CN117398264A - Lower limb rehabilitation system capable of automatically switching active control modes and control method - Google Patents
Lower limb rehabilitation system capable of automatically switching active control modes and control method Download PDFInfo
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Abstract
The invention relates to the technical field of rehabilitation medical appliances, in particular to a lower limb rehabilitation system capable of automatically switching an active control mode and a control method, wherein the system is divided into a sensing layer, a judging layer, a control layer and an execution layer, wherein the sensing layer comprises an acquisition module and a plurality of sensors; the judging layer comprises a switching control module; the control layer comprises a myoelectricity leading control module and an interaction force leading control module; the execution layer comprises a motion execution module; the control method can realize real-time switching between the two active control modes, namely an active mode of electromyographic signal leading control and an active mode of interaction force leading control. The invention can be suitable for patients with different muscle recovery conditions, can realize detection of invalid exercise electromyographic signals, unstable electromyographic signal acquisition and muscle fatigue conditions in the exercise process, is switched into an interactive force signal dominant control mode in real time, has good stability, safety and adaptability, and further has better recovery effect.
Description
Technical Field
The invention relates to the technical field of rehabilitation medical appliances, in particular to a lower limb rehabilitation system capable of automatically switching an active control mode and a control method.
Background
The intelligent lower limb prosthesis, the lower limb exoskeleton and other movable wearable rehabilitation robots can help muscle injury, nerve injury or lower limb amputation patients to recover lower limb movement functions. At present, the problem of control based on close interaction between a person and a robot is a key technical problem for researching the lower limb rehabilitation robot, wherein human movement intention recognition in man-machine interaction is one of the core problems in the interaction control technology.
Signals for human movement intent recognition mainly include mechanical signals, biomechanical signals, and neural signals. The mechanical signals are signals reflecting human kinematics and dynamics, and mainly comprise signals such as speed, joint acceleration and the like; the biomechanical signals are signals reflecting the mechanical characteristics of a human body and mainly comprise signals such as man-machine interaction force, plantar pressure and the like; neural signals are signals reflecting human central information, and mainly include brain electrical signals (EEG) and electromyographic signals (EMG). Mechanical signals and biomechanical signals are widely used for intended identification due to their mature and stable sensing technology.
The exoskeleton active control mode based on the electromyographic signals is used for analyzing the intention of the wearer and executing corresponding actions by collecting the skin bioelectric signals, so that the wearer with difficulty in limb movement is helped to realize autonomous movement. The electromyographic signals are not only direct reflection of central motor nerves, but also can decode the movement intention of the human body, and simultaneously have the characteristics of predictability and accessibility, and become the main input for mapping the movement intention of the human body.
The exoskeleton active control mode based on the interaction force signals is used for analyzing the intention of a wearer and executing corresponding actions by collecting the interaction force signals between a human body and an exoskeleton robot, so that the wearer with difficulty in limb movement is helped to realize autonomous movement. The collection of the interactive force signals is realized through the pressure sensors distributed on the exoskeleton, and the pressure sensing signals have the characteristic of stability.
The above-described technique has the following limitations:
1. there are cases where the recovery of the muscle function of the patient is incomplete, so that the effect of the intended movement is difficult to achieve by using the myoelectric signal for active control.
2. The stable collection of the surface electromyographic signals sEMG is difficult to achieve to a certain extent because the collection of the electromyographic signals is easily affected by factors such as muscle impedance, skin sweat, epidermis hair, external electromagnetic interference and the like, so that the problem of instability exists in a mode of actively controlling the exoskeleton through the electromyographic signals.
3. Active mode control based on interactive force signals has the problem of delay and hysteresis, and cannot respond to the active movement intention of a patient in time.
4. When the muscles are fatigued, abnormal changes can be generated in the electromyographic signals, and the joint moment estimation is not consistent with the initiative intention of a patient.
Disclosure of Invention
The invention aims to provide a lower limb rehabilitation system capable of automatically switching an active control mode, which can adapt to patients with different muscle recovery degrees, avoid the problem of abnormal exoskeleton movement execution caused by insufficient muscle function recovery, unstable myoelectric signal acquisition and muscle fatigue, improve the safety and stability of exoskeleton movement, strengthen nerve circuit remodeling and improve rehabilitation effect.
In order to achieve the above object, there is provided a lower limb rehabilitation system capable of automatically switching an active control mode, applied to an exoskeleton, comprising: the system comprises a sensing layer, a judging layer, a control layer and an executing layer;
the sensing layer comprises an acquisition module; the judging layer comprises a switching control module; the control layer comprises a myoelectricity leading control module and an interaction force leading control module; the execution layer comprises a motion execution module;
and the acquisition module is used for: the method comprises the steps of acquiring myoelectricity data, plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton of a user;
And a switching control module: the gait recognition method is used for recognizing gait based on the gait recognition table and according to plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton, and judging a specific stage of a walking cycle where a user is located; the system is also used for carrying out real-time abnormal judgment on myoelectricity data based on a stage muscle control table and a specific stage of a walking cycle where a user is located, and generating a switching control signal for leading the myoelectricity signal leading control module and the interaction force leading control module to switch according to the judgment result;
electromyographic signal dominant module: when receiving the switching control signal, executing active control of the electromyographic signal leading, and analyzing joint moment control data according to the electromyographic data;
interaction force signal dominant module: when receiving the switching control signal, executing active control of interaction force dominance, and analyzing joint moment control data according to the interaction force data;
the motion execution module: for controlling exoskeleton movements based on the joint moment control data.
Further, the acquisition module includes:
myoelectric signal acquisition submodule: the myoelectricity data acquisition module is used for collecting and preprocessing original myoelectricity data to obtain myoelectricity data; comprises surface electromyographic signal sensors respectively arranged on gluteus maximus, ilium psoas, gluteus medius, rectus femoris and biceps femoris;
Plantar pressure sensing sub-module: the method is used for collecting plantar pressure data; comprises capacitive pressure sensors distributed at the heel, the half sole and the toe parts;
motion gesture sensing sub-module: angle sensors for measuring angle data of a user's lower limb including distributed at the hip, knee and ankle joints; the inertial sensor is used for measuring the movement posture data of the lower limbs of the user and comprises inertial sensors distributed at the root parts of thighs and the root parts of calves; the motion gesture data comprise the number of joint angles of the lower limbs and the motion gesture data of the lower limbs;
interaction force sensing sub-module: the pressure sensor is used for measuring interaction force between a human body and the exoskeleton, and comprises pressure sensors distributed on the front side of thighs, the rear side of thighs and the rear side of calves.
Further, the joint moment control data in the electromyographic signal leading module are obtained by analyzing and estimating the sensing data provided by the acquisition module based on a neuromuscular skeletal model;
the method for estimating the joint moment control data by using the neuromuscular skeletal model method comprises the following steps: and solving the muscle activation degree of the muscle subjected to the nerve signal by using the nerve activation model, bringing the muscle into a nerve musculoskeletal model to calculate muscle force and joint moment arm to obtain joint moment, and optimizing parameters of the whole musculoskeletal model according to the actually measured joint moment to finally obtain the joint moment.
Further, the joint moment control data in the interaction force signal leading module are obtained by predicting the expected angle of the hip-knee joint based on an admittance control model;
the mode of the admittance control model for predicting the expected angle of the hip-knee joint is as follows: the acquired interactive force sensing data is subjected to gravity compensation, and then a main moment is calculated; taking the operation parameters such as the main torque, the exoskeleton position, the speed and the acceleration as inputs, inputting the operation parameters into an admittance controller, outputting a target movement angular speed after the operation of the admittance controller, multiplying the angular speed by a control period, and adding the current joint angle to obtain a desired joint angle;
the transfer function of the admittance controller is as follows:
wherein: omega(s) is the angular velocity of the exoskeleton robot articulation; τ(s) is the main moment corresponding to the interaction force of the legs after the gravity compensation, M d Is of inertia coefficient, B d Is the damping coefficient.
Further, the joint moment control data comprise joint moment and joint angle, and the motion execution module comprises motors which are distributed at the left hip joint and the right hip joint and at the left knee joint and the right knee joint and are provided with rotary encoders, and the motors are used for measuring the rotating speed of the motors and realizing control of the motors with the received joint moment control data.
The second object of the present invention is to provide a lower limb rehabilitation control method capable of automatically switching an active control mode, to which the lower limb rehabilitation system is applied, comprising the following steps:
step 1: after the user wearing equipment starts to move, based on a gait recognition table, performing gait recognition by combining plantar pressure data, movement posture data and interaction force data between a human body and exoskeleton, which are acquired by an acquisition module, and judging the specific stage of a walking cycle where the user is located;
step 2: carrying out real-time preprocessing on the collected original myoelectricity data of the user to obtain myoelectricity data;
step 3: real-time abnormality judgment is carried out on myoelectric data through a switching control module, and a switching control signal for leading the myoelectric signal leading control module and the interaction force leading control module to switch is generated according to a judgment result;
step 4: the electromyographic signal leading control module or the interaction force leading control module is controlled to actively control according to the switching control signal, and joint moment control data is analyzed according to the interaction force data or the electromyographic data; the motion execution module is enabled to control the exoskeleton to move according to the joint moment control data.
Further, the gait recognition table is set based on a walking cycle octant, which divides the walking cycle into a heel strike period, a full plantar landing period, a supportive phase middle period, a heel off period, a toe off period, a swing phase early period, a swing phase middle period and a swing phase end period based on different stages of force muscles and gait in the walking cycle;
In the step 3, when no abnormality occurs in the electromyographic signals in the corresponding stage of the walking cycle, a control signal for electromyographic signal dominant control is generated, and when any electromyographic signal abnormality exists in the corresponding stage of the walking cycle, a control signal for interactive force dominant control is generated.
Further, the gait recognition comprises joint angle judgment, plantar pressure judgment, interaction force judgment and pitch angle judgment;
the joint angle judgment specifically comprises the following steps: setting the joint angles of the hip joint, the knee joint and the ankle joint in a natural state to be 0 degrees, wherein the buckling angle is positive, and the stretching angle is negative; analyzing and judging joint angles of the hip joint, the knee joint and the ankle joint;
the plantar pressure judgment specifically comprises the following steps: setting corresponding first pressure thresholds according to the heel, the half sole and the toe respectively, and judging whether the pressure data values at the heel, the half sole and the toe are larger than the corresponding first pressure thresholds or not;
the interaction force judgment specifically comprises the following steps: respectively setting corresponding second pressure thresholds according to the front thigh, the rear thigh and the rear calf, and judging whether the pressure data values at the front thigh, the rear thigh and the rear calf are larger than the corresponding second pressure thresholds;
the pitch angle judgment specifically comprises the following steps: the absolute pitch angle of the human body when standing is set to be 0 degrees, and the pitch angles of the thigh and the shank are analyzed and judged.
Further, the gait recognition method specifically comprises the following steps:
heel strike period: hip angle 30 °, knee angle 5 °, ankle angle 0 °; the heel reaches a first pressure threshold; the interaction force reaches a second pressure threshold before the thigh and after the calf; the pitch angles of the thigh and the shank are positive;
full plantar grounding period: hip joint angle 20 °, knee joint angle 15 °, ankle joint angle 5-10 °; the heel, the ball and the toe reach a first pressure threshold; the interaction force reaches a second pressure threshold before the thigh and after the calf; the pitch angle of the thigh is positive, and the pitch angle of the shank is 0;
support phase medium term: hip angle 0 °, knee angle 0 °, ankle angle-5 °; the heel, the ball and the toe reach a first pressure threshold; the pitch angles of the thigh and the shank are 0;
heel lift: hip joint angle-10 °, knee joint angle 10 °, ankle joint angle-5 to-10 °; the half sole and toe reach a first pressure threshold; the interaction force reaches a second pressure threshold behind the thigh and behind the calf; the pitch angles of the thigh and the shank are negative;
toe off period: hip angle 0 °, knee angle 30 °, ankle angle 15 °; the toes reach a first pressure threshold, and the interactive forces behind the thighs and the calves reach a second pressure threshold; the pitch angles of the thigh and the shank are negative;
Early swing phase: hip angle 0 °, knee angle 30 °, ankle angle 0 °; after the interactive force shank reaches a second pressure threshold, the pitch angle of the thigh is negative, and the pitch angle of the shank is 0;
mid-swing phase: hip joint angle 35 degrees, knee joint angle 45-55 degrees; the front and rear of the thigh and the shank reach a second pressure threshold, the pitch angle of the thigh is negative, and the pitch angle of the shank is positive;
end of swing phase: hip angle 35 °, knee angle 0 °, ankle angle 0 °; the front of the thigh of the interactive force reaches a second pressure threshold, and pitch angles of the thigh and the shank are positive;
in the gait recognition method, the range of the defined angle error is + -2 degrees.
Further, abnormal myoelectric data includes invalid myoelectric data due to poor muscle function, loss of myoelectric data due to unstable myoelectric data acquisition, and variation of a characteristic value of muscle fatigue due to muscle fatigue; when the electromyographic signals corresponding to the specific stage are abnormally judged according to the stage muscle control table, the judgment method is as follows:
if the user is in the heel strike period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the full plantar grounding period, judging whether the gluteus maximus and rectus femoris electromyographic signals are abnormal or not;
If the user is in the middle stage of the supporting phase, judging whether the myoelectric signals of gluteus maximus and gluteus medius are abnormal or not;
if the user is in the heel lift period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the toe off period, judging whether the ilium and lumbar muscle electrical signals are abnormal or not;
if the user is in the early swing phase, judging whether the ilium muscle electrical signal is abnormal or not;
if the user is in the middle swing phase, judging whether the ilium and lumbar muscle signals are abnormal or not;
if the user is at the end of the swing phase, judging whether the gluteus maximus and biceps femoris electromyographic signals are abnormal.
Principle and advantage:
1. the principle of the scheme is based on a gait recognition table, and carries out gait recognition according to plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton, and judges the specific stage of a walking cycle where a user is located; and the system is also used for carrying out real-time abnormal judgment on myoelectric data based on a phase muscle control table and a specific phase of a walking period where a user is located, such as signal loss, invalid motion myoelectric signals, fatigue myoelectric signals and the like, when no abnormality occurs in the myoelectric data corresponding to the specific phase of the walking period, generating a control signal of myoelectric signal leading control, and when any abnormal condition of the myoelectric signal exists in the specific phase of the walking period, generating a control signal of interaction force leading control. The electromyographic signal leading control module and the interaction force leading control module receive the control signals of the switching control module to control the motion executing module so as to realize real-time switching of the two active control modes. Then analyzing joint moment control data according to myoelectricity data or joint moment control data according to interaction force data; and finally, enabling the motion execution module to control the exoskeleton to move according to the joint moment control data.
2. The lower limb rehabilitation exoskeleton robot system has the functions of identifying abnormal myoelectric signals in real time and adjusting response in real time, avoids the problem of abnormal exoskeleton movement execution caused by insufficient muscle function recovery and unstable myoelectric signal acquisition, improves the safety, stability and fluency of human-computer interaction of the lower limb rehabilitation exoskeleton, and accordingly enhances the rehabilitation effect.
3. The control method of the lower limb rehabilitation exoskeleton robot system considers the condition that partial muscle function recovery of patients is incomplete, does not need personalized treatment, can adapt to patients with different muscle recovery degrees, and has wide adaptability.
4. Has the function of identifying muscle fatigue, and avoids the problem of abnormal exoskeleton movement execution caused by fatigue. The safety and stability of exoskeleton exercise are improved, and the remodeling of the nerve circuit is enhanced, so that the rehabilitation effect is improved.
Drawings
FIG. 1 is a logic block diagram of a lower limb rehabilitation system capable of automatically switching active control modes according to an embodiment of the present invention;
FIG. 2 is a block flow diagram of a lower limb rehabilitation control method capable of automatically switching an active control mode according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a walking cycle octant;
FIG. 4 is a schematic diagram of a gait recognition table;
fig. 5 is a schematic diagram of a phase muscle control table.
Detailed Description
The following is a further detailed description of the embodiments:
examples
A lower limb rehabilitation system capable of automatically switching active control modes, substantially as shown in fig. 1, applied to an exoskeleton, comprising: the system comprises a perception layer, a judgment layer, a control layer and an execution layer.
The sensing layer comprises an acquisition module; the judging layer comprises a switching control module; the control layer comprises a myoelectricity leading control module and an interaction force leading control module; the execution layer comprises a motion execution module;
and the acquisition module is used for: the method comprises the steps of acquiring myoelectricity data, plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton of a user; the acquisition module comprises:
myoelectric signal acquisition submodule: the myoelectricity data acquisition module is used for collecting and preprocessing original myoelectricity data to obtain myoelectricity data; comprises surface electromyographic signal sensors respectively arranged on gluteus maximus, ilium psoas, gluteus medius, rectus femoris and biceps femoris; the preprocessing of the raw myoelectricity data comprises the following steps: firstly, connecting the signal into a high-pass filter circuit and then sending the signal into a high-power amplifying circuit; an amplifier INA118 is adopted in the high-power amplifying circuit; the amplified signal is connected to a low-pass filter; eliminating power frequency interference by using a digital notch filter; then digital band-pass filtering processing is carried out on myoelectricity data; and finally, carrying out normalization processing.
Plantar pressure sensing sub-module: the method is used for collecting plantar pressure data; comprises capacitive pressure sensors distributed at the heel, the half sole and the toe parts;
motion gesture sensing sub-module: angle sensors for measuring angle data of a user's lower limb including distributed at the hip, knee and ankle joints; the inertial sensor is used for measuring the movement posture data of the lower limbs of the user and comprises inertial sensors distributed at the root parts of thighs and the root parts of calves; the motion gesture data comprise the number of joint angles of the lower limbs and the motion gesture data of the lower limbs;
interaction force sensing sub-module: the pressure sensor is used for measuring interaction force between a human body and the exoskeleton, and comprises pressure sensors distributed on the front side of thighs, the rear side of thighs and the rear side of calves.
And a switching control module: the gait recognition method is used for recognizing gait based on the gait recognition table and according to plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton, and judging a specific stage of a walking cycle where a user is located; the system is also used for carrying out real-time abnormal judgment on myoelectricity data based on a stage muscle control table and a specific stage of a walking cycle where a user is located, such as signal loss, invalid motion myoelectricity signals, fatigue myoelectricity signals and the like, and generating a switching control signal for leading the myoelectricity signal leading control module and the interaction force leading control module to switch according to judgment results; when no abnormal myoelectric data of a specific stage corresponds to the walking cycle, generating a control signal of myoelectric signal dominant control, and when any abnormal myoelectric signal exists in the specific stage corresponds to the walking cycle, generating a control signal of interaction force dominant control. The electromyographic signal leading control module and the interaction force leading control module receive the control signals of the switching control module to control the motion executing module so as to realize real-time switching of the two active control modes.
The gait recognition table is set based on a walking cycle octant (as shown in fig. 3), which divides the walking cycle into a heel strike period, a full plantar strike period, a supportive phase middle period, a heel lift period, a toe lift period, a swing phase early period, a swing phase middle period, and a swing phase end period based on different stages of the power generation muscles and gait in the walking cycle, as shown in fig. 4.
The gait recognition comprises joint angle judgment, plantar pressure judgment, interaction force judgment and pitch angle judgment.
The joint angle judgment specifically comprises the following steps: setting the joint angles of the hip joint, the knee joint and the ankle joint in a natural state to be 0 degrees, wherein the buckling angle is positive, and the stretching angle is negative; analyzing and judging joint angles of the hip joint, the knee joint and the ankle joint;
the plantar pressure judgment specifically comprises the following steps: setting corresponding first pressure thresholds according to the heel, the half sole and the toe respectively, and judging whether the pressure data values at the heel, the half sole and the toe are larger than the corresponding first pressure thresholds or not;
the interaction force judgment specifically comprises the following steps: respectively setting corresponding second pressure thresholds according to the front thigh, the rear thigh and the rear calf, and judging whether the pressure data values at the front thigh, the rear thigh and the rear calf are larger than the corresponding second pressure thresholds;
The pitch angle judgment specifically comprises the following steps: the absolute pitch angle of the human body when standing is set to be 0 degrees, and the pitch angles of the thigh and the shank are analyzed and judged.
As shown in fig. 4, the steps of the method for performing gait recognition according to the gait recognition table are specifically as follows:
heel strike period: hip angle 30 °, knee angle 5 °, ankle angle 0 °; the heel reaches a first pressure threshold, corresponding to "/" in fig. 4; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are positive;
full plantar grounding period: hip joint angle 20 °, knee joint angle 15 °, ankle joint angle 5-10 °; the heel, forefoot and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is positive, and the pitch angle of the shank is 0;
support phase medium term: hip angle 0 °, knee angle 0 °, ankle angle-5 °; the heel, forefoot and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are 0;
heel lift: hip joint angle-10 °, knee joint angle 10 °, ankle joint angle-5 to-10 °; the half sole and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the interaction force reaches a second pressure threshold behind the thigh and behind the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are negative;
Toe off period: hip angle 0 °, knee angle 30 °, ankle angle 15 °; the toe reaches a first pressure threshold, corresponding to "∈"; the interaction force reaches a second pressure threshold behind the thigh and behind the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are negative;
early swing phase: hip angle 0 °, knee angle 30 °, ankle angle 0 °; a second pressure threshold is reached after interaction force is applied to the lower leg, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is negative, and the pitch angle of the shank is 0;
mid-swing phase: hip joint angle 35 degrees, knee joint angle 45-55 degrees; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is negative, and the pitch angle of the shank is positive;
end of swing phase: hip angle 35 °, knee angle 0 °, ankle angle 0 °; the interaction force reaches a second pressure threshold before the thigh, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are positive;
in the gait recognition method, the range of the defined angle error is + -2 degrees.
Abnormal myoelectricity data includes ineffective motor myoelectricity data due to poor muscle function, myoelectricity data loss due to unstable myoelectricity data acquisition, and myoelectricity data of variation of a muscle fatigue characteristic value due to muscle fatigue;
The method for judging whether myoelectric data is abnormal myoelectric data comprises the following steps: detecting effective myoelectricity data of human body action execution by using an envelope threshold method; myoelectric data loss detection; muscle fatigue characterization was performed using multi-fractal drop-trend moving average. When the electromyographic signals corresponding to the specific stage are abnormally judged according to the stage muscle control table (shown in fig. 5), the judgment method is as follows:
if the user is in the heel strike period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the full plantar grounding period, judging whether the gluteus maximus and rectus femoris electromyographic signals are abnormal or not;
if the user is in the middle stage of the supporting phase, judging whether the myoelectric signals of gluteus maximus and gluteus medius are abnormal or not;
if the user is in the heel lift period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the toe off period, judging whether the ilium and lumbar muscle electrical signals are abnormal or not;
if the user is in the early swing phase, judging whether the ilium muscle electrical signal is abnormal or not;
if the user is in the middle swing phase, judging whether the ilium and lumbar muscle signals are abnormal or not;
if the user is at the end of the swing phase, judging whether the gluteus maximus and biceps femoris electromyographic signals are abnormal.
Electromyographic signal dominant module: when receiving the switching control signal, executing active control of the electromyographic signal leading, and analyzing joint moment control data according to the electromyographic data; the joint moment control data in the electromyographic signal leading module are obtained by analyzing and estimating the sensing data provided by the acquisition module based on a neuromuscular skeletal model;
the method for estimating the joint moment control data by using the neuromuscular skeletal model method comprises the following steps: and solving the muscle activation degree of the muscle subjected to the nerve signal by using the nerve activation model, bringing the muscle into the nerve musculoskeletal model to calculate the muscle force and the joint moment arm to obtain the joint moment, and optimizing the parameters of the whole nerve musculoskeletal model according to the actually measured joint moment to finally obtain the joint moment.
Interaction force signal dominant module: when receiving the switching control signal, executing active control of interaction force dominance, and analyzing joint moment control data according to the interaction force data; the joint moment control data in the interaction force signal leading module is obtained by predicting the expected angle of the hip-knee joint based on an admittance control model;
the mode of the admittance control model for predicting the expected angle of the hip-knee joint is as follows: the acquired interactive force sensing data is subjected to gravity compensation, and then a main moment is calculated; taking the operation parameters such as the main torque, the exoskeleton position, the speed and the acceleration as inputs, inputting the operation parameters into an admittance controller, outputting a target movement angular speed after the operation of the admittance controller, multiplying the angular speed by a control period, and adding the current joint angle to obtain a desired joint angle;
The transfer function of the admittance controller is as follows:
wherein: omega(s) is the angular velocity of the exoskeleton robot articulation; τ(s) is the main moment corresponding to the interaction force of the legs after the gravity compensation, M d Is of inertia coefficient, B d Is the damping coefficient.
The motion execution module: for controlling exoskeleton movements based on the joint moment control data. The joint moment control data comprise joint moment and joint angle, and the motion execution module comprises motors which are distributed at the left hip joint and the right hip joint and at the left knee joint and the right knee joint and are provided with rotary encoders, and the motors are used for measuring the rotating speed of the motors and realizing the control of the motors with the received joint moment control data.
The angle sensor adopts a rotary encoder. The inertial sensor adopts an IMU inertial sensor. The rotary encoder is used for measuring angles of hip joints, knee joints and ankle joints and closed-loop control of a motor in the motion execution module. And the IMU inertial sensor performs attitude calculation by using Kalman filtering.
The lower limb rehabilitation control method capable of automatically switching the active control mode is applied to the lower limb rehabilitation system, and as shown in fig. 2, the method specifically comprises the following steps:
step S201: after the user wearing equipment starts to move, based on a gait recognition table, performing gait recognition by combining plantar pressure data, movement posture data and interaction force data between a human body and exoskeleton, which are acquired by an acquisition module, and judging the specific stage of a walking cycle where the user is located; the gait recognition table is set based on a walking cycle octant, which is based on different stages of the power generation muscles and gait in the walking cycle, and divides the walking cycle into a heel strike period, a full plantar landing period, a support phase middle period, a heel off period, a toe off period, a swing phase early period, a swing phase middle period and a swing phase end period.
The gait recognition comprises joint angle judgment, plantar pressure judgment, interaction force judgment and pitch angle judgment.
The joint angle judgment specifically comprises the following steps: setting the joint angles of the hip joint, the knee joint and the ankle joint in a natural state to be 0 degrees, wherein the buckling angle is positive, and the stretching angle is negative; analyzing and judging joint angles of the hip joint, the knee joint and the ankle joint;
the plantar pressure judgment specifically comprises the following steps: setting corresponding first pressure thresholds according to the heel, the half sole and the toe respectively, and judging whether the pressure data values at the heel, the half sole and the toe are larger than the corresponding first pressure thresholds or not;
the interaction force judgment specifically comprises the following steps: respectively setting corresponding second pressure thresholds according to the front thigh, the rear thigh and the rear calf, and judging whether the pressure data values at the front thigh, the rear thigh and the rear calf are larger than the corresponding second pressure thresholds;
the pitch angle judgment specifically comprises the following steps: the absolute pitch angle of the human body when standing is set to be 0 degrees, and the pitch angles of the thigh and the shank are analyzed and judged.
As shown in fig. 4, the gait recognition method specifically includes:
heel strike period: hip angle 30 °, knee angle 5 °, ankle angle 0 °; the heel reaches a first pressure threshold, corresponding to "/" in fig. 4; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are positive;
Full plantar grounding period: hip joint angle 20 °, knee joint angle 15 °, ankle joint angle 5-10 °; the heel, forefoot and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is positive, and the pitch angle of the shank is 0;
support phase medium term: hip angle 0 °, knee angle 0 °, ankle angle-5 °; the heel, forefoot and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are 0;
heel lift: hip joint angle-10 °, knee joint angle 10 °, ankle joint angle-5 to-10 °; the half sole and toe reach a first pressure threshold, corresponding to "Γ" in fig. 4; the interaction force reaches a second pressure threshold behind the thigh and behind the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are negative;
toe off period: hip angle 0 °, knee angle 30 °, ankle angle 15 °; the toe reaches a first pressure threshold, corresponding to "∈"; the interaction force reaches a second pressure threshold behind the thigh and behind the calf, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are negative;
early swing phase: hip angle 0 °, knee angle 30 °, ankle angle 0 °; a second pressure threshold is reached after interaction force is applied to the lower leg, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is negative, and the pitch angle of the shank is 0;
Mid-swing phase: hip joint angle 35 degrees, knee joint angle 45-55 degrees; the interaction forces reach a second pressure threshold before the thigh and after the calf, corresponding to "Γ" in fig. 4; the pitch angle of the thigh is negative, and the pitch angle of the shank is positive;
end of swing phase: hip angle 35 °, knee angle 0 °, ankle angle 0 °; the interaction force reaches a second pressure threshold before the thigh, corresponding to "Γ" in fig. 4; the pitch angles of the thigh and the shank are positive;
in the gait recognition method, the range of the defined angle error is + -2 degrees.
Step S202: carrying out real-time preprocessing on the collected original myoelectricity data of the user to obtain myoelectricity data; the preprocessing of the raw myoelectricity data comprises the following steps: firstly, connecting the signal into a high-pass filter circuit and then sending the signal into a high-power amplifying circuit; an amplifier INA118 is adopted in the high-power amplifying circuit; the amplified signal is connected to a low-pass filter; eliminating power frequency interference by using a digital notch filter; then digital band-pass filtering processing is carried out on myoelectricity data; and finally, carrying out normalization processing.
Step S203: real-time abnormality judgment is carried out on myoelectric data through a switching control module, and a switching control signal for leading the myoelectric signal leading control module and the interaction force leading control module to switch is generated according to a judgment result;
In the step 3, when no abnormality occurs in the electromyographic signals in the corresponding stage of the walking cycle, a control signal for electromyographic signal dominant control is generated, and when any electromyographic signal abnormality exists in the corresponding stage of the walking cycle, a control signal for interactive force dominant control is generated.
Abnormal myoelectricity data includes ineffective motor myoelectricity data due to poor muscle function, myoelectricity data loss due to unstable myoelectricity data acquisition, and myoelectricity data of variation of a muscle fatigue characteristic value due to muscle fatigue; when the electromyographic signals corresponding to the specific stage are abnormally judged according to the stage muscle control table, the judgment method is as follows:
if the user is in the heel strike period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the full plantar grounding period, judging whether the gluteus maximus and rectus femoris electromyographic signals are abnormal or not;
if the user is in the middle stage of the supporting phase, judging whether the myoelectric signals of gluteus maximus and gluteus medius are abnormal or not;
if the user is in the heel lift period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the toe off period, judging whether the ilium and lumbar muscle electrical signals are abnormal or not;
If the user is in the early swing phase, judging whether the ilium muscle electrical signal is abnormal or not;
if the user is in the middle swing phase, judging whether the ilium and lumbar muscle signals are abnormal or not;
if the user is at the end of the swing phase, judging whether the gluteus maximus and biceps femoris electromyographic signals are abnormal.
Step S204: the electromyographic signal leading control module or the interaction force leading control module is controlled to actively control according to the switching control signal, and joint moment control data is analyzed according to the interaction force data or the electromyographic data; the motion execution module is enabled to control the exoskeleton to move according to the joint moment control data.
When the electromyographic signal leading module executes electromyographic signal leading active control, joint moment control data are analyzed according to electromyographic data; the joint moment control data in the electromyographic signal leading module are obtained by analyzing and estimating the sensing data provided by the acquisition module based on a neuromuscular skeletal model;
the method for estimating the joint moment control data by using the neuromuscular skeletal model method comprises the following steps: and solving the muscle activation degree of the muscle subjected to the nerve signal by using the nerve activation model, bringing the muscle into a nerve musculoskeletal model to calculate muscle force and joint moment arm to obtain joint moment, and optimizing parameters of the whole musculoskeletal model according to the actually measured joint moment to finally obtain the joint moment.
When the interaction force signal leading module executes interaction force leading active control, joint moment control data are analyzed according to the interaction force data; the joint moment control data in the interaction force signal leading module is obtained by predicting the expected angle of the hip-knee joint based on an admittance control model;
the mode of the admittance control model for predicting the expected angle of the hip-knee joint is as follows: the acquired interactive force sensing data is subjected to gravity compensation, and then a main moment is calculated; taking the operation parameters such as the main torque, the exoskeleton position, the speed and the acceleration as inputs, inputting the operation parameters into an admittance controller, outputting a target movement angular speed after the operation of the admittance controller, multiplying the angular speed by a control period, and adding the current joint angle to obtain a desired joint angle;
the transfer function of the admittance controller is as follows:
wherein: omega(s) is the angular velocity of the exoskeleton robot articulation; τ(s) is the main moment corresponding to the interaction force of the legs after the gravity compensation, M d Is of inertia coefficient, B d Is the damping coefficient.
The lower limb rehabilitation exoskeleton robot system has the functions of identifying abnormal myoelectric signals in real time and adjusting response in real time, avoids the problem of abnormal exoskeleton movement execution caused by insufficient muscle function recovery and unstable myoelectric signal acquisition, improves the safety, stability and fluency of human-computer interaction of the lower limb rehabilitation exoskeleton, and accordingly enhances the rehabilitation effect. The control method of the lower limb rehabilitation exoskeleton robot system considers the condition that partial muscle function recovery of patients is incomplete, does not need personalized treatment, can adapt to patients with different muscle recovery degrees, and has wide adaptability. Has the function of identifying muscle fatigue, and avoids the problem of abnormal exoskeleton movement execution caused by fatigue. The safety and stability of exoskeleton exercise are improved, and the remodeling of the nerve circuit is enhanced, so that the rehabilitation effect is improved.
The foregoing is merely an embodiment of the present invention, and general knowledge of specific structures and features well known in schemes is not described in any way herein, so that a person of ordinary skill in the art would know all of the prior art to which the present invention pertains before the application date or priority date, and would be able to learn all of the prior art in this field, and have the ability to apply conventional experimental means before this date, so that a person of ordinary skill in the art could complete and implement this scheme in combination with his own capabilities, given the benefit of this application, and some typical known structures or known methods should not be an obstacle to the implementation of this application by those of ordinary skill in the art. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (10)
1. A lower limb rehabilitation system capable of automatically switching an active control mode, which is applied to an exoskeleton, and is characterized by comprising: the system comprises a sensing layer, a judging layer, a control layer and an executing layer;
The sensing layer comprises an acquisition module; the judging layer comprises a switching control module; the control layer comprises a myoelectricity leading control module and an interaction force leading control module; the execution layer comprises a motion execution module;
and the acquisition module is used for: the method comprises the steps of acquiring myoelectricity data, plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton of a user;
and a switching control module: the gait recognition method is used for recognizing gait based on the gait recognition table and according to plantar pressure data, movement posture data and interaction force data between a human body and an exoskeleton, and judging a specific stage of a walking cycle where a user is located; the system is also used for carrying out real-time abnormal judgment on myoelectricity data based on a stage muscle control table and a specific stage of a walking cycle where a user is located, and generating a switching control signal for leading the myoelectricity signal leading control module and the interaction force leading control module to switch according to the judgment result;
electromyographic signal dominant module: when receiving the switching control signal, executing active control of the electromyographic signal leading, and analyzing joint moment control data according to the electromyographic data;
interaction force signal dominant module: when receiving the switching control signal, executing active control of interaction force dominance, and analyzing joint moment control data according to the interaction force data;
The motion execution module: for controlling exoskeleton movements based on the joint moment control data.
2. The lower limb rehabilitation system capable of automatically switching active control modes according to claim 1, wherein: the acquisition module comprises:
myoelectric signal acquisition submodule: the myoelectricity data acquisition module is used for collecting and preprocessing original myoelectricity data to obtain myoelectricity data; comprises surface electromyographic signal sensors respectively arranged on gluteus maximus, ilium psoas, gluteus medius, rectus femoris and biceps femoris;
plantar pressure sensing sub-module: the method is used for collecting plantar pressure data; comprises capacitive pressure sensors distributed at the heel, the half sole and the toe parts;
motion gesture sensing sub-module: angle sensors for measuring angle data of a user's lower limb including distributed at the hip, knee and ankle joints; the inertial sensor is used for measuring the movement posture data of the lower limbs of the user and comprises inertial sensors distributed at the root parts of thighs and the root parts of calves; the motion gesture data comprise the number of joint angles of the lower limbs and the motion gesture data of the lower limbs;
interaction force sensing sub-module: the pressure sensor is used for measuring interaction force between a human body and the exoskeleton, and comprises pressure sensors distributed on the front side of thighs, the rear side of thighs and the rear side of calves.
3. The lower limb rehabilitation system capable of automatically switching active control modes according to claim 2, wherein: the joint moment control data in the electromyographic signal leading module are obtained by analyzing and estimating the sensing data provided by the acquisition module based on a neuromuscular skeletal model;
the method for estimating the joint moment control data by using the neuromuscular skeletal model method comprises the following steps: and solving the muscle activation degree of the muscle subjected to the nerve signal by using the nerve activation model, bringing the muscle into a nerve musculoskeletal model to calculate muscle force and joint moment arm to obtain joint moment, and optimizing parameters of the whole musculoskeletal model according to the actually measured joint moment to finally obtain the joint moment.
4. A lower limb rehabilitation system capable of automatically switching active control modes according to claim 3, wherein: the joint moment control data in the interaction force signal leading module is obtained by predicting the expected angle of the hip-knee joint based on an admittance control model;
the mode of the admittance control model for predicting the expected angle of the hip-knee joint is as follows: the acquired interactive force sensing data is subjected to gravity compensation, and then a main moment is calculated; taking the operation parameters such as the main torque, the exoskeleton position, the speed and the acceleration as inputs, inputting the operation parameters into an admittance controller, outputting a target movement angular speed after the operation of the admittance controller, multiplying the angular speed by a control period, and adding the current joint angle to obtain a desired joint angle;
The transfer function of the admittance controller is as follows:
wherein: omega(s) is the angular velocity of the exoskeleton robot articulation; τ(s) is the main moment corresponding to the interaction force of the legs after the gravity compensation, M d Is of inertia coefficient, B d Is the damping coefficient.
5. The lower limb rehabilitation system capable of automatically switching active control modes according to claim 4, wherein: the joint moment control data comprise joint moment and joint angle, and the motion execution module comprises motors which are distributed at the left hip joint and the right hip joint and at the left knee joint and the right knee joint and are provided with rotary encoders, and the motors are used for measuring the rotating speed of the motors and realizing the control of the motors with the received joint moment control data.
6. A lower limb rehabilitation control method capable of automatically switching an active control mode, which is characterized by applying the lower limb rehabilitation system according to any one of claims 1-5, and specifically comprising the following steps:
step 1: after the user wearing equipment starts to move, based on a gait recognition table, performing gait recognition by combining plantar pressure data, movement posture data and interaction force data between a human body and exoskeleton, which are acquired by an acquisition module, and judging the specific stage of a walking cycle where the user is located;
Step 2: carrying out real-time preprocessing on the collected original myoelectricity data of the user to obtain myoelectricity data;
step 3: real-time abnormality judgment is carried out on myoelectric data through a switching control module, and a switching control signal for leading the myoelectric signal leading control module and the interaction force leading control module to switch is generated according to a judgment result;
step 4: the electromyographic signal leading control module or the interaction force leading control module is controlled to actively control according to the switching control signal, and joint moment control data is analyzed according to the interaction force data or the electromyographic data; the motion execution module is enabled to control the exoskeleton to move according to the joint moment control data.
7. The lower limb rehabilitation control method capable of automatically switching the active control mode according to claim 6, wherein the method comprises the following steps: the gait recognition table is set based on a walking cycle octant method, and the walking cycle octant method is based on different stages of power generation muscles and different gaits in the walking cycle, and divides the walking cycle into a heel strike period, a full plantar landing period, a support phase middle period, a heel off period, a toe off period, a swing phase early stage, a swing phase middle period and a swing phase end stage;
in the step 3, when no abnormality occurs in the electromyographic signals in the corresponding stage of the walking cycle, a control signal for electromyographic signal dominant control is generated, and when any electromyographic signal abnormality exists in the corresponding stage of the walking cycle, a control signal for interactive force dominant control is generated.
8. The lower limb rehabilitation control method capable of automatically switching the active control mode according to claim 7, wherein the method comprises the following steps of: the gait recognition comprises joint angle judgment, plantar pressure judgment, interaction force judgment and pitch angle judgment;
the joint angle judgment specifically comprises the following steps: setting the joint angles of the hip joint, the knee joint and the ankle joint in a natural state to be 0 degrees, wherein the buckling angle is positive, and the stretching angle is negative; analyzing and judging joint angles of the hip joint, the knee joint and the ankle joint;
the plantar pressure judgment specifically comprises the following steps: setting corresponding first pressure thresholds according to the heel, the half sole and the toe respectively, and judging whether the pressure data values at the heel, the half sole and the toe are larger than the corresponding first pressure thresholds or not;
the interaction force judgment specifically comprises the following steps: respectively setting corresponding second pressure thresholds according to the front thigh, the rear thigh and the rear calf, and judging whether the pressure data values at the front thigh, the rear thigh and the rear calf are larger than the corresponding second pressure thresholds;
the pitch angle judgment specifically comprises the following steps: the absolute pitch angle of the human body when standing is set to be 0 degrees, and the pitch angles of the thigh and the shank are analyzed and judged.
9. The lower limb rehabilitation control method capable of automatically switching the active control mode according to claim 8, wherein the method comprises the following steps of: the gait recognition method specifically comprises the following steps:
Heel strike period: hip angle 30 °, knee angle 5 °, ankle angle 0 °; the heel reaches a first pressure threshold; the interaction force reaches a second pressure threshold before the thigh and after the calf; the pitch angles of the thigh and the shank are positive;
full plantar grounding period: hip joint angle 20 °, knee joint angle 15 °, ankle joint angle 5-10 °; the heel, the ball and the toe reach a first pressure threshold; the interaction force reaches a second pressure threshold before the thigh and after the calf; the pitch angle of the thigh is positive, and the pitch angle of the shank is 0;
support phase medium term: hip angle 0 °, knee angle 0 °, ankle angle-5 °; the heel, the ball and the toe reach a first pressure threshold; the pitch angles of the thigh and the shank are 0;
heel lift: hip joint angle-10 °, knee joint angle 10 °, ankle joint angle-5 to-10 °; the half sole and toe reach a first pressure threshold; the interaction force reaches a second pressure threshold behind the thigh and behind the calf; the pitch angles of the thigh and the shank are negative;
toe off period: hip angle 0 °, knee angle 30 °, ankle angle 15 °; the toes reach a first pressure threshold, and the interactive forces behind the thighs and the calves reach a second pressure threshold; the pitch angles of the thigh and the shank are negative;
early swing phase: hip angle 0 °, knee angle 30 °, ankle angle 0 °; after the interactive force shank reaches a second pressure threshold, the pitch angle of the thigh is negative, and the pitch angle of the shank is 0;
Mid-swing phase: hip joint angle 35 degrees, knee joint angle 45-55 degrees; the front and rear of the thigh and the shank reach a second pressure threshold, the pitch angle of the thigh is negative, and the pitch angle of the shank is positive;
end of swing phase: hip angle 35 °, knee angle 0 °, ankle angle 0 °; the front of the thigh of the interactive force reaches a second pressure threshold, and pitch angles of the thigh and the shank are positive; in the gait recognition method, the range of the defined angle error is + -2 degrees.
10. The lower limb rehabilitation control method capable of automatically switching the active control mode according to claim 7, wherein the method comprises the following steps of: abnormal myoelectricity data includes ineffective motor myoelectricity data due to poor muscle function, myoelectricity data loss due to unstable myoelectricity data acquisition, and myoelectricity data of variation of a muscle fatigue characteristic value due to muscle fatigue; when the electromyographic signals corresponding to the specific stage are abnormally judged according to the stage muscle control table, the judgment method is as follows:
if the user is in the heel strike period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the full plantar grounding period, judging whether the gluteus maximus and rectus femoris electromyographic signals are abnormal or not;
If the user is in the middle stage of the supporting phase, judging whether the myoelectric signals of gluteus maximus and gluteus medius are abnormal or not;
if the user is in the heel lift period, judging whether the gluteus medius and rectus femoris electromyographic signals are abnormal or not;
if the user is in the toe off period, judging whether the ilium and lumbar muscle electrical signals are abnormal or not;
if the user is in the early swing phase, judging whether the ilium muscle electrical signal is abnormal or not;
if the user is in the middle swing phase, judging whether the ilium and lumbar muscle signals are abnormal or not;
if the user is at the end of the swing phase, judging whether the gluteus maximus and biceps femoris electromyographic signals are abnormal.
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