CN112603296A - Gait analysis method and device based on acceleration sensor - Google Patents
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Abstract
The invention discloses a gait analysis method and a device based on an acceleration sensor, comprising the following steps: acquiring acceleration data of a person during walking through an acceleration sensor, and constructing a walking coordinate system according to the acceleration data; establishing a conversion matrix from an acceleration sensor coordinate system to a terrestrial coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the terrestrial coordinate system through the conversion matrix; acquiring an included angle between a walking coordinate system and a terrestrial coordinate system according to a left-right swing rule when a person walks, and acquiring the acceleration of the walking coordinate system through a cosine algorithm; and constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, and carrying out time domain and frequency domain processing on the oscillogram to obtain a gait analysis result when the person walks. Can accomplish step count statistics, fall down and detect to and through gathering gait analysis result, can detect various gait postures, can effectively calculate equilibrium, wave form power.
Description
Technical Field
The application relates to the field of intelligent analysis, in particular to a gait analysis method based on an acceleration sensor, and also relates to a gait analysis device based on the acceleration sensor.
Background
With the development of informatization, intellectualization and networking, the embedded technology will also obtain a wide development space. Sensing means based on embedded equipment are increasingly abundant, and the application in the fields of health, medical treatment, life, traffic, education, entertainment and the like is endless.
At present, various sensors can be arranged in the embedded device, including an acceleration sensor, a direction sensor, a magnetic sensor, a gyroscope, a rotation vector sensor and the like. The acceleration sensor has wide application, and the micro-electro-mechanical system integrated on the silicon wafer is used for measuring the acceleration value in three axial directions of the coordinate system, thereby providing an excellent way for sensing the motion state of a user
Gait is the normal walking mode of people, and reflects the most common walking movement characteristics of people. Every person has walking gait, and the gait of every person can change along with the change of personal physical condition, and the changes comprise walking speed, walking posture and the like. Therefore, how to judge the physical health of an individual through detecting and recording the walking posture of a user and changing the walking posture is a problem which needs to be solved urgently at present when early warning is made in advance.
Disclosure of Invention
In order to solve the above problem, the present application provides a gait analysis method based on an acceleration sensor, including:
acquiring acceleration data of a person during walking through an acceleration sensor, and constructing a walking coordinate system according to the acceleration data;
establishing a conversion matrix from an acceleration sensor coordinate system to a terrestrial coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the terrestrial coordinate system through the conversion matrix;
acquiring an included angle between a walking coordinate system and a terrestrial coordinate system according to a left-right swing rule when a person walks, and acquiring the acceleration of the walking coordinate system through a cosine algorithm;
and constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, and carrying out time domain and frequency domain processing on the oscillogram to obtain a gait analysis result when the person walks.
Preferably, the acceleration data of the person walking includes:
acceleration data of a left-right swing direction, a gravity acceleration direction and a forward direction of a body when a person walks.
Preferably, constructing a walking coordinate system according to the acceleration data includes:
and constructing a three-axis walking coordinate system by taking the left-right swinging direction of the body of the person during walking as the X axis of the walking coordinate system, the gravity acceleration direction as the Y axis of the walking coordinate system and the advancing direction as the Z axis of the walking coordinate system.
Preferably, the establishing of the transformation matrix from the acceleration sensor coordinate system to the terrestrial coordinate system by the direction cosine transformation algorithm includes:
taking the triaxial acceleration of the acceleration sensor coordinate system as a vector;
and establishing a conversion matrix from the acceleration sensor coordinate system to the earth coordinate system according to the cosine of the three directions of the vector.
Preferably, the method further comprises the following steps:
and converting the acceleration of the acceleration sensor coordinate system into the acceleration of the terrestrial coordinate system through the conversion matrix.
Preferably, an acceleration waveform diagram is constructed according to the acceleration of the walking coordinate system, and the acceleration waveform diagram includes:
an acceleration waveform diagram with the left-right direction as an X axis, an acceleration waveform diagram with the gravity acceleration direction as a Y axis and an acceleration waveform diagram with the walking direction as a Z axis.
Preferably, the processing of the time domain and the frequency domain is performed on the oscillogram to obtain a gait analysis result when the person walks, and the processing includes:
performing time domain processing on the acceleration oscillogram by a second-order derivative inflection point method, segmenting the image, and acquiring single period data so as to acquire the step number, power and balance when a person walks; calibrating the wave crests and the wave troughs of the acceleration oscillogram by a second-order derivative inflection point method, and calculating the single-step time, the walking speed and the stride length according to the number of sampling points in the sampling frequency period of the acceleration sensor;
carrying out frequency domain processing on the acceleration oscillogram through FFT to obtain secondary wave information corresponding to frequency domain data, and analyzing the gait of a person when the person walks by analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
the gait analysis of the walking of the person is completed by the time domain and frequency domain processing of the oscillogram.
Preferably, the method further comprises the following steps:
and comparing the real-time acceleration oscillogram with the falling oscillogram to judge whether the person falls when walking.
This application provides a gait analytical equipment based on acceleration sensor simultaneously, includes:
the walking coordinate system building unit is used for acquiring acceleration data of a person during walking through an acceleration sensor and building a walking coordinate system according to the acceleration data;
the earth coordinate system conversion unit is used for establishing a conversion matrix from the acceleration sensor coordinate system to an earth coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the earth coordinate system through the conversion matrix;
the acceleration acquisition unit acquires an included angle between a walking coordinate system and a terrestrial coordinate system according to the left-right swing rule of a person during walking, and acquires the acceleration of the walking coordinate system through a cosine algorithm;
and the gait analysis unit is used for constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, processing the oscillogram in a time domain and a frequency domain and acquiring a gait analysis result when the person walks.
Preferably, the gait analysis unit includes:
the time domain processing subunit performs time domain processing on the acceleration oscillogram by a second-order derivative inflection point method, divides the image, and obtains data in a single period so as to obtain the step number, the power and the balance when a person walks; calibrating the wave crests and the wave troughs of the acceleration oscillogram by a second-order derivative inflection point method, and calculating the single-step time, the walking speed and the stride length according to the number of sampling points in the sampling frequency period of the acceleration sensor;
the frequency domain processing subunit is used for carrying out frequency domain processing on the acceleration oscillogram through FFT (fast Fourier transform), acquiring secondary wave information corresponding to frequency domain data, and analyzing the gait of a person during walking by analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
and the gait analysis subunit completes the gait analysis of the walking person by processing the time domain and the frequency domain of the oscillogram.
Drawings
Fig. 1 is a schematic flow chart of an acceleration sensor-based gait analysis method provided by the present application;
FIG. 2 is a waveform of X-axis (left and right) acceleration in a human walking coordinate system to which the present application relates;
FIG. 3 is a waveform diagram of acceleration along the Y-axis (gravitational acceleration direction) in the human walking coordinate system according to the present application;
FIG. 4 is a Z-axis (walking direction) acceleration waveform in a human walking coordinate system according to the present application;
FIG. 5 is a peak-to-valley plot of acceleration waveform plot time domain processing marks to which the present application relates;
FIG. 6 is a waveform of the acceleration waveform of FIG. 4 after processing in the frequency domain;
FIG. 7 is an acceleration waveform diagram of clutter and signal weakness to which the present application relates;
FIG. 8 is frequency domain data of the data in the acceleration waveform diagram of FIG. 7 after FFT processing;
fig. 9 is a schematic view of a gait analysis device based on an acceleration sensor provided by the present application.
The figure is a fifth embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
Fig. 1 is a schematic flow chart of an acceleration sensor-based gait analysis method provided by the present application, and the following describes the method provided by the present application in detail with reference to fig. 1.
Step S101, acquiring acceleration data of a person during walking through an acceleration sensor, and constructing a walking coordinate system according to the acceleration data.
In this application, the intelligent device of embedding acceleration sensor is for better carrying out gait analysis, and preferred can fix in the waist, also can put in the coat pocket. When the people walks, the health can produce the swing of equidirectional not, including about, from top to bottom to and fore-and-aft direction, so, acceleration data when the people walks includes: acceleration data of a left-right swing direction, a gravity acceleration direction and a forward direction of a body when a person walks.
The walking coordinate system is a three-dimensional coordinate system formed by three directions of horizontal swing and vertical floating based on the walking fore-and-aft direction of a person. Therefore, a three-axis walking coordinate system is constructed by using the lateral swing direction of the human body when the human is walking as the X-axis of the walking coordinate system, the gravity acceleration direction as the Y-axis of the walking coordinate system, and the forward direction as the Z-axis of the walking coordinate system.
Step S102, establishing a conversion matrix from the acceleration sensor coordinate system to the earth coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the earth coordinate system through the conversion matrix.
The direction cosine transform algorithm is a space analytic geometry, one coordinate system can obtain another new coordinate system after rotating once or for many times relative to the original coordinate system, and the mutual relation between the two coordinate systems can be expressed by direction cosine.
Taking the triaxial acceleration of the acceleration sensor coordinate system as a vector through a direction cosine transform algorithm; the three-axis acceleration is used as a vector, the cosines in the three directions of the three-axis acceleration are respectively the cosines of the angles between the vector and the three coordinate axes, and a conversion matrix from the acceleration sensor coordinate system to the earth coordinate system is established according to the cosines in the three directions of the vector. And finally, converting the acceleration of the sensor coordinate system into the acceleration of the terrestrial coordinate system through the conversion matrix.
The acceleration actually measured by the sensor is different due to the difference of the placing position and the placing angle, so that the measured acceleration data is not influenced by the placing position and the angle of the sensor, and the coordinate system of the sensor needs to be converted according to the three-dimensional angle of the sensor. After the coordinate system is converted, the motion attitude measured by the sensor becomes the motion attitude relative to the earth.
Step S103, acquiring an included angle between a walking coordinate system and a terrestrial coordinate system according to the left-right swing rule when a person walks, and acquiring the acceleration of the walking coordinate system through a cosine algorithm.
And (3) according to the left-right swinging rule of the walking of the person, solving an included angle between a walking coordinate system and a terrestrial coordinate system, completing the conversion of the coordinate system by utilizing a cosine conversion algorithm, and finally obtaining the acceleration of the walking coordinate system of the person.
The acceleration of the earth coordinate system is the motion attitude of the sensor relative to the earth, and the walking attitude of the person needs to be studied, and the earth coordinate system needs to be converted into the walking coordinate system again. Finally, conversion from the sensor coordinate system to the human walking coordinate system is achieved, so that the human walking acceleration data obtained finally are not affected by the arrangement position and the angle of the sensor, and the application flexibility of the sensor is improved.
And step S104, constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, and carrying out time domain and frequency domain processing on the oscillogram to obtain a gait analysis result when the person walks.
The acceleration of the human walking coordinate system comprises the accelerations of the X axis, the Y axis and the Z axis, wherein:
(1) the X-axis (left and right) acceleration waveform in the human walking coordinate system is shown in fig. 2, and it can be seen from the figure that:
a. in the X-axis direction, the period of real walking is a period of two steps, wherein the period comprises a left step and a right step, and the waveform diagram of the left foot and the waveform diagram of the right foot are approximately symmetrical up and down.
b. The processed X-axis acceleration data is stable, and the left and right balance can be well reflected.
(2) The Y-axis (gravitational acceleration direction) acceleration waveform in the human walking coordinate system is shown in fig. 3, and it can be seen from the figure that:
in the Y-axis direction, the body has a large fluctuation once when a person walks by one step, so that the power of the waveform diagram in the Y-axis can represent the magnitude of the fluctuation amplitude of the walking up and down of the person.
(3) The Z-axis (walking direction) acceleration waveform in the human walking coordinate system is shown in fig. 4, and it can be seen from the figure that:
a. the periodicity of the waveform diagram in the Z-axis direction is very obvious, and T1 and T2 respectively represent a right step and a left step, and almost take a single step as a period, so that the statistics of the period (the number of steps) by the Z-axis acceleration and the division of the waveform diagram are very inconvenient.
b. In a single period, the acceleration returns to 0 from 0- > maximum value- > minimum value- > and the whole process is just corresponding, and the toe exerts force- > lifts the foot- > falls the foot- > the sole touches the bottom to reach a stable state.
The time domain processing mainly uses a second-order derivation inflection point method, the acceleration oscillogram is subjected to time domain processing through the second-order derivation inflection point method, an image is segmented, and data of a single period are obtained, so that data such as the step number, the power and the balance when a person walks are obtained. The method comprises the following steps:
(1) real-time updating of step number by second-order derivation inflection point calculation method
a. The first derivative of the continuous function is the corresponding tangent slope, and if the first derivative is greater than 0, the first derivative is increased; if the first reciprocal is less than 0, decreasing; the first derivative is equal to 0, and is not increased or decreased. The second derivative may reflect the relief of the image. The second derivative is greater than 0, the image is concave; the second derivative is less than 0, and the image is convex; the second derivative is equal to 0 and is not concave or convex.
b. And updating the statistics of the step number in real time according to the function inflection point judged by the second-order derivation.
(2) Tumble warning
Through real-time acceleration, compare with falling oscillogram, whether the judgement people takes place to fall when walking.
(3) Single step time, speed, stride
The wave crests and the wave troughs are calibrated by a second-order derivative inflection point method, and the single step time, the walking speed and the stride length can be accurately calculated by the number of sampling points in the sampling frequency period of the sensor. The effect on the waveform icon is shown in fig. 5.
(4) Calculating left and right waveforms, front and back waveforms, marking the power of the upper and lower waveforms by a rear acceleration oscillogram, extracting acceleration numerical value information of x, y and z axes in each period, calculating the energy of the triaxial oscillogram within statistical time, and then respectively calculating the power corresponding to the three axes.
(5) Balance of human walking left and right (x axis), stability of front and back (z axis) and floating treatment of up and down (y axis)
a. Extracting acceleration information of x, y and z axes in each period through the marked acceleration oscillogram, and calculating the energy of the oscillogram in a single period;
b. based on the single period energy and the average period energy, the MSE method is utilized to calculate the numerical values of left-right balance, front-back stability and up-down floating.
The gait of walking difficulty and walking trembling shows that the waveform of the gait is weak in signal and much in clutter, and the acceleration oscillogram is subjected to frequency domain processing through FFT according to the passing condition to obtain secondary wave information corresponding to frequency domain data, and the gait of a person during walking is analyzed through analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
FIG. 6 is a plot of the data of FIG. 4 after FFT processing in MATLAB. In connection with fig. 4, it can be seen from fig. 6 that:
a. the main frequency 1.85hz where the first amplitude is located is the first main frequency taking a single step as a period, and the frequency directly represents the walking speed and is consistent with the single step time obtained by a second-order derivation method.
b. The second main frequency is about 4hz, which is just the waveform corresponding to the process of lifting the legs by the foot with force, namely the first primary wave frequency, and the frequency and the amplitude directly represent the walking force.
For the situation of more clutter and weak signals, as shown in fig. 7, the image is difficult to segment by using a second-order derivation inflection point method according to a group of test data of the old who is inconvenient to walk and is embedded in a jacket pocket of a smart phone of an acceleration sensor. Fig. 8 is the frequency domain data after FFT processing using the data in fig. 7, and it can be seen from fig. 7 and 8 that although the group of data has many clutter and weak signals, the walking frequency can be still obtained, and the step data can still be calculated in combination with the movement.
Counting a and step numbers by the time domain and frequency domain processing of the oscillogram; b. tumble analysis; c. single step time, speed, stride, force; d. the ratio of the first and second secondary frequencies to the secondary frequency; e. left-right, front-back, and up-down balance; f. left and right, front and back, up and down power and individual gait characteristic data, thereby realizing tumble early warning and alarming.
Based on the same inventive concept, the present application further provides an acceleration sensor-based gait analysis device 900, as shown in fig. 9, including:
a walking coordinate system constructing unit 910, which acquires acceleration data of a person walking through an acceleration sensor, and constructs a walking coordinate system according to the acceleration data;
the earth coordinate system conversion unit 920 establishes a conversion matrix from the acceleration sensor coordinate system to the earth coordinate system through a direction cosine conversion algorithm, and converts the acceleration sensor coordinate system into the earth coordinate system through the conversion matrix;
the acceleration obtaining unit 930 obtains an included angle between a walking coordinate system and a terrestrial coordinate system according to a left-right swing rule when a person walks, and obtains an acceleration of the walking coordinate system through a cosine algorithm;
and the gait analysis unit 940 is used for constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, processing the oscillogram in a time domain and a frequency domain and acquiring a gait analysis result of the person during walking.
Preferably, the gait analysis unit includes:
the time domain processing subunit performs time domain processing on the acceleration oscillogram by a second-order derivative inflection point method, divides the image, and obtains data in a single period so as to obtain the step number, the power and the balance when a person walks; calibrating the wave crests and the wave troughs of the acceleration oscillogram by a second-order derivative inflection point method, and calculating the single-step time, the walking speed and the stride length according to the number of sampling points in the sampling frequency period of the acceleration sensor;
the frequency domain processing subunit is used for carrying out frequency domain processing on the acceleration oscillogram through FFT (fast Fourier transform), acquiring secondary wave information corresponding to frequency domain data, and analyzing the gait of a person during walking by analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
and the gait analysis subunit completes the gait analysis of the walking person by processing the time domain and the frequency domain of the oscillogram.
This application is based on embedding acceleration sensor's smart machine, through many people's experiments many times, has verified the reliability, stability and the commonality of algorithm, can accurately accomplish the step count statistics, falls down to detect to and through summarizing gait analysis result, can detect various gait postures, can effectively calculate equilibrium, wave form power.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (10)
1. A gait analysis method based on an acceleration sensor is characterized by comprising the following steps:
acquiring acceleration data of a person during walking through an acceleration sensor, and constructing a walking coordinate system according to the acceleration data;
establishing a conversion matrix from an acceleration sensor coordinate system to a terrestrial coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the terrestrial coordinate system through the conversion matrix;
acquiring an included angle between a walking coordinate system and a terrestrial coordinate system according to a left-right swing rule when a person walks, and acquiring the acceleration of the walking coordinate system through a cosine algorithm;
and constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, and carrying out time domain and frequency domain processing on the oscillogram to obtain a gait analysis result when the person walks.
2. The method of claim 1, wherein the acceleration data of the person while walking comprises:
acceleration data of a left-right swing direction, a gravity acceleration direction and a forward direction of a body when a person walks.
3. The method of claim 1, wherein constructing a walking coordinate system from the acceleration data comprises:
and constructing a three-axis walking coordinate system by taking the left-right swinging direction of the body of the person during walking as the X axis of the walking coordinate system, the gravity acceleration direction as the Y axis of the walking coordinate system and the advancing direction as the Z axis of the walking coordinate system.
4. The method of claim 1, wherein establishing a transformation matrix from the acceleration sensor coordinate system to the terrestrial coordinate system by a direction cosine transformation algorithm comprises:
taking the triaxial acceleration of the acceleration sensor coordinate system as a vector;
and establishing a conversion matrix from the acceleration sensor coordinate system to the earth coordinate system according to the cosine of the three directions of the vector.
5. The method of claim 4, further comprising:
and converting the acceleration of the acceleration sensor coordinate system into the acceleration of the terrestrial coordinate system through the conversion matrix.
6. The method of claim 1, wherein an acceleration waveform map is constructed from the accelerations of the walking coordinate system, the acceleration waveform map comprising:
an acceleration waveform diagram with the left-right direction as an X axis, an acceleration waveform diagram with the gravity acceleration direction as a Y axis and an acceleration waveform diagram with the walking direction as a Z axis.
7. The method according to claim 1, wherein the time domain and the frequency domain processing are performed on the oscillogram to obtain a gait analysis result of a person walking, and the method comprises the following steps:
performing time domain processing on the acceleration oscillogram by a second-order derivative inflection point method, segmenting the image, and acquiring single period data so as to acquire the step number, power and balance when a person walks; calibrating the wave crests and the wave troughs of the acceleration oscillogram by a second-order derivative inflection point method, and calculating the single-step time, the walking speed and the stride length according to the number of sampling points in the sampling frequency period of the acceleration sensor;
carrying out frequency domain processing on the acceleration oscillogram through FFT to obtain secondary wave information corresponding to frequency domain data, and analyzing the gait of a person when the person walks by analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
the gait analysis of the walking of the person is completed by the time domain and frequency domain processing of the oscillogram.
8. The method of claim 7, further comprising:
and comparing the real-time acceleration oscillogram with the falling oscillogram to judge whether the person falls when walking.
9. A gait analysis device based on an acceleration sensor, characterized by comprising:
the walking coordinate system building unit is used for acquiring acceleration data of a person during walking through an acceleration sensor and building a walking coordinate system according to the acceleration data;
the earth coordinate system conversion unit is used for establishing a conversion matrix from the acceleration sensor coordinate system to an earth coordinate system through a direction cosine conversion algorithm, and converting the acceleration sensor coordinate system into the earth coordinate system through the conversion matrix;
the acceleration acquisition unit acquires an included angle between a walking coordinate system and a terrestrial coordinate system according to the left-right swing rule of a person during walking, and acquires the acceleration of the walking coordinate system through a cosine algorithm;
and the gait analysis unit is used for constructing an acceleration oscillogram according to the acceleration of the walking coordinate system, processing the oscillogram in a time domain and a frequency domain and acquiring a gait analysis result when the person walks.
10. The apparatus of claim 9, wherein the gait analysis unit comprises:
the time domain processing subunit performs time domain processing on the acceleration oscillogram by a second-order derivative inflection point method, divides the image, and obtains data in a single period so as to obtain the step number, the power and the balance when a person walks; calibrating the wave crests and the wave troughs of the acceleration oscillogram by a second-order derivative inflection point method, and calculating the single-step time, the walking speed and the stride length according to the number of sampling points in the sampling frequency period of the acceleration sensor;
the frequency domain processing subunit is used for carrying out frequency domain processing on the acceleration oscillogram through FFT (fast Fourier transform), acquiring secondary wave information corresponding to frequency domain data, and analyzing the gait of a person during walking by analyzing the secondary wave information; acquiring corresponding walking frequency through frequency domain data, and acquiring step number by combining with exercise time;
and the gait analysis subunit completes the gait analysis of the walking person by processing the time domain and the frequency domain of the oscillogram.
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