, and if the algorithms such as deep learning are adopted, the calculation efficiency in practical application is greatly reduced.
Therefore, there is an urgent need to develop an easy-to-use system and method for knee joint monitoring that allows for long-term recording of knee joint health and activity without restricting people to hospitals or laboratories and without interfering with their normal activities.
Disclosure of Invention
The technical problem to be solved by the present application is to provide a simple, effective, high-accuracy, and low-cost wearable smart knee joint and gait monitoring method, apparatus, system, and computer-readable storage medium, aiming at the above-mentioned defects in the prior art.
The application provides a wearable intelligent knee joint and gait monitoring method in a first aspect for solving the technical problem, which comprises the following steps:
s1, acquiring accelerometer data and gyroscope data of a person during movement by adopting a first inertial measurement unit worn and fixed on the front side of a thigh and a second inertial measurement unit worn and fixed on the front side of a shank;
s2, preprocessing the collected data of the first inertia measurement unit and the collected data of the second inertia measurement unit;
s3, calculating a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculating a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle;
and S4, calculating horizontal and vertical motions of the second inertial measurement unit during the activity period by using gyroscope data of the second inertial measurement unit, estimating the stride and the minimum foot gap of each stride period, and calculating the gait speed and the pace frequency according to the stride.
In an embodiment of the wearable smart knee joint and gait monitoring method according to the application, the step S2 further comprises:
s21, low-pass filtering is carried out on the collected data of the first inertia measurement unit and the collected data of the second inertia measurement unit, and high-frequency noise is removed;
and S22, carrying out alignment processing on the data of the first inertial measurement unit and the data of the second inertial measurement unit after low-pass filtering so as to align the time of the two data.
In an embodiment of the wearable smart knee joint and gait monitoring method according to the application, the calculating a first knee joint flexion-extension angle in step S3 using accelerometer data of the first inertial measurement unit and the second inertial measurement unit further includes:
calculating the angle change between the static acceleration vector and the walking acceleration vector of the thigh and the calf by using the accelerometer data of the first inertial measurement unit and the second inertial measurement unit respectively, wherein the calculation formula is as follows:
Figure BDA0003987365560000031
wherein, theta acc Is an acceleration vector
Figure BDA0003987365560000032
And &>
Figure BDA0003987365560000033
Angle in between->
Figure BDA0003987365560000034
Is an acceleration vector of the rest position>
Figure BDA0003987365560000035
Is the acceleration vector when walking;
determining the flexion and extension angle of the first knee joint according to the angle change of the thigh and the shank, wherein the calculation formula is as follows:
Figure BDA0003987365560000036
wherein the content of the first and second substances,
Figure BDA0003987365560000037
and &>
Figure BDA0003987365560000038
Is the angle change of the thigh and the calf, respectively>
Figure BDA0003987365560000039
The angle of flexion and extension of the knee joint at rest position, and/or>
Figure BDA00039873655600000310
The angle of flexion and extension of the knee joint during movement is the angle of flexion and extension of the first knee joint.
In an embodiment of the wearable smart knee joint and gait monitoring method according to the application, the calculating the second knee joint flexion and extension angle in step S3 by using the gyroscope data of the first inertial measurement unit and the second inertial measurement unit further includes:
the angular velocity obtained by using the gyroscope data is calculated according to the following formula to obtain the flexion and extension angle of the second knee joint:
Figure BDA00039873655600000311
wherein j is 1 And j 2 Joint axes of the first and second inertial measurement units, g 1 And g 2 Are the respective gyroscope values, tau is the time constant,
Figure BDA00039873655600000312
the flexion and extension angles of the second knee joint.
In an embodiment of the wearable smart knee joint and gait monitoring method according to the application, the step S3 of fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle is implemented by the following formula:
Figure BDA00039873655600000313
/>
wherein, theta K Is the estimated knee flexion and extension angle, alpha is the filter constant,
Figure BDA00039873655600000314
the flexion and extension angle of the first knee joint is,
Figure BDA00039873655600000315
the flexion and extension angles of the second knee joint.
In an embodiment of the wearable smart knee joint and gait monitoring method according to the application, the step S4 of calculating horizontal and vertical movements during the activity using gyroscope data of the second inertial measurement unit, and the estimating a stride and a minimum foot gap for each stride cycle further comprises:
s41, dividing continuous motion information of the second inertia measurement unit into a series of step periods by adopting a signal peak value detection method, wherein each step period consists of a swing stage and a support stage;
s42, calculating the calf angle in the sagittal plane and the transverse plane by integrating the angular velocity measured by the gyroscope of the second inertial measurement unit according to the following formula:
Figure BDA0003987365560000041
wherein, theta s Is the shank angle, ω is the angular velocity, τ is the time constant;
s43, obtaining a linear velocity through the product of the angular velocity and the radius of angular rotation, and decomposing the linear velocity into a horizontal component and a vertical component relative to the earth according to the shank angle;
s44, calculating the horizontal velocity through the linear velocity horizontal components of the sagittal plane and the transverse plane according to the following formula:
Figure BDA0003987365560000042
wherein upsilon is gait Is a horizontal velocity, v hor,s Is the horizontal component of the linear velocity of the sagittal plane, upsilon hor,t Is the horizontal component of the linear velocity of the transverse plane;
s45, performing simple trapezoidal integration on the horizontal speed in one Stride period according to the following formula to obtain Stride Length:
Figure BDA0003987365560000043
s46, vertical velocity v υer The integration is performed according to the following formula to obtain the vertical displacement s υer ：
Figure BDA0003987365560000044
S47, identifying the minimum foot gap position according to the shank angle calculated in the step S42, and then according to the vertical displacement S υer A minimum foot gap is determined for each stride cycle.
In one embodiment of the wearable smart knee joint and gait monitoring method of the present application, the method further comprises:
and S5, decomposing the data preprocessed in the step S2 by wavelet packet decomposition to obtain the frequency spectrum characteristics of the knee joint movement signals.
This application provides a wearable intelligent knee joint and gait monitoring system in the second aspect for solving its technical problem, includes:
the data acquisition module is used for acquiring accelerometer data and gyroscope data of a person during movement by adopting a first inertial measurement unit worn and fixed on the front side of a thigh and a second inertial measurement unit worn and fixed on the front side of a shank;
the data preprocessing module is used for preprocessing the acquired data of the first inertia measurement unit and the acquired data of the second inertia measurement unit;
the first calculation module is used for calculating a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculating a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle;
and the second calculation module is used for calculating the horizontal and vertical motions of the second inertial measurement unit during the activity period by using the gyroscope data of the second inertial measurement unit, estimating the stride and the minimum foot gap of each stride period, and calculating the gait speed and the pace frequency according to the stride.
In a third aspect, the present application provides a wearable smart knee joint and gait monitoring system, comprising a processor and a memory, the memory storing a computer program, the computer program when executed by the processor implementing the steps of the wearable smart knee joint and gait monitoring method as described above.
In order to solve the technical problem, in a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the wearable smart knee joint and gait monitoring method as described above.
The wearable intelligent knee joint and gait monitoring method, device, system and computer readable storage medium have the following advantages: according to the wearable intelligent knee joint and gait detection method and device, the two IMUs respectively worn and fixed on the front sides of the thigh and the shank are used for collecting activity data of a patient, the knee joint flexion and extension angle obtained through accelerometer data calculation and the knee joint flexion and extension angle obtained through gyroscope data calculation are fused through a complementary filtering algorithm to obtain a final knee joint flexion and extension angle, horizontal and vertical motions of a sensor during the activity period are calculated based on the gyroscope data of the shank IMU, stride and minimum foot gap estimation is carried out, and gait speed and frequency are deduced. The wearable intelligent knee joint and gait monitoring method and device according to the embodiment of the application adopt a simple and effective knee joint flexion and extension angle evaluation algorithm and a gait parameter estimation algorithm, and prove to be strong in practicability and high in reliability, so that the wearable intelligent knee joint and gait monitoring method and device according to the embodiment of the application are accurate, easy to use and low in cost, can be used for evaluating the health condition of the knee joint, can be combined with internet of things (IoT) to promote remote long-term joint health monitoring and real-time evaluation, and can be potentially used for predicting neurodegenerative diseases (such as dementia, alzheimer's disease, parkinson's disease and the like) and evaluating diseases related to cardiovascular systems at the same time,
drawings
The present application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic diagram of a model for knee joint and gait monitoring based on two IMUs according to the present application;
fig. 2 is a flow chart of a wearable smart knee joint and gait monitoring method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a knee flexion-extension angle fusion algorithm in an embodiment of the present application;
fig. 4 is a logic block diagram of a wearable smart knee joint and gait monitoring device according to an embodiment of the present application;
fig. 5 is a logic block diagram of a wearable smart knee joint and gait monitoring system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. Also, the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
An Inertial Measurement Unit (IMU), also called an Inertial Measurement sensor, is a sensor for measuring a three-axis attitude angle (or angular velocity) and an acceleration of an object. Generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, the accelerometers detect acceleration signals of an object in three independent axes of a carrier coordinate system, and the gyroscopes detect angular velocity signals of the carrier relative to a navigation coordinate system, measure angular velocity and acceleration of the object in three-dimensional space, and solve the attitude of the object accordingly.
The wearable intelligent knee joint and gait monitoring method is provided aiming at the problem that knee joint and gait cannot be simply and effectively monitored for a long time in the home rehabilitation training of a patient with the knee arthritis, and two inertia measurement units, namely an IMU 21 and an IMU 22, are adopted to be respectively worn and fixed on the front surfaces of a thigh 11 and a shank 12 of the patient to collect motion data of the patient during the activity, as shown in figure 1. Wherein, the included angle of the thigh 11 relative to the ground vertical line is the thigh angle theta t The angle between the lower leg 12 and the vertical line of the ground is the angle theta of the lower leg s The included angle between the thigh 11 and the shank 12 is the knee flexion-extension angle theta k . The data collected by the IMU 21 and IMU 22 undergoes pre-processing and data analysis steps to determine the necessary knee and gait parameters, such as knee flexion and extension angle, stride length, minimum foot gap, gait speed, cadence (steps per minute), etc., for use in assessing the patient's activity state.
Fig. 2 illustrates a flow diagram of a wearable smart knee joint and gait monitoring method 100 according to one embodiment of the present application. As shown in fig. 2, the wearable smart knee joint and gait monitoring method 100 includes the steps of:
in step S110, accelerometer data and gyroscope data of the person during the activity are collected by using the first inertial measurement unit 21 worn and fixed on the front surface of the thigh 11 and the second inertial measurement unit 22 worn and fixed on the front surface of the shank 12. In particular, the two IMUs 21 and 22 may be worn and secured to the upper and lower legs 11 and 12 using various suitable means known in the art, such as straps, elastic bands, hook and loop fasteners, etc.
Then, in step S120, the acquired data of the first inertial measurement unit and the acquired data of the second inertial measurement unit are preprocessed. In a specific embodiment, the preprocessing of the data in step S120 further includes:
step S121, low-pass filtering the acquired data of the first inertia measurement unit and the acquired data of the second inertia measurement unit to remove high-frequency noise;
and step S122, carrying out alignment processing on the data of the first inertial measurement unit and the data of the second inertial measurement unit after low-pass filtering so as to align the time of the two data.
There are device vibrations and high frequency ambient noise in the raw data of the IMU that can degrade the sensor readings and thereby affect their accuracy. To eliminate this high frequency noise, the present application low pass filters the data acquired by the first IMU 21 and the second IMU 22 using, for example, a fifth order low pass Butterworth filter. Since most of the salient features in the knee joint movement signal remain in the low frequency region, the cut-off frequency of the filter can be set to, for example, 12Hz. Although both IMUs are calibrated to acquire data simultaneously at a fixed sample rate (100 Hz), the total sample count for both IMUs is not necessarily the same for longer data sets. This is because the IMUs use a Bluetooth Low Energy (BLE) connection to transmit data, the sensors conduct data faster than the devices exchange data over the BLE connection, and the total time span over which the two IMUs collect data is the same, resulting in a data loss situation. Thus, the present application regenerates a time array for the total time span that is 0.01s (1/100 Hz) apart, and then re-samples the low pass filtered data of the two IMUs according to this time array to align the time of the two IMU data. Through the alignment treatment, the knee joint flexion and extension angle and the calculation precision of the motion parameters can be improved.
Then, in step S130, the method 100 calculates a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculates a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fuses the first knee joint flexion and extension angle and the second knee joint flexion and extension angle by complementary filtering to obtain an estimated knee joint flexion and extension angle.
The calculation of accelerometer data provides more absolute angle information since the orientation of the sensors is independent of each other. However, the measurement accuracy may be affected by high-frequency noise (mechanical noise) of the sensor vibration and translational motion of the object. On the other hand, it is simpler to calculate the knee flexion-extension angle using gyroscope data because its data is less affected by high frequency noise. However, the gyroscope data may experience low frequency drift over time. Therefore, the method 100 of the present application applies a fusion method (complementary filter) in step S130, which combines the knee flexion-extension angle obtained from the accelerometer data and the knee flexion-extension angle obtained from the gyroscope data to eliminate the accelerometer noise and overcome the gyroscope drift, and calculates the knee flexion-extension angle during the activity with higher accuracy, as shown in fig. 3.
In a specific embodiment, the specific implementation steps of calculating the flexion-extension angle of the first knee joint by using the accelerometer data of the first inertial measurement unit and the second inertial measurement unit in step S130 are as follows:
firstly, the accelerometer data of the first inertial measurement unit and the second inertial measurement unit are respectively used for calculating the angle change between the static acceleration vector and the walking acceleration vector of the thigh and the calf, and the calculation formula is as follows:
Figure BDA0003987365560000081
wherein, theta acc Is the acceleration vector
Figure BDA0003987365560000082
And &>
Figure BDA0003987365560000083
The angle therebetween; />
Figure BDA0003987365560000084
Is the acceleration vector in the rest position (standing upright, maximum knee extension state), is->
Figure BDA0003987365560000091
Is the acceleration vector when walking, whose value and direction change with the sagittal motion of the thigh and calf. According to the above formula (1), the thigh @duringwalking can be calculated>
Figure BDA0003987365560000092
And the lower leg>
Figure BDA0003987365560000093
While also passing the thigh rest position acceleration vector->
Figure BDA0003987365560000094
And a shank rest position acceleration vector->
Figure BDA0003987365560000095
Knee joint flexion and extension angle determining resting position>
Figure BDA0003987365560000096
Then, determining a first knee joint flexion and extension angle based on accelerometer data according to the angle change of the thigh and the shank, wherein the calculation formula is as follows:
Figure BDA0003987365560000097
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003987365560000098
and &>
Figure BDA0003987365560000099
Angle changes in the thigh and the calf, respectively>
Figure BDA00039873655600000910
The angle of flexion and extension of the knee joint at rest position, and/or>
Figure BDA00039873655600000911
The flexion-extension angle of the knee joint during movement is the first flexion-extension angle of the knee joint.
In a specific embodiment, the specific implementation steps of calculating the flexion-extension angle of the second knee joint by using the gyroscope data of the first inertial measurement unit and the second inertial measurement unit in step S130 are as follows: the angular velocity obtained by using the gyroscope data is calculated according to the following formula to obtain the flexion and extension angle of the second knee joint:
Figure BDA00039873655600000912
wherein j is 1 And j 2 Joint axes of the first and second inertial measurement units, g 1 And g 2 The angular velocity values of the first inertial unit and the second inertial unit, respectively, τ is a time constant, d τ represents the differential to τ,
Figure BDA00039873655600000913
the flexion and extension angles of the second knee joint. The present application calculates the second knee flexion-extension angle by integrating the difference in angular velocity about the joint axis with respect to time using equation (3) above.
In a specific embodiment, the step S130 of fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle is implemented by the following formula:
Figure BDA00039873655600000914
/>
wherein, theta K Is the estimated knee flexion and extension angle, alpha is the filter constant,
Figure BDA00039873655600000915
the angle of flexion and extension of the first knee joint,
Figure BDA00039873655600000916
the flexion and extension angles of the second knee joint.
The present application combines the calculated angles of accelerometer data and gyroscope data using sensor fusion, thereby reducing the limitations of each sensor. The present application uses a complementary filter that estimates knee flexion-extension angles with very low computational complexity. The complementary filters function to low pass filter the accelerometer data, high pass filter the gyroscope data, and then combine them to output a result. In equation (4) above, the filter constant α determines the cutoff time for trusting gyroscope and filtering accelerometer data. By choosing an appropriate time constant τ, α:
Figure BDA0003987365560000101
wherein τ is a time constant, T s Is the sampling time.
By calculation, the optimal parameters here are: α =0.93, τ =0.25s (4 Hz is the cut-off frequency) and the sampling time T s =20ms. This indicates that the gyroscope data is retained for a period of time less than 0.25s and the accelerometer data is removed; meanwhile, the accelerometer data is retained for a period of time exceeding 0.25 s.
Then, the method 100 calculates horizontal and vertical movements during the activity using the gyroscope data of the second inertial measurement unit, estimates a stride and a minimum foot gap for each stride period, and calculates a gait speed and a gait frequency from the stride in step S140.
The stride and Minimum Foot Clearance (MCF) are two key parameters for knee joint function assessment and gait analysis. Short strides and low MFC often indicate reduced knee joint motion and angulation during walking. The stride length may be calculated by measuring the distance covered by a stride cycle, and the MFC indicates the vertical distance of the sole/sole above the ground during the mid-swing (calf vertical, foot parallel to the ground for gait cycle). With the greatest horizontal velocity and the smallest ground clearance, the motion of the foot during the midswing phase is considered to be the most critical event in walking. Thus, low MFC can give rise to the possibility of tripping and falling. The present application utilizes gyroscope data of a second IMU, which is secured to the lower leg, worn to calculate horizontal and vertical motion of the sensor during walking to estimate stride and MFC. The specific implementation process is as follows:
in step S141, the continuous motion information of the second inertial measurement unit is divided into a series of step periods by using a simple signal peak detection method, where each step period is composed of a swing stage and a support stage.
In step S142, to calculate the displacement along the horizontal and vertical ground axes for each stride period, the calf angles in the sagittal and transverse planes are first calculated by integrating the angular velocities measured by the gyroscopes of the second inertial measurement unit, as follows:
Figure BDA0003987365560000102
wherein, theta s Is the calf angle, ω is the angular velocity and τ is the time constant.
Step S143, obtaining a linear velocity v through the product of the angular velocity omega and the radius r of the angular rotation, and decomposing the linear velocity v into a horizontal component upsilon relative to the earth according to the shank angle hor And a vertical component v ver The following are:
υ(t)＝ω(t)×r (7)
υ hor (t)＝υ(t)×cosθ S (t) (8)
υ ver (t)＝υ(t)×sinθ S (t) (9)
here, the radius r of angular rotation is the length of the knee to the bottom of the heel.
Step S144, linear velocity horizontal component upsilon through two planes of sagittal plane and transverse plane hor,s And upsilon hor,t The horizontal velocity upsilon is calculated according to the following formula gait ：
Figure BDA0003987365560000111
While considering horizontal velocity, the sensor has motion in both the sagittal and transverse planes. Therefore, in order to accurately estimate gait, the linear velocity component upsilon of the two planes is used hor,s And upsilon hor,t The horizontal velocity is calculated.
Step S145, will go from t =0 to t onecycle The horizontal velocity in one step cycle is simply trapezoidal integrated according to the following formula to obtain the step StrideLength:
Figure BDA0003987365560000112
step S146, regarding the vertical velocity upsilon υer The integration is performed according to the following formula to obtain the vertical displacement s υer ：
Figure BDA0003987365560000113
Step S147, based on the calf angle θ calculated in step S142 s To identify a minimum foot gap position and then based on said vertical displacement s υer A minimum foot gap is determined for each stride cycle. When the foot is parallel and the lower leg is perpendicular to the ground, a minimum foot gap occurs, indicating that the lower leg angle at the MFC is zero. Thus, the present application employs the calculated calf angle to identify the MFC position.
In addition, the method 100 calculates a gait speed and a pace frequency (i.e. number of steps per minute, also called rhythm) according to the stride obtained in the step S145 in step S140 for evaluating the overall performance of walking. The calculation formula of the gait speed is as follows:
Figure BDA0003987365560000114
wherein the total walking distance D total By starting time t in each stride period start To the stop time t stop The sum of the stride lengths of (a) is calculated as:
Figure BDA0003987365560000121
where N is the total number of gait cycles. The step frequency cadence is calculated through the total number N of gait cycles as follows:
Figure BDA0003987365560000122
since each gait cycle consists of two phases and the stride frequency is expressed in steps per minute, in equation (15) above, the stride frequency (rhythm) of the individual is determined by first multiplying by 2 and 60s and then dividing by the duration of the stride.
Further, the wearable smart knee joint and gait monitoring method 100 according to the embodiment of the present application may also use Wavelet Packet Decomposition (WPD) to decompose the data preprocessed in step S120, so as to obtain the spectrum characteristics of the knee joint motion signal.
Knee joint motion signals are complex, nonlinear, non-stationary, and have variable spectral characteristics that can be efficiently analyzed by decomposing them into spectral components. To obtain spectral components, the present application uses wavelet packet decomposition WPD, which has a fast and hierarchical tree decomposition algorithm making it suitable for real-time applications. WPD is a wavelet transform in which the signal is passed through discrete-time low-pass and high-pass quadrature mirror filters to decompose the approximation and detail coefficients of the first stage. The following levels are then calculated by passing the detail and approximation coefficients of the previous level through similar low-pass and high-pass filters. Thus, WPD allows a signal to be decomposed uniformly throughout its spectrum. It is preferred in this application to use the WPD of stage 8 to decompose the sensor signal preprocessed in step S120 and to calculate the energy information using the following equations, these energy characteristics derived from the signal components are closely related to the mechanical work done during knee joint movement.
Figure BDA0003987365560000123
In the above equation (16), N represents the total number of gait cycles in the signal, M is the total number of samples, and S represents the decomposed signal component.
According to the wearable intelligent knee joint and gait monitoring method 100 of the embodiment of the application, the simple and effective knee joint flexion and extension angle evaluation algorithm and gait parameter estimation algorithm are adopted, and the related information of the knee joint and the gait of the patient during different activities can be observed and recorded. According to the method, through rehabilitation training experiments of 40 knee joint inflammation and knee joint replacement patients, key typical key characteristics such as knee joint angle, stride, minimum foot gap, gait speed and step frequency are extracted, through the adoption of methods such as a complementary filter and wavelet packet decomposition, characteristic changes of relevant parameters are well analyzed, and the monitoring method can be used for real-time monitoring, rehabilitation assessment and early diagnosis, falling detection, activity monitoring and the like of joint diseases. The characteristic data obtained by the wearable smart knee joint and gait monitoring method 100 of the above-described embodiment of the present application can be further utilized and interpreted by medical personnel for clinical diagnosis and treatment of knee joint and activity related problems. Thus, the method can be used for a wide range of motion-related applications, such as rehabilitation, sports medicine, assessment of human activity, and virtual guide training.
Based on the wearable smart knee joint and gait monitoring method 100 introduced in the above embodiment, the present application also provides a wearable smart knee joint and gait monitoring device. Fig. 4 illustrates a logical block diagram of a wearable smart knee and gait monitoring device 200 according to an embodiment of the application. As shown in fig. 2, the wearable smart knee joint and gait monitoring device 200 includes a data acquisition module 210, a data preprocessing module 220, a first calculation module 230 and a second calculation mode 240. The data acquisition module 210 is configured to acquire accelerometer data and gyroscope data of a person during activity by using a first inertial measurement unit worn and fixed on the front of a thigh and a second inertial measurement unit worn and fixed on the front of a calf. The data preprocessing module 220 is configured to preprocess the acquired data of the first inertial measurement unit and the acquired data of the second inertial measurement unit. The first calculating module 230 is configured to calculate a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculate a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fuse the first knee joint flexion and extension angle and the second knee joint flexion and extension angle by complementary filtering to obtain an estimated knee joint flexion and extension angle. The second calculation module 240 is configured to calculate horizontal and vertical movements during the activity using the gyroscope data of the second inertial measurement unit, estimate a stride and a minimum foot gap for each stride period, and calculate a gait speed and a stride frequency from the stride. Further, the wearable smart knee and gait monitoring device 200 also includes a third computing module 250 for decomposing the data preprocessed by the data preprocessing module 220 using wavelet packet decomposition to obtain the spectrum characteristics of the knee motion signal.
The wearable smart knee joint and gait monitoring device 200 according to the above-described embodiment of the present application is used to implement the aforementioned wearable smart knee joint and gait monitoring method 100. For further implementation of the various modules of the wearable smart knee joint and gait monitoring device 200, reference may be made to the foregoing detailed description of the various steps of the wearable smart knee joint and gait monitoring method 100.
Based on the wearable smart knee joint and gait monitoring method 100 of the foregoing embodiment of the present application, the present application also provides a wearable smart knee joint and gait monitoring system 300. Referring to fig. 5, the wearable smart knee and gait monitoring system 300 includes a processor 310 and a memory 320, the processor 310 and the memory 320 being communicatively coupled. The memory 302 stores computer programs that, when executed by the processor 310, cause the processor 310 to implement the steps of the wearable smart knee and gait monitoring method 100 of the foregoing embodiment of the application.
Processor 310 may be a Central Processing Unit (CPU). The Processor 310 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or any combination thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 320 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the wearable smart knee joint and gait monitoring method in the embodiments of the invention. The processor 310 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 320, namely, implementing the wearable smart knee joint and gait monitoring method in the foregoing embodiments.
The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the wearable smart knee joint and gait monitoring method, and the like. Further, the memory 320 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from processor 310, which may be connected to processor 310 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The present application also proposes a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the wearable smart knee joint and gait monitoring method 100 of the aforementioned embodiment of the present application. The computer-readable storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk Drive (HDD), or a Solid-State Drive (SSD). The computer readable storage medium may also include a combination of memories of the above kinds.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A wearable intelligent knee joint and gait monitoring method is characterized by comprising the following steps:
s1, acquiring accelerometer data and gyroscope data of a person during movement by adopting a first inertial measurement unit worn and fixed on the front side of a thigh and a second inertial measurement unit worn and fixed on the front side of a shank;
s2, preprocessing the collected data of the first inertia measurement unit and the collected data of the second inertia measurement unit;
s3, calculating a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculating a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle;
and S4, calculating horizontal and vertical movement of the second inertial measurement unit during activity by using gyroscope data of the second inertial measurement unit, estimating the stride and the minimum foot gap of each stride period, and calculating the gait speed and the gait frequency according to the strides.
2. The wearable smart knee joint and gait monitoring method according to claim 1, wherein said step S2 further comprises:
s21, low-pass filtering is carried out on the collected data of the first inertia measurement unit and the collected data of the second inertia measurement unit, and high-frequency noise is removed;
and S22, carrying out alignment processing on the data of the first inertial measurement unit and the data of the second inertial measurement unit after low-pass filtering so as to align the time of the two data.
3. The wearable smart knee joint and gait monitoring method according to claim 1, wherein the step S3 of calculating a first knee joint flexion-extension angle using the accelerometer data of the first inertial measurement unit and the second inertial measurement unit further comprises:
calculating the angle change between the static acceleration vector and the walking acceleration vector of the thigh and the calf by using the accelerometer data of the first inertial measurement unit and the second inertial measurement unit respectively, wherein the calculation formula is as follows:
Figure FDA0003987365550000011
wherein, theta acc Is the acceleration vector
Figure FDA0003987365550000012
And &>
Figure FDA0003987365550000013
Angle in between->
Figure FDA0003987365550000014
Is an acceleration vector of the rest position>
Figure FDA0003987365550000015
Is the acceleration vector when walking;
determining the flexion and extension angle of the first knee joint according to the angle change of the thigh and the shank, wherein the calculation formula is as follows:
Figure FDA0003987365550000021
wherein the content of the first and second substances,
Figure FDA0003987365550000022
and &>
Figure FDA0003987365550000023
Is the angle change of the thigh and the calf, respectively>
Figure FDA0003987365550000024
The angle of flexion and extension of the knee joint at rest position, and/or>
Figure FDA0003987365550000025
The angle of flexion and extension of the knee joint during movement is the angle of flexion and extension of the first knee joint.
4. The wearable smart knee joint and gait monitoring method according to claim 3, wherein the step S3 of calculating a second knee joint flexion and extension angle using the gyroscope data of the first inertial measurement unit and the second inertial measurement unit further comprises:
the angular velocity obtained by using the gyroscope data is calculated according to the following formula to obtain the flexion and extension angle of the second knee joint:
Figure FDA0003987365550000026
wherein j is 1 And j 2 Joint axes of the first and second inertial measurement units, g 1 And g 2 Respectively, the respective gyroscope values, tau is a time constant,
Figure FDA0003987365550000027
the flexion and extension angles of the second knee joint. />
5. The wearable smart knee joint and gait monitoring method according to claim 4, wherein the step S3 of fusing the first knee joint flexion-extension angle and the second knee joint flexion-extension angle by complementary filtering to obtain the estimated knee joint flexion-extension angle is implemented by the following formula:
Figure FDA0003987365550000028
wherein, theta K Is the estimated knee flexion and extension angle, alpha is the filter constant,
Figure FDA0003987365550000029
for the flexion and extension angle of the first knee joint, and/or>
Figure FDA00039873655500000210
The flexion and extension angles of the second knee joint.
6. The wearable smart knee joint and gait monitoring method of claim 1, wherein the step S4 of calculating the horizontal and vertical movements during the activity using the gyroscope data of the second inertial measurement unit, and wherein estimating the stride and minimum foot gap for each stride cycle further comprises:
s41, dividing continuous motion information of the second inertia measurement unit into a series of step periods by adopting a signal peak value detection method, wherein each step period consists of a swing stage and a support stage;
s42, calculating the shank angle in the sagittal plane and the transverse plane by integrating the angular velocity measured by the gyroscope of the second inertial measurement unit according to the following formula:
Figure FDA00039873655500000211
wherein, theta S Is the shank angle, ω is the angular velocity, τ is the time constant;
s43, obtaining a linear velocity through the product of the angular velocity and the radius of angular rotation, and decomposing the linear velocity into a horizontal component and a vertical component relative to the earth according to the shank angle;
s44, calculating the horizontal velocity through the linear velocity horizontal components of the sagittal plane and the transverse plane according to the following formula:
Figure FDA0003987365550000031
wherein, upsilon gait Is a horizontal velocity, v hor,s Is the horizontal component of the linear velocity of the sagittal plane, upsilon hor,t Is the horizontal component of the linear velocity of the transverse plane;
s45, performing simple trapezoidal integration on the horizontal speed in one Stride period according to the following formula to obtain Stride Length:
Figure FDA0003987365550000032
s46, vertical velocity v υer The integration is performed according to the following formula to obtain the vertical displacement s υer ：
Figure FDA0003987365550000033
S47, identifying the minimum foot gap position according to the shank angle calculated in the step S42, and then according to the vertical displacement S υer A minimum foot gap is determined for each stride cycle.
7. The wearable smart knee joint and gait monitoring method of claim 1, further comprising:
and S5, decomposing the data preprocessed in the step S2 by wavelet packet decomposition to obtain the frequency spectrum characteristics of the knee joint movement signals.
8. A wearable intelligent knee joint and gait monitoring device, comprising:
the data acquisition module is used for acquiring accelerometer data and gyroscope data of a person during movement by adopting a first inertial measurement unit worn and fixed on the front side of a thigh and a second inertial measurement unit worn and fixed on the front side of a shank;
the data preprocessing module is used for preprocessing the acquired data of the first inertia measurement unit and the acquired data of the second inertia measurement unit;
the first calculation module is used for calculating a first knee joint flexion and extension angle by using accelerometer data of the first inertial measurement unit and the second inertial measurement unit, calculating a second knee joint flexion and extension angle by using gyroscope data of the first inertial measurement unit and the second inertial measurement unit, and fusing the first knee joint flexion and extension angle and the second knee joint flexion and extension angle through complementary filtering to obtain an estimated knee joint flexion and extension angle;
and the second calculation module is used for calculating the horizontal and vertical motions of the second inertial measurement unit during the activity period by using the gyroscope data of the second inertial measurement unit, estimating the stride and the minimum foot gap of each stride period, and calculating the gait speed and the pace frequency according to the stride.
9. A wearable smart knee joint and gait monitoring system, comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, carries out the steps of the wearable smart knee joint and gait monitoring method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored which, when being executed by a processor, realizes the steps of the wearable smart knee joint and gait monitoring method according to any of claims 1-7.
CN202211575559.8A 2022-12-08 2022-12-08 Wearable intelligent knee joint and gait monitoring method, device, system and storage medium Pending CN115969355A (en)

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