Control method and system for electro-hydrostatic active ankle joint prosthesis

Abstract

The invention discloses a control method and system for an electro-hydrostatic active ankle joint artificial limb, and relates to the field of lower limb gait recognition and artificial limb control. The method comprises the steps that plantar pressure features are extracted according to plantar pressure signals; according to plantar pressure features, based on the trained SVM model, obtaining division results of four gait stages of passive plantar flexion, passive dorsal flexion, active plantar flexion and active dorsal flexion; according to the division results of the four gait stages of passive plantar flexion, passive dorsal flexion, active plantar flexion and active dorsal flexion, in combination with finite state machine state division based on an ankle angle signal and a sole switch, a real-time gait stage is obtained through a weighted decision model; according to the real-time gait stage and the liquid pressure signal, a control signal of artificial limb movement is generated; and controlling the electro-hydrostatic active ankle joint artificial limb to move according to the control signal of the artificial limb movement. The lower limb gait can be accurately recognized, and therefore accurate control over the artificial limb is achieved according to the lower limb gait.

Description

Technical Field

[0001] The present application relates to the field of lower limb gait recognition and prosthetic control, and in particular to a control method and system for an electrostatic-hydraulic active ankle prosthesis. Background Art

[0002] Limb loss brings great inconvenience to the daily life and work of disabled people. Prostheses are important tools to help people with limb disabilities regain their mobility, among which the stability and flexibility of the ankle joint play a vital role in the movement and coordination of the human body. The ankle joint contributes 60% of the force in the process of pushing the body forward and upward. For patients with transtibial amputation, the installation of an active ankle prosthesis, such as an electrostatic-hydraulic active ankle prosthesis (i.e., an electrostatic-hydraulic ankle prosthesis), can restore basic motor functions and help them regain confidence. Among them, the electrostatic-hydraulic active ankle prosthesis is an active ankle prosthesis based on the principle of electrostatic-hydraulic actuation, that is, an active ankle prosthesis driven by an electrostatic-hydraulic actuator.

[0003] However, to achieve perfect adaptation of prostheses to disabled people, the mechanical and circuitry of prostheses are far from enough. The key lies in how to ensure that the prostheses can be coordinated with the physiological movements of disabled people during use, so as to help them achieve stable and comfortable walking. At this point, accurate gait recognition and appropriate control methods become crucial technical support. Accurate gait recognition technology can help us gain a deep understanding of the walking patterns and habits of disabled people, thereby providing key information for personalized adjustment of prostheses. By analyzing gait characteristics, we can determine important parameters such as gait phase, stride speed, and state stage of each individual, providing basic data for subsequent prosthetic control. At the same time, appropriate control methods are the key to achieving coordinated movement between prostheses and disabled people. Appropriate control algorithms and strategies can ensure that the prostheses can move at a timing and force that matches the user's physiological movements, so as to provide a more stable and natural walking experience. Therefore, accurate lower limb gait recognition and its extended prosthetic control methods are not only a supplement to prosthetic design and production, but also a key technology to ensure that prostheses are adapted to disabled people and achieve smooth interactive movement. However, the current control methods used for electrostatic-hydraulic active ankle prostheses are unable to accurately identify the gait of the lower limbs, and therefore are unable to achieve accurate control of the prosthesis based on the gait of the lower limbs. Summary of the invention

[0004] The purpose of the present application is to provide a control method and system for an electrostatic-hydraulic active ankle prosthesis, which can accurately identify the gait of the lower limbs, thereby achieving accurate control of the prosthesis according to the gait of the lower limbs.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a control method for an electrostatic-hydraulic active ankle joint prosthesis, the control method for an electrostatic-hydraulic active ankle joint prosthesis comprising:

[0007] Acquire original fluid pressure signal, original ankle angle signal and original plantar pressure signal;

[0008] Preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal; the plantar pressure signal includes pressure signals of each point in the plantar pressure array;

[0009] Extracting plantar pressure features according to the plantar pressure signal; the plantar pressure features include overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse;

[0010] According to the plantar pressure characteristics, the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion are obtained based on the trained SVM model; the trained SVM model is trained by using the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse of different subjects in walking mode to distinguish the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion;

[0011] According to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the state division of the finite state machine based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through a weighted decision model; the plantar switch is a pressure switch; the pressure signals of the frontmost and rearmost two areas in the plantar pressure array are used as a pressure switch;

[0012] generating a control signal for prosthetic movement according to the real-time gait phase and the fluid pressure signal;

[0013] The movement of the electrostatic-hydraulic active ankle joint prosthesis is controlled according to the control signal of the prosthesis movement.

[0014] Optionally, preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal specifically includes:

[0015] Performing Fourier transformation on the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain frequency domain distribution of the original liquid pressure signal, frequency domain distribution of the original ankle angle signal and frequency domain distribution of the original plantar pressure signal;

[0016] The frequency domain distribution of the original liquid pressure signal, the frequency domain distribution of the original ankle angle signal and the frequency domain distribution of the original plantar pressure signal are subjected to low-pass filtering to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal.

[0017] Optionally, extracting a plantar pressure feature according to the plantar pressure signal specifically includes:

[0018] According to the plantar pressure signal, the plantar pressure center trajectory and the pressure cloud map during walking are used to extract the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse.

[0019] Optionally, the weighted decision model is expressed as:

[0020]

[0021] Among them, x i represents the plantar pressure signal, y i represents the ankle angle signal, represents the weight of the plantar pressure signal in the i-th gait stage, represents the weight of the ankle angle signal in the i-th gait stage, i∈{1,2,3,4} represents four gait stages respectively, i=1 represents passive plantar flexion, i=2 represents passive dorsiflexion, i=3 represents active plantar flexion, i=4 represents active dorsiflexion, S d represents the weighted decision result, and round represents the rounding function, which is used to convert the weighted average result into the nearest integer gait phase.

[0022] Optionally, generating a control signal for prosthetic movement according to the real-time gait phase and the liquid pressure signal specifically includes:

[0023] According to the real-time gait phase and the liquid pressure signal, a control signal for prosthetic movement is generated in combination with a finite state machine triggered by optimized typical events and an electrostatic-hydraulic active ankle prosthesis hydraulic model, kinematics and dynamics model; the control signal for prosthetic movement includes motor output power and high-speed switch valve opening.

[0024] Optionally, controlling the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement specifically includes:

[0025] The motor of the electrostatic-hydraulic active ankle prosthesis is controlled according to the output power of the motor, and the high-speed switch valve of the electrostatic-hydraulic active ankle prosthesis is controlled according to the opening of the high-speed switch valve. The electrostatic-hydraulic active ankle prosthesis moves under the control of the motor and the high-speed switch valve.

[0026] In a second aspect, the present application provides a control system for an electrostatic-hydraulic active ankle prosthesis, based on the control method for the electrostatic-hydraulic active ankle prosthesis; the control system for the electrostatic-hydraulic active ankle prosthesis comprises:

[0027] A liquid pressure sensor is installed on the hydraulic integrated block of the electrostatic-hydraulic active ankle joint prosthesis and connected to the internal hydraulic oil circuit of the electrostatic-hydraulic active ankle joint prosthesis to obtain an original liquid pressure signal;

[0028] An ankle angle sensor, installed at the ankle of the electrostatic-hydraulic active ankle joint prosthesis, is used to obtain the original ankle angle signal;

[0029] A multi-point plantar pressure array sensor is installed in the insole of the electrostatic-hydraulic active ankle prosthesis to obtain the original plantar pressure signal;

[0030] The signal and control circuit board is respectively connected to the liquid pressure sensor, the ankle angle sensor and the multi-point plantar pressure array sensor, and is used for preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal, extracting the plantar pressure feature according to the plantar pressure signal, and obtaining the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature, and according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the finite state machine state division based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through the weighted decision model, and the false alarm is generated according to the real-time gait stage and the liquid pressure signal. limb movement control signal; wherein, the plantar pressure signal includes the pressure signals of each point in the plantar pressure array; the plantar pressure characteristics include the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rear foot area ground reaction force, the plantar pressure center trajectory x coordinate, the plantar pressure center trajectory y coordinate and the maximum single point impulse; the trained SVM model is trained by using the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rear foot area ground reaction force, the plantar pressure center trajectory x coordinate, the plantar pressure center trajectory y coordinate and the maximum single point impulse of different subjects in walking mode, and distinguishing the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion; the plantar switch is a pressure switch; the pressure signals of the two front and rear end areas in the plantar pressure array are used as a pressure switch;

[0031] The power supply and drive circuit board are respectively connected to the signal and control circuit board and the high-speed switch valve, motor and battery of the electrostatic-hydraulic active ankle joint prosthesis, and are used to control the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement.

[0032] Optionally, the ankle angle sensor is a potentiometer angle sensor, which is installed at the ankle of the electro-static-hydraulic active ankle prosthesis and fixed to the electro-static-hydraulic active ankle prosthesis through a stepped shaft and a flange structure, and is used to obtain the real-time angle of the electro-static-hydraulic active ankle prosthesis foot.

[0033] Optionally, the multi-point plantar pressure array sensor is composed of 18 single-point resistive thin film pressure sensors, which are installed in the insole of the electrostatic-hydraulic active ankle prosthesis and in contact with the silicone foot skin worn on the footplate of the electrostatic-hydraulic active ankle prosthesis.

[0034] Optionally, the signal and control circuit board is provided with a single-chip microcomputer; the single-chip microcomputer is used to pre-process the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal, extract the plantar pressure feature according to the plantar pressure signal, and obtain the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature; according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the finite state machine state division based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through a weighted decision model; according to the real-time gait stage and the liquid pressure signal, a control signal for the prosthetic movement is generated, and the control signal for the prosthetic movement is transmitted to the power supply and drive circuit board.

[0035] According to the specific embodiments provided in this application, this application has the following technical effects:

[0036] The present application provides a control method and system for an electrostatic-hydraulic active ankle prosthesis, according to the extracted plantar pressure characteristics including the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rearfoot area ground reaction force, the plantar pressure center trajectory x-coordinate, the plantar pressure center trajectory y-coordinate and the maximum single-point impulse, the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion are divided based on a trained SVM model, thereby obtaining the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion according to the division results of the four gait stages, combined with the ankle angle signal and the plantar pressure characteristics. The finite state machine state division of the switch is used to obtain the real-time gait stage through the weighted decision model. Since the three-foot area pressure distribution characteristics are added on the basis of traditional plantar pressure gait and other characteristics, it can better capture the multivariate nonlinear characteristics in the gait data and improve the accuracy of gait division. By adopting a weighted decision strategy, combining multi-source information and sensor reliability, precise control of key events and optimization of gait stages are achieved, ensuring that the prosthesis can adapt to the needs of the wearer during walking, and ultimately achieving the effect of accurately identifying the lower limb gait, thereby achieving accurate control of the prosthesis according to the lower limb gait. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1A flow chart of a control method for an electrostatic-hydraulic active ankle prosthesis provided in one embodiment of the present application;

[0039] Figure 2 This is the overall flow chart of the ankle prosthesis gait detection method of this application;

[0040] Figure 3 This is the overall design block diagram of the controller in the ankle prosthesis of this application;

[0041] Figure 4 A schematic diagram of the dynamic structure of the ankle prosthesis in this application;

[0042] Figure 5 This is a diagram showing the correspondence between the four gait phases during human walking and the working conditions of the ankle joint prosthesis hydraulic system;

[0043] Figure 6 It is the equivalent circuit diagram of the hydraulic drive system in the passive state;

[0044] Figure 7 This is the control block diagram of ankle joint impedance characteristics in passive state;

[0045] Figure 8 It is the equivalent circuit diagram of the hydraulic drive system under active plantar flexion actuation state;

[0046] Fig. 9 This is the control block diagram of the joint output torque in the active plantar flexion state;

[0047] Fig.10 It is the equivalent circuit diagram of the hydraulic drive system in the active dorsiflexion state;

[0048] Fig.11 It is a schematic diagram of the ankle angle servo of the ankle joint prosthesis in the active dorsiflexion state;

[0049] Fig.12 This is the hardware layout diagram of the ankle prosthesis gait detection system for this application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] The purpose of the present application is to provide a control method and system for an electrostatic-hydraulic active ankle prosthesis, which can accurately identify the gait of the lower limbs, thereby achieving accurate control of the prosthesis according to the gait of the lower limbs.

[0052] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] like Figure 1 As shown, the present application provides a control method for an electrostatic-hydraulic active ankle joint prosthesis, comprising:

[0054] Step 101: Acquire an original fluid pressure signal, an original ankle angle signal, and an original plantar pressure signal.

[0055] Step 102: pre-processing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal; the plantar pressure signal includes the pressure signal of each point in the plantar pressure array.

[0056] The step 102 specifically includes:

[0057] The original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal are subjected to Fourier transformation to obtain the frequency domain distribution of the original liquid pressure signal, the frequency domain distribution of the original ankle angle signal and the frequency domain distribution of the original plantar pressure signal.

[0058] The frequency domain distribution of the original liquid pressure signal, the frequency domain distribution of the original ankle angle signal and the frequency domain distribution of the original plantar pressure signal are processed by low-pass filtering to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal.

[0059] Step 103: extracting plantar pressure features according to the plantar pressure signal; the plantar pressure features include the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rear foot area ground reaction force, the plantar pressure center trajectory x-coordinate, the plantar pressure center trajectory y-coordinate and the maximum single-point impulse.

[0060] The step 103 specifically includes:

[0061] According to the plantar pressure signal, the plantar pressure center trajectory and the pressure cloud map during walking are used to extract the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse.

[0062] Step 104: According to the plantar pressure characteristics, the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion are obtained based on the trained SVM model; the trained SVM model is trained by using the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse of different subjects in walking mode to distinguish the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion.

[0063] In step 104, the weighted decision model is expressed as:

[0064]

[0065] Among them, x i Represents the plantar pressure signal, y i represents the ankle angle signal, represents the weight of the plantar pressure signal in the i-th gait stage, represents the weight of the ankle angle signal in the i-th gait stage, i∈{1,2,3,4} represents four gait stages, i=1 represents passive plantar flexion, i=2 represents passive dorsiflexion, i=3 represents active plantar flexion, i=4 represents active dorsiflexion, S d represents the weighted decision result, and round represents the rounding function, which is used to convert the weighted average result into the nearest integer gait phase.

[0066] Step 105: Based on the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the state division of the finite state machine based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through a weighted decision model; the plantar switch is a pressure switch; the pressure signals of the frontmost and rearmost areas in the plantar pressure array are used as a pressure switch.

[0067] Step 106: Generate a control signal for prosthetic movement according to the real-time gait phase and the fluid pressure signal.

[0068] The step 106 specifically includes:

[0069] According to the real-time gait phase and fluid pressure signal, the control signal of prosthetic movement is generated by combining the finite state machine triggered by optimized typical events and the hydraulic model, kinematic and dynamic model of the electrostatic-hydraulic active ankle prosthesis; the control signal of prosthetic movement includes the motor output power and the opening of the high-speed switching valve.

[0070] Step 107: Control the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement.

[0071] The step 107 specifically includes:

[0072] The motor of the electrostatic-hydraulic active ankle prosthesis is controlled according to the motor output power, the high-speed switch valve of the electrostatic-hydraulic active ankle prosthesis is controlled according to the opening of the high-speed switch valve, and the electrostatic-hydraulic active ankle prosthesis moves under the control of the motor and the high-speed switch valve.

[0073] The following is a specific example to illustrate the technical solution of the control method for the electrostatic-hydraulic active ankle joint prosthesis of the present application:

[0074] The present application is a control method for an electrostatic-hydraulic active ankle prosthesis, which is a gait detection and control method for a wearable ankle prosthesis. The gait detection and control method of the present application includes the acquisition and preprocessing of original signals from sensors such as plantar pressure and ankle angle, feature extraction such as pressure distribution in three-foot areas and pressure center trace in plantar pressure, optimization of misclassification of the SVM classification model training set in the gait stage, top-level state classification combining plantar pressure array and ankle angle information fusion, prosthetic dynamics model and top, mid and bottom multi-layer control. Ultimately, the prosthesis can effectively identify and judge human gait information to determine the control strategy, and regulate walking damping and output timing.

[0075] The working principle of the hydraulic drive of ankle prosthesis is usually as follows: the driving motor drives the hydraulic pump to operate, and the hydraulic oil in the oil tank is supplied to the hydraulic cylinder through the pipeline according to the state of the reversing valve to provide power for the foot.

[0076] like Figure 2 Shown is the overall flow chart of the ankle prosthesis gait detection method of the present application, which consists of the steps of sensor raw signal acquisition, raw signal preprocessing, plantar pressure feature extraction, gait stage classification model (i.e., classification SVM model) deployment, ankle angle finite state machine determination (i.e., ankle angle threshold determination), top-level state classification of multi-sensor information fusion (i.e., multi-source information fusion state classification), and prosthesis dynamics model and control, reflecting the whole process from human-machine walking information acquisition to human-machine collaborative control.

[0077] The sensor's raw signal acquisition is divided into plantar pressure, ankle angle and fluid pressure signal acquisition. Plantar pressure refers to the pressure generated between the sole of the foot and the ground when a person walks, runs, jumps or stands. With every step, the sole of the foot bears the pressure of body weight. The plantar pressure signal is obtained through a multi-point plantar pressure array sensor installed in the insole. Each point is the plantar pressure in the area (i.e., the pressure value of each point in the array), which is used to determine the gait stage (see plantar pressure feature extraction for details. By extracting and analyzing the data, combined with machine learning, gait stage judgment can be achieved); the plantar pressure is a 3×6 sensor array, and each area can sense the pressure value corresponding to the point based on strain; the ankle angle signal (i.e., the specific angle change data of the ankle angle) is obtained through a potentiometer-type angle sensor installed at the ankle of the prosthesis, which is used to set the switching threshold (according to the experimental data obtained from multiple tests, the angle change during each gait switching is very clear, and the switching threshold can be set); the liquid pressure signal is obtained through a liquid pressure sensor installed on the hydraulic integrated block that is connected to the oil circuit, which is used to detect the internal state of the prosthetic power system (the prosthesis is hydraulically driven, and the internal state of the prosthesis can be known by measuring the oil pressure at the upper and lower ends of the hydraulic cylinder and the motor outlet).

[0078] In the original signal preprocessing, Fourier transform is performed on the plantar pressure and other signals (i.e., Fourier transform is performed on the plantar pressure signal, ankle angle signal and liquid pressure signal, the purpose is to reduce high-frequency noise information), and the frequency domain distribution of the signal can be obtained. The results of Fourier transform show that the signals generated by plantar pressure and other signals are concentrated in the low-frequency band, and there is almost no high-frequency information. Therefore, the signals greater than 5Hz are filtered, and the subsequent analysis and feature extraction of plantar pressure and other signals are focused on the time domain information; then the data is standardized and preprocessed, and the data set is extracted using the sliding window method to eliminate the scale differences between the features, so as to ensure that the optimization algorithm can converge faster, thereby improving the training speed and performance of the classifier. In short, the original signal preprocessing is to perform low-pass filtering on the frequency domain distribution of the plantar pressure signal, the frequency domain distribution of the ankle angle signal and the frequency domain distribution of the liquid pressure signal in turn, and finally eliminate the influence of high-frequency noise to obtain more realistic sensor data.

[0079] In the extraction of plantar pressure features, the plantar pressure signal features are first analyzed (similar to an exploratory analysis and regularity summary of preprocessed signals). In gait analysis, the analysis of the center of pressure (COP) is usually used to evaluate the stability of the foot and the health of the walking posture. The center of pressure (i.e., the plantar pressure center trajectory) describes the trajectory of the reaction force point of the sole of the foot on the ground changing over time during standing or walking. COP is usually measured by devices such as pressure sensor arrays or force platforms, indicating the pressure distribution applied at different positions on the sole of the foot. However, relying solely on COP as an analysis indicator may not provide detailed information on the pressure distribution between different regions inside the foot. By observing the pressure cloud map during walking (drawing the sensor array as an XY plane image, and the pressure at each point on the image can obtain a pressure cloud map), the foot is divided into three regions: the forefoot area, the midfoot area, and the hindfoot area according to the physiological structure and functional characteristics of the foot. Each area is subjected to a pressure load that is staggered in the time domain. For example, when the rear foot lands, the rear foot area bears the greatest impact force; in the propulsion phase of walking, the pressure in the forefoot area reaches the maximum, and the midfoot area provides stability and cushioning throughout the gait cycle. At the same time, in the support phase (a medical name for a gait of the human body), the pressure cloud map has obvious maximum values ​​when the rear foot lands and the forefoot pushes off the ground. Therefore, a total of 7 features, including the local reaction force of the three-foot area, the center trace of the plantar pressure, and the maximum single-point impulse, are extracted as the basis for classification.

[0080] Among them, the ground reaction force of the foot area is extracted according to the data of each point in the plantar pressure array: Foot pressure center trace: Maximum single-point impulse (i.e. the pressure value of the maximum pressure point in the plantar pressure array at a certain moment): x, y are the pressure coordinates of each point in the plantar pressure array. In the formula, x(i) represents the value of the i-th data point, MAV represents the mean absolute value, N represents the total number of data, P(x,y) represents the pressure value of the point with coordinates (x,y) in the plantar pressure matrix, and P(x i ,y i ) represents the pressure value of the i-th point, x i and i Respectively represent the X-axis and Y-axis coordinates of the i-th point in the plantar pressure matrix, COP X and COP Y Represent the X-axis and Y-axis coordinates of the plantar pressure center, P i represents the pressure value of the ith pressure point, A i represents the area of ​​the ith pressure point, f s Indicates the sampling frequency, used to calculate impulse.

[0081] The difficulty in deploying gait phase classification models is that gait data often contains complex nonlinear relationships. Human motion gait can be assumed to be a nonlinear function of human-machine interaction information, which is difficult to obtain through mathematical modeling. Machine learning methods have strong nonlinear modeling capabilities and can better capture the nonlinear characteristics in gait data. Among them, SVM seeks the best compromise between model complexity and learning ability based on limited sample information and has good generalization ability.

[0082] The SVM model was trained using seven gait features, including the overall foot ground reaction force, forefoot ground reaction force, midfoot ground reaction force, rearfoot ground reaction force, COP trajectory x-coordinate, COP trajectory y-coordinate, and maximum single-point pressure (i.e., maximum single-point impulse) in different walking modes (including passive plantar flexion, passive dorsiflexion, active plantar flexion, and active dorsiflexion, and four gait cycle switching during walking), to distinguish four gait stages: passive plantar flexion, passive dorsiflexion, active plantar flexion, and active dorsiflexion. The recognition accuracy was verified by 5-fold cross-validation, and the misclassification cost function was adjusted to improve the recognition accuracy of the active gait stage, resulting in an SVM classification model for individual optimization (since the model can be trained on data sets of different subjects, including the subject's unique walking habits and information, a more accurate and personalized gait stage can be obtained). Among them, the forefoot area includes the toes (phalanges) and metatarsals, which is the part of the foot closest to the toes; the midfoot area is located between the forefoot area and the hindfoot area, and is mainly composed of the tarsal bones; the hindfoot area includes the calcaneus and talus, which is the part of the foot closest to the ankle; the sum of the pressure values ​​of the corresponding plantar pressure array sensors in each foot area is the ground reaction force value of the area.

[0083] The ankle angle-based finite state machine determines the four gaits by using the front and rear single points on the plantar pressure sensor (the pressure values ​​of the front and rear areas in the plantar pressure array are used as a simple pressure switch) and completing the judgment of the ankle angle sensor switching threshold through key events in gait such as heel touchdown (HS), maximum dorsiflexion angle (MD), and toe lift-off (TO). This part is actually the ankle angle threshold switching idea of ​​the traditional finite state machine, which is part of the sensor fusion. After fusion, the recognition accuracy is higher. The specific implementation steps of this part include:

[0084] Phase control of the stance phase: The three states (CP, CD, TS) are the three phases of the stance phase. Each state is described as follows: (1) The CP phase starts from the heel touching the ground and ends with the sole of the foot touching the ground. During the CP phase, the output characteristic of the prosthesis is impedance servo, which prevents the toes from touching the ground prematurely and provides a certain degree of vibration reduction and cushioning for the human body. (2) The CD phase starts from the sole of the foot touching the ground and ends before the TS phase or the toes leave the ground, depending on whether the ankle angle exceeds the maximum dorsiflexion angle. During the CD phase, the output characteristic of the prosthesis is impedance servo, which is used to maintain the smooth movement of the body and the energy recovery of the accumulator. (3) The TS phase is initiated only when the sole of the foot touches the ground and the ankle angle exceeds the maximum dorsiflexion angle. Otherwise, the prosthesis will remain in the CD phase until the sole of the foot leaves the ground. During the TS phase, the prosthesis outputs torque to propel the human body forward.

[0085] Swing phase control: Three states (SW1, SW2, SW3) are used to control the swing phase. The specific description of each state is as follows: (4) SW1 starts from the toe leaving the ground and is switched on after a given delay t H (5) SW2 starts after SW1 and ends after the ankle angle reaches a predetermined value. During this period, the ankle angle reaches the state before the CP phase, preparing for the heel strike in the next cycle. (6) SW3 starts after SW2 and ends when the heel strikes the ground in the next cycle.

[0086] The top-level state classification of multi-sensor information fusion uses the plantar pressure center point and three-foot zone information measured by the plantar pressure array sensor to understand and simulate the dynamics of human walking, and combines the traditional finite state machine state based on ankle angle and plantar switch to realize the division of the four gait stages of the ankle joint when the human body walks, namely, Figure 3 The top-level control part in .

[0087] The present application fuses two sensor information (i.e., plantar pressure signal and ankle angle signal) to complete the state switching of the prosthetic finite state machine (in simple terms, a weighted control strategy). Its state transfer trigger information (the output after fusion is four real-time gait stage information, and the transfer of the gait stage is the state transfer trigger information, that is, the transfer of the gait stage is synchronized with the controller state stage transfer) not only considers the gait stage division result based on the plantar pressure characteristics, but also combines the ankle joint angle measurement and the traditional plantar switch signal in the traditional finite state machine. Specifically, it is a weight distribution method of the importance of two sensor data obtained through experimental analysis. Assume that S = {CP, CD, TS, PF} is a gait stage set, corresponding to (1) passive plantar flexion, (2) passive dorsiflexion, (3) active plantar flexion and (4) active dorsiflexion. Considering that the sensitivity and importance of the plantar pressure and ankle angle sensors may be different in different gait stages, the present application introduces a weighted decision model to divide the gait stages.

[0088] Let x i is the data obtained from the plantar pressure array sensor, y i is the data obtained from the ankle angle sensor, where i∈{1,2,3,4} represents the four gait stages. The weighted decision model can be expressed as:

[0089]

[0090] in, and are the weights of the plantar pressure array sensor data and the ankle joint angle data in the i-th gait stage, S d is the weighted decision result, and round is the rounding function, which is used to convert the weighted average result into the closest integer gait phase, that is, the corresponding real-time gait phase, and then switch to the underlying servo model of the corresponding gait phase.

[0091] It can be seen from the plantar pressure experiment that the plantar pressure changes more significantly in the passive plantar flexion, passive dorsiflexion and active plantar flexion stages. At this time, the ankle angle trigger condition of the traditional finite state machine has a poor degree of reproduction in the change trend and threshold switching between the two passive states. Therefore, the weight of the plantar pressure array sensor is and On the contrary, in the active dorsiflexion phase, the plantar pressure array recognition accuracy is lower and the change of ankle joint angle is more critical, so the weight of the sensor is The weight of the weighted decision model and The optimization can be adjusted through experiments on actual gait data of different populations to minimize the error between the model-predicted gait phase and the actual gait phase to ensure the accuracy and robustness of the model.

[0092] Prosthetic dynamics model and control Based on the gait characteristics and actual working conditions of the prosthesis, servo impedance, torque and displacement controllers are designed in combination with hydraulic models, kinematics and dynamic models. These controllers can finely adjust the torque output and real-time damping of the prosthetic joint through the PWM-PFM strategy (a general control strategy) (torque output is motor control, damping is high-speed switch valve control, and the combination of the two can obtain a more simulated and natural gait) to simulate the physical behavior and mechanical response of the human ankle joint in different gait stages, and achieve mechanical characteristics and dynamic responses similar to those of the human ankle joint. The specific dynamics model can be seen in Figure 4 Among them, the gait characteristics are the medical gait characteristics of the ankle joint when the human body walks, and the working condition is walking on flat ground. The combination of the two determines the basis for the division of gait stages and the specific movement of the ankle joint in each gait stage, providing a factual basis for the gait stage obtained by sensor fusion.

[0093] The above introduces the state recognition and gait division of sensor fusion. The following introduces the hydraulic model (prosthetic hydraulic formula), that is, the correspondence between the four gait stages of human walking and the working conditions of the ankle prosthesis hydraulic system. The purpose is to use it as a theoretical calculation basis together with the subsequent kinematic and dynamic models. After optimizing the event-triggered finite state machine state to realize the division of the four gait stages of the ankle joint when the human body walks, it is calculated how to accurately realize the control of the underlying driver (motor and high-speed switch valve):

[0094] like Figure 5 The figure shows the corresponding relationship between the four gait stages of human walking and the working conditions of the ankle prosthesis hydraulic system. The physiological gait division of the human body and the working conditions of the active ankle prosthesis are composed of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, namely the following four parts:

[0095] 1. In the passive plantar flexion stage, the upper and lower chambers of the hydraulic cylinder are connected through valves 3 and 4. When the heel is impacted by the ground, the hydraulic oil flows from the lower chamber to the upper chamber. At this time, the dynamic model of the hydraulic cylinder is obtained by the following formula:

[0096] m A a A =F A -(P3-P2)A a -F s ;

[0097] Among them, m A is the load and piston mass, aA 为 The acceleration of the hydraulic cylinder piston rod, P3 and P2 represent the pressure of the upper and lower chambers of the hydraulic cylinder, respectively. A is the external load force, F s is the internal friction, A a It is the oil action area of ​​the hydraulic cylinder.

[0098] In order to ensure the consistency of the hydraulic cylinder activity with the human body activity, the present application realizes the PWM duty cycle of the bionic regulating valve 3 of the prosthetic hydraulic system by controlling the valve opening of the high-speed switch valve to adjust the hydraulic overall damping, and the damping of the hydraulic system will be adjusted. The average flow through the valve 3 It can be expressed as:

[0099]

[0100] Where ρ represents the density of the oil, c d is the flow coefficient, is the equivalent flow cross-sectional area of ​​the switch valve, and its calculation formula is:

[0101]

[0102] Where, θ represents the switching angle of the high-speed switching valve, D is the diameter of the high-speed switching valve, is the average displacement of the high-speed switch valve, which is opened and closed by the PWM wave. By controlling the PWM duty cycle, the opening time of the high-speed switch valve within a period of time can be controlled, which is equivalent to controlling the size of the throttle hole. Therefore, the purpose of adjusting the output torque of the prosthesis can be achieved by adjusting the PWM duty cycle. At the same time, the DC motor works continuously to charge the high-voltage accumulator A. The volume change of the accumulator is:

[0103]

[0104] Among them, V1 and V'1 represent the volume of the accumulator at different times, P1 and P'1 represent the pressure of the accumulator at different times, V0 is the accumulator capacity, P a is the charging pressure of accumulator A, ΔV is the volume change of the accumulator, and n is the index of the gas polymorphic process in the accumulator airbag.

[0105] 2. In the passive dorsiflexion stage, the upper and lower chambers of the hydraulic cylinder are connected through valves 3 and 4. The hydraulic oil flows from the upper chamber to the lower chamber. Similar to the above, the dynamic equation and pressure equation of the hydraulic cylinder are:

[0106] m A a A =F A -(P2-P3)A a -F s

[0107]

[0108] Among them, m A is the mass of the piston, a A is the acceleration of the piston, F AA is the force acting on the piston. a Indicates the force area of ​​the piston, F s represents the static friction of the system, β e Represents the bulk elastic modulus of hydraulic oil, v A represents the speed of the piston, Q2 and Q3 represent the flow rates of different pipelines, Q2 corresponds to the pipeline where P2 is located, Q3 corresponds to the pipeline where P3 is located, y a represents the position of the piston, Indicates the rate of change of P2, represents the rate of change of P3, V2 represents the volume of accumulator B, and V3 represents the volume of accumulator C. At this time, by adjusting the PWM duty cycle of valve 3, the damping of the hydraulic system circuit can be adjusted in real time, and the ankle joint torque can be adjusted in real time, and the ankle joint impedance characteristics can be adjusted in real time. In addition, the motor keeps working to charge the high-pressure accumulator A, and the pressure of the high-pressure accumulator keeps increasing:

[0109]

[0110] Among them, P acc Indicates the power of the accumulator, P a Indicates the charging pressure of accumulator A, q acc represents the flow rate of the accumulator, P1 and P'1 represent the pressure of accumulator A at different times, V1 and V'1 represent the volume of accumulator A at different times (i.e., different moments), ΔV represents the volume difference at different moments. At the same time, the human body gravity does work on the hydraulic cylinder, and the accumulator C recovers the energy brought by the gravity potential energy of the human body at this time:

[0111]

[0112] Among them, V0 is the accumulator capacity, P a is the charging pressure of accumulator A, P c represents the charging pressure of accumulator C, V3 and V'3 represent the volume of accumulator C at different times, and P3 and P'3 represent the pressure of accumulator C at different times.

[0113] 3. In the active plantar flexion stage, the upper chamber of the hydraulic cylinder is connected to the low-pressure accumulator B through valve 1. At this time, the high-pressure accumulator releases the high-pressure oil accumulated in the two passive stages. The released energy drives the hydraulic cylinder to output an instantaneous high thrust, so that the ankle pushes the human body forward. The dynamic equation and pressure equation of the prosthesis are:

[0114] m A a A =F A +(P2-P3)A a -F s

[0115]

[0116] In this process, the pressure in the lower chamber of the hydraulic cylinder can be adjusted by adjusting the PWM duty cycle of valve 3, so that the output of the ankle prosthesis is close to the output of the biological ankle joint. In particular, in this state, the hydraulic pump and the accumulator act on the actuator at the same time. The power of the hydraulic pump is:

[0117] P pum p=P1V pumpn η0 / 60;

[0118] Among them, V pump is the displacement of the hydraulic pump, P pump is the power of the hydraulic pump, η0 refers to the total efficiency of the hydraulic pump, and n represents the speed of the hydraulic pump. At this time, the total input power of the hydraulic system is the sum of the input power of the hydraulic pump and the power of the accumulator.

[0119] 4. The hydraulic system in the active dorsiflexion phase is similar to the traditional hydraulic drive system. The motor drives the hydraulic pump, which drives the hydraulic cylinder to move in preparation for the next gait cycle. The dynamic equation and pressure equation of the hydraulic cylinder are:

[0120] m a a A =F A +(P3-P2)A a -F s

[0121]

[0122] So far, the corresponding relationship between the four gait stages of human walking and the working conditions of the ankle joint prosthesis hydraulic system can be obtained, and the influence of motor input and switch valve opening on the displacement, velocity, acceleration and output force of the hydraulic cylinder can be obtained. Figure 4 The prosthetic kinematics content can be used to construct a complete prosthetic mathematical model.

[0123] like Figure 3 The figure shows the overall design block diagram of the controller in the ankle prosthesis of this application. The controller is designed to achieve accurate fitting of the real biological ankle gait. The system consists of three core logic layers: decision fusion layer (top layer), state transition control layer (middle layer) and servo impedance control layer (bottom layer).

[0124] The top layer is responsible for integrating the information of the plantar pressure array sensor and the ankle angle sensor, and performing gait phase identification, that is, gait phase identification and division of multi-sensor information fusion (top-level state classification of multi-sensor information fusion). The plantar pressure sensor provides accurate judgment of each stage of the gait cycle and generates gait phase information by analyzing the characteristic changes such as the pressure distribution and pressure center trace of the three-foot area. The ankle angle sensor and the plantar switch provide gait phase information through traditional finite state machine logic and division. This information is fused through the decision-making layer advantage weight algorithm to obtain the final gait phase trigger signal, providing an accurate trigger point for the system state transition.

[0125] The middle layer uses a finite state machine that optimizes key event triggering to process the top-level gait phase information. At this layer, the system combines the preset threshold limits to identify the current gait phase and control the state transition, which is determined by the finite state machine based on the ankle angle and combined with the plantar pressure. The state transition control layer responds to the fused trigger information provided by the decision layer and ensures that the prosthesis smoothly transfers and switches between key events in the gait cycle, achieving the continuity and naturalness of the prosthetic gait.

[0126] The underlying control logic designs servo impedance, torque, and displacement controllers based on the gait characteristics and actual working conditions of the prosthesis, combined with hydraulic and dynamic models. These controllers can finely adjust the torque output and real-time damping of the prosthetic joint through the PWM-PFM strategy to simulate the physical behavior and mechanical response of the human ankle joint in different gait stages, and achieve mechanical properties and dynamic responses similar to those of the human ankle joint. The overall control strategy ensures the coordinated work of controllers at all levels to simulate the complex movements of the biological ankle joint under different ground and walking conditions.

[0127] The following is an introduction to the kinematic and dynamic models, i.e. the kinematic and dynamic formulas of the prosthesis:

[0128] like Figure 4 As shown, it is a simplified diagram of the dynamic structure of the ankle prosthesis in the present application. It analyzes the relationship between the movement of the hydraulic cylinder and the movement of the ankle joint, which is an important link from the control to the drive of the prosthesis. Since the connection between the hydraulic cylinder and the carbon fiber foot plate is rigid, the ankle joint angle and the hydraulic cylinder displacement have a fixed mapping relationship. By controlling the force, displacement and speed of the hydraulic cylinder, the output of the ankle prosthesis can be controlled. The mechanical structure of the prosthesis is equivalent to a crank-connecting rod mechanism, the hydraulic cylinder performs a linear translational motion, and the connecting rod BC rotates around the fixed axis of point C, so it can be equivalent to the fixed axis rotation of AC, and the AB and BC rods have fixed lengths. In addition, the rotation angle around point C is defined as the ankle joint rotation angle.

[0129] The geometric relationship between the hydraulic cylinder displacement and the ankle joint rotation angle is:

[0130]

[0131] Among them, L BC and L AB Represents the length of connecting rods BC and AB, d x and d y is the fixed length on the ankle prosthesis, α represents the angle between the connecting rod AB and the x-axis, β represents the angle between the rod BC and the horizontal direction, and y represents the displacement of the hydraulic cylinder, which can be calculated by the following formula:

[0132]

[0133] The relationship between the hydraulic cylinder movement speed and the ankle joint speed is:

[0134]

[0135] v BA represents the relative velocity of point B relative to point A, v A represents the linear velocity of point A, v B represents the linear velocity of rod BC, from which we can know:

[0136] v B =ω BC L BC ;

[0137] We can calculate ω AB and v A :

[0138]

[0139] Among them, ω BC represents the angular velocity of rod BC, ω AB represents the angular velocity of rod AB.

[0140] Hydraulic cylinder acceleration a A and ankle joint angular acceleration α BA The relationship is:

[0141]

[0142] in, Represents the axial acceleration and tangential acceleration of rods BC and AB, respectively, namely:

[0143]

[0144] Among them, α BC represents the acceleration of point B relative to point C, α AB It represents the acceleration of point A relative to point B.

[0145] Hydraulic cylinder output force F AThe relationship between the ankle joint torque T is:

[0146]

[0147] have:

[0148]

[0149] in, represents the axial force of rod BC, F BC It represents the force on rod BC. Therefore, the hydraulic cylinder output force F A for:

[0150]

[0151] It can be seen that the angle, angular velocity, angular acceleration and torque of the ankle joint can be derived from the displacement, velocity, acceleration and output force of the hydraulic cylinder. Figure 5 The hydraulic system model in the paper constitutes the whole system model, which can realize the precise drive control of the whole prosthesis.

[0152] The hydraulic and dynamic models are used jointly: by constructing hydraulic and dynamic models, after identifying the real-time motion state of the prosthesis (i.e., the optimized finite state machine state output based on sensor fusion), the angle, angular velocity, angular acceleration, and torque required by the ankle joint at this time can be inferred according to the motion state and phase, and then the displacement, velocity, acceleration, and output force of the hydraulic cylinder can be obtained, and finally the motor output power and switch valve opening data in the underlying servo controller can be obtained, and overall real-time and accurate control can be achieved through feedback.

[0153] Output force F in the dynamics formula A , the hydraulic pump displacement V in the hydraulic formula pump The pressure difference before and after the high-speed switching valve (P1-P2, P2-P3, etc.) is the final control target obtained by the prosthesis dynamics model and control steps.

[0154] The underlying control layer is a well-known technology. This application corresponds different underlying controls to their appropriate gait phases to achieve effective control. For details, see the following underlying controller details:

[0155] The design of the underlying controller (underlying servo controller), namely the servo impedance, torque and displacement controller, is introduced below:

[0156] 1. Joint impedance controller design

[0157] Since the ankle joint prosthesis has a high actuation frequency, and the output torque of the ankle joint in the passive state is related to the actuation speed of the ankle joint and the driving signal of the high-speed switch valve, the speed disturbance has a greater impact on the ankle joint torque output, and the input signal and the disturbance signal change relatively quickly. Therefore, the damping control system ensures the rapidity and robustness of the system. Therefore, the PID combined with PWM-PFM control strategy is used to control the prosthesis output torque. This control strategy can improve the linearity of the system, minimize the dead zone range of the high-speed switch valve flow characteristics, and linearize it, which is conducive to accurately controlling the system flow, thereby completing the real-time and accurate adjustment of the prosthesis damping.

[0158] In the passive state, the prosthetic hydraulic drive system can be equivalent to Figure 6 The power source of this system is the gravity of the human body, that is, the downward pressure of the human body causes the hydraulic system oil to flow, and the resistance of the hydraulic system is controlled by controlling the high-speed switch valve. Figure 7 Impedance control system.

[0159] Among them, the impedance characteristic curve is the relationship between the ankle joint output torque and the ankle joint angle under the target motion mode and walking speed, that is, the resistance torque corresponding to the characteristic angle is used as the input signal, which is finally applied to the hydraulic cylinder through the PID controller, the PWM-PFM driver, and the high-speed switch valve to control the pressure difference between the two chambers of the hydraulic cylinder. At the same time, the pressure of the two chambers of the hydraulic cylinder is used as the feedback signal to realize the closed loop of the system. The hydraulic cylinder is used as an actuator to realize the actuation of the prosthesis. During this process, the external environment exerts a load force on the hydraulic cylinder rod through the prosthetic joint hinge, which ultimately affects the output angular acceleration, angular velocity and angle of the ankle joint. The angular velocity of the ankle joint will also affect the flow rate of the hydraulic cylinder oil, thereby disturbing the pressure of the two chambers of the hydraulic cylinder.

[0160] 2. Joint output torque controller design

[0161] In active plantar flexion, Figure 8 The high-speed switch valve in the prosthetic hydraulic drive system shown in the figure controls the flow rate of the oil by controlling the damping between the high-pressure accumulator and the hydraulic cylinder, thereby controlling the pressure of the hydraulic cylinder actuating chamber. The power source of the system is the charged high-pressure accumulator and the hydraulic pump. Fig. 9 The torque control system shown.

[0162] Among them, the output characteristic curve is obtained according to the relationship between the ankle joint output torque and the ankle joint angle under the target motion mode and walking speed, that is, the output torque corresponding to the characteristic angle is used as the input signal, which is finally applied to the hydraulic cylinder through the PID controller, the PWM-PFM driver, and the high-speed switch valve to control the pressure difference between the two chambers of the hydraulic cylinder. At the same time, the pressure of the two chambers of the hydraulic cylinder is used as the feedback signal to realize the closed loop of the system. The hydraulic cylinder is used as an actuator to realize the actuation of the prosthesis. In this process, the external environment exerts a load force on the hydraulic cylinder rod through the prosthetic joint hinge, which ultimately affects the output angular acceleration, angular velocity and angle of the ankle joint.

[0163] 3. Joint angle servo controller design

[0164] In the swing phase active dorsiflexion state, the ankle joint prosthesis mainly undergoes active dorsiflexion, and the hydraulic system can be simplified as follows: Fig.10 In this process, since the load of the prosthesis is small and the main task is to restore the ankle joint angle, the classic PID-based joint angle servo system is used to control the rotation angle of the ankle joint prosthesis. Fig.11 Ankle angle servo system shown.

[0165] The system feedback is mainly the ankle joint rotation angle measured by the angle sensor, and the system input is the target angle θ of the ankle joint during the active dorsiflexion phase of the swing phase. d The output of the controller is the input of the PWM driver to control the speed of the DC motor, thereby controlling the EHA actuator to achieve control of the ankle angle.

[0166] Based on the control method for the electrostatic-hydraulic active ankle joint prosthesis, the present application also provides a control system for the electrostatic-hydraulic active ankle joint prosthesis, comprising:

[0167] The liquid pressure sensor is installed on the hydraulic integrated block of the electrostatic-hydraulic active ankle joint prosthesis and connected to the internal hydraulic oil circuit of the electrostatic-hydraulic active ankle joint prosthesis to obtain the original liquid pressure signal.

[0168] The ankle angle sensor is installed at the ankle of the electrostatic-hydraulic active ankle joint prosthesis to obtain the original ankle angle signal.

[0169] The multi-point plantar pressure array sensor is installed in the insole of the electrostatic-hydraulic active ankle prosthesis to obtain the original plantar pressure signal.

[0170] The signal and control circuit board is connected to the liquid pressure sensor, the ankle angle sensor and the multi-point plantar pressure array sensor respectively, and is used for preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal, extracting the plantar pressure feature according to the plantar pressure signal, and obtaining the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature, and according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the state division of the finite state machine based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through the weighted decision model, and the control signal of the prosthetic movement is generated according to the real-time gait stage and the liquid pressure signal. number; wherein, the plantar pressure signal includes the pressure signal of each point in the plantar pressure array; the plantar pressure characteristics include the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rearfoot area ground reaction force, the plantar pressure center trajectory x-coordinate, the plantar pressure center trajectory y-coordinate and the maximum single-point impulse; the trained SVM model is trained by using the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rearfoot area ground reaction force, the plantar pressure center trajectory x-coordinate, the plantar pressure center trajectory y-coordinate and the maximum single-point impulse of different subjects in walking mode, and distinguishing the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion; the plantar switch is a pressure switch; the pressure signals of the two front and rear end areas in the plantar pressure array are used as a pressure switch.

[0171] The power supply and drive circuit board are respectively connected to the signal and control circuit board and the high-speed switch valve, motor and battery of the electrostatic-hydraulic active ankle joint prosthesis, and are used to control the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement.

[0172] Among them, the ankle angle sensor is a potentiometer angle sensor, which is installed at the ankle of the electrostatic-hydraulic active ankle prosthesis and fixed to the electrostatic-hydraulic active ankle prosthesis through a stepped shaft and a flange structure, and is used to obtain the real-time angle of the electrostatic-hydraulic active ankle prosthesis foot.

[0173] The multi-point plantar pressure array sensor is composed of 18 single-point resistive thin film pressure sensors, which are installed in the insole of the electrostatic-hydraulic active ankle prosthesis and contact the silicone foot skin worn on the foot plate of the electrostatic-hydraulic active ankle prosthesis.

[0174] The signal and control circuit board is provided with a single-chip microcomputer; the single-chip microcomputer is used to pre-process the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal, extract the plantar pressure feature according to the plantar pressure signal, and obtain the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature, and according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the state division of the finite state machine based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through the weighted decision model, and the control signal of the prosthetic movement is generated according to the real-time gait stage and the liquid pressure signal, and the control signal of the prosthetic movement is transmitted to the power supply and drive circuit board.

[0175] The following is a specific example to illustrate the technical solution of the control system of the present application for an electrostatic-hydraulic active ankle joint prosthesis:

[0176] The present application is for a control system for an electrostatic-liquid active ankle prosthesis, which is a gait detection and control system for a wearable ankle prosthesis. The gait detection and control system of the present application is installed on an active ankle prosthesis, and is composed of components such as a liquid pressure sensor, an ankle angle sensor, a plantar pressure array sensor, a signal and control circuit board, and a power supply and drive circuit board, etc., to complete the detection of prosthesis and human body information, and control the prosthesis drive element according to the gait division results and the real-time gait phase to achieve real-time adjustment of damping and output during walking. Finally, the two are combined to complete the flexible control for the entire walking cycle and meet the dynamic coupling relationship between the prosthesis and the wearer.

[0177] like Fig.12 Shown is the hardware layout diagram of the ankle prosthesis gait detection system of the present application, which is installed on an active ankle prosthesis 601 and consists of a liquid pressure sensor 602, an ankle angle sensor 603, a plantar pressure array sensor 604, a signal and control circuit board 605, and a power supply and drive circuit board 606.

[0178] Among them, the multi-point plantar pressure array sensor 604 is installed in the insole and contacts the silicone foot skin worn on the prosthetic foot to ensure the force-bearing area. It is composed of 18 single-point resistive thin film pressure sensors, each point is the plantar pressure in the area, which is used to judge the human body pressure in the area; the potentiometer angle sensor 603 is installed at the ankle of the ankle prosthesis 601, and is fixed to the prosthesis through a stepped shaft and flange structure, and is used to record the real-time angle of the prosthetic foot; the liquid pressure sensor 602 is connected to the internal hydraulic oil circuit of the ankle prosthesis 601, and is used to detect the real-time liquid pressure of the upper and lower chambers of the prosthetic hydraulic cylinder and the front and rear ends of the high-speed switching valve.

[0179] The signal and control circuit board 605 and the power supply and drive circuit board 606 are fixed on one side of the active ankle joint prosthesis 601. The signal and control circuit board 605 is connected to the above-mentioned sensor and is also the calculation and processing center of the prosthesis signal. A single-chip microcomputer is set on it. The main function is to obtain the data of each sensor and transmit it to the single-chip microcomputer, complete feature extraction and state division, and transmit the control signal of the specific component to the drive circuit board. The power supply and drive circuit board 606 is connected to the high-speed switch valve, motor, and battery of the prosthesis, and mainly completes the connection and drive of the high-power components of the prosthesis. It can complete the precise control (PWM control) of the corresponding structure of the prosthesis (motor and high-speed switch valve) according to the state calculated by the signal and control circuit board and the component control signal, thereby realizing actuation. Separating the drive circuit part from the control circuit can improve the circuit's ability to resist electromagnetic interference and is also convenient for placement on the prosthesis; in the control circuit part, interfaces for various external sensors and the DSP microcontroller interface are set, and interfaces are reserved for subsequent sensor expansion, completing the overall data acquisition and calculation functions of the prosthesis; in the drive circuit part, an overall power supply, high-speed switch valve and motor drive are designed, and a plug-in terminal interface is selected to facilitate the connection of battery, motor and high-speed switch valve terminals.

[0180] The gait detection, control method and system for wearable ankle prosthesis provided in the present application are specifically divided into two levels: gait detection, control method and supporting gait detection and control system, that is, they are divided into gait detection, control method and supporting gait detection and control system. The two cooperate to judge the gait information of the human body to determine the control strategy, adjust the walking damping and output timing, complete the compliant control of the prosthesis for the whole gait stage, and meet the dynamic coupling relationship between the prosthesis and the wearer. The gait detection and control method for wearable ankle prosthesis in the present application includes: acquiring and preprocessing the original signal; completing the gait division of plantar pressure feature extraction and machine learning classification model; integrating plantar pressure and ankle angle information to realize the top-level controller decision; constructing an active ankle prosthesis hydraulic model and dynamic model; the control method consists of a decision fusion layer, a state control layer and a servo impedance control layer, and its core is a finite state machine and a bottom-level servo controller that optimizes the transfer of key events. Among them, the plantar pressure feature extraction and gait classification of the machine learning classification model are completed. That is, on the basis of the traditional plantar pressure gait feature center (COP) trace and other features, the pressure distribution characteristics of the three-foot area based on the plantar physiological structure are combined to further capture the multivariate nonlinear characteristics in the gait data to achieve more accurate gait classification. The plantar pressure and ankle angle information are integrated to realize the top-level controller decision, that is, the multi-source information and the reliability of sensors at different stages are integrated to realize the triggering of the key information transfer of the finite state machine, and the formula is used. The weighted decision strategy is used to achieve the overall gait phase information fusion. Where i∈{1,2,3,4} represents four gait phases, and are the weights of the plantar pressure array sensor data and the ankle joint angle data in the i-th gait stage, S d is the weighted decision result, round is the rounding function, and the final decision of the state stage is obtained. The present application is used for the gait detection and control system of the wearable ankle prosthesis, which is installed on the active ankle prosthesis and consists of a liquid pressure sensor that monitors the pressure in the hydraulic system to adjust the output force of the hydraulic cylinder, an ankle angle sensor that provides real-time joint angle information and the position and posture of the prosthesis in the gait cycle, a plantar pressure array sensor that detects the pressure distribution and characteristics of different parts of the wearer's sole, and circuit boards such as signals and drivers. This sensor combination can comprehensively monitor and control the key parameters of the hydraulically driven prosthesis to ensure the stability, flexibility and functionality of the prosthesis.

[0181] The present application is specifically a method and system suitable for the recognition of human lower limb motion and human-computer interactive control of active electro-hydraulic ankle prosthesis. The present application is designed for a wearable active electro-static hydraulic ankle prosthesis, including a gait detection and control method, and a matching gait detection and control system. The system contains components such as a liquid pressure sensor, an ankle angle sensor, a plantar pressure array sensor, a signal and control circuit board, and a power supply and drive circuit board. The combination of these sensors and control circuits can complete the comprehensive monitoring and precise control of the key parameters of the prosthesis, thereby ensuring the stability, flexibility and functionality of the prosthesis. The gait detection and control method of the present application includes the acquisition and preprocessing of the original signal, the extraction of plantar pressure characteristics, the gait division based on machine learning, and the fusion of plantar pressure and ankle The top-level control decision of angle information is based on the traditional plantar pressure gait characteristics, combined with the pressure distribution of the three-foot area, which can better capture the multivariate nonlinear characteristics in the gait data and significantly improve the accuracy of gait division. Through the weighted decision strategy and the optimization of the finite state machine, the real-time adjustment and smooth control of the prosthesis are completed, ensuring the dynamic adaptation of the prosthesis during walking and improving the overall motion experience. The construction of the hydraulic and dynamic models provides detailed dynamic information of the prosthetic movement, so that the prosthesis can better realize the underlying walking damping and output timing control to adapt to the wearer's gait changes and further improve the overall performance and comfort of the prosthesis.

[0182] Compared with the existing classic prosthetic finite state machine that completes state switching through sensor thresholds, the plantar pressure therein only serves as a single-point switch for the sole and toes, and does not reveal the large amount of human walking characteristic information it contains. In addition, all triggering information of its typical events is directly provided by the sensors integrated in the prosthetic system. The prosthetic detection system of this application optimizes the triggering of key events, and understands and simulates the dynamics of human walking through the plantar pressure center point and three-foot area information measured by the plantar pressure array sensor, and realizes the division of the four gait stages of the ankle joint when the human body walks. Combined with the traditional finite state machine state division based on ankle angle and plantar switch, the decision-making layer fusion of multi-sensor information is realized to complete the recognition. At the same time, the method of this application constructs a specific prosthetic hydraulic and dynamic model, and more accurately completes the control of the prosthetic actuator through the underlying model. Compared with the traditional method, this application can more accurately control the prosthetic power output and damping change timing, thereby achieving a better and more natural power assistance effect. The advantages of this application are specifically introduced below:

[0183] (1) This application realizes comprehensive gait detection and control. By combining gait detection and control methods and systems, it can comprehensively monitor the key parameters of the prosthesis, independently and accurately judge the gait information of the human body, and formulate corresponding control strategies to adjust the walking damping and output timing, thereby realizing smooth control of the prosthesis in the entire gait stage.

[0184] (2) This application optimizes the feature capture of nonlinear plantar data. Based on the traditional plantar pressure gait and other features, it adds the pressure distribution characteristics of the three-foot area, which can better capture the multivariate nonlinear characteristics in the gait data and improve the accuracy of gait division.

[0185] (3) This application has completed the optimization of the prosthetic finite state machine controller: using a weighted decision strategy, combining multi-source information and sensor reliability, to achieve precise control of key events and optimization of gait phases, ensuring that the prosthesis can adapt to the wearer's needs during walking. The existing prosthetic finite state machine control is completed through sensor thresholds and empirical formulas when switching between multiple states, while this application optimizes the state machine using SVM machine learning, introduces the three-foot zone characteristics of plantar pressure, and fuses multiple sensor states to obtain more accurate key state identification for different individuals.

[0186] (4) This application constructs a prosthetic hydraulic and dynamic model, provides detailed dynamic information of prosthetic movement, and dynamically adjusts the walking damping and output timing of the prosthesis to adapt to the wearer's gait changes and improve the overall performance and comfort of the prosthesis. Figure 5The figure shows the working state of the prosthesis of the present application in the four gait stages. The key point is that by constructing a hydraulic and dynamic model, after identifying the real-time motion state of the prosthesis, the angle, angular velocity, angular acceleration, and torque required by the ankle joint at this time can be inferred according to the motion state and phase, and then the displacement, speed, acceleration, and output force of the hydraulic cylinder can be obtained, and finally the motor output power and switch valve opening data in the underlying servo controller can be obtained, and overall real-time and accurate control can be achieved through feedback.

[0187] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A control method for an electrostatic-hydraulic active ankle prosthesis, characterized in that: The control method for the electrostatic-hydraulic active ankle prosthesis comprises: Acquire original fluid pressure signal, original ankle angle signal and original plantar pressure signal; Preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal; the plantar pressure signal includes pressure signals of each point in the plantar pressure array; Extracting plantar pressure features according to the plantar pressure signal; the plantar pressure features include overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse; According to the plantar pressure characteristics, the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion are obtained based on the trained SVM model; the trained SVM model is trained by using the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse of different subjects in walking mode to distinguish the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion; According to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the state division of the finite state machine based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through a weighted decision model; the plantar switch is a pressure switch; the pressure signals of the frontmost and rearmost two areas in the plantar pressure array are used as a pressure switch; generating a control signal for prosthetic movement according to the real-time gait phase and the fluid pressure signal; The movement of the electrostatic-hydraulic active ankle joint prosthesis is controlled according to the control signal of the prosthesis movement.

2. The control method for an electrostatic-hydraulic active ankle prosthesis according to claim 1, characterized in that: Preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal specifically includes: Performing Fourier transformation on the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain frequency domain distribution of the original liquid pressure signal, frequency domain distribution of the original ankle angle signal and frequency domain distribution of the original plantar pressure signal; The frequency domain distribution of the original liquid pressure signal, the frequency domain distribution of the original ankle angle signal and the frequency domain distribution of the original plantar pressure signal are subjected to low-pass filtering to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal.

3. The control method for an electrostatic-hydraulic active ankle prosthesis according to claim 1, characterized in that: Extracting the plantar pressure feature according to the plantar pressure signal specifically includes: According to the plantar pressure signal, the plantar pressure center trajectory and the pressure cloud map during walking are used to extract the overall foot area ground reaction force, forefoot area ground reaction force, midfoot area ground reaction force, rearfoot area ground reaction force, plantar pressure center trajectory x-coordinate, plantar pressure center trajectory y-coordinate and maximum single-point impulse.

4. The control method for an electrostatic-hydraulic active ankle prosthesis according to claim 1, characterized in that: The weighted decision model is expressed as: Among them, x i represents the plantar pressure signal, y i represents the ankle angle signal, represents the weight of the plantar pressure signal in the i-th gait stage, represents the weight of the ankle angle signal in the i-th gait stage, i∈{1,2,3,4} represents four gait stages respectively, i=1 represents passive plantar flexion, i=2 represents passive dorsiflexion, i=3 represents active plantar flexion, i=4 represents active dorsiflexion, S d represents the weighted decision result, and round represents the rounding function, which is used to convert the weighted average result into the nearest integer gait phase.

5. The control method for an electrostatic-hydraulic active ankle prosthesis according to claim 1, characterized in that: Generating a control signal for prosthetic movement according to the real-time gait phase and the liquid pressure signal, specifically comprising: According to the real-time gait phase and the liquid pressure signal, a control signal for prosthetic movement is generated in combination with a finite state machine triggered by optimized typical events and an electrostatic-hydraulic active ankle prosthesis hydraulic model, kinematics and dynamics model; the control signal for prosthetic movement includes motor output power and high-speed switch valve opening.

6. The control method for an electrostatic-hydraulic active ankle prosthesis according to claim 5, characterized in that: Controlling the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement specifically includes: The motor of the electrostatic-hydraulic active ankle prosthesis is controlled according to the output power of the motor, and the high-speed switch valve of the electrostatic-hydraulic active ankle prosthesis is controlled according to the opening of the high-speed switch valve. The electrostatic-hydraulic active ankle prosthesis moves under the control of the motor and the high-speed switch valve.

7. A control system for an electrostatic-hydraulic active ankle prosthesis, characterized in that: A control method for an electrostatic-hydraulic active ankle prosthesis based on any one of claims 1-6; The control system for the electrostatic-hydraulic active ankle prosthesis comprises: A liquid pressure sensor is installed on the hydraulic integrated block of the electrostatic-hydraulic active ankle joint prosthesis and connected to the internal hydraulic oil circuit of the electrostatic-hydraulic active ankle joint prosthesis to obtain an original liquid pressure signal; An ankle angle sensor, installed at the ankle of the electrostatic-hydraulic active ankle joint prosthesis, is used to obtain the original ankle angle signal; A multi-point plantar pressure array sensor is installed in the insole of the electrostatic-hydraulic active ankle prosthesis to obtain the original plantar pressure signal; The signal and control circuit board is respectively connected to the liquid pressure sensor, the ankle angle sensor and the multi-point plantar pressure array sensor, and is used for preprocessing the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain the liquid pressure signal, the ankle angle signal and the plantar pressure signal, extracting the plantar pressure feature according to the plantar pressure signal, and obtaining the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature, and according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the finite state machine state division based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through the weighted decision model, and the false alarm is generated according to the real-time gait stage and the liquid pressure signal. limb movement control signal; wherein, the plantar pressure signal includes the pressure signals of each point in the plantar pressure array; the plantar pressure characteristics include the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rear foot area ground reaction force, the plantar pressure center trajectory x coordinate, the plantar pressure center trajectory y coordinate and the maximum single point impulse; the trained SVM model is trained by using the overall foot area ground reaction force, the forefoot area ground reaction force, the midfoot area ground reaction force, the rear foot area ground reaction force, the plantar pressure center trajectory x coordinate, the plantar pressure center trajectory y coordinate and the maximum single point impulse of different subjects in walking mode, and distinguishing the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion; the plantar switch is a pressure switch; the pressure signals of the two front and rear end areas in the plantar pressure array are used as a pressure switch; The power supply and drive circuit board are respectively connected to the signal and control circuit board and the high-speed switch valve, motor and battery of the electrostatic-hydraulic active ankle joint prosthesis, and are used to control the movement of the electrostatic-hydraulic active ankle joint prosthesis according to the control signal of the prosthesis movement.

8. The control system for an electrostatic-hydraulic active ankle prosthesis according to claim 7, characterized in that: The ankle angle sensor is a potentiometer angle sensor, which is installed at the ankle of the electrostatic-hydraulic active ankle prosthesis and fixed to the electrostatic-hydraulic active ankle prosthesis through a stepped shaft and a flange structure, and is used to obtain the real-time angle of the electrostatic-hydraulic active ankle prosthesis foot.

9. The control system for an electrostatic-hydraulic active ankle prosthesis according to claim 7, characterized in that: The multi-point plantar pressure array sensor is composed of 18 single-point resistive film pressure sensors, which are installed in the insole of the electrostatic-hydraulic active ankle joint prosthesis and contact the silicone foot skin worn on the footboard of the electrostatic-hydraulic active ankle joint prosthesis.

10. The control system for an electrostatic-hydraulic active ankle prosthesis according to claim 7, characterized in that: The signal and control circuit board is provided with a single-chip microcomputer; the single-chip microcomputer is used to pre-process the original liquid pressure signal, the original ankle angle signal and the original plantar pressure signal to obtain a liquid pressure signal, an ankle angle signal and a plantar pressure signal, extract the plantar pressure feature according to the plantar pressure signal, and obtain the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion based on the trained SVM model according to the plantar pressure feature; according to the division results of the four gait stages of passive plantar flexion, passive dorsiflexion, active plantar flexion and active dorsiflexion, combined with the finite state machine state division based on the ankle angle signal and the plantar switch, the real-time gait stage is obtained through a weighted decision model; according to the real-time gait stage and the liquid pressure signal, a control signal for the prosthetic movement is generated, and the control signal for the prosthetic movement is transmitted to the power supply and drive circuit board.

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