CN109199786B - Lower limb rehabilitation robot based on bidirectional neural interface - Google Patents
Lower limb rehabilitation robot based on bidirectional neural interface Download PDFInfo
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Abstract
The invention relates to a lower limb rehabilitation robot based on a bidirectional neural interface, belongs to the technical field of medical rehabilitation, and solves the problem that the existing rehabilitation robot is low in active participation of patients. The method comprises the following steps: the electroencephalogram system is used for collecting electroencephalogram signals of a patient, identifying the motor intention and the rehabilitation training requirement of the patient, converting the electroencephalogram signals into motion instructions and outputting the motion instructions to the lower limb motion system; the electromyographic signal system is used for collecting the lower limb electromyographic signals of the patient, analyzing and processing the lower limb electromyographic signals and feeding the motion state of the lower limb back to the lower limb motion system and the electroencephalographic signal system; the lower limb movement system is used for generating gait according to the received movement instruction and guiding the lower limb of the patient to move; meanwhile, the lower limb movement state fed back by the electromyographic signal system is received, and the affected limb movement is appropriately intervened. The invention realizes the high-efficiency interaction between the patient and the rehabilitation robot by using the closed-loop human-machine neural information perception bidirectional loop, improves the participation initiative of the patient and improves the exercise rehabilitation efficiency.
Description
Technical Field
The invention relates to the technical field of medical rehabilitation, in particular to a lower limb rehabilitation robot based on a bidirectional neural interface.
Background
At present, the lower limb rehabilitation robot represents the development trend of products in the aspects of application and market, the development of the lower limb rehabilitation robot enters a bottleneck stage, at present, home and abroad devices adopt rehabilitation exoskeletons to guide patients to carry out basic natural gait training, most of physical substitution work of rehabilitation therapists can be basically met, the lower limb rehabilitation robot is limited in the range of automatic rehabilitation devices, only can obtain the passive training effect, and does not have the connotation of an external dynamics mechanism of nerve rehabilitation.
At present, China is in the starting stage of developing rehabilitation medical equipment, researches on the motion mechanical characteristics, the electrophysiological characteristics and the like of human bodies of patients are insufficient, and the market of high-end medical lower limb rehabilitation robots exerting strength by exploring scientific laws and solving key technologies is urgently needed.
The main problems of the existing rehabilitation robot are as follows: the passive training mode causes low active participation of stroke hemiplegia patients, insufficient active contraction of muscles on the affected side, lack of muscle state monitoring and mismatching of training gait and mechanical environment in traditional sports rehabilitation.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a lower limb rehabilitation robot based on a bidirectional neural interface, so as to solve the problems of low active participation of a patient, insufficient active contraction of muscles on an affected side, lack of monitoring of muscle states, and mismatch between a training gait and a mechanical environment in the conventional exercise rehabilitation when the conventional rehabilitation robot adopts a passive training mode.
The purpose of the invention is mainly realized by the following technical scheme:
provided is a lower limb rehabilitation robot based on a bidirectional neural interface, comprising: an electroencephalogram signal system, an electromyogram signal system and a lower limb movement system;
the electroencephalogram signal system is used for collecting electroencephalogram signals of a patient, identifying the movement intention and the rehabilitation training requirement of the patient, converting the electroencephalogram signals into movement instructions and outputting the movement instructions to the lower limb movement system;
the electromyographic signal system is used for collecting lower limb electromyographic signals of a patient, analyzing and processing the lower limb electromyographic signals and feeding the motion state of the lower limb back to the lower limb motion system and the electroencephalographic signal system;
the lower limb movement system generates gait according to the received movement instruction and guides the lower limb of the patient to move; meanwhile, according to the lower limb movement state fed back by the electromyographic signal system, the lower limb movement of the patient is properly intervened.
The invention has the following beneficial effects: the invention applies the brain-computer interface technology with high communication rate and the brain-computer interface technology with brain electricity, myoelectricity and other two-way neural interface technologies to realize the quick, convenient and self-help control of the lower limb exoskeleton; the human-machine reverse regulation and control channel based on muscle nerve stimulation realizes the monitoring and proper intervention of the lower limb rehabilitation robot on the human body state; and constructing a man-machine coupled control loop to realize the personalized weight-reducing gait training. The intelligent rehabilitation lower limb exoskeleton brain-controlled robot for establishing the nerve perception channel of the biological-robot can enable a patient to actively participate in a training process to carry out human-computer integrated rehabilitation training, and the patient can carry out autonomous control to achieve a nerve feedback training state and effect, thereby greatly improving the neural plasticity recovery process of the patient.
On the basis of the scheme, the invention is further improved as follows:
further, the electroencephalogram signal system includes: the system comprises an electroencephalogram signal collector, a visual stimulator, an electroencephalogram signal analyzing and processing module, an instruction sending module and a visual feedback module;
the visual stimulator is used for displaying flashing lamps representing different control instructions, and a patient can watch the flashing lamps to generate different electroencephalogram signals;
the electroencephalogram collector is used for collecting electroencephalogram signals of a patient and transmitting the electroencephalogram signals to the electroencephalogram signal analysis processing module;
the electroencephalogram signal analysis processing module converts the acquired electroencephalogram signals into motion instructions and sends the motion instructions to the lower limb motion system through the instruction sending module;
and the visual feedback module receives the feedback signal of the motion state of the lower limb and displays the feedback signal to the patient through the visual stimulator.
Further, the electromyographic signal system includes: the system comprises an electromyographic signal collector, an electromyographic signal analysis processing module and a motion information feedback module;
the electromyographic signal collector is used for collecting electromyographic signals of the lower limbs of the patient;
the electromyographic signal analyzing and processing module is used for analyzing and processing the collected lower limb electromyographic signals to acquire the motion state information of the lower limbs;
and the motion information feedback module feeds the motion state information back to the lower limb motion system and the brain electrical signal system.
Further, the lower limb movement system comprises: medical treadmill, lower limb exoskeleton and handrail;
the lower limb exoskeleton generates an individualized gait according to the received motion instruction and the motion state information fed back by the electromyographic signal system, and controls the motion of the lower limbs;
the armrest is used for assisting a patient to stand;
the medical treadmill is used for adjusting the speed in real time in a training process in cooperation with the lower limb exoskeleton.
Furthermore, the lower limb movement system comprises a muscle electrical stimulation instrument, and the muscle electrical stimulation instrument stimulates the muscle of the lower limb at the affected side according to the movement instruction sent by the electroencephalogram signal system to guide the movement of the lower limb.
Furthermore, the muscle electrical stimulation instrument is matched with the lower limb exoskeleton to guide the lower limb of the affected side to move together.
Further, the muscle electrical stimulation instrument adjusts the stimulation time sequence and intensity through an FES control strategy based on the mutual reference of the healthy/affected side limbs, and the gait difference of the lower limbs on both sides of the patient is eliminated.
Further, the following procedures are executed to adjust the stimulation time sequence and intensity of the muscle electrical stimulation instrument:
collecting a healthy side electromyographic signal of a patient by an electromyographic signal collector;
the electromyographic signal analyzing and processing module analyzes and processes the healthy side electromyographic signal to obtain the contraction time sequence of different muscles when the healthy side lower limb walks in the complete gait cycle, and the contraction time sequence is used as the time sequence mode of stimulating the affected side lower limb by the muscle electrical stimulation instrument;
comparing the kinematic parameter change of the affected lower limb by taking the healthy side kinematic parameter before the constant time as a template according to the kinematic data of the bilateral lower limbs to obtain the intensity mode of the muscle electrical stimulation instrument for stimulating the affected lower limb;
the muscle electrical stimulation instrument adjusts the time sequence and the intensity of stimulation according to the time sequence mode and the intensity mode of stimulating the lower limb of the affected side.
Further, the lower limb exoskeleton receives the lower limb motion state fed back by the electromyographic signal system in real time, judges and intervenes the motion of the affected limb when the motion state is judged to be abnormal.
Further, the lower limb movement system further comprises: the dynamic weight reduction device reduces the influence of gravity on the lower limb of a patient in the motion process.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic structural view of a lower limb rehabilitation robot according to an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating human-computer interaction control of a lower limb rehabilitation robot according to an embodiment of the present invention;
FIG. 3 is a block diagram of an electroencephalogram system for directly controlling a lower limb rehabilitation robot based on a brain-computer interface in an embodiment of the present invention;
FIG. 4 is a schematic diagram of an FES dynamic control method according to an embodiment of the present invention.
The attached drawings are as follows:
1-an electroencephalogram signal system, 2-a myoelectricity signal system, 3-a muscle electrical stimulation instrument, 4-a medical running table, 5-a lower limb exoskeleton, 6-a handrail and 7-dynamic weight reduction equipment.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In an embodiment of the present invention, a lower limb rehabilitation robot based on a bidirectional neural interface is disclosed, as shown in fig. 1 and 2, including: an electroencephalogram signal system (1), an electromyogram signal system (2) and a lower limb movement system;
the electroencephalogram signal system is used for collecting electroencephalogram signals of a patient, identifying the movement intention and the rehabilitation training requirement of the patient, converting the electroencephalogram signals into movement instructions and outputting the movement instructions to the lower limb movement system;
the electromyographic signal system is used for collecting the lower limb electromyographic signals of the patient, analyzing and processing the lower limb electromyographic signals and feeding the motion state of the lower limb back to the lower limb motion system and the electroencephalographic signal system;
the lower limb movement system is used for generating gait according to the received movement instruction and guiding the lower limb of the patient to move; meanwhile, according to the lower limb movement state fed back by the electromyographic signal system, the lower limb movement of the patient is properly intervened.
When the device is implemented, a patient wears the lower limb exoskeleton and holds the handrail by hand, stands on the medical jogging table and wears the electroencephalogram collector on the head. Through an electroencephalogram signal system, acquiring and processing electroencephalograms, extracting characteristics, identifying subjective walking willingness of a wearer, transmitting the walking willingness to a lower limb movement system movement instruction, controlling an exoskeleton movement mode, and guiding to complete rehabilitation movement treatment; through the organic combination of all the systems, the patient can realize the remodeling of motor function and nerve circuit through long-term rehabilitation training.
Compared with the prior art, the lower limb rehabilitation robot based on the bidirectional neural interface provided by the embodiment; the brain-computer interface with high communication rate is adopted by applying the brain-electricity and myoelectricity bidirectional neural interface technology, so that the quick, convenient and self-help control of the lower limb exoskeleton is realized; the human-machine reverse regulation and control channel based on muscle nerve stimulation realizes the monitoring and proper intervention of the lower limb rehabilitation robot on the human body state; and constructing a man-machine coupled control loop to realize the personalized weight-reducing gait training. The intelligent rehabilitation lower limb exoskeleton brain-controlled robot for establishing the nerve perception channel of the biological-robot can enable a patient to actively participate in a training process to carry out human-computer integrated rehabilitation training, and the patient can carry out autonomous control to achieve a nerve feedback training state and effect, thereby greatly improving the neural plasticity recovery process of the patient.
Specifically, a patient adopts an SSVEP (Steady State Visual Evoked Potential) brain-computer interaction instruction to complete direct control on a lower limb motion system through an electroencephalogram system, the lower limb motion system completes the behavior corresponding to the brain-computer interaction instruction, and the behavior is presented to a subject in a Visual feedback mode, so that the lower limb rehabilitation robot is directly controlled by the brain-computer interaction instruction. For complex tasks, a hierarchical adaptive menu can be adopted to realize direct control of the lower limb exoskeleton.
Fig. 3 is a block diagram of an electroencephalogram system for directly controlling an exoskeleton of a lower limb based on a brain-computer interface in the embodiment, and the system includes: the device comprises an electroencephalogram signal collector, a visual stimulator, an electroencephalogram signal analyzing and processing module, an instruction sending module and a visual feedback module. Wherein,
the visual stimulator is used for displaying flashing lamps representing different control instructions so that a patient can control the robot through an SSVEP brain-computer interaction instruction, or stares at the corresponding flashing lamps according to the instruction requirement or the movement intention of the patient so as to generate corresponding electroencephalogram signals;
the electroencephalogram collector is used for collecting electroencephalogram signals (dry electrodes can be adopted) of a patient and transmitting the electroencephalogram signals to the electroencephalogram signal analysis processing module, preferably, the electroencephalogram signal analysis processing module can be in a wireless transmission mode;
the electroencephalogram signal analysis processing module is used for analyzing and processing the acquired electroencephalogram signals, converting the electroencephalogram signals into motion instructions through an electroencephalogram decoding algorithm, and sending the motion instructions to the lower limb motion system through the instruction sending module;
and the visual feedback module is used for receiving a lower limb movement state feedback signal of the lower limb movement system or the electromyographic signal system and displaying the lower limb movement state feedback signal to a patient through the visual stimulator.
Lamps with different frequencies flash simultaneously on the visual stimulator (representing a plurality of selectable visual stimulation targets), different flashing frequencies represent different control instructions (preferably, the size of a brain-computer interaction instruction set is greater than 8), a patient can watch the lamps representing the control instructions when wanting to execute a certain instruction, the control intention of the user can be judged only by detecting electroencephalograms of the patient and inducing SSVEP (steady state visual evoked potential) without using hands or speaking of the patient, and further the control on the lower limb exoskeleton is realized.
Preferably, the visual stimulator may display flashing lights representing instructions such as start, end of training, speed of travel (plus, -fine-tune on initial set), stride (plus, -fine-tune on initial set), weight loss (plus, -fine-tune on initial set), etc.
According to the characteristics of SSVEP and spontaneous electroencephalogram, the rest and working states of an electroencephalogram signal system are distinguished, the system state is automatically detected in the robot control process, and a corresponding brain-computer interface and visual stimulation are switched. In a rest state, the electroencephalogram signal system only sets a starting function, and after the SSVEP signal for starting the brain-computer interface is detected, the visual stimulator automatically switches to the robot control interface, and the electroencephalogram signal system starts to work.
In order to realize the monitoring and the proper intervention on the human body state, the lower limb rehabilitation robot is also provided with an electromyographic signal system to construct a human-machine reverse regulation and control channel based on muscle nerve stimulation; the electromyographic signal system includes: the system comprises an electromyographic signal collector, an electromyographic signal analysis processing module and a motion information feedback module; wherein,
the electromyographic signal collector is used for collecting the electromyographic signals of the lower limbs of the patient;
the electromyographic signal analyzing and processing module is used for analyzing and processing the collected lower limb electromyographic signals; preferably, the method comprises the steps of filtering, rectifying, normalizing and the like to obtain a pure electromyographic signal, and further obtain motion information such as the lower limb motion intention and the motion state;
the motion information feedback module is used for sending the information such as the motion state, the motion intention and the like of the lower limbs, which is acquired by the electromyographic signal analysis processing module, to the lower limb exoskeleton and the electroencephalographic signal system; the lower limb exoskeleton and the electroencephalogram signal system analyze the feedback information, and according to the analysis result, the electroencephalogram signal system resends the motion instruction or adjusts the motion gait of the lower limb exoskeleton, so that the monitoring and the proper intervention on the human body state are realized.
A lower extremity movement system comprising: the medical treadmill (4), the lower limb exoskeleton (5), the armrests (6) and the dynamic weight reduction equipment (7); according to the received motion instruction sent by the electroencephalogram signal system, the individual natural gait training (preferably, the gait training speed range is 1 km/h-4 km/h) of the patient is generated and guided based on the real weight-reducing mechanical environment of the human body, and meanwhile, the individual natural gait training can be matched with the electromyogram signal system to carry out rehabilitation training on the affected limb. In particular, the amount of the solvent to be used,
the lower limb exoskeleton mainly provides generation and control of weight-losing personalized gait;
the patient can keep body balance through the handrails and can stand autonomously as a support;
the medical treadmill mainly cooperates with the lower limb exoskeleton to adjust the speed in real time in the training process,
aiming at the problem of insufficient supporting force of the lower limbs of the patient, the influence of gravity on the lower limbs of the patient in the motion process is relieved through the motion weight reduction equipment, so that the patient can more easily and freely complete ideal gait motion under the action of active guiding and driving of the lower limb exoskeleton.
The lower limb exoskeleton generates a movement gait to guide the lower limb to move, and the affected limb can be directly guided to move according to a movement instruction sent by an electroencephalogram signal system; meanwhile, since the lower limb rehabilitation robot interacts with the affected limb with impaired motor function, and the patient is a subject with the awareness of autonomous movement, interactive control between the robot and the patient is indispensable. The interactive control can create a safe, comfortable and natural training environment with active flexibility for the patient, and the affected limb is prevented from confronting the robot due to abnormal muscle activities such as spasm, trembling and the like; specifically, the myoelectric signals of the affected limb can be collected in real time through a myoelectric signal system, and are analyzed and processed to obtain the motion state of the affected limb, and the motion state is fed back to the lower limb exoskeleton in real time, and when the lower limb exoskeleton judges that the motion state of the affected limb is abnormal, intervention is carried out, so that the patient is protected from secondary damage.
Considering that the active contraction of the muscle on the affected side is insufficient due to the fact that the affected limb movement is controlled by only relying on the lower limb exoskeleton, and the recovery of the affected limb is affected, the muscle electrical stimulation instrument (3) is arranged in the lower limb movement system, and the muscle movement is induced or the normal autonomous movement is simulated by stimulating the muscle of the affected limb, so that the purpose of improving or recovering the function of the stimulated muscle is achieved. In the actual rehabilitation training, the brain control of the electroencephalogram signal system on the lower limb of the affected side can be matched with the muscle electrical stimulation, and the muscle electrical stimulation instrument analyzes and generates stimulation time sequence and strength according to a motion instruction sent by the electroencephalogram signal system and in combination with the kinematic parameters of the limb of the patient, so as to stimulate the corresponding muscle of the affected side, thereby achieving better rehabilitation effect; in addition, the device can be matched with an electromyographic signal system to guide the movement of the affected limb together, for example, the device can be matched with a lower limb exoskeleton to limit the patient from overtravel during recovery training; the device can also be matched with a lower limb exoskeleton to guide the motion of the affected limb together.
On the basis, considering the difference of the motion data such as the gaits of the lower limbs on both sides of the patient, a Functional Electrical Stimulation (FES) control strategy based on the mutual reference of the healthy and affected limbs can be adopted; as shown in fig. 4, aiming at the nerve perception requirement of the motion state of the affected lower limb, the FES dynamic adaptive control is completed through the extraction of the kinematic data and healthy side electromyographic signals of the lower limbs at two sides, and the rehabilitation motion training is completed by the two legs according to the gait rule of the habit of the patient through the dynamic time-sequence positive neuromuscular stimulation of the affected leg, so that the motion coordination training of the legs at two sides of the patient is facilitated, an ascending nerve feedback path is formed, the motion connection between the affected side and the healthy side of the patient is established, and the rehabilitation process is controlled by taking the patient as the center to the maximum extent.
Specifically, in the rehabilitation training process, the electromyographic signal system collects the kinematic data of the lower limbs at both sides of the patient and the healthy side electromyographic signals in real time. The electromyographic signal analysis processing module carries out filtering and rectification pretreatment on the collected surface electromyographic signals, and calculates the muscle activation degree through second-order autoregressive filtering and exponential transformation. Through a set muscle activation threshold value, the activation degree curves of a plurality of muscles in a gait cycle are binarized to obtain the contraction time sequence of different muscles when the healthy lower limbs walk in the complete gait cycle, and the contraction time sequence is used as the stimulation time sequence mode of the lower limb muscle electrical stimulation instrument of the affected side.
And (3) respectively calculating kinematic parameters such as joint angles, angular speeds and the like of the healthy and affected lower limbs according to kinematic information of the lower limbs at two sides, comparing the kinematic parameter change of the affected lower limb by taking the healthy side kinematic parameter before constant time as a template, adjusting the stimulation intensity of a specific channel (a channel which corresponds to a stimulation time sequence on and is related to the kinematic parameter) of the muscle electrical stimulation instrument, completing FES dynamic self-adaptive control, and realizing natural mode activation of muscles of the affected lower limb.
Through this embodiment, to the recovered demand of cerebral apoplexy hemiplegia patient low limbs motion function, utilize the two-way loop of closed loop people-machine neural information perception, realize the high-efficient interaction between patient and the recovered robot, improve the disease and participate in the initiative, promote the recovered efficiency of motion.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by hardware associated with computer program instructions, and the program may be stored in a computer readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (7)
1. A lower limb rehabilitation robot based on a bidirectional neural interface, comprising: an electroencephalogram signal system, an electromyogram signal system and a lower limb movement system;
the electroencephalogram signal system is used for collecting electroencephalogram signals of a patient, identifying the movement intention and the rehabilitation training requirement of the patient, converting the electroencephalogram signals into movement instructions and outputting the movement instructions to the lower limb movement system;
the electromyographic signal system is used for collecting lower limb electromyographic signals of a patient, analyzing and processing the lower limb electromyographic signals and feeding the motion state of the lower limb back to the lower limb motion system and the electroencephalographic signal system;
the lower limb movement system generates gait according to the received movement instruction and guides the lower limb of the patient to move; the lower extremity locomotion system comprises a lower extremity exoskeleton; the lower limb exoskeleton receives and judges a lower limb movement state fed back by the electromyographic signal system in real time, and intervenes the movement of the affected limb when the movement state is judged to be abnormal;
wherein the electromyographic signal system comprises: the system comprises an electromyographic signal collector, an electromyographic signal analysis processing module and a motion information feedback module;
the electromyographic signal collector is used for collecting electromyographic signals of the lower limbs of the patient;
the electromyographic signal analyzing and processing module is used for analyzing and processing the collected lower limb electromyographic signals to acquire the motion state information of the lower limbs;
the motion information feedback module feeds the motion state information back to a lower limb motion system and an electroencephalogram system;
the lower limb movement system further comprises: the dynamic weight reduction device is used for reducing the influence of gravity on the lower limb movement process of a patient, and the lower limb exoskeleton is also used for providing generation and control of weight reduction individual gait.
2. The bi-directional neural interface-based lower limb rehabilitation robot of claim 1, wherein the electroencephalogram signal system comprises: the system comprises an electroencephalogram signal collector, a visual stimulator, an electroencephalogram signal analyzing and processing module, an instruction sending module and a visual feedback module;
the visual stimulator is used for displaying flashing lamps representing different control instructions, and a patient can watch the flashing lamps to generate different electroencephalogram signals;
the electroencephalogram collector is used for collecting electroencephalogram signals of a patient and transmitting the electroencephalogram signals to the electroencephalogram signal analysis processing module;
the electroencephalogram signal analysis processing module converts the acquired electroencephalogram signals into motion instructions and sends the motion instructions to the lower limb motion system through the instruction sending module;
and the visual feedback module receives the feedback signal of the motion state of the lower limb and displays the feedback signal to the patient through the visual stimulator.
3. The bi-directional neural interface-based lower limb rehabilitation robot of claim 2, wherein the lower limb movement system further comprises: medical treadmill and armrest;
the lower limb exoskeleton generates an individualized gait according to the received motion instruction and the motion state information fed back by the electromyographic signal system, and controls the motion of the lower limbs;
the armrest is used for assisting a patient to stand;
the medical treadmill is used for adjusting the speed in real time in a training process in cooperation with the lower limb exoskeleton.
4. The robot for rehabilitation of lower limbs based on bidirectional neural interface according to any of claims 1 to 3, wherein said lower limb movement system further comprises a muscle electrical stimulation device for stimulating the muscles of the affected lower limb according to the movement commands from the EEG signal system to guide the movement of the lower limb.
5. The bi-directional neural interface-based lower limb rehabilitation robot of claim 4, wherein the muscle electrostimulator, in cooperation with the lower limb exoskeleton, collectively guides the movement of the affected lower limb.
6. The bi-directional neural interface-based lower limb rehabilitation robot according to claim 5, wherein the muscle electrical stimulator adjusts stimulation timing and intensity to eliminate gait differences of bilateral lower limbs of the patient through an FES control strategy based on mutual reference of healthy/affected limbs.
7. The lower limb rehabilitation robot based on the bidirectional neural interface as claimed in claim 6, wherein the following procedures are performed to adjust the stimulation timing and intensity of the muscle electrical stimulator:
collecting a healthy side electromyographic signal of a patient by an electromyographic signal collector;
the electromyographic signal analyzing and processing module analyzes and processes the healthy side electromyographic signal to obtain the contraction time sequence of different muscles when the healthy side lower limb walks in the complete gait cycle, and the contraction time sequence is used as the time sequence mode of stimulating the affected side lower limb by the muscle electrical stimulation instrument;
comparing the kinematic parameter change of the affected lower limb by taking the healthy side kinematic parameter before the constant time as a template according to the kinematic data of the bilateral lower limbs to obtain the intensity mode of the muscle electrical stimulation instrument for stimulating the affected lower limb;
the muscle electrical stimulation instrument adjusts the time sequence and the intensity of stimulation according to the time sequence mode and the intensity mode of stimulating the lower limb of the affected side.
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| CN201810833639.6A CN109199786B (en) | 2018-07-26 | 2018-07-26 | Lower limb rehabilitation robot based on bidirectional neural interface |
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| CN119153026A (en) * | 2024-09-23 | 2024-12-17 | 中国人民解放军总医院第二医学中心 | Brain control gait training method and system for parkinsonism based on artificial intelligence |
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