Method for generating exoskeleton bionic power-assisted curve for non-stationary movement of human body

Abstract

The invention relates to an exoskeleton bionic power-assisted curve generation method for human body non-stationary motion. The synchronization and bionic capability of an ankle joint exoskeleton robot power-assisted curve for human body non-stationary motion is improved. The method comprises the following steps: acquiring direction angle data of a shank of a human body by using an inertial measurement unit, and taking the data as an independent variable of a power-assisted curve; designing a power-assisted curve model modeling method based on a double-Gaussian function to realize shape planning of a power-assisted curve; estimation of model parameters is realized by utilizing measurement data of the two inertial measurement units on the shank part and the instep part of the human body, and the similarity between an exoskeleton assistance curve and a human body biological moment curve is improved. According to the method, design is conducted from the two dimensions of the independent variable and the model of the power-assisted curve, the man-machine motion synchronization capacity of the power-assisted curve under the non-stable motion of the human body and the bionic capacity of the power-assisted curve and the biological moment curve of the human body are guaranteed respectively, and the exoskeleton robot can be promoted to be applied to complex and unstructured task scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of exoskeleton robots, and in particular to a method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body. Background Art

[0002] Exoskeleton robots are wearable smart devices that can assist patients in limb movement and enhance the physical functions of healthy people. However, the current exoskeleton robot technology has defects, mainly manifested in the problem that the generated power curve is not synchronized with human-machine movement and cannot meet the biomechanical requirements of the human body. This phenomenon is particularly reflected in the non-stationary movement of the human body that is irregular and periodic, such as emergency speed changes, gait transitions, and crossing obstacles. Therefore, in order to promote the application of exoskeleton robots in complex scenarios, it is key to realize the generation of exoskeleton bionic power curves for non-stationary human movement.

[0003] At present, there are two methods for generating the power assistance curve of exoskeleton robots: first identify the type of human activity and then switch to different power assistance curves; and second, collect sensor data to directly adjust the power assistance curve in stages. However, there are two problems with the current methods: (1) they often rely on equipment and instruments in laboratory environments, such as respirators, functional treadmills, and motion capture systems; and (2) they only consider the optimization of the longitudinal dimension of the power assistance curve (y-axis, curve amplitude and shape), while ignoring the synchronization problem between the transverse dimension (x-axis, curve model independent variable) and human movement. At present, some work has begun to try to use small devices such as electromyography sensors, foot pressure sensors, and inertial measurement units to achieve the optimization design of the power assistance curve, but there is still no simultaneous consideration of the optimization design problem of the two dimensions of the power assistance curve. When time is used as the independent variable of the curve model, if the human body suddenly changes speed, it is difficult to synchronize the power assistance curve points with the human body movement points, that is, the power assistance curve is no longer suitable for human needs. Therefore, some studies have proposed using the human body state quantity as the independent variable of the power assistance curve to improve the synchronization between the power assistance curve and human body movement. The human body state quantity is a potential feasible solution for optimizing the design of the horizontal dimension of the power assist curve, but what is urgently needed now is to conduct simultaneous and integrated analysis of the optimization design of the two dimensions of the power assist curve, and to design a power assist curve that can simultaneously meet the human body's power assist needs in the vertical dimension and continuously synchronize human body movements in the horizontal dimension. The generation and realization of such a power assist curve will greatly enhance the exoskeleton robot's ability to adapt to complex scenarios and extreme tasks. Summary of the invention

[0004] In view of the defects and deficiencies of the above prior art, the purpose of the present invention is to provide a method for generating an exoskeleton bionic power-assistance curve for non-stationary human motion, which acts on an exoskeleton robot system including human ankle joint motion assistance, and improves the synchronization and bionic ability of the exoskeleton robot power-assistance curve for non-stationary human motion. This improves the motion synchronization ability of the exoskeleton robot and the human in non-stationary motion and complex tasks, and realizes the bionic power assistance of the exoskeleton robot to the human joints.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a method for generating an exoskeleton bionic power-assistance curve for non-stationary motion of the human body, which is used to improve the synchronization and bionic ability of the power-assistance curve of the exoskeleton robot for non-stationary motion of the human body, comprising the following steps:

[0006] Two inertial measurement units are used to collect the calf swing angle and the state data of the dorsum of the foot during human movement;

[0007] Identify the gait phase and events of the current limb side according to the dorsal state data, obtain the calf swing angle values ​​at the two moments of the foot landing and the foot leaving the ground; calculate the human ankle joint angle on the current limb side;

[0008] Using the data of human ankle joint angle during the entire stance phase of gait, the continuous correspondence between the maximum value of human ankle joint dorsiflexion angle and the maximum value of ankle joint plantar flexion torque is analyzed, and the calf swing angle corresponding to the moment of maximum ankle joint dorsiflexion angle is extracted;

[0009] Using the calf swing angle corresponding to the foot landing event, the foot leaving the ground event, and the maximum dorsiflexion angle of the ankle joint, the parameters of a power assistance curve model based on a double Gaussian function are estimated to generate an ideal curve model;

[0010] The ideal power assistance curve model is used to estimate the power assistance value of the exoskeleton robot that meets the synchronization and bionic requirements for any calf swing angle.

[0011] The calf inertial measurement unit is set at any position between the ankle and the knee joint to measure the swing angle of the calf in the sagittal plane, and the instep inertial measurement unit is set at any position of the foot to measure the foot state data.

[0012] The inertial measurement units are arranged at locations where there are few muscles, skin and soft tissues that are not easy to move.

[0013] The inertial measurement unit is reset to zero when the human body is standing still and upright.

[0014] The gait phase and gait event recognition refers to the use of the foot movement angle, angular velocity and acceleration data collected by the inertial measurement unit of the back of the foot, and the use of pattern recognition methods to realize the recognition of the stance phase and swing phase, as well as the foot landing and foot leaving events.

[0015] The human ankle angle measurement is obtained by subtracting the human sagittal plane direction angles measured by the two inertial measurement units of the calf and the dorsum of the foot under the premise of ensuring synchronization of the data measurements.

[0016] The moment of maximum ankle joint dorsiflexion angle is obtained by obtaining the extreme point of the ankle joint angle data curve through an extreme value recognition method to obtain the corresponding moment.

[0017] The power assistance curve model based on the double Gaussian function is:

[0018]

[0019] Where τ(θ) represents the power assist curve value when the calf swing angle is θ, where the calf swing angle θ takes the backward position of the ankle joint relative to the knee joint as a negative value, the forward position as a positive value, and when they are on the same vertical plane, it is 0; A represents the maximum value of the power assist curve, μ represents the middle position of the power assist curve, σ1 and σ2 determine the shapes of the power assist curve in the ascending stage and the descending stage, respectively, and θ TO represents the calf swing angle at the moment of foot leaving the ground, θ HS Represents the shank swing angle at the moment of foot contact event.

[0020] The estimated parameters of the double Gaussian boost curve model refer to the online estimation of the three parameters μ, σ1, and σ2.

[0021] Furthermore, the online parameter estimation method for estimating the dual Gaussian boost curve model is:

[0022] (1) where μ is estimated to be the calf swing angle corresponding to the maximum ankle dorsiflexion angle, symbolized by θ MDF ;

[0023] (2) where σ1 is estimated as:

[0024]

[0025] (3) where σ2 is estimated as:

[0026]

[0027] The exoskeleton robot assist value for any calf swing angle is estimated by using an ideal curve model, by inputting the latest measured calf swing angle θ, and calculating the exoskeleton robot assist value τ(θ) corresponding to the calf swing angle, as at least one of a rotational assist torque or a linear assist force, for the exoskeleton robot to provide walking assistance.

[0028] The present invention has the following beneficial effects and advantages:

[0029] The present invention only uses two inertial measurement units, namely the calf and the instep, and proposes a method for generating an exoskeleton bionic power-assistance curve for non-steady human motion. The method innovatively uses the calf swing angle as the process variable (horizontal axis variable or function independent variable) of the power-assistance curve to ensure the continuous synchronization of the human-machine motion state; a double Gaussian function is used as the model function of the power-assistance curve, and online estimation and updating of the model parameters can be realized, thereby ensuring that even in complex and non-steady motion scenarios such as sudden acceleration and deceleration, sudden stop, and crossing obstacles of the human body, the synchronization of the exoskeleton robot and the human body's motion can be guaranteed, and the bionics of the power-assistance curve to the human body's biological torque can be guaranteed, thereby greatly improving the adaptability of the exoskeleton robot, meeting the user's diverse assistance needs, and promoting the daily application of the exoskeleton robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is an overall flow chart of the implementation process of the present invention and a schematic diagram of the position of the inertial measurement unit;

[0031] Figure 2 is a corresponding relationship diagram between the ankle joint angle and the joint torque in the present invention;

[0032] Figure 3 It is a proof diagram that the present invention is applicable to activities such as walking on flat ground, running and walking on a slope;

[0033] Figure 4 It is a schematic diagram of generating a power assistance curve based on a double Gaussian function in the present invention;

[0034] Figure 5 It is a diagram of the actual generation of the power assistance curve in the experiment of the present invention when facing different types of activities;

[0035] Figure 6 It is a similarity comparison diagram of the power assistance curve and the human body torque curve when facing non-steady motion in different types of activities. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation method of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the invention, so the present invention is not limited by the specific implementation disclosed below.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0038] The method of the present invention is used for an exoskeleton robot system including human ankle joint motion assistance, and is used to improve the synchronization and bionic ability of the exoskeleton robot's assistance curve for the non-stationary motion of the human body. The method uses an inertial measurement unit to collect the angular data of the human calf, and uses the data as the independent variable of the assistance curve; designs a modeling method of the assistance curve model based on a double Gaussian function to realize the shape planning of the assistance curve; uses the measurement data of two inertial measurement units of the human calf and the instep to estimate the parameters of the double Gaussian function, and the estimated frequency is equivalent to the gait frequency, so as to improve the similarity between the exoskeleton robot's assistance curve and the human ankle joint biological torque curve, that is, bionics. The present invention is designed from two dimensions of the independent variable and model of the assistance curve, respectively ensuring the human-machine motion synchronization ability of the assistance curve under the non-stationary motion of the human body, and ensuring the bionic ability of the assistance curve and the human biological torque curve, which is helpful to promote the application of exoskeleton robots in complex and unstructured task scenes, and realize the daily and practical application of exoskeleton robots.

[0039] The power assist curve of the exoskeleton robot is completely determined by two aspects: (1) the independent variable of the curve function, and (2) the model of the curve function, and the output is the power assist value. The independent variable determines the change process of the curve and determines the synchronization of human-machine movement, while the function model determines the shape of the curve and determines the bionics of the power assist curve. The current independent variables of the power assist curve are time and gait cycle percentage. However, because the non-stationary motion of the human body has temporal inhomogeneity, that is, the motion process is not linearly increasing in time, considering that the slope of time is 1 and the slope of the gait cycle percentage is 1 / T, where T is the current gait cycle value, both are constants, so they cannot be used to describe the non-stationary motion process. Figure 1 As shown, the method in the invention utilizes the inertial measurement unit of the calf to collect the calf swing angle of the human body during movement, and collects and analyzes the calf swing angle between the moment when the foot touches the ground and the moment when the foot leaves the ground. Taking into account the monotonicity of the calf swing angle in this time period, the invention uses the calf swing angle as the independent variable of the power assistance curve, wherein the start and end times of the time period can be determined by common pattern recognition methods, such as KNN, hidden Markov model, etc., and the endpoint time can be obtained by identifying gait events.

[0040] The bionic characteristics of the exoskeleton robot's power-assist curve are the expectations and needs of users. In terms of the bionics of the power-assist curve shape, the method of the invention utilizes the continuous correspondence between the maximum dorsiflexion angle of the human ankle joint and the maximum plantar flexion torque of the ankle joint. Figure 2 As shown in , during walking on flat ground, the maximum dorsiflexion moment of the human ankle joint and the maximum plantar flexion moment have a corresponding relationship, and this corresponding relationship is also applicable in other types of human activities. Figure 3 As shown in the figure, in human activities such as walking on flat ground with variable speed, walking on a slope with variable slope, and running on flat ground with variable speed, the maximum dorsiflexion angle of the human ankle joint and the maximum plantar flexion torque maintain a corresponding rule. Therefore, the method of the invention is to make the maximum moment of the power assist curve correspond to the maximum moment of the ankle joint dorsiflexion angle, that is, the model parameter μ is estimated as the calf swing angle θ corresponding to the maximum moment of the dorsiflexion angle MDF , thereby achieving bionics at the peak moment.

[0041] As shown in Figure 4, this method models the power assist curve as a double Gaussian function, where the red dashed line represents the first segment of the Gaussian function, and the action time is from the moment of the foot touching the ground to the moment of the ankle dorsiflexion angle maximum; and the black solid line represents the second segment of the Gaussian function, and the action time is from the moment of the ankle dorsiflexion angle maximum to the moment of the foot leaving the ground. The mathematical expression of the overall power assist curve is as follows:

[0042]

[0043] Among them, τ and θ represent the power assist value and the calf swing angle respectively. The calf swing angle is 0 when the human body stands vertically, negative when the ankle joint is located behind the knee joint in the sagittal plane, and positive when the ankle joint is located in front of the knee joint in the sagittal plane. Therefore, during the development of the human foot standing phase, the calf swing angle gradually transitions from positive to negative; and the power assist effect interval is θ TO and θ HS , respectively represent the calf swing angle corresponding to the moment of foot leaving the ground and the moment of foot touching the ground; the double Gaussian function has four parameters, A, μ, σ1, σ2, among which parameter A determines the amplitude of the entire power curve, which is specified as 15% of the subject's current weight in this method; and the parameter estimation method of μ has been explained above, which is equivalent to the calf swing angle θ corresponding to the maximum dorsiflexion angle moment MDF ; The parameter estimation methods of σ1 and σ2 are as follows Figure 4 As shown below:

[0044]

[0045]

[0046] The division by 4 is to reduce the numerical jump of the power assist curve at the endpoints on both sides, so as to achieve a smooth transition of the power assist phase. This value can be changed according to actual needs, and can even be changed online to adapt to more tasks and demand scenarios.

[0047] A human motion experiment was conducted for the above-mentioned power-assistance curve generation method, which respectively proved (1) the curve generation capability of the method in the face of different types of human activities, and (2) the similarity between the generated curve and the human biological torque curve when the method encountered non-stationary human motion, that is, the method has bionic ability. A total of 7 subjects participated in the experiment (6 males and 1 female; the human body information was expressed in the form of mean ± standard deviation: age 26.9 ± 2.7 years, height 1.72 ± 0.07m, weight 62.9 ± 3.8kg), and all were required to wear a flexible ankle joint power-assistance exoskeleton robot and walk on a treadmill. The exoskeleton robot has inertial measurement units on the lower leg and the dorsum of the foot, so it meets the number and position requirements of sensors required for the implementation of the present invention. The speed of the treadmill was selected as the optimal speed for the subject. Before the experiment, the subjects will be familiar with walking on the treadmill and determine the optimal speed (once the optimal speed is determined, the treadmill speed will remain unchanged for the subject throughout the experiment, but the subject can freely change his or her favorite walking rhythm and gait pattern, such as changing the step length or phase ratio, etc.). The subjects will be asked to perform four types of activities: flat walking, flat running, uphill walking (+10deg) and downhill walking (-10deg).

[0048] like Figure 5 The figure shows the assistance curve generated by the exoskeleton robot collected during the experiment, where the multiple curves in the figure refer to the data of multiple gait cycles. The figure shows that this technical method can generate different types of assistance curves according to the difference in activity types, and can change the range and shape of the curve through the online estimated curve model parameters μ, σ1 and σ2, as well as the measured calf swing angle, so as to meet the assistance needs of the current subject in the current activity type.

[0049] At the same time, in order to further verify the bionic function of the curve generation method in the face of non-steady motion of the human body, the experiment requires the treadmill to randomly perform rapid acceleration (increase the treadmill speed by 80% within 0.05s, and then return to the original speed within the next 0.05s) and rapid deceleration (similar to the rapid acceleration process), and induce the human body to produce non-steady motion through the rapid change of the treadmill speed. Figure 6As shown in the figure, the power assistance curve generated by the exoskeleton robot and the ankle biotorque curve of the current subject (the human body data is first collected through two devices, motion capture and plantar pressure treadmill, and then the human body biotorque curve is obtained through biomechanical analysis software), where the values ​​are normalized for similarity or biomimetic comparison. The Pearson Correlation Coefficient is used to quantify the similarity of the two curves. Among them, the biomimetic coefficients are 0.90 and 0.92 respectively in the case of rapid deceleration and rapid acceleration in flat walking; 0.94 and 0.98 respectively for flat running; 0.90 and 0.97 respectively for uphill walking; and 0.90 and 0.96 respectively for downhill walking. When the coefficient is greater than 0.6-0.8, it means that there is a clear similarity between the two curves. Therefore, it can be proved that the curve generated by this method can still ensure the biomimetic nature of the torque curve of the target joint of the human body even in different types of activities and in the face of different non-stationary movements.

[0050] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should be regarded as within the scope of protection of the present invention.

Claims

1. A method for generating an exoskeleton bionic power-assistance curve for non-steady motion of the human body, characterized in that: The method is used to improve the synchronization and bionic ability of the exoskeleton robot's power curve for the non-steady motion of the human body, including the following steps: Two inertial measurement units are used to collect the calf swing angle and the state data of the dorsum of the foot during human movement; Identify the gait phase and events of the current limb side according to the dorsal state data, obtain the calf swing angle values ​​at the two moments of the foot landing and the foot leaving the ground; calculate the human ankle joint angle on the current limb side; Using the data of human ankle joint angle during the entire stance phase of gait, the continuous correspondence between the maximum value of human ankle joint dorsiflexion angle and the maximum value of ankle joint plantar flexion torque is analyzed, and the calf swing angle corresponding to the moment of maximum ankle joint dorsiflexion angle is extracted; Using the calf swing angle corresponding to the foot landing event, the foot leaving the ground event, and the maximum dorsiflexion angle of the ankle joint, the parameters of a power assistance curve model based on a double Gaussian function are estimated to generate an ideal curve model; The ideal power assistance curve model is used to estimate the power assistance value of the exoskeleton robot that meets the synchronization and bionic requirements for any calf swing angle.

2. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of the human body according to claim 1, characterized in that: The calf inertial measurement unit is set at any position between the ankle and the knee joint to measure the swing angle of the calf in the sagittal plane, and the instep inertial measurement unit is set at any position of the foot to measure the foot state data.

3. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The inertial measurement units are arranged at locations where there are few muscles, skin and soft tissues that are not easy to move.

4. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The inertial measurement unit is reset to zero when the human body is standing still and upright.

5. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The gait phase and gait event recognition refers to the use of the foot movement angle, angular velocity and acceleration data collected by the inertial measurement unit of the back of the foot, and the use of pattern recognition methods to realize the recognition of the stance phase and swing phase, as well as the foot landing and foot leaving events.

6. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The human ankle angle measurement is obtained by subtracting the human sagittal plane direction angles measured by the two inertial measurement units of the calf and the dorsum of the foot under the premise of ensuring synchronization of the data measurements.

7. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The moment of maximum ankle joint dorsiflexion angle is obtained by obtaining the extreme point of the ankle joint angle data curve through an extreme value recognition method to obtain the corresponding moment.

8. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The power assistance curve model based on the double Gaussian function is: Where τ(θ) represents the power assist curve value when the calf swing angle is θ, where the calf swing angle θ takes the backward position of the ankle joint relative to the knee joint as a negative value, the forward position as a positive value, and when they are on the same vertical plane, it is 0; A represents the maximum value of the power assist curve, μ represents the middle position of the power assist curve, σ1 and σ2 determine the shapes of the power assist curve in the ascending stage and the descending stage, respectively, and θ TO represents the calf swing angle at the moment of foot leaving the ground, θ HS Represents the shank swing angle at the moment of foot contact event.

9. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The estimated parameters of the double Gaussian boost curve model refer to the online estimation of the three parameters μ, σ1, and σ2.

10. The method for generating an exoskeleton bionic power-assistance curve for non-steady motion of a human body according to claim 1, characterized in that: The exoskeleton robot assist value for any calf swing angle is estimated by using an ideal curve model, by inputting the latest measured calf swing angle θ, and calculating the exoskeleton robot assist value τ(θ) corresponding to the calf swing angle, as at least one of a rotational assist torque or a linear assist force, for the exoskeleton robot to provide walking assistance.

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