CN114371695B

CN114371695B - Method, device and equipment for determining position of landing point and storage medium

Info

Publication number: CN114371695B
Application number: CN202111462254.1A
Authority: CN
Inventors: 付成龙; 陈欣星; 冷雨泉; 张贶恩; 陈楚衡
Original assignee: Southern University of Science and Technology
Current assignee: Southern University of Science and Technology
Priority date: 2021-12-02
Filing date: 2021-12-02
Publication date: 2022-09-16
Anticipated expiration: 2041-12-02
Also published as: CN114371695A

Abstract

The application discloses a method, a device, equipment and a storage medium for determining a foot drop point position, and belongs to the technical field of walking-aid robots. The method comprises the following steps: the method comprises the steps of firstly determining an initial probability distribution function according to the historical landing point position of a heel, then obtaining the motion state characteristics of the heel in the swinging process after the heel leaves the ground, updating the initial probability distribution function according to the obtained motion state characteristics to obtain a target probability distribution function, and then determining the landing point position of the heel after the heel leaves the ground according to the target probability distribution function. The motion state characteristics are acquired in a swing process before the heel falls to the ground, so that the probability distribution function is updated according to the motion state characteristics, and the determination of the position of the foot falling point is completed before the heel falls to the ground, so that the position of the foot falling point is accurately predicted in advance, the walking-assisting robot is effectively assisted to adjust the pose before the foot of the human body falls to the ground, the walking-assisting robot can assist the foot of the human body more flexibly, and the assistance performance of the walking-assisting robot is improved.

Description

Method, device and equipment for determining position of landing point and storage medium

Technical Field

The application relates to the technical field of walking-aid robots, in particular to a method, a device, equipment and a storage medium for determining a foot-landing point position.

Background

The walking-aid robot is a service robot developed with the main aim of assisting walking, and can assist the elderly with inflexible lower limbs or patients with lower limb mobility disorder to walk. The early prediction of the position of the foot-falling point of the walking robot can help the walking robot to adjust the pose before the foot of the walking robot falls to the ground, so that the walking robot can assist the foot more flexibly, the assistance performance of the walking robot is improved, and therefore the prediction of the position of the foot-falling point of the walking robot is one of key technologies of the walking robot. However, most of the research on walking robots is limited to measuring the position of the foot-landing point after the person walks and lands on the foot, and few researches are made on the method for predicting the position of the foot-landing point in advance.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for determining a landing foot point position, which can predict the landing foot point position in advance. The technical scheme is as follows:

in a first aspect, a method for determining a location of a landing point is provided, where the method includes:

determining an initial probability distribution function according to the historical landing point position of the heel, wherein the initial probability distribution function is used for indicating the probability that the landing point position of the heel after liftoff is located at each position in a landing area;

acquiring the motion state characteristics of the heel in the swinging process after liftoff, wherein the motion state characteristics are characteristics related to the swinging speed and/or the swinging position of the heel;

updating the initial probability distribution function according to the acquired motion state characteristics to obtain a target probability distribution function;

and determining the position of the foot landing point of the heel after liftoff according to the target probability distribution function.

As one example, determining an initial probability distribution function based on historical landing foot positions of a heel includes:

determining the historical step length and the historical step width of the heel walking according to the historical landing point position of the heel;

and determining the initial probability distribution function according to the historical step length and the historical step width.

As an example, determining the initial probability distribution function according to the history step size and the history step width includes:

determining the initial probability distribution function according to the history step size and the history step width by the following formula:

wherein, P ₀ Is the initial probability distribution function; c ═ x _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a plurality of grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₀ (C＝(x _i ,y _i ) Is located on the ith grid (x) for the position of the foot landing point of the heel after liftoff _i ,y _i ) The probability of (d); x is the number of _h For the history step, y _h For the history step width, beta and sigma are preset parameters, alpha ₀ Are normalized coefficients.

As an example, the obtaining the motion state characteristics of the heel in the swinging process after liftoff comprises:

acquiring a first angular velocity and a first acceleration of the heel in a swinging process after the heel lifts off the ground through an inertial sensor worn by the heel;

determining the swing speed and the swing position of the heel according to the first angular speed and the first acceleration;

and determining the motion state characteristic according to the swing speed and the swing position.

As one example, the first angular velocity and the first acceleration are an angular velocity and an acceleration in an inertial coordinate system measured by the inertial sensor;

determining a swing velocity and a swing position of the heel based on the first angular velocity and the first acceleration, comprising:

converting the first angular velocity into a second angular velocity in a global coordinate system, wherein the global coordinate system is a coordinate system established by taking the position of the heel when leaving the ground as a coordinate origin;

integrating the second angular velocity to obtain a swing angle of the heel under the global coordinate system;

determining a second acceleration of the heel under the global coordinate system according to the swing angle and the first acceleration;

integrating the second acceleration to obtain the swing speed;

and integrating the swing speed to obtain the swing position.

As one example, the state of motion feature includes one or more of a first feature, a second feature, a third feature, a fourth feature, a fifth feature, and a sixth feature;

wherein, the first characteristic is the first maximum value of the swing speed of the X-axis direction of the global coordinate system, the second characteristic is the first maximum value of the swing speed of the Y-axis direction of the global coordinate system, the third characteristic is the first maximum value of the maximum swing speed of the Z-axis direction of the global coordinate system, the fourth characteristic is the first maximum value of the swing position of the Z-axis direction of the global coordinate system, the fifth characteristic is the swing position corresponding to the X-axis direction when the swing position of the Z-axis direction of the global coordinate system is the first maximum value, the sixth characteristic is the swing position corresponding to the Y-axis direction when the swing position of the Z-axis direction of the global coordinate system is the first maximum value, the global coordinate system is that the position when the heel leaves the ground is the origin of coordinates, the direction right ahead of the heel swing is the X-axis, And the direction vertical to the right front direction is a Y axis, and the direction vertical to the ground is a Z axis.

As an example, the motion state feature includes s features, s being a positive integer;

the updating the initial probability distribution function according to the obtained motion state characteristics to obtain a target probability distribution function includes:

when the updating end condition is not met, when any one of the s characteristics is obtained and the currently obtained characteristic is the n-th acquired characteristic, updating the probability distribution function updated for the (n-1) th time according to the n-th characteristic to obtain the probability distribution function updated for the n-th time;

if n is 1, the probability distribution function updated at the (n-1) th time is the initial probability distribution function; if n is larger than 1, the probability distribution function updated for the (n-1) th time is obtained by updating the probability distribution function updated for the (n-2) th time according to the obtained (n-1) th feature;

and when the updating end condition is met, determining the probability distribution function updated for the last time as the target probability distribution function.

As an example, the updating the n-1 th updated probability distribution function according to the nth feature to obtain the nth updated probability distribution function includes:

according to the nth characteristic, updating the probability distribution function updated for the (n-1) th time through the following formula to obtain the probability distribution function updated for the nth time:

wherein, P _n A probability distribution function for the nth update; c ═ x _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a plurality of grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P _n (C＝(x _i ,y _i )|f _n,m ,f _n-1,m ,…,f _1,m ) For the position of the foot drop point of the heel after liftoff to be positioned at the ith grid (x) _i ,y _i ) The probability of the nth update; f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features is obtained; alpha is alpha _n Is a normalized coefficient; f. of _n,e (x _i ,y _i ) For a characteristic expectation function, γ, corresponding to said nth characteristic _n And the root mean square error corresponds to the nth characteristic.

As an example, before the updating the n-1 th updated probability distribution function according to the nth feature to obtain the nth updated probability distribution function, the method further includes:

obtaining sample motion state characteristics of a heel of a subject during a plurality of swings, the sample motion state characteristics including at least one sample characteristic;

and performing curve fitting on each sample characteristic in the at least one sample characteristic to obtain a characteristic expected value function and a root mean square error corresponding to each sample characteristic.

As an example, the determining the position of the foot landing point of the heel after liftoff according to the target probability distribution function includes:

according to the target probability distribution function, determining the position of the foot landing point of the heel after liftoff through the following formula:

wherein the content of the first and second substances,

the position of a foot drop point of the heel after being off the ground; (x) _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a x b grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is the target probability distribution function for indicating that the position of the foot landing point of the heel after liftoff is located in the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features.

In a second aspect, there is provided a device for determining a location of a landing point, the device comprising:

the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining an initial probability distribution function according to the historical foot landing point position of the heel, and the initial probability distribution function is used for indicating the probability that the foot landing point position of the heel after leaving the ground is located at each position in a foot landing area;

the first acquisition module is used for acquiring the motion state characteristics of the heel in the swinging process after liftoff, wherein the motion state characteristics are characteristics related to the swinging speed and/or the swinging position of the heel;

the updating module is used for updating the initial probability distribution function according to the acquired motion state characteristics to obtain a target probability distribution function;

and the second determining module is used for determining the position of the foot landing point of the heel after liftoff according to the target probability distribution function.

In one embodiment, the first determination module is to:

wherein, P ₀ Is the initial probability distribution function; c ═ x _i ,y _i ) E is A, A is the landing area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₀ (C＝(x _i ,y _i ) Is located on the ith grid (x) for the position of the foot landing point of the heel after liftoff _i ,y _i ) The probability of (d); x is the number of _h For the history step, y _h For the history step width, beta and sigma are preset parameters, alpha ₀ Are normalized coefficients.

In one embodiment, the first obtaining module is configured to:

acquiring a first angular velocity and a first acceleration of the heel in a swinging process after liftoff through an inertial sensor worn by the heel;

In one embodiment, the first angular velocity and the first acceleration are angular velocities and accelerations in an inertial coordinate system measured by the inertial sensor, and the first acquisition module is configured to:

converting the first angular velocity into a second angular velocity in a global coordinate system, wherein the global coordinate system is a coordinate system established by taking the position of the heel in the ground as a coordinate origin;

integrating the second acceleration to obtain the swing speed;

and integrating the swing speed to obtain the swing position.

In one embodiment, the motion state feature includes s features, s being a positive integer, and the update module is configured to:

if n is 1, the probability distribution function updated at the (n-1) th time is the initial probability distribution function; if n is larger than 1, the probability distribution function of the (n-1) th update is obtained by updating the probability distribution function of the (n-2) th update according to the obtained (n-1) th feature;

In one embodiment, the update module is to:

wherein, P _n Probability distribution function for the nth update；C＝(x _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a plurality of grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P _n (C＝(x _i ,y _i )|f _n,m ,f _n-1,m ,…,f _1,m ) For the position of the foot drop point of the heel after liftoff to be positioned at the ith grid (x) _i ,y _i ) The probability of the nth update; f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features is obtained; alpha is alpha _n Is a normalized coefficient; f. of _n,e (x _i ,y _i ) For a characteristic expectation function, γ, corresponding to said nth characteristic _n And the root mean square error corresponds to the nth characteristic.

In one embodiment, the apparatus further comprises:

a second obtaining module for obtaining sample motion state characteristics of the heel of the subject during a plurality of swings, the sample motion state characteristics including at least one sample characteristic;

and the curve fitting module is used for performing curve fitting on each sample characteristic in the at least one sample characteristic to obtain a characteristic expected value function and a root mean square error corresponding to each sample characteristic.

In one embodiment, the second determination module is to:

wherein the content of the first and second substances,

the position of a foot drop point of the heel after being off the ground; (x) _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a x b grids, C ═ x _i ,y _i ) Is the ith grid in A(x _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is the target probability distribution function for indicating that the position of the foot landing point of the heel after liftoff is located in the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features.

In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when executed by the processor, the computer program implements the method for determining a location of a foothold as described above.

In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the method for determining a location of a foothold as described above.

The technical scheme provided by the embodiment of the application has the following beneficial effects:

in the embodiment of the application, an initial probability distribution function is determined according to the historical foot-drop point position of the heel, the motion state characteristics of the heel in the swing process after the heel leaves the ground are obtained, the initial probability distribution function is updated according to the obtained motion state characteristics to obtain a target probability distribution function, and then the foot-drop point position of the heel after the heel leaves the ground is determined according to the target probability distribution function. Wherein the initial probability distribution function indicates the probability that the position of the foot landing point of the heel after liftoff is positioned at each position in the foot landing area, and the motion state characteristic is a characteristic related to the swing speed and/or the swing position of the heel. The probability distribution function is updated according to the motion state characteristics in a swing process, so that the position of the foot-landing point is accurately predicted in advance. In addition, the motion state characteristics are acquired before the heel falls to the ground, so that the probability distribution function is updated according to the motion state characteristics, and the position of the foot falling point is determined before the heel falls to the ground, so that the walking-assisting robot can be effectively assisted to adjust the pose before the foot of the human body falls to the ground, the walking-assisting robot can assist the foot more flexibly, and the assistance performance of the walking-assisting robot is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a method for determining a location of a landing point according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a global coordinate system provided in an embodiment of the present application;

FIG. 3 is a schematic view of a foot swing trajectory provided by an embodiment of the present application;

fig. 4 is a schematic structural diagram of a device for determining a location of a landing point according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

It should be understood that reference to "a plurality" in this application means two or more. In the description of the present application, "/" means "or" unless otherwise stated, for example, a/B may mean a or B; "and/or" herein is only an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, for the convenience of clearly describing the technical solutions of the present application, the words "first", "second", and the like are used to distinguish the same items or similar items having substantially the same functions and actions. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.

The method for determining the position of the foot-falling point provided by the embodiment of the application can be applied to predicting the position of the foot-falling point of the walking of the human body in advance. For example, the position of the foot falling point of the walking robot can be predicted in advance to assist the walking robot to adjust the pose before the foot of the walking robot falls to the ground, so that the walking robot can assist the foot more flexibly, and the assistance performance of the walking robot is improved. Of course, the method can also be applied to other scenarios, and the embodiment of the present application is not limited thereto.

The method for determining the landing point position provided in the embodiments of the present application is explained in detail below.

Referring to fig. 1, fig. 1 is a flowchart of a method for determining a location of a landing point according to an embodiment of the present disclosure. The method provided by the embodiment of the application is applied to an electronic device, for example, the electronic device can be a walking-assistant robot or other electronic devices connected with the walking-assistant robot, and the method comprises the following steps:

step 101, determining an initial probability distribution function according to the historical landing point position of the heel.

The initial probability distribution function is used for indicating the probability that the foot drop point position of the heel after being lifted off the ground is located at each position in the foot drop area, and the embodiment of the application predicts the foot drop point position of the heel after being lifted off the ground.

Wherein the historical landing point location refers to a location on the ground prior to heel lift.

As an example, in order to accurately predict the position of the foot-landing point, a global coordinate system may be established in advance, and the position of the foot-landing point in the global coordinate system may be determined. Accordingly, the historical landing point location, the position at heel lift, and the landing point location after heel lift refer to locations in the global coordinate system.

The global coordinate system is a rectangular coordinate system established by taking the position of the heel when leaving the ground as the origin of coordinates, the direction right ahead of the heel swing as an X axis, the direction perpendicular to the right ahead direction as a Y axis and the direction perpendicular to the ground as a Z axis.

Referring to fig. 2, fig. 2 is a schematic diagram of a global coordinate system according to an embodiment of the present disclosure. As shown in fig. 2, o is the position of the heel off the ground, and a rectangular coordinate system established with the position o of the heel off the ground as the origin of coordinates and the direction right in front of the heel swing as the X axis is used as the global coordinate system. The right front direction of the heel swing is the positive direction of the X axis of the global coordinate system, the left direction perpendicular to the right front direction of the heel swing is the positive direction of the Y axis of the global coordinate system, and the upward direction perpendicular to the ground is the positive direction of the Z axis of the global coordinate system.

As an example, a historical step size and a historical step width of a heel walking may be determined according to a historical landing point position of a heel, and then an initial probability distribution function may be determined according to the historical step size and the historical step width.

For example, the distance between the historical footfall position in the global coordinate system and the position of the heel off the ground in the global coordinate system is determined, and the historical step length and the historical step width of the driving are determined according to the distance. For example, as shown in fig. 2, p is the historical foot landing point position, which is the historical foot landing point last time before the heel lifts off, o is the position of the heel when the heel lifts off, and the historical step length and the historical step width are determined according to the distance between p and o. For example, the distance between p and o in the X-axis direction of the global coordinate system is taken as the history step width, and the distance between p and o in the Y-axis direction of the global coordinate system is taken as the history step width.

In addition, the historical step length and the historical step width can also be determined according to two or more historical foothold positions. For example, a plurality of step sizes and a plurality of step widths are determined according to a plurality of historical foothold positions, an average value of the plurality of step sizes is used as a historical step size, and an average value of the plurality of step widths is used as a historical step width.

As an example, a first history step size and a first history step width are determined according to a distance between two history foot landing positions, a second history step size and a second history step width are determined according to a distance between a position of the ground (history foot landing position) and a position of the heel at the time of ground before ground lift, a third history step size determined according to the first history step size and the second history step size is taken as the history step size, and a third history step width determined according to the first history step width and the second history step width is taken as the history step width. For example, the average of the first history step and the second history step may be used as the third history step.

As an example, the historical step length and the historical step width may be used as a priori knowledge to initialize the probability that the landing point position is located at each position in the landing area, so as to obtain an initial probability distribution function.

For example, the positions of the landing points obey gaussian distribution, the initial probability distribution function is a gaussian probability distribution function, the gaussian probability distribution function added with the bias term can be obtained according to the historical step length and the historical step width, and the gaussian probability distribution function added with the bias term is the initial probability distribution function.

Wherein, according to the history step size and the history step width, the initial probability distribution function can be determined by the following formula (1):

wherein, P ₀ Is an initial probability distribution function; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₀ (C＝(x _i ,y _i ) Is located at the ith grid (x) for the position of the foot landing point of the heel after liftoff _i ,y _i ) The probability of (d); x is the number of _h For historical step size, y _h For the history step width, β and σ are preset parameters, α ₀ Are normalized coefficients.

Wherein, a is divided into a × b grids, a refers to the number of horizontal grids into which the footable area a is divided, and b refers to the number of vertical grids into which the footable area a is divided. For example, a is divided into a grids in the X-axis direction of the global coordinate system, and a is divided into b grids in the Y-axis direction of the global coordinate system.

For example, as shown in fig. 2, a is a foot-drop area of the foot-drop position of the person walking ahead and after the heel lift off, that is, an accessible range of the foot-drop position of the person walking behind the heel lift off, and the area a is divided into 6 × 5 grids.

And the historical step length and the historical step width are used for initializing the probability that the position of the foothold point of the heel after liftoff is in the ith grid in the A.

And 102, acquiring the motion state characteristics of the heel in the swing process after liftoff.

It should be noted that, in the embodiment of the present application, a swing process from heel off to heel on the ground is referred to as a swing phase, and the embodiment of the present application predicts a foot drop point position of a swing process after heel off.

Wherein the motion state characteristic is a characteristic related to the swing speed and/or the swing position of the heel during the swing after liftoff, and the motion state characteristic comprises at least one characteristic. Swing velocity and swing position refer to the velocity and displacement of the heel in global coordinates during the swing after ground off.

For example, the motion state characteristic is used to indicate a qualified swing speed and/or a qualified swing position of the heel at an early stage of the swing process of the heel after liftoff. The eligible swing speed and/or eligible swing position are in the early phase of the swing process of the heel after liftoff.

Wherein, the earlier stage of the swing process refers to the swing process from the heel off the ground to the heel before landing, or the earlier stage of the swing process refers to the swing process in the preset condition after the heel off the ground. Therefore, the position of the foot falling point is determined before the heel falls to the ground according to the motion state characteristics of the early stage of the swing process, the position of the foot falling point is predicted in advance, the walking-assisting robot is effectively assisted to adjust the pose before the foot of the human body falls to the ground, the walking-assisting robot can assist the human body more flexibly, and the power assisting performance of the walking-assisting robot is improved.

For example, the early stage of the swing process refers to the swing process within a preset time or a preset distance after the heel leaves the ground.

In addition, the qualified oscillation speed and/or the qualified oscillation position may comprise one or more, i.e. the motion state feature comprises at least one feature.

As an example, the motion state feature includes s features, s being a positive integer. For example, the state of motion feature may include one or more of the first feature, the second feature, the third feature, the fourth feature, the fifth feature, and the sixth feature.

The first characteristic is a first maximum value of the swing speed in the X-axis direction of the global coordinate system, the second characteristic is a first maximum value of the swing speed in the Y-axis direction of the global coordinate system, the third characteristic is a first maximum value of the maximum swing speed in the Z-axis direction of the global coordinate system, the fourth characteristic is a first maximum value of the swing position in the Z-axis direction of the global coordinate system, the fifth characteristic is the swing position corresponding to the X-axis direction when the swing position in the Z-axis direction of the global coordinate system is the first maximum value, and the sixth characteristic is the swing position corresponding to the Y-axis direction when the swing position in the Z-axis direction of the global coordinate system is the first maximum value.

The first, second, and third characteristics are qualified oscillation speeds, and the fourth, fifth, and sixth characteristics are qualified oscillation positions. It should be understood that the first feature, the second feature, the third feature, the fourth feature, the fifth feature and the sixth feature are only one example of the qualified swing speed and the qualified swing position, and are not limited, that is, the qualified swing speed and/or the qualified swing position indicated by the motion state feature may be other features, and the embodiment of the present application is not limited thereto.

In addition, the method for determining the motion state characteristics according to the swing speed and the swing position may include the steps of:

1) and acquiring the swing speed and the swing displacement of the global coordinate system in the swing process of the heel after liftoff.

For example, the swing speed and the swing position of the heel during the swing process after liftoff can be continuously obtained in real time, or the swing speed and the swing position of the heel during the swing process after liftoff can be periodically obtained, so that a plurality of swing speeds and a plurality of swing displacements of the heel during the swing process after liftoff can be obtained, and at least one of the motion state characteristics, namely the characteristics related to the swing speed and/or the swing position of the heel, can be determined according to the plurality of swing speeds and the plurality of swing displacements of the heel.

As one example, the swing speed and swing position may be obtained by an inertial sensor worn by the heel. For example, a first angular velocity and a first acceleration of the heel in the swing process after liftoff are obtained through an inertial sensor worn by the heel, and then the swing velocity and the swing position of the heel are determined according to the first angular velocity and the first acceleration.

Wherein the first angular velocity and the first acceleration are an angular velocity and an acceleration in an inertial coordinate system measured by the inertial sensor. Since the swing velocity and the swing position refer to the velocity and the position under the global coordinate system, the conversion of the coordinate system is involved in determining the swing velocity and the swing position of the heel from the first angular velocity and the first acceleration.

For example, the first angular velocity is converted into a second angular velocity in the global coordinate system, and the second angular velocity is integrated to obtain the swing angle of the heel in the global coordinate system. Then, a second acceleration of the heel in the global coordinate system is determined based on the swing angle and the first acceleration. And integrating the second acceleration to obtain the swing speed, and integrating the swing speed to obtain the swing position.

Based on the above, the current swing speed and the current swing position of the heel in the swing process after liftoff can be obtained in real time through the inertial sensor.

Of course, the current swing speed and the current swing position of the heel in the swing process after liftoff can also be obtained in real time through other modes, which is not limited in the embodiment of the application. For example, the image is obtained by photographing the feet of the walking person with a camera.

2) And determining the motion state characteristic according to the swing speed and the swing position.

For example, the currently acquired swing speed is compared with the last acquired swing speed. If the X-axis direction component of the swing speed obtained at present is smaller than the X-axis direction component of the swing speed obtained at the last time, the X-axis direction component of the swing speed obtained at the last time is used as a first maximum value of the swing speed of the global coordinate system in the X-axis direction after the heel leaves the ground, the X-axis direction component of the swing speed obtained at the last time is the swing speed meeting the condition, and the X-axis direction component of the swing speed obtained at the last time is used as a first feature in the motion state features. Similarly, the first maximum value of the swing speed in the Y-axis direction of the global coordinate system may be determined as the second feature, and the first maximum value of the swing speed in the Z-axis direction may be determined as the third feature.

In addition, the currently acquired swing position may also be compared with the swing position acquired last time. If the Z-axis direction component of the currently acquired swing position is smaller than the Z-axis direction component of the last acquired swing position, the Z-axis direction component of the last acquired swing position is used as a first maximum value of the swing position of the global coordinate system in the Z-axis direction after the heel is lifted off the ground, the Z-axis direction component of the last acquired swing position is a swing position meeting the conditions, the Z-axis direction component of the last acquired swing position is used as a fourth feature in the motion state features, the X-axis direction component of the last acquired swing position is used as a fifth feature in the motion state features, and the Y-axis direction component of the last acquired swing position is used as a sixth feature in the motion state features.

It should be noted that each of the motion state characteristics is acquired only once during one oscillation.

In addition, since different features have different dimensions and dimension units, to eliminate such differences, normalization processing may be performed on the different features. I.e. the first, second, third, fourth, fifth and sixth features are normalized features.

Wherein the first, second, third, fourth, fifth and sixth features do not indicate an order of acquisition. For example, the third feature is obtained according to the swing speed and the swing position obtained in real time. And then, other characteristics included in the motion state characteristics are continuously determined according to the swing speed and the swing position acquired in real time.

Of course, at least one of the motion state characteristics related to the swing speed and/or the swing position of the heel may be determined in other manners according to the swing speed and the swing position, that is, at least one of the motion state characteristics may be other characteristics, and the embodiment of the present application is only an example and is not limited.

The motion state characteristics are characteristics of the heel in the swing process after being lifted off the ground, namely the motion state characteristics are acquired before the heel falls to the ground, so that the probability distribution function is updated according to the motion state characteristics, and the determination of the position of the foot falling point can be completed before the heel falls to the ground, so that the position of the foot falling point can be predicted in advance, the walking-assisting robot is effectively assisted to adjust the pose before the foot of the human body falls to the ground, the walking-assisting robot can assist the foot more flexibly, and the assistance performance of the walking-assisting robot is improved.

As an example, when the update end condition is met, the motion state characteristic is not determined according to the swing speed and the swing position, so that the obtained motion state characteristic is in an early stage of a swing process after the heel leaves the ground, and the early stage of the swing process refers to the swing process in a preset condition after the heel leaves the ground, so that the early prediction of the position of the foot drop point can be realized according to the motion state characteristic in the early stage of the swing process, the pose of the walking-aid robot can be adjusted early, the walking-aid robot can further assist the walking-aid robot more flexibly, and the assistance performance of the walking-aid robot can be further improved.

As an example, the motion state feature includes s features, and the update end condition may be whether all of the motion state features have been acquired, that is, whether s features have been acquired. In this case, the time for acquiring s features in the motion state features is in the early stage of a swing process after the heel lift off, so that whether all the features in the motion state features are acquired or not can be used as an update end condition, and the early prediction of the position of the foot falling point can be realized according to the motion state features in the early stage of the swing process.

For example, the motion state characteristics include a first characteristic, a second characteristic, a third characteristic, a fourth characteristic, a fifth characteristic, and a sixth characteristic. The time for acquiring the first feature, the second feature, the third feature, the fourth feature, the fifth feature and the sixth feature is in an early stage of a swing process after heel lift off, so that whether all the features in the motion state features are acquired or not can be used as update ending conditions.

As an example, please refer to fig. 3, fig. 3 is a schematic diagram of a swing trajectory of a foot according to an embodiment of the present application.

Fig. 3(a) is a schematic diagram of a plurality of foot swing trajectories of the swing speed in the X-axis direction of the global coordinate system. The abscissa is the duration of the swing phase after the start, that is, the duration of a swing process from heel off to heel on the ground, and the ordinate is the speed value of the swing speed in the X-axis direction. As can be seen from fig. 3(a), the first maximum of the ordinate in the swing trajectories of the plurality of feet is located at the early stage of the swing process after heel lift, i.e. the first feature f _A Corresponding abscissa t _fA In the early phase of the oscillation phase. For example, the abscissa corresponding to the first feature is 50% in front of the swing phase.

Fig. 3 (b) is a schematic diagram of a plurality of foot swing trajectories of the swing speed in the Y-axis direction of the global coordinate system. The abscissa is the time duration after the start of the swing phase, and the ordinate is the speed value of the swing speed in the Y-axis direction. As can be seen from fig. 3 (b), the first maximum values of the ordinate in the plurality of foot swing trajectories are all at the initial stage of the swing phase, i.e., the second feature f _B Corresponding abscissa t _fB In the early phase of the oscillation phase. For example, the abscissa corresponding to the second feature is 50% in front of the swing phase.

Fig. 3 (c) is a schematic diagram of a plurality of foot swing trajectories for the swing speed in the Z-axis direction of the global coordinate system. The abscissa is the time duration after the start of the swing phase, and the ordinate is the speed value of the swing speed in the Z-axis direction. As can be seen from the graph (c) in fig. 3, the first maximum value of the ordinate in the plurality of foot swing trajectories is at the initial stage of the swing phase, i.e., the third feature f _C Corresponding abscissa t _fC In the early phase of the oscillation phase. For example, the third feature corresponds to the cross barThe coordinates are 50% in front of the swing phase.

Fig. 3 (d) is a schematic diagram of a plurality of foot swing trajectories of the swing position in the Z-axis direction of the global coordinate system. The abscissa is a time period after the start of the oscillation phase, and the ordinate is a displacement value of the oscillation position in the Z-axis direction. As can be seen from the graph (d) in fig. 3, the first maximum value of the ordinate in the plurality of foot swing trajectories is in the initial phase of the swing phase, i.e., the fourth feature f _D Corresponding abscissa t _fD In the early phase of the oscillation phase. For example, the abscissa corresponding to the fourth feature is 50% in front of the swing phase.

In addition, the fifth characteristic is a rocking position in the X-axis direction when the fourth characteristic is obtained, and the sixth characteristic is a rocking position in the Y-axis direction when the fourth characteristic is obtained. Based on this, the fifth feature and the sixth feature are also in the initial period of the oscillation phase. For example, fig. 3(e) is a schematic diagram of a foot swing trajectory. As can be seen from the graph (e) in fig. 3, the swing position in the Z-axis direction is the first maximum value f _D When the position of the corresponding X-axis swing is f _E And a swing position in the Y-axis direction is f _F ，f _E As a fifth feature, f _F The sixth feature f _E And the sixth feature f _F Corresponding abscissa t _fD In the early phase of the oscillation phase.

That is, as can be seen from fig. 3(a) to 3(e), the abscissa corresponding to the first feature, the second feature, the third feature, the fourth feature, the fifth feature, and the sixth feature is in the phase preceding the swing phase, so that the motion state features including the first feature, the second feature, the third feature, the fourth feature, the fifth feature, and the sixth feature may be set, and whether all of the motion state features have been acquired or not may be used as the update end condition.

Of course, as can be seen from fig. 3(a) -3 (e), the abscissa corresponding to the first feature, the second feature, the third feature, the fourth feature, the fifth feature and the sixth feature is located 50% before the swing phase, so that the preset time after the heel lift off may be used as an update end condition, the motion state feature obtained from the swing process within the preset time after the heel lift off may be obtained, and the obtained motion state feature may be one or more of the first feature, the second feature, the third feature, the fourth feature, the fifth feature and the sixth feature. Of course, the obtained motion state features may be other, which is not limited in this application.

The update end condition may be other conditions, which is not limited in the embodiment of the present application. For example, whether the swing position in the X-axis direction of the global coordinate system is greater than one-half of the history step is used as the update end condition, and the fact that the swing position in the X-axis direction is greater than one-half of the history step means that the heel has swung by one-half of the history step. In this case, the acquired movement state characteristic can also be located in the early phase of a swing process, so that an early prediction of the position of the footfall point is achieved.

As one example, from the swing speed and swing position, a foot swing trajectory may also be determined. Such as the wobble track shown in fig. 2.

And 103, updating the initial probability distribution function according to the acquired motion state characteristics to obtain a target probability distribution function.

The target probability distribution function is a probability distribution function for determining the position of the foot landing point after the heel lift off, that is, a probability distribution function for predicting the position of the foot landing point after the heel lift off.

Wherein, the initial probability distribution function can be updated 1 time or repeatedly according to the motion state characteristics. For example, if the number of features in the motion state features is 1, the initial probability distribution function is updated 1 time. And if the number of the features in the motion state features is two or more, iteratively updating the initial probability.

As an example, the motion state feature includes s features, s is a positive integer, and updating the initial probability distribution function according to the obtained motion state feature to obtain the target probability distribution function may include the following steps:

and step 1031, when the update end condition is not met, each time any one of the s features is acquired and the currently acquired feature is the nth feature of the acquired features, updating the probability distribution function updated for the (n-1) th time according to the nth feature to obtain the probability distribution function updated for the nth time.

It should be noted that the target probability distribution function can be obtained in the early stage of a swing process by setting the update ending condition, so that the position of the foot-falling point can be obtained in the early stage of the swing process, and the position of the foot-falling point can be predicted in advance, so that the pose of the walking-assisting robot can be adjusted before the foot of the human body falls to the ground, the walking-assisting robot can assist the foot more flexibly, and the assistance performance of the walking-assisting robot can be improved.

If n is 1, the probability distribution function updated at the (n-1) th time is the initial probability distribution function; and if n is larger than 1, updating the probability distribution function updated for the n-1 st time according to the acquired n-1 st characteristic to obtain the probability distribution function updated for the n-2 th time.

For example, according to the nth feature, the probability distribution function of the (n-1) th update can be updated by the following formula (2), so as to obtain the probability distribution function of the nth update:

wherein, P _n A probability distribution function for the nth update; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P _n (C＝(x _i ,y _i )|f _n,m ,f _n-1,m ,…,f _1,m ) The position of the foot drop point after the heel lift off is positioned in the ith grid (x) _i ,y _i ) The probability of the nth update; f. of _n,m For the n-th feature already acquired in the motion state feature, f _1,m The acquired 1 st feature in the motion state features; alpha is alpha _n Is a normalized coefficient; f. of _n,e (x _i ,y _i ) For a characteristic expectation function, γ, corresponding to the nth characteristic _n The root mean square error corresponding to the nth feature.

As an example, if the update end condition is not satisfied, the motion state feature is continuously determined according to the swing speed and the swing position, so that the probability distribution function is continuously updated according to the motion state feature to obtain an updated probability distribution function, until the update end condition is satisfied, the motion state feature is no longer determined according to the swing speed and the swing position, and the probability distribution function is no longer updated.

For example, when the update end condition is not satisfied, the update of the probability distribution function is implemented by the following steps:

1) and determining the 1 st feature in the s features according to the swing speed and the swing position, and obtaining the 1 st updated probability distribution function according to the determined 1 st feature. For example, the 1 st feature determined according to the swing speed and the swing position is a third feature, and the probability distribution function of the 1 st update is obtained according to the third feature.

For example, the probability distribution function of the 1 st update can be obtained by the following formula (3):

wherein, P ₁ Probability distribution function for 1 st update; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₁ (C＝(x _i ,y _i )|f _1,m ) The position of the foot drop point after the heel lift off is positioned in the ith grid (x) _i ,y _i ) Probability of the 1 st update; f. of _1,m The acquired 1 st feature (the third feature in the embodiment of the present application) in the motion state features; f. of _1,e (x _i ,y _i ) A characteristic expectation function corresponding to the 1 st characteristic (in the embodiment of the present application, a characteristic expectation function corresponding to the third characteristic); gamma ray ₁ The root mean square error corresponding to the 1 st feature (in the embodiment of the present application, the root mean square error corresponding to the third feature); p ₀ Is an initial probability distribution function; alpha is alpha ₁ Are normalized coefficients.

2) And continuously determining the 2 nd feature in the s features according to the swinging speed and the swinging position, and continuously obtaining the probability distribution function updated for the 2 nd time according to the determined 2 nd feature. For example, the 2 nd feature determined according to the swing speed and the swing position is the first feature, and the probability distribution function of the 2 nd update is obtained according to the first feature.

For example, the probability distribution function of the 2 nd update can be obtained by the following formula (4):

wherein, P ₂ Probability distribution function for 2 nd update; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₂ (C＝(x _i ,y _i )|f _2,m ,f _1,m ) The position of the foot drop point after the heel lift off is positioned in the ith grid (x) _i ,y _i ) Probability of the 2 nd update of (1); f. of _2,m The acquired 1 st feature (the first feature in the embodiment of the present application) in the motion state features; f. of _1,m The acquired 1 st feature (the third feature in the embodiment of the present application) in the motion state features; f. of _2,e (x _i ,y _i ) A characteristic expectation function corresponding to the 2 nd characteristic (in the embodiment of the present application, the characteristic expectation function corresponding to the first characteristic), γ ₁ The root mean square error corresponding to the 1 st feature (in the embodiment of the present application, the root mean square error corresponding to the first feature); p ₁ Probability distribution function for 1 st update; alpha is alpha ₂ Are normalized coefficients.

3) And continuously determining the mth feature in the s features according to the swinging speed and the swinging position, continuously obtaining the probability distribution function updated for the mth time according to the determined mth feature, and stopping updating the probability distribution function until the motion state feature is not determined when the updating ending condition is met.

Wherein, the update end condition may be whether all of the motion state features have been acquired. The characteristics in the motion state characteristics may be preset. Of course, the update end condition may be other, which is not limited in the embodiment of the present application.

For example, if the motion state feature includes a first feature, a second feature, a third feature, a fourth feature, a fifth feature, and a sixth feature, the 6 th-time probability distribution function is updated and then the operation is stopped.

In addition, the characteristic expectation function and the root mean square error corresponding to the nth characteristic can be obtained in advance.

For example, the motion state characteristics of the heel of the subject during multiple swings are obtained, and then curve fitting is performed on each sample characteristic of at least one sample characteristic to obtain a characteristic expectation function and a root mean square error corresponding to each sample characteristic. Wherein the sample motion state features include at least one sample feature, the sample feature corresponding to a feature in the motion state features.

As an example, a sample swing speed and a sample swing position of the heel of the subject during multiple swings may be obtained, and then the motion state characteristics of the sample may be obtained according to the sample swing speed and the sample swing position.

For example, a plurality of cameras are arranged around a running machine without an inclination angle, the feet of a human body carry mark points, a plurality of subjects walk on the running machine without the inclination angle at different speeds and gaits, and images including the mark points are shot by the cameras to obtain a plurality of sample swing speeds and a plurality of sample swing positions included in each swing process in a plurality of swing processes. Each sample characteristic in the sample motion state characteristics can be obtained according to the sample swinging speeds and the sample swinging positions. The number of each sample feature in the sample motion state features that can be determined by one swing process is 1, for example, position coordinates corresponding to 1 first sample feature, 1 second sample feature, 1 third sample feature, 1 fourth sample feature, 1 fifth sample feature, 1 sixth sample feature, and each sample feature are obtained. Based on this, the number of each sample feature that can be determined by the plurality of swing processes is plural, such as obtaining a plurality of first sample features, a plurality of second sample features, a plurality of third sample features, a plurality of fourth sample features, a plurality of fifth sample features, a plurality of sixth sample features, and the corresponding position coordinates of each sample feature.

Of course, the motion state feature of the sample may also be obtained in other manners, which is not limited in this application. The sample motion status characteristics are obtained, for example, by an inertial sensor worn by the heel.

In addition, a plurality of foot swing tracks can be obtained according to the sample swing speed and the sample swing position. For example, the plurality of foot swing trajectories shown in fig. 3 may be sample foot swing trajectories obtained from a sample swing speed and a sample swing position.

In addition, since different sample features have different dimensions and dimension units, to eliminate such differences, normalization processing may be performed on the different sample features. That is, the first sample feature, the second sample feature, the third sample feature, the fourth sample feature, the fifth sample feature, and the sixth sample feature are normalized sample features, and the normalization processing method of the sample features is the same as the normalization processing method of the features in the motion state features.

As one example, the sample motion state feature includes a first sample feature, a second sample feature, a third sample feature, a fourth sample feature, a fifth sample feature, or a sixth sample feature, and the number of each sample feature includes a plurality.

As an example, a quadratic polynomial surface interpolation fitting may be performed on each sample feature in the at least one sample feature to obtain a feature expectation function and a root mean square error corresponding to each sample feature.

For example, according to each sample feature and the corresponding position coordinate of each sample feature, the feature expectation function can be obtained by the following equation (5):

f _e (x,y)＝z＝a ₂₀ x ² +a ₀₂ y ² +a ₁₁ xy+a ₁₀ x+a ₀₁ y+a ₀₀ (5)

wherein f is _e (x, y) is the same asThe characteristic expectation function corresponding to the characteristic, z is the sample characteristic, (x, y) is the position coordinate corresponding to the sample characteristic, a ₂₀ 、a ₀₂ 、a ₁₁ 、a ₁₀ 、a ₀₁ And a ₀₀ Is the unknown to be solved.

For example, the expected feature value function and the root mean square error corresponding to the first sample feature are obtained through the above formula (5) according to the plurality of first sample features and the position coordinates corresponding to the plurality of first sample features, that is, the expected feature value function and the root mean square error corresponding to the first feature are obtained. Then, if the first feature is determined, the probability distribution function may be updated according to the expected feature value function and the root mean square error corresponding to the first feature.

And 1032, when the updating ending condition is met, determining the probability distribution function updated last time as the target probability distribution function.

It should be noted that, through steps 1032 to 1032, the probability distribution function may be updated to obtain the target probability distribution function. The motion state characteristics are acquired in a swing process before the heel falls to the ground, so that a target probability distribution function can be acquired before the heel falls to the ground, the position of a foot falling point can be predicted before the heel falls to the ground according to the target probability distribution function, the walking assisting robot is effectively assisted in adjusting the pose of the walking assisting robot before the foot of a human body falls to the ground, the walking assisting robot can assist in assisting more flexibly, and the assisting performance of the walking assisting robot is improved.

And step 104, determining the position of a foot landing point of the heel after the heel leaves the ground according to the target probability distribution function.

For example, according to the target probability distribution function, the position of the foot landing point after heel lift off can be determined by the following formula (6):

wherein the content of the first and second substances,

after leaving the ground for the heelThe position of the foot-drop point; (x) _i ,y _i ) e.A, A is the area where feet can fall, A is divided into a x b grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is a target probability distribution function for indicating that the position of the foot landing point of the heel after liftoff is located in the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature already acquired in the motion state feature, f _1,m The acquired 1 st feature in the motion state features.

It should be noted that, since the target probability distribution function can be obtained at the early stage of the heel swing process, the position of the foot drop point can be predicted in advance at the early stage of the heel swing process according to the target probability distribution function.

Fig. 4 is a schematic structural diagram of a device for determining a location of a foot point according to an embodiment of the present disclosure. The foothold position determination apparatus may be implemented by software, hardware, or a combination of the two as part or all of an electronic device, which may be an electronic device shown in fig. 5 below. Referring to fig. 3, the apparatus includes: a first determining module 401, a first obtaining module 402, an updating module 403 and a second determining module 404.

A first determining module 401, configured to determine an initial probability distribution function according to a historical foot drop point position of a heel, where the initial probability distribution function is used to indicate a probability that the foot drop point position of the heel after liftoff is located at each position in a foot-drop-able area;

a first obtaining module 402, configured to obtain a motion state characteristic of the heel in a swinging process after liftoff, where the motion state characteristic is a characteristic related to a swinging speed and/or a swinging position of the heel;

an updating module 403, configured to update the initial probability distribution function according to the obtained motion state characteristic, so as to obtain a target probability distribution function;

and a second determining module 404, configured to determine a position of a foot landing point after the heel lifts off the ground according to the target probability distribution function.

In one embodiment, the first determination module 401 is configured to:

determining the historical step length and the historical step width of heel walking according to the historical position of the foot drop point of the heel;

and determining an initial probability distribution function according to the history step length and the history step width.

In one embodiment, the first determination module 401 is configured to:

determining an initial probability distribution function according to the history step size and the history step width by the following formula:

wherein, P ₀ Is an initial probability distribution function; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P ₀ (C＝(x _i ,y _i ) Is located at the ith grid (x) for the position of the foot landing point after the heel off the ground _i ,y _i ) The probability of (d); x is the number of _h For historical step size, y _h For the history step width, β and σ are preset parameters, α ₀ Are normalized coefficients.

In one embodiment, the first obtaining module 402 is configured to:

In one embodiment, the first angular velocity and the first acceleration are angular velocities and accelerations in an inertial coordinate system measured by an inertial sensor, and the first obtaining module 402 is configured to:

converting the first angular velocity into a second angular velocity under a global coordinate system, wherein the global coordinate system is a coordinate system established by taking the position of the heel when leaving the ground as a coordinate origin;

integrating the second angular velocity to obtain a swing angle of the heel under a global coordinate system;

integrating the second acceleration to obtain a swing speed;

and integrating the swing speed to obtain the swing position.

In one embodiment, the motion status feature includes s features, s is a positive integer, and the update module 403 is configured to:

and when the updating end condition is met, determining the probability distribution function updated for the last time as a target probability distribution function.

In one embodiment, the update module 403 is configured to:

wherein, P _n A probability distribution function for the nth update; c ═ x _i ,y _i ) E is A, A is a foot-falling area, A is divided into a plurality of grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P _n (C＝(x _i ,y _i )|f _n,m ,f _n-1,m ,…,f _1,m ) The position of the foot drop point after the heel lift off is positioned in the ith grid (x) _i ,y _i ) The probability of the nth update; f. of _n,m For the n-th feature already acquired in the motion state feature, f _1,m The acquired 1 st feature in the motion state features; alpha is alpha _n Is a normalized coefficient; f. of _n,e (x _i ,y _i ) For the characteristic expectation function, gamma, corresponding to the nth characteristic _n The root mean square error corresponding to the nth feature.

In one embodiment, the apparatus further comprises:

a second obtaining module, configured to obtain sample motion state characteristics of the heel of the subject during a plurality of swings, where the sample motion state characteristics include at least one sample characteristic;

In one embodiment, the second determination module 404 is configured to:

according to the target probability distribution function, determining the position of a foot landing point of the heel after liftoff through the following formula:

wherein the content of the first and second substances,

the position of a foot drop point after the heel leaves the ground; (x) _i ,y _i ) e.A, A is the area where feet can fall, A is divided into a x b grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is a target probability distribution function for indicating that the position of the foot landing point of the heel after liftoff is located in the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature already acquired in the motion state feature, f _1,m The acquired 1 st feature in the motion state features.

It should be noted that: the device for determining a landing position provided in the above embodiment is only illustrated by dividing the above functional modules, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions.

Each functional unit and module in the above embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present application.

The embodiments of the device and the method for determining a location of a foot-down point provided in the above embodiments belong to the same concept, and for specific working processes of units and modules and technical effects brought by the working processes, reference may be made to the method embodiments, and details are not described here.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 5, the electronic apparatus includes: a processor 501, a memory 502 and a computer program 503 stored in the memory 502 and operable on the processor 501, wherein the steps in the method for determining the location of a landing point in the above embodiment are implemented when the processor 501 executes the computer program 503.

The electronic device may be a general-purpose electronic device or an application-specific electronic device. In a specific implementation, the electronic device may be a desktop computer, a laptop computer, a network server, a palmtop computer, a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device, and the embodiment of the present application does not limit the type of the electronic device. Those skilled in the art will appreciate that fig. 5 is merely an example of an electronic device and is not intended to be limiting, and may include more or fewer components than those shown, or some components may be combined, or different components may be included, such as input output devices, network access devices, etc.

The Processor 501 may be a Central Processing Unit (CPU), and the Processor 501 may also be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor.

The storage 502 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device, in some embodiments. The memory 502 may also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the electronic device. Further, the memory 502 may also include both an internal memory unit and an external memory device of the electronic device TH. The memory 502 is used to store an operating system, application programs, a Boot Loader (Boot Loader), data, and other programs. The memory 502 may also be used to temporarily store data that has been output or is to be output.

The present application further provides an electronic device, where the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps in any of the above method embodiments are implemented.

The computer device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor being implemented when executing the computer program.

The embodiments of the present application also provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

The embodiments of the present application provide a computer program product, which when run on a computer causes the computer to perform the steps of the above-described method embodiments.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above method embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or apparatus capable of carrying computer program code to a photographing apparatus/terminal device, a recording medium, computer Memory, ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, optical data storage device, etc. The computer-readable storage medium referred to herein may be a non-volatile storage medium, in other words, a non-transitory storage medium.

It should be understood that all or part of the steps for implementing the above embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions may be stored in the computer-readable storage medium described above.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the above-described apparatus/computer device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A method for determining a location of a landing point, the method comprising:

determining the historical step length and the historical step width of the heel walking according to the historical foot drop point position of the heel;

determining an initial probability distribution function according to the historical step length and the historical step width, wherein the initial probability distribution function is used for indicating the probability that the foot landing point position of the heel after liftoff is located at each position in a foot landing area, and the initial probability distribution function is a Gaussian probability distribution function;

acquiring motion state characteristics of the heel in a swinging process after liftoff, wherein the motion state characteristics are characteristics related to the swinging speed and/or the swinging position of the heel, the motion state characteristics comprise s characteristics, and s is a positive integer;

determining the position of a foot drop point of the heel after liftoff according to the target probability distribution function;

wherein, the updating the initial probability distribution function according to the obtained motion state characteristics to obtain a target probability distribution function includes:

when the updating end condition is not met, when any one of the s characteristics is obtained and the currently obtained characteristic is the n-th acquired characteristic, updating the probability distribution function updated for the (n-1) th time according to the n-th characteristic to obtain the probability distribution function updated for the n-th time; if n is 1, the probability distribution function updated at the (n-1) th time is the initial probability distribution function; if n is larger than 1, the probability distribution function updated for the (n-1) th time is obtained by updating the probability distribution function updated for the (n-2) th time according to the obtained (n-1) th feature;

when the updating end condition is met, determining the probability distribution function updated for the last time as the target probability distribution function;

determining the position of the foot landing point of the heel after liftoff according to the target probability distribution function by the following formula:

wherein the content of the first and second substances,

the position of a foot drop point of the heel after being off the ground; (x) _i ,y _i ) E is A, A is the landing area, A is divided into a x b grids, C is (x) _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is the target profileA rate distribution function indicating that the location of the foot landing point of the heel after liftoff is located on the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features.

2. The method of claim 1, wherein determining an initial probability distribution function based on the historical step size and the historical step width comprises:

3. The method for determining the position of a landing point according to claim 1, wherein the obtaining the motion state characteristics of the heel in the swinging process after liftoff comprises:

4. The landing foot point position determination method according to claim 3, wherein the first angular velocity and the first acceleration are an angular velocity and an acceleration in an inertial coordinate system measured by the inertial sensor;

integrating the second acceleration to obtain the swing speed;

and integrating the swing speed to obtain the swing position.

5. A method of determining a location of a foothold as claimed in claim 1, wherein the state of motion features comprise one or more of a first feature, a second feature, a third feature, a fourth feature, a fifth feature and a sixth feature;

6. The method for determining a landing point position according to any one of claims 1 to 5, wherein the updating the n-1 th updated probability distribution function according to the nth feature to obtain the nth updated probability distribution function comprises:

7. The method according to claim 6, wherein before the step of updating the n-1 th updated probability distribution function according to the nth feature to obtain the nth updated probability distribution function, the method further comprises:

8. A foothold position determination apparatus, comprising:

the first determination module is used for determining the historical step length and the historical step width of the heel walking according to the historical foot drop point position of the heel;

the first determining module is further configured to determine an initial probability distribution function according to the historical step length and the historical step width, where the initial probability distribution function is used to indicate the probability that the foot landing point position of the heel after liftoff is located at each position in a foot landing area, and the initial probability distribution function is a gaussian probability distribution function;

the first acquisition module is used for acquiring motion state characteristics of the heel in a swinging process after liftoff, wherein the motion state characteristics are characteristics related to the swinging speed and/or the swinging position of the heel, the motion state characteristics comprise s characteristics, and s is a positive integer;

an updating module, configured to, when an update end condition is not met, whenever any one of the s features is obtained and a currently obtained feature is an obtained nth feature, update the n-1 th updated probability distribution function according to the nth feature to obtain an nth updated probability distribution function, and if n is equal to 1, the n-1 th updated probability distribution function is the initial probability distribution function; if n is larger than 1, the probability distribution function updated for the (n-1) th time is obtained by updating the probability distribution function updated for the (n-2) th time according to the obtained (n-1) th feature; when the updating end condition is met, determining the probability distribution function updated for the last time as a target probability distribution function;

a second determining module, configured to determine, according to the target probability distribution function, a foot landing point position of the heel after liftoff by using the following formula:

wherein the content of the first and second substances,

the position of the foot drop point of the heel after being off the ground; (x) _i ,y _i ) e.A, A is the area which can be fallen into the foot, A is divided into a x b grids, C ═ x _i ,y _i ) Is the ith grid (x) in A _i ,y _i )；P(C＝(x _i ,y _i )|f _n,m ,…,f _1,m ) Is the target probability distribution function for indicating that the landing point position of the heel after liftoff is located at the ith grid (x) _i ,y _i ) The probability of (d); f. of _n,m For the n-th feature, f, already acquired from the state of motion features _1,m The acquired 1 st feature in the motion state features.

9. An electronic device, characterized in that the electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, which computer program, when executed by the processor, implements the method according to any of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.