WO2018230787A1

WO2018230787A1 - Device and method for analyzing gait by using acceleration sensor worn on ankle

Info

Publication number: WO2018230787A1
Application number: PCT/KR2017/013842
Authority: WO
Inventors: 남윤영; 이수환
Original assignee: 순천향대학교 산학협력단
Priority date: 2017-06-15
Filing date: 2017-11-29
Publication date: 2018-12-20
Also published as: KR101998465B1; KR20180136789A

Abstract

The present invention provides a device and method for analyzing a gait by using an acceleration sensor worn on an ankle. A device for analyzing a gait by using an acceleration sensor worn on an ankle according to an aspect of the present invention comprises: a reception unit for receiving a signal according to the gait of a patient from an acceleration sensor included in a band worn on the ankle of the patient; and an analysis unit for preprocessing the received signal according to the gait of the patient, extracting features from the preprocessed signal, and calculating the energy of the gait and the correlation coefficient for characteristics of the gait by using the extracted features, so as to analyze the gait of the patient.

Description

Gait analysis device and method using an acceleration sensor worn on the ankle

The present invention relates to a gait analysis device and method using an acceleration sensor worn on the ankle, more specifically gait using an acceleration sensor worn on the ankle that can analyze the gait of a patient having difficulty gait. An analysis device and method.

This application claims priority based on Korean Patent Application No. 10-2017-0075982 filed on June 15, 2017, and all the contents disclosed in the specification and drawings of the application are incorporated in this application.

Walking is one of the most basic human behaviors. At this time, the gait may be used as a measure to check basic body condition or to find signs of abnormality in the body. Therefore, by analyzing a person's gait, it is possible to examine the state of the body or detect an abnormality in the body early.

At this time, the gait analysis method uses image processing, flow sensors, and wearable sensors.

On the other hand, it is very important to extract the gait characteristics in patients with neurological diseases such as Parkinson's disease. In other words, patients with nervous system disorders who feel uncomfortable walking may analyze gait as a way to see the recovery process after surgery.

Therefore, there is a need for research on technology for more accurately analyzing the gait of patients with neurological diseases.

(Patent Document 1) Korean Patent Publication No. 2012-0085064 (2012.07.31 publication)

The present invention has been proposed to solve the above problems, an ankle that is attached to the acceleration sensor to the ankle of the patient having difficulty walking, ankle that can analyze the gait of the patient using the signal obtained from the acceleration sensor It is an object of the present invention to provide a gait analysis device and method using an acceleration sensor worn on the.

Other objects and advantages of the present invention can be understood by the following description, and will be more clearly understood by one embodiment of the present invention. It will also be appreciated that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the claims.

Gait analysis device using an acceleration sensor worn on the ankle according to an aspect of the present invention for achieving the above object, the signal according to the gait of the patient from the acceleration sensor included in the band worn on the patient's ankle Receiving unit for receiving; And preprocessing the signal according to the gait of the received patient, extracting a feature from the preprocessed signal, calculating the energy of the gait using the extracted feature, and calculating a correlation coefficient for the gait characteristics. Includes; an analysis unit for analyzing the gait of the.

The analysis unit, a pre-processing unit for removing noise from the signal; And a feature extractor which extracts a feature on a time domain, a feature on a frequency domain, and an energy feature from the signal from which the noise is removed.

The preprocessor may remove noise from the signal using a digital low pass filter.

The feature extractor may extract a feature on a time domain by calculating a vertical component from the preprocessed signal.

The feature extractor may extract a feature in a frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform.

The feature extractor may calculate the energy value of the gait by Equation X, and calculate a correlation coefficient for the gait characteristic by Equation Y.

[Equation X]

Here, x _i are values converted into a frequency band using an FFT, and w is a window size.

[Equation Y]

Here, x and y are the values calculated by the energy formula for the walking of the left foot and the right foot,

Is the standard deviation of x and y values, and cov (x, y) is the covariance of x and y. P is a correlation coefficient value of x and y.

Step analysis method in the gait analysis device using an acceleration sensor worn on the ankle according to another aspect of the present invention for achieving the above object, from the acceleration sensor included in the band worn on the patient's ankle Receiving a signal according to a gait; Preprocessing the signal according to the gait of the received patient; Extracting features from the preprocessed signal; And calculating the energy of the gait using the extracted features and calculating a correlation coefficient for the characteristics of the gait.

In the preprocessing of the signal according to the step of the patient, the digital low pass filter may be used to remove noise from the signal.

Extracting a feature from the preprocessed signal includes: extracting a feature in a time domain by calculating a vertical component in the preprocessed signal; And extracting a feature on a frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform.

In the step of calculating the energy of the gait using the extracted feature and calculating the correlation coefficient for the characteristics of the gait, the energy value of the gait is calculated by Equation X, and the gait characteristic is calculated by Equation Y. The correlation coefficient can be calculated.

[Equation X]

[Equation Y]

According to an aspect of the present invention, by using a small acceleration sensor can minimize the inconvenience that the patient feels in the analysis.

The gait analysis algorithm can analyze the gait of the patient more accurately.

The effect obtained in the present invention is not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. .

The following drawings attached to this specification are illustrative of the preferred embodiments of the present invention, and together with the specific details for carrying out the invention serve to further understand the technical spirit of the present invention, the present invention to such drawings It should not be construed as limited to the matters described.

1 is a view showing a schematic configuration of a gait analysis system according to an embodiment of the present invention,

2 is a view showing a stance and a swing according to an embodiment of the present invention;

3 is a view showing a schematic configuration of a gait analysis device according to an embodiment of the present invention,

Figure 4 is a diagram showing the flow of the gait analysis method according to an embodiment of the present invention.

The above objects, features, and advantages will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, whereby those skilled in the art may easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, when it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Throughout the specification, when a part is said to "include" a certain component, it means that it can further include other components, without excluding other components unless otherwise stated. In addition, the “…” described in the specification. “Unit” refers to a unit that processes at least one function or operation, which may be implemented in hardware or software, or a combination of hardware and software.

1 is a view showing a schematic configuration of a gait analysis system according to an embodiment of the present invention, Figure 2 is a view showing a stance and a swing according to an embodiment of the present invention, Figure 3 is a view of the present invention It is a figure which shows schematic structure of the gait analyzer according to the embodiment.

Referring to FIG. 1, the gait analysis system according to the present embodiment includes an acceleration sensor 100 and a gait analyzer 200 attached to an ankle of a patient. In this case, the patient may be a patient suffering from a neurological disease such as Parkinson's disease. As the acceleration sensor 100 and the gait analyzer 200 are connected by a network, a signal generated by the acceleration sensor 100 may be transmitted to the gait analyzer 200. In this case, the network may be a wireless communication such as Bluetooth.

The acceleration sensor 100 is for acquiring a signal according to a patient's walking and may be included in a band. Accordingly, the patient may wear the band on the ankle, so that the acceleration sensor 100 may be attached to the ankle of the patient. In this case, as the band is worn on both ankles of the patient, the acceleration sensor 100 included in the band may acquire a signal according to the patient's walking. The signal according to the walking of the patient may be a signal generated according to the walking of the patient. The acceleration sensor 100 may be a very small Attitude Heading Reference System (AHRS) module having a three-axis acceleration sensor, a three-axis gyroscope, and a three-axis geomagnetic sensor. The acceleration sensor 100 may support data update and output speeds up to 1000 Hz, and output a pure acceleration value from which gravity components are removed. The acceleration sensor 100 according to the present embodiment may be EBIMU-9DOFV2 manufactured by E2BOX. The acceleration sensor 100 may include three data output modes, such as an ASCII output mode, a hex (binary) output mode, and a polling output mode. In this embodiment, it is assumed that the ASCII output mode is used as the data output mode. The operating power of the three-axis acceleration sensor 100 may use a 4.5V power supplied from a battery (not shown). In addition, a digital low pass fitter designed inside the sensor may be used to remove noise included in the signal output from the acceleration sensor 100. On the other hand, since the acceleration sensor 100 is small, it takes up less space and can reduce discomfort for the patient wearing the band.

The gait analyzing apparatus 200 may analyze the gait of the patient using a signal according to the walking of the patient received from the acceleration sensor 100.

Referring to FIG. 3, the gait analyzing apparatus 200 includes a receiver 210 and an analyzer 230.

The receiver 210 receives a signal according to the walking of the patient from the acceleration sensor 100. In this case, the signal according to the walking of the patient may be a signal according to the walking of the patient. In other words, the receiver 210 receives a signal according to the walking of the patient from the acceleration sensor 100 included in the band worn on the ankle of the patient.

The analysis unit 230 may analyze the gait of the patient using a signal according to the walking of the patient received by the receiver 210. The analyzer 230 preprocesses the signal according to the walking of the patient, extracts a feature from the preprocessed signal, and uses the extracted feature to calculate a correlation coefficient for the energy of the gait and the characteristics of the gait. Calculate to analyze the patient's gait. In this case, the analyzer 230 may include a preprocessor 231 and a feature extractor 233.

The preprocessor 231 may perform preprocessing by removing noise from a signal according to a patient's walking. In this case, the preprocessor 231 may remove noise from the signal using a digital low pass filter. The noise may be noise in a high-frequency noise band. In addition, a moving average filter may be used to give an overall smoothing effect of the signal. In this case, the signal may be a signal according to the gait of the patient measured by the acceleration sensor 100. The signal can be measured by the acceleration sensor 100 included in the band worn on the left and right ankles of the patient, the signal is transmitted to the gait analysis device 200 through a network to be stored in a separate storage It may be. On the other hand, the gait analysis device 200 may be a smart phone with a gait analysis application. In this case, when the gait analysis device 200 is a smartphone, a signal may be transmitted by Bluetooth communication.

The feature extractor 233 may extract a feature from a signal from which noise is removed by the preprocessor 231. In detail, the feature extractor 233 may include a feature on the time domain, a feature on the frequency domain, and an energy feature on the signal from which the noise is removed by the preprocessor 231. Can be extracted.

The feature extractor 233 may extract a feature on a time domain by calculating a vertical component from a preprocessed signal. At this time, the reason for extracting the feature on the time domain is to divide a human's gait into a stance and a swinging step. As shown in FIG. 1, a human gait consists of a stance and a swing. Stance refers to the foot touching the ground, and swinging refers to the foot being held up until one foot hits the ground again. In this case, an event that occurs at the start of a stance phase is called a heel strike, and an event that occurs at the start of a swing phase (or the end of a stance phase) is heeled off. off and / or toe off. Accordingly, the feature extractor 233 may find a heel off and / or a toe off and a heel strike point, divide the stance and the swing, and the heel off And / or compute the vertical component from the signal obtained by the acceleration sensor 100 to find the toe off and heel strike points. In this case, the vertical component may be calculated by Equation 1 below.

Where vertical component vector

Then

Mx ', my' and mz 'are the mean values for the intervals sampled on each axis. In this case, x ', y', z 'is the acceleration signal value coming from the three-axis acceleration sensor.

In this case, it is a vector value of a specific point in the sampled section, and N is the length of the sampled section.

For

The estimated value of

It is calculated from the internal vertical components.

Is the result calculated by the internal components,

Is an estimated vector value.

On the other hand, the characteristics of the gait such as movement time, stride length, period, speed, etc. may be calculated based on heel off and / or toe off and heel strike.

The feature extractor 233 may extract a feature in the frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform (FFT). The feature extractor 233 may convert a calculated vertical component into a frequency band by using a fast Fourier transform in order to check the value of the frequency band represented by gait between normal and abnormal persons. In this case, various frequency characteristics may appear according to the walking style of the person according to the converted result.

The feature extractor 233 may calculate an energy value of the gait to estimate a correlation coefficient of the gait of the left and right feet of the patient. In this case, the energy value of the gait can be calculated by Equation 2 below.

Here, x _i is a value converted to the frequency band using the FFT, w is the window size, and the overlapping setting so as not to miss the components of the gait according to the walking characteristics of the patient.

In addition, the correlation may be calculated by Equation 3 below.

Hereinafter, with reference to FIG. 4, the black gait analysis method in the gait analysis device 200 as described above will be described.

Referring to FIG. 4, first, the gait analyzing apparatus 200 receives a signal according to the gait of the patient from the acceleration sensor 100 included in the band worn on the ankle of the patient (S410).

The gait analyzing apparatus 200 preprocesses the signal by using the signal according to the gait of the patient (S420). The gait analyzing apparatus 200 may perform preprocessing by removing noise from a signal according to a patient's walking. In this case, the gait analyzer 200 may remove noise from a signal using a digital low pass filter.

The gait analyzing apparatus 200 extracts a feature from a preprocessed signal (S430). The gait analyzer 200 may extract a feature on a time domain and a feature on a frequency domain from a signal from which noise is removed. The gait analyzer 200 may extract a feature on a time domain by calculating a vertical component from a preprocessed signal. In this case, the vertical component may be calculated by Equation 1 described above. The gait analyzing apparatus 200 may extract a feature on the frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform (FFT).

The gait analyzing apparatus 200 calculates the energy of the gait using the extracted feature and calculates a correlation coefficient with respect to the gait of the gait (S440). In this case, the energy value of the gait may be calculated by Equation 2 described above, and the correlation may be calculated by Equation 3 described above.

Methods according to an embodiment of the present invention may be implemented in the form of program instructions that may be implemented as an application or executed through various computer components, and may be recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be those specially designed and constructed for the present invention, and may be known and available to those skilled in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs, DVDs, and magneto-optical media such as floptical disks. media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the process according to the invention, and vice versa.

While this specification includes many features, such features should not be construed as limiting the scope of the invention or the claims. In addition, the features described in the individual embodiments of the present specification can be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment of the present specification can be implemented individually in various embodiments or in combination as appropriate.

Although the operations have been described in a particular order in the drawings, they should not be understood as being performed in a particular order as shown or in a sequence of successive orders, or all of the described actions being performed to obtain a desired result. Multitasking and parallel processing may be advantageous in certain circumstances. In addition, it should be understood that the division of various system components in the above-described embodiments does not require such division in all embodiments. The app components and systems described above may generally be packaged in a single software product or multiple software products.

The present invention described above is capable of various substitutions, modifications, and changes without departing from the technical spirit of the present invention for those skilled in the art to which the present invention pertains. It is not limited by the drawing.

Claims

Receiving unit for receiving a signal according to the step of the patient from the acceleration sensor included in the band worn on the ankle of the patient; And

By preprocessing the signal according to the gait of the received patient, extracting features from the preprocessed signal, using the extracted feature to calculate the energy of the gait and calculate the correlation coefficient for the gait characteristics of the patient Gait analysis device using an acceleration sensor worn on the ankle, including; analysis unit for analyzing the gait.
The method of claim 1,

The analysis unit,

A preprocessor to remove noise from the signal; And

A gait analysis device using an acceleration sensor worn on ankle, including; a feature extractor extracting a feature on a time domain, a feature on a frequency domain, and an energy feature from the signal from which the noise is removed.
The method of claim 2,

The preprocessing unit,

Gait analysis device using an acceleration sensor worn on the ankle to remove noise from the signal using a digital low pass filter.
The method of claim 2,

The feature extraction unit,

A gait analysis device using an acceleration sensor worn on the ankle to extract a feature on a time domain by calculating a vertical component from the preprocessed signal.
The method of claim 2,

The feature extraction unit,

The gait analysis device using the acceleration sensor worn on the ankle to extract the feature on the frequency domain by converting the calculated vertical components into a frequency band using a fast Fourier transform.
The method of claim 2,

The feature extraction unit,

A gait analysis device using an acceleration sensor, which is worn on the ankle to calculate the energy value of the gait by Equation X, and calculates a correlation coefficient for the gait characteristic by Equation Y.

[Equation X]

Here, x i are values converted into a frequency band using an FFT, and w is a window size.

[Equation Y]

Here, x and y are the values calculated by the energy formula for the walking of the left foot and the right foot,
Is the standard deviation of x and y values, and cov (x, y) is the covariance of x and y. P is a correlation coefficient value of x and y.
In the gait analysis method in a gait analysis device using an acceleration sensor worn on the ankle,

Receiving a signal according to the step of the patient from the acceleration sensor included in the band worn on the ankle of the patient;

Preprocessing the signal according to the gait of the received patient;

Extracting features from the preprocessed signal; And

The step analysis method comprising the step of calculating the energy of the gait using the extracted features and calculating the correlation coefficient for the characteristics of the gait.
The method of claim 7, wherein

In the step of pre-processing the signal according to the step of the received patient,

A gait analysis method for removing noise from the signal using a digital low pass filter.
The method of claim 7, wherein

Extracting a feature from the preprocessed signal,

Extracting features on a time domain by calculating vertical components in the preprocessed signal; And

A step of extracting a feature on a frequency domain by converting the calculated vertical component into a frequency band using a fast Fourier transform.
The method of claim 7, wherein

In the step of calculating the energy of the gait using the extracted features and calculating the correlation coefficient for the characteristics of the gait,

A gait analysis method for calculating an energy value of gait by Equation X, and calculating a correlation coefficient for gait characteristics by Equation Y.

[Equation X]

Here, x i are values converted into a frequency band using an FFT, and w is a window size.

[Equation Y]

Here, x and y are the values calculated by the energy formula for the walking of the left foot and the right foot,
Is the standard deviation of x and y values, and cov (x, y) is the covariance of x and y. P is a correlation coefficient value of x and y.