CN109998551B - Gait phase analysis method for sectional type local peak detection - Google Patents
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
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- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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Abstract
A gait phase analysis method for sectional type local peak detection relates to the field of gait analysis, in particular to a gait phase analysis method for foot position angular velocity. The invention provides a gait phase analysis method for sectional type local peak detection aiming at the problem of poor adaptivity of a double-peak detection method used for gait phase division according to inertia data, which has the technical key points that: for the acquired pitch angle speed data of the foot in gait motion, according to the motion characteristics of the swing phase and the landing phase in the gait motion, the extraction of two key point information of heel landing and toe off in the gait motion is realized by extracting the forward swing angular speed and segmenting the non-forward swing angular speed and using the extracted local maximum value to replace the traditional peak value detection method. The invention can be applied to the field of gait analysis.
Description
One, the technical field
The invention relates to the field of gait analysis, in particular to a gait phase analysis method based on foot position angular velocity.
Second, background Art
Gait phase analysis is a branch of the gait analysis field, and has wide application in medical rehabilitation diagnosis, athlete training evaluation, human-computer interaction and the like. With the increasing development of MEMS technology, wearable equipment based on MEMS devices is gradually popularized, inertial data such as acceleration, angular velocity and the like of a wearing part in gait movement can be acquired by wearing the MEMS equipment, and the method is low in cost, high in precision, small in limitation of testing environment and testing time, and wide in application prospect in the field of gait analysis.
The common inertial data used for gait analysis are three-axis angular velocity and three-axis acceleration, and the gait phase analysis mainly analyzes the periodic characteristics of the data and extracts the distribution information of each key point on a time axis. In view of the fact that the pitch angle velocity of a foot part has good periodic characteristics when a human body walks, at present, the single-axis angular velocity is often selected to perform double-peak detection to extract key point information, the double-peak detection often adopts a transverse time window length threshold value and a longitudinal peak value threshold value detection as judgment criteria, the scheme has poor adaptability for different test individuals and different test conditions, after a scheme of dynamically updating the threshold value is introduced, the adaptability of the scheme to the individuals is enhanced, but the adaptability to the different test conditions is still poor, such as a gait initialization stage and a gait stagnation stage, the threshold value is deviated from an ideal value due to the updating of the threshold value, and before the threshold value is updated to the ideal value, reasonable measurement data often obtain wrong detection results due to the deviation from the threshold value of the ideal value.
Third, the invention
In order TO improve the robustness of peak detection in gait phase division, the invention provides a gait phase analysis method of sectional type local peak detection, which is used for extracting key point information of heel landing HS and toe off TO in gait motion,
according to the motion characteristics of the swing phase and the landing phase in gait motion, the information of two key points of heel landing and toe-off in the gait motion is extracted by extracting the forward swing angular velocity and segmenting the non-forward swing angular velocity and using the extracted local maximum value to replace the traditional peak value detection method.
The invention adopts the following technical scheme: a gait phase analysis method for sectional type local peak detection comprises the following steps:
(1) acquiring pitch angle speed data of the foot part in gait motion for a period of time through a single-axis gyroscope positioned at the foot part, and defining the positive direction of the angular speed as the right side of the advancing direction;
(2) and extracting characteristic moments of the heel landing HS and the toe off TO, and dividing the single-foot step period into a swing period and a support period.
In the step (2), the extraction of the HS and TO characteristic moments of the sectional local peak detection method comprises the following steps:
(2.1) extracting characteristic moments of the highest BT of backward swing and the highest FT of forward swing from all the forward angular velocity data;
and (2.2) extracting two negative peak values of the angular velocity data, namely HS and TO characteristic moments, from all the negative-direction angular velocity data.
The BT and FT are extracted in the step (2.1), and the BT-FT angular velocity and the BT and FT are extracted by utilizing the characteristic that the angle obtained by the integral of the BT-FT angular velocity is the largest, and the BT and FT are extracted, and the method comprises the following steps:
(2.1.1) the angular velocity value obtained by sampling is expressed as Ω ═ ω k1, 2,.. N }, with a sampling interval Δ t, omega is satisfied in the extraction of omegakFor sampling values > 0, n subsequences are obtained which are consecutive in time and are denoted as S ═ S1，S2，...，SnIn which S isj＝{ωk|k＝pj，pj+1，...，pj+qj}；
(2.1.2) to SjThe angular velocity in (1) is integrated to obtain an angle which is recorded as
Let Θ be { θ ═ θj|j＝1，2，...，n}；
(2.1.3) arranging the angles in Θ in descending order of numerical magnitude to obtain Θ ', Θ ' ═ θ 'jI j | 1, 2., n }, and calculating the first-order difference value of Θ 'yields Δ Θ', i.e., Δ Θ '═ Δ θ'j＝θ′j-θ′j+1|j＝1，2，...，n-1}；
(2.1.4) the maximum value Deltatheta 'was determined'rIf the term "r + 1" indicates that there is a mutation in term "Θ", the sequence of positive angular velocities in S corresponding to the preceding term "r" in term "Θ" belongs to the forward swing angular velocity, and for the terms "r + 1" to "n" of term "Θ", k is taken as "r + 1", r +2 ",.k/θ′r< α (α is used for judging whether the mutation term and the subsequent term belong to the forward swing angular velocity, 0.5-0.6 is taken), if k meeting the condition exists, the minimum value is recorded as kminIf the number of walking steps s is kmin-1, if there is no k that satisfies the condition, the number of walking steps s ═ n;
(2.1.5) the sequence of positive angular velocity values in S corresponding to the first S term in Θ' belongs to the sequence of forward oscillating angular velocities, the start and end of each sequence of segmentsThe moments in time are respectively denoted as tBT(i) And tFT(i) Namely BT and FT.
In the step (2.2), two negative peak values of negative angular velocity data are extracted by utilizing the characteristic that two main negative peak values of FT-BT angular velocities are distributed on two sides of the flat foot stage, and a dynamic segmentation threshold strategy is adopted, and the method comprises the following steps:
(2.2.1) taking out the angular velocity FT-BT in omega to obtain s-1 subsequences, which are marked as Ai＝{ωk|tFT(i)≤k≤tBT(i+1)}，A＝{A1，A2，...，As-1Get subsequence AiAll maxima in (1) are denoted as di(k)，k＝1，2，...；
(2.2.2) determination of di(k) Maximum value max ofi(1) And the sub-maximum maxi(2) Computing a segmentation threshold βi＝(maxi(1)+maxi(2) 2, if d)i(k) Only one element in (A)iHas only one maximum value, βi＝maxi(1)；
(2.2.3) definition of yi(xi) Is the interval [ tFT(i)，tBT(i+1)]Upper βiIs as an indication of
When y isi(xi) 0, otherwise yi(xi)＝1，yi(xi) As a binary function, the interval tFT(i)，tBT(i+1)]Into three, four or five segments (the interval is divided into three segments when the segmentation threshold is greater than the angular velocity sample values at both ends of the interval, or into four or five segments otherwise), and, correspondingly, yi(xi) The four possible distribution forms of the function values are
Three stages, {1, ·, 1, 0, ·, 0, 1, ·, 1}
Four stages, {0, · 0, 1,. 7, 1, 0,. 0, 1,. 1, or {1,. 1, 0,. 0, 1,. 1, 0,. 0}
Fifth paragraph, {0, · 0, 1,. talks, 1, 0,. talks, 0 };
(2.2.4) extraction of yi(xi) The first point where the mutation from 1 to 0 is denoted ta(i) Re-extraction is at ta(i) The right-hand point of the mutation from 0 to 1 is denoted as tb(i)；
(2.2.5) determining the section [ tFT(i)，ta(i)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tHS(i) I.e., HS, to find the interval [ t [ [ t ]b(i)，tBT(i+1)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tTO(i) I.e., TO.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. only the specific direction of the foot position and the angular velocity data of a single shaft are utilized, and the hardware requirement is relatively simple;
2. according TO the characteristics of each stage of gait movement and the corresponding pitch angle speed data characteristics of the gait movement, two key point information of heel landing HS and heel lift TO in the gait movement are designed and extracted, peak value detection is carried out by using local maximum detection, threshold judgment is avoided as far as possible as a final judgment criterion of the peak value detection, and the robustness is good.
Description of the drawings
FIG. 1 is a schematic representation of the pitch angular velocity and various feature points of the foot in gait;
FIG. 2 is a schematic representation of the invention with phase division of gait according to pitch angular velocity of the foot;
FIG. 3 is a schematic diagram of the present invention for extracting BT and FT in terms of pitch rate of the foot;
fig. 4 is a schematic diagram of the present invention for extracting HS and TO from the pitch rate of the foot.
Fifth, detailed description of the invention
The following detailed description of the invention refers to the accompanying drawings.
FIG. 1 is a graph of pitch rate (in:/s) of a foot in gait motion collected by a single axis gyroscope located at the foot, plotted against time, with the sampling frequency of the data being 200Hz and the positive direction of the angular rate data being to the right of the direction of travel. The data curve of each gait cycle includes four segments, namely a positive direction (forward swing) angular velocity segment, two negative direction (backward swing) angular velocity segments, and a relatively stable (flat foot) angular velocity segment, as shown by dashed frames 1-4 in fig. 1. In gait motion, when the heel swings backwards due to the inertia of pedaling and stretching the ground after leaving the ground, the foot swings backwards to the maximum angle, and then quickly swings forwards to the maximum angle after reaching the BT point and reaches the FT point. The foot then falls TO HS into the support phase until TO into the swing phase. HS and TO correspond TO the troughs of two segments of negative (back-swing) angular velocity, respectively, due TO the "violent" action of the foot and the ground, as shown by the dashed boxes 2 and 4. The dotted frame 5 is the stagnation state in walking, the dotted frame 6 is the first step of the transition from the stagnation state to the walking state, and neither of the dotted frames 5 and 6 appears in the detection result of the present invention.
Fig. 2 shows the angular velocities of two gait cycles taken from fig. 1, and the gait cycle is divided by detecting BT and FT, and then detecting HS and TS, and the gait phase is divided according to the support phase and the swing phase.
Extracting HS and TS of FIG. 1 or FIG. 2, which comprises the following steps:
(1) acquiring pitch angle speed data of a foot part in gait motion for a period of time by using a single-shaft gyroscope positioned at the foot part, and requiring a sensitive shaft of the gyroscope to point to the right side vertical to the advancing direction;
(2) and extracting characteristic moments of the heel landing HS and the toe off TO, and dividing the single-foot step period into a swing period and a support period.
In step (2), the extraction of the characteristic times of the heel strike HS and the toe-off TO includes the steps of:
(2.1) extracting characteristic moments of the highest BT of backward swing and the highest FT of forward swing from all the forward angular velocity data;
and (2.2) extracting two negative peak values of the angular velocity data, namely HS and TO characteristic moments, from all the negative-direction angular velocity data.
And (3) extracting BT and FT in the step (2.1), and extracting BT-FT angular velocity and BT and FT by utilizing the characteristic that the angle obtained by BT-FT angular velocity integration is the largest, wherein the BT-FT angular velocity and FT are extracted by the following steps:
(2.1.1) the angular velocity value obtained by sampling is expressed as Ω ═ ω k1, 2,.. N }, with a sampling interval Δ t, omega is satisfied in the extraction of omegakFor sample values > 0, 10 subsequences are obtained as indicated by the thick and thin solid lines in fig. 3-a, which are consecutive in time and denoted as S ═ S1，S2，...，SnIn which S isj＝{ωk|k＝pj，pj+1，...，pj+qj}；
(2.1.2) to SjThe angular velocity in (1) is integrated to obtain an angle which is recorded as
Let Θ be { θ ═ θj1, 2.., 10}, as shown in fig. 3-b;
(2.1.3) arranging the angles in Θ in descending order of numerical magnitude to obtain Θ ', Θ ' ═ θ 'j1, 2., 10}, as shown in fig. 3-c, calculating the first order difference value of Θ ' yields Δ Θ ', i.e., Δ Θ ' ═ Δ θ ″.j＝θ′j-θ′j+11, 2.., 9}, as shown in fig. 3-d;
(2.1.4) the maximum value Deltatheta 'was determined'6If the term Θ ' has a mutation in the 7 th term, the sequence of positive angular velocity values in S corresponding to the first 6 terms in the term Θ ' belongs to the forward swing angular velocity, and for the 7 to 10 terms of the term Θ ', k is taken to be 7, 8, 9, 10, and the term θ ' is determined one by one 'k/θ′r< α (α is used to determine whether the mutation term and the subsequent term belong to the forward swing angular velocity, 0.6 is taken), and if there is k satisfying the condition, the minimum value 7 is recorded as kminIf the number of walking steps s is kmin-1＝6；
(2.1.5) the angular velocity positive value sequence in S corresponding to the first 6 items in the theta' belongs to the forward swing angular velocity sequence, and the starting time and the ending time of each segment of sequence are respectively marked as tBT(i) And tFT(i) I.e., BT and FT, as shown in fig. 3-a.
In the step (2.2), two negative peak values of the negative angular velocity data are extracted by utilizing the characteristic that two main negative peak values of the FT-BT angular velocities are distributed on two sides of the flat foot period, and the method comprises the following steps:
(2.2.1) taking out the angular velocity FT-BT from omega to obtain 5 subsequences, which are marked as Ai＝{ωk|tFT(i)≤k≤tBT(i+1)}，A＝{A1，A2，...，A5Get subsequence AiAll maxima in (1) are denoted as di(k)，k＝1，2，...；
(2.2.2) determination of di(k) Maximum value max ofi(1) And the sub-maximum maxi(2) Computing a segmentation threshold βi＝(maxi(1)+maxi(2) 2, if d)i(k) Only one element in (A)iHas only one maximum value, βi＝maxi(1)；
(2.2.3) definition of yi(xi) Is the interval [ tFT(i)，tBT(i+1)]Upper βiIs as an indication of
When y isi(xi) 0, otherwise yi(xi)＝1，yi(xi) As a binary function, the interval tFT(i)，tBT(i+1)]Into three, four or five segments (the interval is divided into three segments when the segmentation threshold is greater than the angular velocity sample values at both ends of the interval, or into four or five segments otherwise), and, correspondingly, yi(xi) The four possible distribution forms of the function values are
Three stages, {1, ·, 1, 0, ·, 0, 1, ·, 1}
Fourth, a {0, a., 0, 1, a., 1, 0, a., 0, 1, a., 1} or {1, a., 1, 0, a., 0, 1, a., 1, 0, a., 0} (e.g., a in fig. 4-c)1And A4Corresponding interval shows)
Paragraph v, {0,. a., 0, 1,. a., 1, 0,. a., 0, 1,. 1, 0,. a., 0} (see, e.g., a in fig. 4-c)2、A3And A5Indicated by the corresponding interval);
(2.2.4) extraction of yi(xi) The first point where the mutation from 1 to 0 is denoted ta(i) Re-extraction is at ta(i) The right-hand point of the mutation from 0 to 1 is denoted as tb(i) As shown in FIGS. 4- (1) and 4- (2);
(2.2.5) determining the section [ tFT(i)，ta(i)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tHS(i) I.e., HS, to find the interval [ t [ [ t ]b(i)，tBT(i+1)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tTO(i) I.e., TO.
It should be noted that the above-mentioned embodiments are only used for explaining the present invention, and do not limit the present invention.
Claims (1)
1. A gait phase analysis method for sectional type local peak detection comprises the following steps:
(1) acquiring pitch angle speed data of the foot part in gait motion for a period of time through a single-axis gyroscope positioned at the foot part, and defining the positive direction of the angular speed as the right side of the advancing direction;
(2) extracting characteristic moments of heel landing HS and toe off TO, and dividing a single-foot step period into a swing period and a support period;
the method comprises the following steps:
(2.1) extracting characteristic moments of the highest BT of backward swing and the highest FT of forward swing from all the forward angular velocity data;
the step (2.1) of extracting BT and FT and utilizing the characteristic that the angle obtained by integrating BT-FT angular velocity is the largest comprises the following steps:
(3.1) the angular velocity value obtained by sampling is Ω ═ ω { [ ω } { (ω) }k1, 2,.. N }, with a sampling interval Δ t, omega is satisfied in the extraction of omegakFor sampling values > 0, n subsequences are obtained which are consecutive in time and are denoted as S ═ S1，S2，...，SnIn which S isj＝{ωk|k＝pj，pj+1，...，pj+qj}；
(3.2) to SjThe angular velocity in (1) is integrated to obtain an angle which is recorded as
Let Θ be { θ ═ θj|j＝1，2，...，n}；
(3.3) arranging the angles in Θ in descending order of numerical magnitude to obtain Θ ', Θ ═ θ'jI j | 1, 2., n }, and calculating the first-order difference value of Θ 'yields Δ Θ', i.e., Δ Θ '═ Δ θ'j＝θ′j-θ′j+1|j＝1，2，...，n-1}；
(3.4) the maximum value Deltatheta 'was obtained'rIf the term "r + 1" of Θ ' has a mutation, the sequence of positive angular velocities in S corresponding to the preceding term "r" in Θ ' belongs to the forward swing angular velocity, and for the terms "r + 1" to "n" of Θ ', k is taken as "r +1, r + 2.k/θ′r< α, wherein α is used for determining whether the mutation term and the subsequent terms belong to the forward swing angular velocity, and α has a value range of 0.5-0.6, if there is k satisfying the condition, the minimum value is recorded as kminIf the number of walking steps s is kmin-1, if there is no k that satisfies the condition, the number of walking steps s ═ n;
(3.5) the angular velocity positive value sequence in S corresponding to the previous S term in the theta' belongs to the forward swing angular velocity sequence, and the starting time and the ending time of each sequence are respectively marked as tBT(i) And tFT(i) BT and FT;
(2.2) extracting two negative peak values of the angular velocity data, namely HS and TO characteristic moments, from all negative angular velocity data;
in the step (2.2), two negative peak values of negative angular velocity data are extracted by utilizing the characteristic that two main negative peak values of FT-BT angular velocities are distributed on two sides of the flat foot stage, and a dynamic segmentation threshold strategy is adopted, and the method comprises the following steps:
(4.1) taking out the angular velocity FT-BT in omega to obtain s-1 subsequences, which are marked as Ai＝{ωk|tFT(i)≤k≤tBT(i+1)}，A＝{A1，A2，...，As-1Get subsequence AiAll maxima in (1) are denoted as di(k)，k＝1，2，...；
(4.2) determining di(k) Is the most important ofLarge value maxi(1) And the sub-maximum maxi(2) Computing a segmentation threshold βi＝(maxi(1)+maxi(2) 2, if d)i(k) Only one element in (A)iHas only one maximum value, βi＝maxi(1)；
(4.3) definition of yi(xi) Is the interval [ tFT(i)，tBT(i+1)]Upper βiIs as an indication of
When y isi(xi) 0, otherwise yi(xi)＝1，yi(xi) As a binary function, the interval tFT(i)，tBT(i+1)]Into three, four or five segments (the interval is divided into three segments when the segmentation threshold is greater than the angular velocity sample values at both ends of the interval, or into four or five segments otherwise), and, correspondingly, yi(xi) The four possible distribution forms of the function values are
Three stages, {1, ·, 1, 0, ·, 0, 1, ·, 1}
Four stages, {0, · 0, 1,. 7, 1, 0,. 0, 1,. 1, or {1,. 1, 0,. 0, 1,. 1, 0,. 0}
Fifth paragraph, {0, · 0, 1,. talks, 1, 0,. talks, 0 };
(4.4) extraction of yi(xi) The first point where the mutation from 1 to 0 is denoted ta(i) Re-extraction is at ta(i) The right-hand point of the mutation from 0 to 1 is denoted as tb(i)；
(4.5) obtaining the section [ tFT(i)，ta(i)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tHS(i) I.e., HS, to find the interval [ t [ [ t ]b(i)，tBT(i+1)]The minimum value of the sampling values of the internal angular velocity, the corresponding time instant, is denoted as tTO(i) I.e., TO.
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