CN-121979398-A - Method and system for controlling lower limb exoskeleton through brain-computer interface intention confidence level driving

CN121979398ACN 121979398 ACN121979398 ACN 121979398ACN-121979398-A

Abstract

The application discloses a method and a system for driving and controlling lower limb exoskeleton by brain-computer interface intention confidence, and relates to the technical field of medical rehabilitation. The method comprises the steps of firstly identifying an original electroencephalogram signal to obtain an intended confidence coefficient, mapping an impedance parameter set comprising joint rigidity and damping coefficients by combining gait phase information, secondly extracting a motion position and interaction force error, comparing the intended confidence coefficient with an actual execution state by utilizing a sliding time window to obtain a neural matching error, fusing the neural matching error, the interaction position and the interaction force error into a total composite error, meanwhile, evaluating man-machine coupling compliance based on the angle of a human joint and the change quantity of exoskeleton moment, judging and generating a safe gain coefficient, and finally performing gain operation on the total composite error based on the impedance parameter set, and correcting the safe gain coefficient to obtain a target control moment to be output to a driving actuator. Thus, a nerve-force-motion three-closed loop architecture is constructed, and the high-safety man-machine cooperative rehabilitation of the compliant self-adaptive on-demand assistance and the defending protection is realized.

Inventors

WANG TIAN
WANG LEI
ZHAO QINGYU
ZHANG JIYU
SUN QINGLIN
LIU DAGANG

Assignees

杭州程天科技发展有限公司

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (10)

1. A method for brain-computer interface intent confidence level driven control of a lower extremity exoskeleton, comprising: S1, performing motion intention recognition on an acquired original electroencephalogram signal to obtain intention confidence degrees representing the intensity or the confidence degree of the motion intention of a user; S2, acquiring gait phase information, and performing nerve driving type variable impedance parameter mapping on the gait phase information and the intention confidence level to obtain an impedance parameter set consisting of a joint stiffness coefficient and a joint damping coefficient; S3, acquiring a motion position error and an interaction force error, comparing the intention confidence coefficient with an actual execution state by utilizing a sliding time window to obtain a neural matching error, and further performing closed-loop fusion processing on at least two of the motion position error, the interaction force error and the neural matching error to obtain a total composite error; S4, performing human-machine coupling compliance assessment on the obtained human joint angle variation and exoskeleton moment variation to obtain a human-machine coupling compliance index, and comparing and judging the human-machine coupling compliance index with a preset safety threshold to obtain a safety gain coefficient; And S5, performing gain operation on the total composite error based on the impedance parameter set to obtain an initial control moment, and performing safety correction on the initial control moment based on a safety gain coefficient to obtain a target control moment, wherein the target control moment is output to a driving actuator of the lower limb exoskeleton so as to drive the lower limb of the patient to complete auxiliary movement.
2. The method of brain-computer interface intention-to-confidence-level drive control of a lower-limb exoskeleton of claim 1, wherein step S2 comprises: Acquiring gait phase information; The method comprises the steps of calling a preset minimum stiffness boundary value and a preset maximum stiffness boundary value from a storage register of a bottom layer controller, and carrying out stiffness parameter mapping on the intention confidence coefficient based on the minimum stiffness boundary value and the maximum stiffness boundary value to obtain a joint stiffness coefficient; the method comprises the steps of calling a minimum damping boundary value and a maximum damping boundary value which are preset for the physical sign of a current patient in a control system, and carrying out damping parameter mapping on gait phase information based on the minimum damping boundary value and the maximum damping boundary value to obtain joint damping coefficients; And dynamically constructing an impedance parameter set based on the joint damping coefficient and the joint stiffness coefficient.
3. The method of brain-computer interface intention-to-confidence-level drive control of a lower-limb exoskeleton of claim 1, wherein step S3 comprises: Extracting the neural matching error based on the sliding time window to obtain the neural matching error according to the acquired intention confidence coefficient and the actual execution state; Performing multidimensional error fusion weight distribution on the received neural matching error, the motion position error and the interaction force error to obtain a weight factor group; and based on the weight factor group, performing three-closed loop composite error weighted fusion on the neural matching error, the motion position error and the interaction force error to obtain a total composite error.
4. The method of brain-computer interface intention-to-confidence-level drive control of a lower-limb exoskeleton of claim 1, wherein step S4 comprises: Acquiring a human joint angle variation and an exoskeleton moment variation, and determining a human-computer coupling compliance index representing the limb following capacity of a patient under unit auxiliary moment based on the human joint angle variation and the exoskeleton moment variation; A preset lower safety threshold and an upper safety threshold are called from a system nonvolatile memory, a human-computer coupling compliance index is compared with a safety threshold interval to calculate, if the index is in the interval, the system is judged to be in a safety follow-up state, if the index exceeds the safety threshold interval, the abnormal risk state is judged, and the judged logic state is packaged as a safety judgment result; based on the security determination result, a security gain coefficient is generated.
5. The method for controlling the exoskeleton of a lower limb driven by the confidence level of the brain-computer interface intention according to claim 4, wherein the steps of obtaining the angle change of the joints of the human body and the moment change of the exoskeleton and determining the human-computer coupling compliance index for representing the following capacity of the limbs of the patient under the unit auxiliary moment based on the angle change of the joints of the human body and the moment change of the exoskeleton comprise the steps of determining the human-computer coupling compliance index according to the following formula: Wherein, the Is the angle change quantity of the joints of the human body, As the moment variation of the exoskeleton, Is a human-computer coupling compliance index.
6. The method of brain-computer interface intention-confidence-driven control of lower-extremity exoskeleton of claim 4, wherein obtaining a human joint angle change and an exoskeleton moment change and determining a human-machine coupling compliance index characterizing a patient's limb following capability at a unit assist moment based on the human joint angle change and the exoskeleton moment change, comprises: acquiring a joint type identifier; Determining a gait phase weight factor and a joint normalization coefficient based on the joint type identification and the gait phase information; Carrying out time sequence smoothing treatment on the angle change quantity of the human joint and the moment change quantity of the exoskeleton to obtain a filtered angle change quantity and a filtered moment change quantity, carrying out ratio operation on the filtered angle change quantity and the filtered moment change quantity, and introducing joint normalization coefficients to carry out normalization treatment to obtain a basic compliance index; and taking the gait phase weight factor as a dynamic modulation coefficient to carry out self-adaptive weighted fusion on the basic compliance index so as to obtain the man-machine coupling compliance index.
7. The method of brain-computer interface intention-confidence-driven control of a lower-extremity exoskeleton of claim 4, wherein generating a safety gain factor based on the safety decision result comprises: if the safety judgment result is in a safety follow-up state, setting the safety gain coefficient to be 1.0; if the safety judgment result is in a first abnormal risk state, namely the human-computer coupling compliance index is lower than the safety threshold lower limit, determining a safety gain coefficient as a preset attenuation proportionality constant so as to carry out proportionality reduction on the initial control moment; if the safety judgment result is in the second type of abnormal risk state, namely the man-machine coupling compliance index is higher than the upper limit of the safety threshold, setting the safety gain coefficient to be 0 or 0.05, and issuing an emergency parking instruction to enable the driving actuator to enter a high-damping self-locking mode.
8. The method of brain-computer interface intention-to-confidence-level drive control of a lower-limb exoskeleton of claim 1, wherein step S5 comprises: Dividing the difference value by the system sampling period based on the total composite error value between the current sampling time and the last control period to obtain an error change rate; Extracting joint stiffness coefficients and joint damping coefficients from the impedance parameter sets, and determining an initial control moment based on the joint stiffness coefficients, the joint damping coefficients, the total composite errors and the error change rate; and carrying out safety correction on the initial control moment based on the safety gain coefficient to obtain the target control moment.
9. The method of brain-computer interface intention-confidence-driven control of a lower-limb exoskeleton of claim 1, further comprising: acquiring joint angle data and interaction force data in real time through a joint encoder and a force sensor to serve as execution feedback data; And the execution feedback data is transmitted to the step S3 for updating the motion position error and the interaction force error, so that the closed-loop self-adaptive control of the nerve-motion-force is realized.
10. A system for brain-computer interface intent confidence level driven control of a lower extremity exoskeleton, comprising: The intention confidence identification module is used for carrying out movement intention identification on the acquired original electroencephalogram signals so as to obtain intention confidence degrees for representing the strength and the credibility of the movement intention of the user; The impedance parameter mapping module is used for acquiring gait phase information, and performing nerve driving type variable impedance parameter mapping on the gait phase information and the intention confidence level to obtain an impedance parameter set consisting of a joint stiffness coefficient and a joint damping coefficient; the error fusion module is used for acquiring a motion position error and an interaction force error, comparing the intention confidence coefficient with the actual execution state by utilizing a sliding time window to obtain a neural matching error, and further carrying out closed-loop fusion processing on at least two of the motion position error, the interaction force error and the neural matching error to obtain a total composite error; The compliance safety evaluation module is used for evaluating human-machine coupling compliance of the obtained human joint angle variation and exoskeleton moment variation to obtain a human-machine coupling compliance index, and comparing and judging the human-machine coupling compliance index with a preset safety threshold to obtain a safety gain coefficient; The moment gain correction module is used for carrying out gain operation on the total composite error based on the impedance parameter set to obtain an initial control moment, and carrying out safety correction on the initial control moment based on the safety gain coefficient to obtain a target control moment, wherein the target control moment is output to a driving actuator of the lower limb exoskeleton so as to drive the lower limb of the patient to complete auxiliary movement.

Description

Method and system for controlling lower limb exoskeleton through brain-computer interface intention confidence level driving Technical Field The application relates to the technical field of medical rehabilitation, in particular to a method and a system for driving and controlling lower limb exoskeleton by brain-computer interface intention confidence. Background In recent years, the exoskeleton of lower limbs plays an important role in rehabilitation training of patients with damaged nervous systems. To motivate the patient's active participation awareness, brain-computer interface technology is widely introduced into exoskeleton control. However, existing control schemes typically only parse electroencephalograms into simple start-stop trigger instructions, failing to make deep use of continuous confidence of movement intent for dynamic control system design. The single control mode causes that the system cannot be combined with gait phases to carry out self-adaptive adjustment on impedance parameters such as joint rigidity, damping and the like, and the provided auxiliary moment is often too hard. In addition, the traditional bottom control mostly only depends on the motion position error or man-machine interaction force error of a physical layer to carry out closed-loop feedback, and the neural matching error between the central neural instruction and the actual execution state is ignored. The control architecture for performing the cleavage with the brain intention and the physics is difficult to realize deep coordination of nerve-force-movement, and restricts the rehabilitation effect. On the other hand, the compliance interaction and the safety protection mechanism in the rehabilitation training process have obvious defects. Affected by nerve injury, the lower limb muscle tone of the patient tends to be unstable, and sudden muscle spasms or joint stiffness are very likely to occur during training. Existing exoskeleton systems lack a quantitative evaluation mechanism for instantaneous human-machine coupling compliance (i.e., coupling relationship between human joint angle changes and exoskeleton moment changes). When human-machine motion conflict and sharp degradation of compliance occur, the underlying driver may still continue to output a huge forced compensation moment due to lack of a pre-arranged compliance early warning and real-time adaptive safety gain correction strategy. The control mode of stiffness not only destroys the stability of gait assistance, but also is very easy to violate the physiological stress limit of the joints of the patient, thereby causing serious secondary injury risk, and a control method which combines the intention confidence driving and the compliance safety monitoring is needed. Disclosure of Invention The application provides a method for driving and controlling lower limb exoskeleton by brain-computer interface intention confidence, which breaks the limitation of traditional single physical quantity-based fracture control, and constructs a flexible closed loop control link which deeply fuses continuous movement intention, gait time-varying characteristics and real-time physical compliance state of a patient, thereby realizing man-machine collaborative rehabilitation training which takes natural self-adaption and high safety protection into consideration. According to the method, the device and the system for controlling the lower limb exoskeleton through the brain-computer interface intention confidence level driving is provided, wherein the method comprises the steps of S1, carrying out movement intention recognition on an obtained original brain-computer signal to obtain intention confidence level representing movement intention strength or credibility of a user, S2, obtaining gait phase information, carrying out nerve driving type variable impedance parameter mapping on the gait phase information and the intention confidence level to obtain an impedance parameter set consisting of joint stiffness coefficients and joint damping coefficients, S3, obtaining movement position errors and interaction force errors, comparing the intention confidence level with actual execution states by utilizing a sliding time window to obtain nerve matching errors, carrying out closed-loop fusion processing on at least two of the movement position errors, the interaction force errors and the nerve matching errors to obtain total composite errors, S4, carrying out man-machine coupling compliance assessment on the obtained human body joint angle change amount and exoskeleton moment change amount to obtain a man-machine coupling degree index, carrying out comparison judgment on the man-machine coupling compliance index and a preset safety threshold to obtain a safety gain coefficient, S5, carrying out operation on the total composite errors to obtain initial control moment based on the impedance parameter set, carrying out operation on the initial control moment to obtain initial contr