CN111631722A - Parkinson's gait analysis system and method based on optical fiber microbend pressure sensing - Google Patents
Parkinson's gait analysis system and method based on optical fiber microbend pressure sensing Download PDFInfo
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Abstract
A gait analysis system and method for Parkinson's disease people based on optical fiber microbending pressure sensing comprises an insole, wherein a deformer is arranged below the heel of the insole; the optical fiber microbend pressure sensor is used for collecting plantar pressure data; the Arduino yun board is used for processing and wirelessly and remotely transmitting plantar pressure data; and the server receives the processed data from the Arduino yun board, further transmits the data to a mobile phone APP software interface for display after gait related processing analysis. And the mobile phone APP software is used for displaying gait analysis information. In addition, a gait processing method combining a time domain and a frequency domain is provided, and APP design facing a doctor user group for displaying patient gait analysis information is completed. The invention has the advantages of no radio frequency interference and electromagnetic interference on output, high sensitivity, detachability, convenient maintenance, comfortable use, wireless transmission, convenient gait information checking and the like.
Description
Technical Field
The invention relates to the field of rehabilitation detection, in particular to a system and a method for analyzing gait of a Parkinson's disease person based on optical fiber microbending pressure sensing.
Background
Gait is the most intuitive factor reflecting lower limb movement ability, and objective and accurate gait analysis can provide quantitative evaluation for various diseases causing abnormal gait. Among these, rigidity and tremor of the patient's limbs can lead to typical parkinsonian gait, such as a panic gait, frozen gait, immobilized gait, etc., which causes great inconvenience to the patient's daily life. Therefore, the gait characteristics are researched, and the gait characteristics are greatly helpful for understanding the Parkinson disease principle and judging the state of illness of the Parkinson patients.
The plantar pressure sensor realizes the basic gait data processing and analysis by collecting the data of the plantar pressure of the patient during walking. The traditional plantar pressure sensor is mainly a piezoelectric type or piezoresistive type sensor, is mainly a metal device, is easy to be interfered by radio frequency and electromagnetic, is hard in texture and is not comfortable when being contacted with a human body. In addition, the traditional plantar pressure sensor system only analyzes and processes the plantar pressure sensor system according to a time domain, and ignores information of a frequency domain. Further, the general plantar pressure sensor system does not consider the convenience of the user in viewing information. Therefore, a system and a method for analyzing gait of Parkinson's disease people based on optical fiber microbending pressure sensing are designed to solve the problems.
Disclosure of Invention
Aiming at the defects or shortcomings in the prior art, the invention provides a system and a method for analyzing gait of a Parkinson's disease person based on optical fiber microbending pressure sensing.
The technical scheme of the invention is as follows:
the system for analyzing gait of Parkinson's disease people based on optical fiber microbending pressure sensing is characterized by comprising a flexible pressure device, wherein the flexible pressure device comprises a left foot insole and a right foot insole, a left deformer is arranged below the heel of the left foot insole, a right deformer is arranged below the heel of the right foot insole, a left foot microbending pressure sensing optical fiber is arranged in the left deformer, a right foot microbending pressure sensing optical fiber is arranged in the right deformer, one end of the left foot microbending pressure sensing optical fiber and one end of the right foot microbending pressure sensing optical fiber are respectively connected with a laser through a beam splitter, the other end of the left foot microbending pressure sensing optical fiber is connected with a first signal input pin of a central processing unit through a left detector, the other end of the right foot microbending pressure sensing optical fiber is connected with a second signal input pin of the central processing unit through a right detector, the central processing unit is connected with the server, and the server is connected with the mobile phone APP software client.
Central processing unit adopts Arduino yun board, the last A0 pin of Arduino yun board does first signal input pin, the last A1 pin of Arduino yun board does the second signal input pin, the plantar pressure data that the detector conveying was come is handled and wireless remote transmission to Arduino yun board, the server is right plantar pressure data after Arduino yun board was handled carries out gait analysis after gives gait analysis with gait analysis result information transmission for cell-phone APP software client with show on the cell-phone APP software interface.
The laser is connected with the optical splitter by a coupler, the left deformer and the right deformer both comprise upper and lower sawtooth occlusion structures, the microbending pressure sensing optical fiber is positioned on the occlusal surfaces of the upper and lower sawtooth occlusion structures, when the deformer is under pressure, the optical fiber is correspondingly microbending due to extrusion, so that microbending loss of optical fiber optical signals is caused, and the optical fiber optical signals with microbending loss are converted into electric signals by the detector and transmitted to the Arduino yun board; the left deformer and the right deformer both comprise detachable structures with dovetail grooves and dovetail bodies embedded, and the deformer is made of soft rubber with Shore hardness of 70 degrees manufactured by a compound molding process.
A gait processing method combining a time domain and a frequency domain is characterized by comprising the following steps:
step 1, preprocessing data, namely performing low-pass filtering on original pressure data by using a Butterworth low-pass filter to remove interference of noise points;
and 6, extracting the dominant frequency value of the power spectrum, namely the frequency value corresponding to the peak value of the power spectrum curve.
The step 1 comprises a Butterworth low-pass filter which realizes the function of removing noise by suppressing high-frequency components, and the square function of a transformation function H (w) is as follows:
wherein n is the order of the Butterworth low-pass filter, the higher the order is, the faster the amplitude attenuation speed of the filter in the stop band is, and WCIs the cut-off frequency, i.e. the frequency at which the amplitude drops by 3 db, W is the independent variable of the transformation function, representing the frequency value; the step 1 includes performing discrete fourier transform on an acquired original data sequence x (n), where n is a positive integer, n is 1,2, …, and L (L is an acquired data length), to obtain an acquired original data sequence x (n)
F (w) is a discrete fourier transform function, j is an imaginary unit, and is multiplied by a transform function h (w) of a Butterworth low-pass filter to obtain g (w) f (w) h (w), g (w) is a fourier transform function of a denoised data sequence, high-frequency components in original data are suppressed to realize a denoising and smoothing function, and g (w) is subjected to inverse fourier transform to obtain a denoised data sequence g (x), wherein x is 1,2, …, and L is a final sequence length.
The step 2 comprises the following steps: finding the highest peak sequence f of the waveform by using findpeaks functiondatamax(i) And its corresponding time series ftimemax(i) And lowest valley sequence fdatamin(i) And its corresponding time series ftimemin(i) I is a positive integer, i is 1,2, …, n is the length of a wave crest sequence (or a wave trough sequence), namely the pressure amplitude of the highest point and the lowest point in each gait cycle and the corresponding time; in order to filter out peaks which are erroneously counted due to small fluctuations of the curve, a threshold value for the amplitude of the peak protrusion is set, which is set as the average of the amplitude differences of all peaks and their adjacent valleys, i.e. the average of the amplitude differences
n is the length of the peak sequence (or trough sequence) and peaks below this threshold are ignored.
The step 3 comprises a pressure peak value, a step frequency P, a support phase time ST, a swing phase time WT and a gait cycle proportion, wherein the pressure peak value represents the maximum value of a pressure sequence in a single gait cycle, and the unit N is the expression of the maximum peak value sequence f obtained in the step 2datamax(i) (ii) a The step frequency P tableThe number of steps taken in unit time is shown, unit step/s, expression: p ═ num (f)datamax(i) T, where num (f)datamax(i) Is the number of peaks (i.e., steps) obtained in one experiment, and t is the length of the experiment; the support phase time ST represents the contact time of the foot and the ground in a single gait cycle, and the unit s is the difference between the moments corresponding to the two adjacent lowest wave troughs, and the expression is as follows: (st (i) ═ f)timemin(i+1)-ftimemin(i) I-1, 2, …, n-1, n being the length of the trough sequence; the swing phase time WT represents the time that the foot is not in contact with the ground in a single gait cycle, and the unit s is the difference between the moments corresponding to the two adjacent highest peak values, and the expression: WT (i) ═ ftimemax(i+1)-ftimemax(i) I is 1,2, …, n-1, n is the length of the peak sequence; the gait cycle proportion represents the proportion of the support phase and the swing phase in a single gait cycle, and the expression is as follows: ratio of supporting phase time
Swing phase time ratio
In the formula
Is the sum of the support phase time in one experiment,
is the sum of the swing phase times in one experiment.
is provided with
Is x1,x2,…,xnThe arithmetic mean of (A) is
And because of
So when
The above equation is minimum, i.e. the best value of measurement is the arithmetic mean of the measured values, the direct current component is approximately considered to be equal to the arithmetic mean of the measured values, and the original pressure data is subtracted by the mean value, i.e. the direct current component of the filtered signal.
The step 5 comprises the following steps:
(1) the power spectral density equation is defined as follows:
wherein F (w) is a Fourier transform function of the original sequence, T represents the period of the original sequence, and the power spectrum of the signal is estimated by using a set of observed data in the actual calculation of the power spectrum curve;
(2) segmenting the acquired data sequence, dividing the signal serial number x (N) with the length of N into L segments, wherein the length of each segment of the sequence is M, and then respectively calculating a power spectrum for each segment and then averaging to reduce the random fluctuation phenomenon;
(3) selecting a proper window function to multiply the segmented data sequence, and reducing the sudden change at two ends of signal truncation by using the window function;
(4) respectively calculating the power spectrum of each section of data obtained in the step 5, wherein the windowed data x (n) is obtained for each section, n is 1,2 …, M, n is a positive integer, M is the length of each section of sequence, and the power spectrum is estimated by using the discrete fourier transform, and the calculation formula is as follows:
wherein x (n) is the data sequence after windowing, M is the sequence length, P (i) is the power spectrum estimated value of the ith sequence, and i is a positive integer.
(5) Averaging the power spectrum of the L-segment data to obtain the final power spectrum value of the data, i.e. the power spectrum value
Wherein i is a positive integer, i is 1,2, …, L is the number of segments divided by the original data sequence,
the final power spectrum value.
A physician-user-population oriented APP design for displaying patient gait analysis information, comprising: the system comprises a user login module, a patient management module, a patient information menu module, a patient basic information module, an analysis data display module, a disease grading module and a data visualization module.
The invention has the following technical effects: 1. the optical fiber microbend pressure sensor is based on the optical fiber microbend loss principle, and has the advantages that the output is free from radio frequency interference and electromagnetic interference, the sensitivity is high, the optical fiber microbend pressure sensor is detachable and convenient to maintain; the flexible deformer is used, so that the contact comfort of the human body is improved; the Arduino yun board comprises a Wi-Fi module and can carry out wireless remote transmission. 2. The gait analysis processing method combining the time domain and the frequency domain gives consideration to the advantages that the time domain can intuitively and conveniently describe the gait characteristics and the frequency domain can carry out pathological study on the gait from a deeper degree. 3. Completes the APP design facing the doctor user group for displaying the gait analysis information of the patient, has the advantages of large user capacity, capability of managing multiple objects, lower development cost, providing professional angles of multiple disease analysis and the like
Drawings
FIG. 1 is a system block diagram of the present invention.
FIG. 2 is a schematic diagram of the hardware apparatus of the present invention.
Fig. 3 is a detailed view of the connection structure of the present invention.
Figure 4 is a schematic representation of the insole and deformer of the present invention.
Fig. 5 is a schematic diagram of a deformer of the present invention.
FIG. 6 is a schematic view of an Arduino yun plate of the present invention.
Fig. 7 is a flow chart of the overall steps of gait data processing by time domain and frequency domain combination according to the invention.
FIG. 8 is a flow chart of the present invention for estimating the power spectral density of data using the Welch method.
FIG. 9 is a comparison of the original pressure profile of the present invention and the denoised and smoothed pressure profile.
FIG. 10 is a comparison graph of temporal characteristic parameters of gait of left and right feet of experimenters in the embodiment of the invention.
FIG. 11 is a comparison of power spectrum curves of the left and right feet of an experimenter in an embodiment of the present invention.
FIG. 12 is a functional block diagram of the APP of the present invention.
FIG. 13 is a diagram of a user login module of the present invention.
Figure 14 is a patient management module of the present invention.
Figure 15 is a block diagram of a patient information menu of the present invention.
Fig. 16 is a diagram of a basic information module of a patient according to the present invention.
FIG. 17 is a diagram of a disorder scoring module of the present invention.
FIG. 18 is a data visualization module of the present invention.
The reference numbers are listed below: 1-a laser; 11-a coupler; 2-a beam splitter; 3-an optical fiber; 4-flexible pressure means (deformer and insole combination); 41-insole (left insole for left foot and right insole for right foot); 42-deformer (left deformer for left foot, right deformer for right foot); 5-probe ((left probe for left foot, right probe for right foot)); 421-upper and lower sawtooth engaging structure; 422-detachable structure for embedding the dovetail groove and the dovetail body; 6-central processing unit (for example, Arduino yun board, Arduino is an open source electronic prototype platform, including Arduino hardware boards of various types such as Arduino Uno board, Arduino yun board, etc., the central processing unit is sometimes referred to by Arduino yun board in the present invention, that is, 6 is also a reference numeral of Arduino yun board at the same time); a 7-Wi-Fi wireless connection; 8-a server; 9-mobile phone APP software client.
Detailed Description
The invention is described below with reference to the accompanying drawings (fig. 1-18).
Referring to fig. 1 to 6, the system for analyzing gait of parkinson's disease based on optical fiber microbending pressure sensing comprises a flexible pressure device 4, wherein the flexible pressure device 4 comprises a left insole (fig. 4 left insole 41) and a right insole (fig. 4 right insole 41), a left deformer (fig. 4 left deformer 42) is arranged below the heel of the left insole, a right deformer (fig. 4 right deformer 42) is arranged below the heel of the right insole, a left microbending pressure sensing optical fiber (i.e. optical fiber 3) is arranged in the left deformer, a right microbending pressure sensing optical fiber is arranged in the right deformer, one end of the left microbending pressure sensing optical fiber and one end of the right microbending pressure sensing optical fiber are respectively connected with a laser 1 through a beam splitter 2, the other end of the left microbending pressure sensing optical fiber is connected with a first signal input pin of a central processing unit through a left detector (fig. 5 below), the other end of right foot microbending pressure sensing optical fiber is connected through right detector (detector 5 above figure 5) the second signal input pin of central processing unit 6, central processing unit 6 is connected server 8 (for example, through Wi-Fi wireless connection 7), cell-phone APP software client 9 is connected to server 8. Central processing unit 6 adopts Arduino yun board, the A0 pin on the Arduino yun board does first signal input pin, the A1 pin on the Arduino yun board does the second signal input pin, the plantar pressure data that detector 5 conveying was come is handled and wireless remote transmission to Arduino yun board, server 8 is right plantar pressure data after Arduino yun board was handled carries out gait analysis after with gait analysis result information transmission cell-phone APP software client 9 in order to show on the cell-phone APP software interface. The laser 1 is connected with the optical splitter 2 by a coupler 11, the left deformer and the right deformer both comprise upper and lower sawtooth occlusion structures 421, microbending pressure sensing optical fibers are positioned on the occlusal surfaces of the upper and lower sawtooth occlusion structures 421, when the deformer 42 is under pressure, the optical fibers 3 are correspondingly microbending due to extrusion, microbending loss of optical fiber optical signals is caused, and the optical fiber optical signals with microbending loss are converted into electric signals by the detector 5 and transmitted to the Arduino yun board; the left deformer and the right deformer both comprise detachable structures 422 embedded with dovetail grooves and dovetail bodies, and the deformer 42 is made of soft rubber with Shore hardness of 70 degrees manufactured by a compound molding process.
As shown in fig. 1, the gait analysis system of the invention is an overall design diagram, and the system includes: an insole in direct contact with the foot of the patient, a deformer attached below the heel; the optical fiber microbend pressure sensor is used for collecting plantar pressure data; the Arduinoyun board is used for processing and wirelessly and remotely transmitting plantar pressure data; the server receives the data processed by the Arduino yun board, further transmits the data to a mobile phone APP software interface for display after gait related processing analysis; and the mobile phone APP software is used for displaying gait analysis information. As an embodiment of the invention, the insole and the deformer are placed in the shoe, so that a patient can walk with the shoe. In the patient walking process, plantar pressure information that time variation will be gathered to the little curved pressure sensor of optic fibre, give Arduino yun board with information transmission, Arduino yun board carries out wireless remote transmission for the server through Wi-Fi after the information preprocess, carry out gait analysis and processing in the server to give cell-phone APP software with data transmission, the user alright look up corresponding gait analysis information in APP software, thereby as the relevant foundation of judging the patient's state of an illness.
FIG. 2 is a schematic diagram of a hardware apparatus according to the present invention. As an embodiment of the invention, the laser 1 outputs optical signals and is connected with the input end of the optical splitter 2, the output end of the optical splitter is respectively connected with the optical fibers 3, and the optical fibers are respectively communicated with the insoles of left and right feet and the deformer 4. In an embodiment of the present invention, the optical fiber 3 passes through the deformer 42 and then is connected to the detector 5, and the detector 5 converts the optical signal into an electrical signal. As an embodiment of the present invention, the detector 5 is connected to the Arduino yun board 6, and transmits the electrical signal to the Arduino yun board 6 for processing.
Fig. 3 is a schematic view of the connection structure of the laser and the optical splitter according to the present invention. In an embodiment of the present invention, the laser 1 and the optical splitter 2 are connected by a coupler 11. Fig. 4 is a schematic view of the insole and the deformer of the present invention. As an embodiment of the present invention, the deformer 42 is placed under the heel of the insole 41. As an embodiment of the invention, the direction of the optical fiber 3 is arranged in a direction perpendicular to the saw teeth in the deformer 42 and is located in the middle of the deformer 42, and when the deformer 42 is subjected to corresponding pressure, the optical fiber 3 is pressed to generate corresponding micro-bending, so that the micro-bending loss of the optical fiber 3 is caused. Fig. 5 is a schematic diagram of the deformer of the present invention. According to an embodiment of the invention, the deformer 42 comprises an upper part and a lower part, the shape of the deformer is square, the shape of the saw teeth 421 is trapezoidal, the period length of the saw teeth 421 and the number of the teeth are correspondingly designed through theoretical analysis, and the sensitivity is improved, so that the purpose of accurately measuring the pressure is achieved. As an embodiment of the present invention, the deformer 42 is structurally configured to have the upper and lower portions fixed by the grooves 422 for the purpose of being detachable. As an embodiment of the present invention, the deformer 42 is made of soft rubber with shore hardness of 70 degrees manufactured by a double molding process, so as to improve comfort of contacting with a human body. FIG. 6 is a schematic view of an Arduino yun plate used in the present invention. As an embodiment of the invention, the Arduino yun board comprises a Wi-Fi module, and data can be transmitted to the server through Wi-Fi in a wireless and remote mode.
As shown in fig. 7, is a flow chart of the overall steps for parkinson gait data processing, comprising the following steps:
step 1: and (4) preprocessing data, namely performing low-pass filtering on the original pressure data by using a Butterworth low-pass filter to remove the interference of noise. The Butterworth low-pass filter achieves the function of removing noise by suppressing high-frequency components, and the square function of a transformation function H (w) is as follows:
wherein n is the order of the Butterworth low-pass filter, the higher the order is, the faster the amplitude attenuation speed of the filter in the stop band is, and WCIs the cut-off frequency, i.e. the frequency at which the amplitude drops by 3 db, and W is the argument of the transformation function, representing the frequency value.
And (3) carrying out discrete Fourier transform on an original data sequence x (n), wherein n is a positive integer, n is 1,2, …, and L (L is the length of the acquired data), and then multiplying the result by a transform function H (w), so that high-frequency components in the original data are suppressed, and the function of denoising and smoothing is realized.
As an embodiment of the present invention, the order n of the Butterworth low-pass filter is 8, and the cutoff frequency W is set to be equal toC1.6Hz, since the sampling frequency of the experiment is FCAt 32Hz, normalized cut-off frequency Wn＝2*WC/FCFig. 8 shows the pressure curve after denoising and smoothing, which is 0.1.
Step 2: finding the highest peak sequence f of the waveformdatamax[i]And its corresponding time series ftimemax(i) And lowest valley sequence fdatamin[i]And its corresponding time series ftimemin[i]I is a positive integer, i is 1,2, …, n is the length of the wave crest sequence (or the wave trough sequence), i.e. the pressure amplitude of the highest point and the lowest point in each gait cycle and the corresponding time. Meanwhile, in order to filter out peaks that are erroneously counted due to minute fluctuations of the curve, a threshold value of the peak protrusion amplitude is set, which is set as an average value of amplitude differences between all peaks and their adjacent valleys, that is, a threshold value of the peak protrusion amplitude is set
n is the length of the peak sequence (or trough sequence) and peaks below this threshold are ignored.
And step 3: extracting specific time domain gait characteristic parameters, such as parameter sequences of pressure peak values, step frequency, support phase time, swing phase time, gait cycle proportion and the like of the left foot and the right foot, and calculating the formula as follows:
(1) pressure peak value: representing the maximum of the pressure sequence in a single gait cycle, singlyThe bit N is the highest peak value sequence f obtained in the step 2datamax[i]；
(2) Step frequency (P): represents the number of steps taken in unit time, unit step/s, expression: p ═ num (f)datamax[i]) T, where num (f)datamax[i]) The number of peak values (namely, the number of steps) obtained in one experiment, and t is the experiment duration;
(3) support phase time (ST): the contact time of the foot and the ground in a single gait cycle is represented by a unit s, namely the difference between the corresponding moments of the two adjacent lowest wave troughs, and the expression is as follows:
ST(i)＝ftimemin(i+1)-ftimemin(i) -1, 2, …, n-1, n being the length of the trough sequence;
(4) swing phase time (WT): the time that the foot is not in contact with the ground in a single gait cycle is represented, the unit s is the difference between the moments corresponding to the two adjacent highest peak values, and the expression is as follows:
WT(i)＝ftimemax(i+1)-ftimemax(i) i is 1,2, …, n-1, n is the length of the peak sequence;
(5) gait cycle proportion: expressing the proportion of the support phase and the swing phase in a single gait cycle respectively, and expressing the following expression: ratio of supporting phase time
Swing phase time ratio
In the formula
Is the sum of the support phase time in one experiment,
is the sum of the swing phase times in one experiment. .
As an embodiment of the present invention, the average value of each gait parameter is obtained after statistical processing of plantar pressure data of 15 groups of experimenters, as shown in fig. 10.
And 4, step 4: filtering signalThe direct current component in the power spectrum peak value extraction device avoids the influence of the direct current component on the extraction of the subsequent power spectrum peak value. Because a large direct current component exists in the signal, the amplitude of the power spectrum around 0Hz is very large, and the extraction of the real peak value of the power spectrum is influenced, so that the direct current component needs to be filtered. Using the principle of least squares, let x1,x2,…,xnIs any one of the measured values, n is the length of the measured sequence, and the most reliable value to be measured is assumed to be x0Then the condition should be satisfied:
is provided with
Is x1,x2,…,xnThe arithmetic mean of (A) is
And because of
So when
The above equation is minimum, i.e. the optimum value of the measurement is the arithmetic mean of the measured values, and the direct current component can be approximately considered to be equal to the arithmetic mean of the measured values, so that the direct current component of the signal can be filtered by subtracting the mean value from the original pressure data.
And 5: the power spectral density value of the original data sequence is estimated using the Welch (Welch) method, which combines a windowing process with an averaging process. The power spectral density represents the signal power per unit frequency band, and the formula is defined as:
where f (w) is the fourier transform function of the original sequence, T denotes the period of the original sequence, and the power spectrum of the signal needs to be estimated from the set of data that has been observed in the actual calculation of the power spectrum curve.
As an embodiment of the present invention, the window function used is the Kaiser window, which is defined as
In the formula I0In this embodiment, β is taken as 14, and data is not segmented, after the Kaiser window is multiplied by the original data sequence, the windowed data x (n), n is 1,2, …, M, and the discrete fourier transform of the data is used to estimate the power spectrum, and the calculation formula is:
wherein x (n) is the data sequence after windowing, M is the sequence length, P (i) is the power spectrum estimated value of the ith sequence, and i is a positive integer.
Averaging the power spectrum of the L-segment data to obtain the final power spectrum value of the data, i.e. the power spectrum value
Wherein i is a positive integer, i is 1,2, …, L is the number of segments divided by the original data sequence,
the final power spectrum value.
By this we have done the raw pressure signal power spectral density curve, as shown in fig. 11.
Step 6: and extracting the dominant frequency value of the power spectrum, namely the corresponding frequency value at the peak value of the power spectrum curve.
Fig. 12 is a functional block diagram of the APP of the present invention.
As shown in fig. 13-18, the specific method of use of APP is as follows:
step 1: and inputting a user name and a password in a user login interface, clicking 'Enter', and entering App.
Step 2: and clicking the name of the patient in the patient management interface to enter the patient information menu interface.
And step 3: clicking 'personnel basic information' in a patient information menu interface, entering a patient basic information interface, and checking the name, age and weight of a patient;
and 4, step 4: clicking 'intelligence score' in the basic information interface of the patient to enter an intelligence score questionnaire. After the questionnaire is filled in, if the user clicks 'submit' and returns to the basic information interface of the patient, the intellectual scoring score can be checked. If "reset" is clicked, the questionnaire can be re-filled. (the other three categories of symptom scores were used in the same way as in this step).
And 5: and returning to a user information menu interface, clicking the voltage peak value, entering a voltage peak value data interface, and checking a line graph and a data list of the double-pin voltage peak value data for nearly 10 times. Clicking "see nearly 50 times" and "see all" can see the line graph and data list of nearly 50 times and all the existing peak data of the two-pin voltage respectively. (the data viewing methods of "swing phase relative time", "support phase relative time", and "spectrum analysis" are the same as in this step).
Those skilled in the art will appreciate that the invention may be practiced without these specific details. Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (10)
1. The system for analyzing gait of Parkinson's disease people based on optical fiber microbending pressure sensing is characterized by comprising a flexible pressure device, wherein the flexible pressure device comprises a left foot insole and a right foot insole, a left deformer is arranged below the heel of the left foot insole, a right deformer is arranged below the heel of the right foot insole, a left foot microbending pressure sensing optical fiber is arranged in the left deformer, a right foot microbending pressure sensing optical fiber is arranged in the right deformer, one end of the left foot microbending pressure sensing optical fiber and one end of the right foot microbending pressure sensing optical fiber are respectively connected with a laser through a beam splitter, the other end of the left foot microbending pressure sensing optical fiber is connected with a first signal input pin of a central processing unit through a left detector, the other end of the right foot microbending pressure sensing optical fiber is connected with a second signal input pin of the central processing unit through a right detector, the central processing unit is connected with the server, and the server is connected with the mobile phone APP software client.
2. The system of claim 1, wherein the central processing unit employs an Arduino yun board, a0 pin on the Arduino yun board is the first signal input pin, a1 pin on the Arduino yun board is the second signal input pin, the Arduino yun board processes and wirelessly remotely transmits plantar pressure data transmitted from the detector, and the server transmits gait analysis result information to the mobile phone APP software client for displaying on the mobile phone APP software interface after gait analysis of the plantar pressure data processed by the Arduino yun board.
3. The system for analyzing gait of Parkinson's disease people based on optical fiber microbending pressure sensing according to claim 1, wherein the laser is connected with the optical splitter by a coupler, the left deformer and the right deformer both comprise upper and lower sawtooth occlusion structures, microbending pressure sensing optical fibers are located on the occlusal surfaces of the upper and lower sawtooth occlusion structures, when the deformer is subjected to pressure, corresponding microbending occurs due to extrusion of the optical fibers, microbending loss of optical fiber optical signals is caused, and the optical fiber optical signals with microbending loss are converted into electric signals by the detector and transmitted to the Arduino yun board; the left deformer and the right deformer both comprise detachable structures with dovetail grooves and dovetail bodies embedded, and the deformer is made of soft rubber with Shore hardness of 70 degrees manufactured by a compound molding process.
4. A gait processing method combining a time domain and a frequency domain is characterized by comprising the following steps:
step 1, preprocessing data, namely performing low-pass filtering on original pressure data by using a Butterworth low-pass filter to remove interference of noise points;
step 2, finding out the highest peak value sequence f of the waveformdatamax(i) And its corresponding time series ftimemax(i) And lowest valley sequence fdatamin(i) And its corresponding time series ftimemin(i) I is a positive integer;
step 3, extracting specific time domain gait characteristic parameters, such as parameter sequences of pressure peak values, step frequencies, support phase time, swing phase time, gait cycle proportion and the like of the left foot and the right foot;
step 4, filtering the direct current component in the signal, and avoiding the direct current component from influencing the extraction of the subsequent power spectrum peak value;
step 5, estimating the power spectral density value of the original data sequence by adopting a Welch method combining windowing processing and averaging processing;
and 6, extracting the dominant frequency value of the power spectrum, namely the frequency value corresponding to the peak value of the power spectrum curve.
5. A gait processing method according to claim 4, characterized in that, the step 1 comprises a Butterworth low-pass filter for removing noise by suppressing high frequency components, and the square function of the transform function is:
wherein n is the order of the Butterworth low-pass filter, the higher the order is, the faster the amplitude attenuation speed of the filter in the stop band is, and WCIs the cut-off frequency, i.e. the frequency at which the amplitude drops by 3 db, W is the independent variable of the transformation function, representing the frequency value; the step 1 includes performing discrete fourier transform on an acquired original data sequence x (n), where n is a positive integer, n is 1,2, …, and L (L is an acquired data length), to obtain an acquired original data sequence x (n)
F (w) is a discrete fourier transform function, j is an imaginary unit, and is multiplied by a transform function h (w) of a Butterworth low-pass filter to obtain g (w) f (w) h (w), g (w) is a fourier transform function of a denoised data sequence, high-frequency components in original data are suppressed to realize a denoising and smoothing function, and g (w) is subjected to inverse fourier transform to obtain a denoised data sequence g (x), wherein x is 1,2, …, and L is a final sequence length.
6. The time domain and frequency domain combined gait processing method according to claim 4, characterized in that the step 2 comprises: finding the highest peak sequence f of the waveform by using findpeaks functiondatamax(i) And its corresponding time series ftimemax(i) And lowest valley sequence fdatamin(i) And its corresponding time series ftimemin(i) I is a positive integer, i is 1,2, …, n is the length of a wave crest sequence (or a wave trough sequence), namely the pressure amplitude of the highest point and the lowest point in each gait cycle and the corresponding time; in order to filter out peaks which are erroneously counted due to small fluctuations of the curve, a threshold value for the amplitude of the peak protrusion is set, which is set as the average of the amplitude differences of all peaks and their adjacent valleys, i.e. the average of the amplitude differences
n is the length of the peak sequence (or trough sequence) and peaks below this threshold are ignored.
7. A gait processing method according to claim 4, characterized in that step 3 comprises a pressure peak, a step frequency P, a support phase time ST, a swing phase time WT and a gait cycle ratio, said pressure peak representing the maximum of the pressure sequence in a single gait cycle in N, the expression being the maximum peak sequence f obtained in step 2datamax(i) (ii) a The step frequency P represents the number of steps taken in unit time, unit step/s, expression: p ═ num (f)datamax(i) T, where num (f)datamax(i) Is the number of peaks (i.e., steps) obtained in one experiment, and t is the length of the experiment; the support phase time ST represents the contact time of the foot and the ground in a single gait cycle, and the unit s is the difference between the moments corresponding to the two adjacent lowest wave troughs, and the expression is as follows: (st (i) ═ f)timemin(i+1)-ftimemin(i) I-1, 2, …, n-1, n being the length of the trough sequence; the swing phase time WT represents the time that the foot is not in contact with the ground in a single gait cycle, and the unit s is the difference between the moments corresponding to the two adjacent highest peak values, and the expression: WT (i) ═ ftimemax(i+1)-ftimemax(i) I is 1,2, …, n-1, n is the length of the peak sequence; the gait cycle proportion represents the proportion of the support phase and the swing phase in a single gait cycle, and the expression is as follows: ratio of supporting phase time
Swing phase time ratio
In the formula
Is the sum of the support phase time in one experiment,
is at a timeSum of swing phase times in the experiment.
8. The time domain and frequency domain combined gait processing method according to claim 4, characterized in that the step 4 comprises filtering out the DC component, and using the principle of least squares, setting x1,x2,…,xnIs any one of the measured values, n is the length of the measured sequence, and the most reliable value to be measured is assumed to be x0Then the condition should be satisfied:
is provided with
Is x1,x2,…,xnThe arithmetic mean of (A) is
And because of
So when
The above equation is minimum, i.e. the best value of measurement is the arithmetic mean of the measured values, the direct current component is approximately considered to be equal to the arithmetic mean of the measured values, and the original pressure data is subtracted by the mean value, i.e. the direct current component of the filtered signal.
9. The time domain and frequency domain combined gait processing method according to claim 4, characterized in that the step 5 comprises:
(1) the power spectral density equation is defined as follows:
wherein F (w) is a Fourier transform function of the original sequence, T represents the period of the original sequence, and the power spectrum of the signal is estimated by using a set of observed data in the actual calculation of the power spectrum curve;
(2) segmenting the acquired data sequence, dividing the signal serial number x (N) with the length of N into L segments, wherein the length of each segment of the sequence is M, and then respectively calculating a power spectrum for each segment and then averaging to reduce the random fluctuation phenomenon;
(3) selecting a proper window function to multiply the segmented data sequence, and reducing the sudden change at two ends of signal truncation by using the window function;
(4) respectively calculating the power spectrum of each section of data obtained in the step 5, wherein the windowed data x (n) is obtained for each section, n is 1,2 …, M, n is a positive integer, M is the length of each section of sequence, and the power spectrum is estimated by using the discrete fourier transform, and the calculation formula is as follows:
wherein x (n) is the data sequence after windowing, M is the sequence length, P (i) is the power spectrum estimated value of the ith sequence, and i is a positive integer.
(5) Averaging the power spectrum of the L-segment data to obtain the final power spectrum value of the data, i.e. the power spectrum value
Wherein i is a positive integer, i is 1,2, …, L is the number of segments divided by the original data sequence,
the final power spectrum value.
10. A physician-user-population-oriented APP for displaying patient gait analysis information, characterized by: the system comprises a user login module, a patient management module, a patient information menu module, a patient basic information module, an analysis data display module, a disease grading module and a data visualization module.
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