Human-computer interaction method, system, device and medium for mobile rehabilitation robot

Abstract

The present application provides a human-computer interaction method, system, device and medium for a mobile rehabilitation robot. The method includes using the display prompt interface to divide the planar motion space into a number of predefined independent areas; randomly selecting a predefined area as a target area through the display prompt interface, and clearly indicating the target area through visual prompts on the display prompt interface to guide the mobile rehabilitation robot to perform a specified movement; when the mobile rehabilitation robot detects that it has moved to the target area, it automatically updates and lights up the new target area, and records the order of each of the lit target areas to form a display sequence; based on the display sequence, generates and issues specific prompt information to guide the mobile rehabilitation robot to perform a series of predetermined motion paths in a specific order to complete the rehabilitation training task.

Description

Technical Field

[0001] The present application relates to the technical field of rehabilitation robots, and in particular to a human-computer interaction method, system, device and medium for a mobile rehabilitation robot. Background Art

[0002] In the field of rehabilitation medicine, especially for the rehabilitation of upper limb movement disorders and cognitive decline caused by stroke or other neurological diseases, traditional rehabilitation training methods mostly rely on direct intervention by physical therapists and a series of standardized physical exercises. Although these methods are effective to a certain extent, they usually lack sufficient personalization and dynamic adaptability to meet the specific needs of all patients. In addition, traditional methods have limited effects in promoting the recovery of memory and cognitive abilities, and the rehabilitation training needs in these areas are far from being fully met.

[0003] In recent years, mobile rehabilitation robots and gamification-based rehabilitation training methods have attracted widespread attention. They aim to improve patient engagement and training effectiveness by providing highly interactive and interesting training methods. Despite these technological innovations, existing rehabilitation robot systems tend to focus on assisting physical movements, while there is still much room for development in integrated memory and cognitive function training. In particular, how to effectively combine upper limb movement training with memory and cognitive ability recovery training, and how to provide personalized and adaptive support through intelligent assistance modes are issues that existing technologies have not fully addressed. In addition, the human-computer interaction methods in existing systems usually lack targeted memory training tasks and dynamic adjustment mechanisms based on user performance. This limits the effectiveness of rehabilitation training, especially in training that needs to promote memory and cognitive function recovery. Summary of the invention

[0004] One purpose of the present application is to provide a human-computer interaction method, system, device and medium for a mobile rehabilitation robot, at least to enable the method to combine upper limb rehabilitation training movements with memory and cognitive training to improve the rehabilitation training effect.

[0005] To achieve the above-mentioned objectives, some embodiments of the present application provide a human-computer interaction method for a mobile rehabilitation robot, the method comprising using the display prompt interface to divide the planar motion space into a number of predefined independent areas, each of which can be identified and used to display motion targets; randomly selecting a predefined area as a target area through the display prompt interface, and clearly indicating the target area through visual prompts on the display prompt interface to guide the mobile rehabilitation robot to perform specified movements; when the mobile rehabilitation robot detects that it has moved to the target area, it automatically updates and lights up the new target area, and records the order of each of the lit target areas to form a display sequence; based on the display sequence, generates and issues specific prompt information to guide the mobile rehabilitation robot to perform a series of predetermined motion paths in a specific order to complete the rehabilitation training task.

[0006] Furthermore, the method also includes: the mobile rehabilitation robot monitors and analyzes its own motion trajectory and speed and its positional relationship relative to the target area in real time through the motion sensor to obtain motion data; when the mobile rehabilitation robot determines, based on the motion data, that it has failed to reach the target area along a predetermined trajectory or within a specified time, it starts a power-assist mode to provide power-assistance in the direction of the target area.

[0007] Furthermore, the power-assistance mode includes: defining the inertia, damping and stiffness of the mobile rehabilitation robot as M1, B1 and K1 respectively; obtaining the external force F and power-assistance F of the mobile rehabilitation robot; d , displacement x0, speed and acceleration The impedance model of the mobile rehabilitation robot has the following relationship:

[0008]

[0009] Furthermore, the boost F d A plurality of power assistance levels are set; and the power assistance output by the mobile rehabilitation robot is dynamically adjusted according to the motion data.

[0010] Furthermore, the method also includes: when the mobile rehabilitation robot detects that it has reached the target area, or its movement trend satisfies the target movement trajectory, the power-assisting mode is automatically released.

[0011] Furthermore, the generating and issuing of specific prompt information includes: generating prompt information requiring to repeat the target area reaching actions in a previous specific order.

[0012] Furthermore, the method also includes: automatically evaluating the training individual's memory and training execution status according to the mobile rehabilitation robot's response and execution of the prompt information, and adjusting the implementation of the assistance mode according to the training status.

[0013] Some embodiments of the present application also provide a human-computer interaction system for a mobile rehabilitation robot, the system comprising: a motion sensor module, used to monitor the position and motion state of the mobile rehabilitation robot in real time, and detect whether the mobile rehabilitation robot has moved to a predetermined target area; a display prompt interface, used to provide visual prompts, including dividing the planar motion space into a number of predefined independent areas, displaying the target area and instructing the mobile rehabilitation robot to perform a specified movement; a display sequence module, used to randomly select a target area and update and light up a new target area after the robot reaches each target area, and at the same time record the order of each lit target area to form a display sequence; a power assistance module, used to control the mobile rehabilitation robot to provide corresponding power assistance.

[0014] Some embodiments of the present application also provide a human-computer interaction device for a mobile rehabilitation robot, the device comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to execute the method described above.

[0015] Some embodiments of the present application also provide a computer-readable medium having computer program instructions stored thereon, and the computer program instructions can be executed by a processor to implement the human-computer interaction method of the mobile rehabilitation robot.

[0016] Compared with the prior art, the human-computer interaction method of the mobile rehabilitation robot in the solution provided in the embodiment of the present application effectively combines upper limb physical rehabilitation training with memory and cognitive function training through intelligent human-computer interaction. It can adjust the power output in real time according to the sports performance of the individual participating in the training, and provide individuals with precise physical assistance and dynamic training difficulty adjustment. Compared with traditional, static or completely manual rehabilitation training methods, this significantly improves the personalization and adaptability of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a human-computer interaction method for a mobile rehabilitation robot provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the effect of a human-computer interaction method for a mobile rehabilitation robot provided in an embodiment of the present application;

[0019] Figure 3 A schematic diagram of the structure of a human-computer interaction system of a mobile rehabilitation robot provided in an embodiment of the present application;

[0020] Figure 4 A schematic structural diagram of a human-machine interaction device of a mobile rehabilitation robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] Some similar products on the market, such as Fourier and Zhuodao's upper limb rehabilitation products, usually implement assistance by detecting that the patient has not actively moved for a period of time, and then switching the current mode to passive mode, allowing the robot to drive the patient to move to the designated position. Once the assistance mechanism is triggered, the robot changes to passive mode and will no longer interact with the patient. This limits the patient's autonomous movement, thereby weakening the rehabilitation effect.

[0023] In response to the above technical problems, an embodiment of the present application provides a human-computer interaction method for a mobile rehabilitation robot, and the core of the embodiment of the present application lies in: S101, using the display prompt interface to divide the planar motion space into several predefined independent areas, each of which can be identified and used to display motion targets; S102, randomly selecting a predefined area as a target area through the display prompt interface, and clearly indicating the target area through visual prompts on the display prompt interface to guide the mobile rehabilitation robot to perform specified movements; S103, when the mobile rehabilitation robot detects that it has moved to the target area, it automatically updates and lights up the new target area, and records the order of each lit target area to form a display sequence; S104, based on the display sequence, generates and issues specific prompt information to guide the mobile rehabilitation robot to perform a series of predetermined motion paths in a specific order to complete the rehabilitation training task.

[0024] The mobile rehabilitation robot is equipped with high-precision motion sensors, a graphical display prompt interface, and built-in processors and software algorithms to control the training process and implement human-computer interaction. The display prompt interface can clearly display the training tasks and instructions, and record key data during the training process. The robot's display prompt interface divides the planar motion space into several predefined independent areas, which are distributed on the display interface, and each area represents a potential motion target. A region is randomly selected as the initial target area, and the area is indicated on the display interface through visual cues (such as highlighting) to guide the mobile rehabilitation robot (and the patient participating in the training) to perform the specified movement. The patient participating in the training uses the upper limbs to operate the mobile rehabilitation robot to move to the specified target area. The robot's motion sensor monitors and analyzes the motion trajectory in real time. Once it detects that the robot has successfully reached the target area, it automatically updates the target area, lights up the new target area and indicates it through the display prompt interface. This process is repeated, and the system records the order of each illuminated target area to form a display sequence.

[0025] At a certain stage of exercise training, based on the previously recorded display sequence, specific memory training prompts are issued to the patient through the display prompt interface, such as requiring the patient to repeat a specific sequence of movement paths. This step is intended to combine memory and cognitive function training, and further stimulate and train the patient's memory and cognitive abilities by requiring the patient to memorize and execute a specific sequence of movement paths. The training effect is evaluated based on the patient's response to the prompt information and training performance, and the subsequent training plan can be adjusted based on the evaluation results. The training difficulty can also be automatically adjusted according to the patient's progress to ensure that the training is challenging but does not exceed the patient's ability.

[0026] This embodiment provides a comprehensive and effective rehabilitation training method by combining upper limb physical exercise with memory and cognitive function training. This method can significantly improve the patient's rehabilitation efficiency and training participation through intelligent human-computer interaction and personalized training adjustment, and provides an innovative rehabilitation training program, especially for patients who need to improve both physical movement ability and cognitive function.

[0027] In some embodiments of the present application, the method also includes: the mobile rehabilitation robot monitors and analyzes its own motion trajectory and speed and its positional relationship relative to the target area in real time through the motion sensor to obtain motion data; when the mobile rehabilitation robot determines, based on the motion data, that it has failed to reach the target area along a predetermined trajectory or within a specified time, it starts a power-assist mode to provide power-assistance in the direction of the target area.

[0028] The mobile rehabilitation robot is equipped with high-precision motion sensors that can capture the robot's motion status in real time, including speed, trajectory, and relative position between the robot and the predefined target area. These data are transmitted in real time to the robot's central processing unit, where built-in algorithms analyze the data to evaluate the execution of the training. When the analysis results show that the robot (and the robot operated by the patient) fails to move according to the predetermined trajectory, or fails to reach the target area within the specified time, it is automatically determined that additional assistance is needed. On this basis, the robot automatically starts the power-assist mode and provides an appropriate amount of power in the direction of the target area through its drive system. This power-assistance is intended to assist patients in overcoming movement obstacles and help the robot reach the target area more accurately and efficiently. The size and direction of the power-assistance are dynamically calculated by the robot's processor based on the motion data to ensure that the assistance provided can effectively help patients complete the training tasks, but will not completely replace the patient's active movement, so as to maintain the effectiveness of the training and the patient's participation.

[0029] This embodiment significantly improves the personalization and adaptability of rehabilitation training through intelligent motion monitoring and analysis combined with adaptive power-assistance mode. This method can provide immediate support when patients encounter difficulties, optimize the training process, thereby improving rehabilitation efficiency and accelerating the patient's recovery process. In addition, the introduction of the power-assistance mode increases the flexibility and interactivity of training, making rehabilitation training more humane and patient-friendly.

[0030] In some embodiments of the present application, the power-assistance mode includes: defining the inertia, damping and stiffness of the mobile rehabilitation robot as M1, B1 and K1 respectively; obtaining the external force F and power-assistance F of the mobile rehabilitation robot; d , displacement x0, speed and acceleration The impedance model of the mobile rehabilitation robot has the following relationship:

[0031]

[0032] By implementing the impedance control model, the mobile rehabilitation robot of this embodiment can provide fine-tuned assistance, greatly enhancing the interactivity and adaptability of training. This intelligent assistance can be dynamically adjusted according to the user's real-time sports performance and needs, which not only ensures the effectiveness of training, but also improves the user's comfort and satisfaction. In addition, this method also helps to improve the accuracy and personalization level of rehabilitation training, providing users with more customized rehabilitation solutions.

[0033] In some embodiments of the present application, the booster F d A plurality of power assistance levels are set; and the power assistance output by the mobile rehabilitation robot is dynamically adjusted according to the motion data.

[0034] The control system of the mobile rehabilitation robot has preset multiple levels of assistance, each corresponding to a different amount of assistance. These levels of assistance can be set according to the specific needs of rehabilitation training and the individual differences of users, for example, from slight assistance (low level) to significant assistance (high level). The setting of the assistance level allows rehabilitation training to provide appropriate physical support at different stages or for different training tasks, making the training process more flexible and adaptable.

[0035] The mobile rehabilitation robot uses its motion sensors to monitor its own displacement, speed, acceleration and other motion data in real time, and obtains the external force applied by the user to the robot. These data are transmitted to the robot's central processing unit in real time for analysis. Based on the analyzed motion data, the robot's control system will evaluate the execution of the current training task and the user's motor ability. If it is detected that the user encounters difficulties in performing the task, such as slowing down the movement speed or failing to reach the target area according to the predetermined trajectory, the power level will be automatically increased to provide greater power assistance. The adjustment of the power is based not only on the user's current performance and needs, but also on the user's rehabilitation progress and long-term training goals. The system can continuously adjust the power level throughout the training process to ensure that the user always performs rehabilitation training with the most appropriate support.

[0036] By setting multiple power levels and dynamically adjusting the power according to motion data, the mobile rehabilitation robot in this embodiment can provide users with more personalized and refined rehabilitation training assistance. This method not only increases the flexibility and adaptability of training, but also helps to improve the training efficiency and satisfaction of users. In addition, the ability to dynamically adjust the power output allows rehabilitation training to be optimized according to the specific needs and rehabilitation stages of users, thereby accelerating the rehabilitation process and improving the rehabilitation effect.

[0037] In some embodiments of the present application, the method further includes: when the mobile rehabilitation robot detects that it has reached the target area, or its movement trend satisfies the target movement trajectory, automatically releasing the power-assist mode.

[0038] The mobile rehabilitation robot monitors its position and motion state in real time through its sophisticated sensor system. When the robot detects that it has successfully reached the preset target area, or its motion trend fully matches the target motion trajectory, it will automatically determine that the current training task or motion goal has been completed. The determination of reaching the target area can be based on the robot's displacement sensor and positioning technology; while the determination of the motion trend involves the analysis of the robot's motion parameters such as speed and acceleration, and the comparison with the predetermined trajectory. When one of the above conditions is met, the control system of the mobile rehabilitation robot will automatically release the power-assist mode. This means that the robot will stop providing additional power assistance, return to a normal motion state, and wait for the user's next operation or transfer to the next training task. The function of automatically releasing the power-assist mode not only reduces excessive dependence on the user, promotes the user's active participation and self-control ability, but also improves the safety and efficiency of training.

[0039] In some embodiments of the present application, the generating and issuing of specific prompt information includes: generating prompt information requiring the repetition of a previous specific order of target area reaching actions.

[0040] During the rehabilitation training, the mobile rehabilitation robot divides the planar motion space into several predefined areas by displaying a prompt interface, and guides the user to move the robot to these areas through upper limb movements. Each time the target area is successfully reached, the robot automatically records the serial number of the area to form a training sequence. Based on the completed training sequence, the robot's control system will generate specific prompt information, requiring the user to repeat the target area arrival action in the previous specific order. For example, if the previous training sequence is 1-3-2-4, the system may generate a prompt information requiring "Please repeat the position of the second movement", that is, pointing to area 3. The generated prompt information is issued through the display prompt interface or audio output module of the mobile rehabilitation robot to guide the user to perform memory training. This training not only involves the user's physical movement ability, but more importantly, it involves the training of memory and cognitive function. The user tries to repeat the specified motion path according to the prompt information. The robot monitors the user's motion execution through sensors and evaluates the user's response to the prompt information to determine the effect of memory training.

[0041] By generating prompts that require the user to repeat the target area arrival action in a specific order, a rehabilitation method combining physical exercise and memory training is realized. The application of this method can not only improve the user's upper limb motor ability, but more importantly, it promotes the recovery of cognitive function through memory training tasks, which is especially helpful for improving memory. Through this highly interactive training method, the user's training participation and training effect are significantly improved.

[0042] In some embodiments of the present application, the method further includes: automatically evaluating the training individual's memory and training execution status based on the mobile rehabilitation robot's response and execution of the prompt information, and adjusting the implementation of the assistance mode based on the training status.

[0043] After guiding the completion of a specific memory training task (for example, repeating the target area arrival action in a specific order before), the mobile rehabilitation robot monitors and analyzes the training individual's response to the prompt information and the execution status in real time through its sensor system and built-in algorithm. The robot evaluation includes but is not limited to whether the training individual can accurately recall and execute the specified movement sequence, the speed and accuracy of execution, and the consistency of the movement trajectory during execution with the predetermined trajectory. Based on the evaluation results, the training individual's performance in memory and execution tasks will be automatically determined. If it is found that the training individual has difficulty in performing the memory training task, the system may decide to increase the assistance of the power-assistance mode to help the individual better complete the training task. On the contrary, if the training individual can complete the task easily and accurately, the system may reduce the provision of assistance to encourage more autonomous movement, thereby promoting the further improvement of the training individual's motor ability and memory. The implementation adjustment of the power-assistance mode is based on a dynamic feedback loop, that is, according to the real-time performance and progress of the training individual, the size and timing of the assistance provided are continuously adjusted to achieve the optimal training effect.

[0044] The following is a specific application example to explain in detail the implementation details of the human-computer interaction method of the mobile rehabilitation robot in the embodiment of the present application. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this solution.

[0045] like Figure 2 As shown in the figure, the mobile rehabilitation robot divides the screen into 9 areas and numbers them. Assume that the current device is located at (x0, y0), the current desired target point is (x1, y1), the system inertia coefficient is M1, the damping coefficient is B1, the stiffness is K1, the device displacement is x0, and the speed is Acceleration The magnitude of the external force applied by the system is F. The system impedance model equation is as follows:

[0046]

[0047] In this application scenario, the device needs to follow the force applied by the patient. When the external force disappears, the device should not return to the initial position, so the stiffness K1=0 is required. The force that needs to be introduced into the system is F d , its size can be set through system parameters, such as different levels of assistance, level 1 assistance level corresponds to 2N assistance, level 3 assistance level corresponds to 6N assistance. dThe direction is determined by the vector between the current position of the device and the desired target position, (x1 - x0, y1 - y0). Such assistance can better help the user by providing certain prompt information. When the user has a direction, they can independently move to the target position, greatly enhancing the initiative of the patient. The initiative of the user's autonomous movement will not be restricted during the entire training process.

[0048] Among them, the trigger and implementation of the assistance include entering the answering session of the above-mentioned cognitive rehabilitation. At this time, the device switches to the active mode. When the device satisfies the relationship in formula (1), at this time F d = 0; Record the distance between the initial position of the device and the desired target position at this time. Assume that the current real-time position of the device is (x, y). If the distance S between the current device and the target position (x1, y1) is S < S0, then let S0 = S; If S - S0 > 5mm and S > S0 for more than 3 seconds, then introduce the assistance mechanism; According to the above process, assign F d At this time, the patient can feel that the device is moving the patient in a certain direction, which will help the patient recall which target area they should go to. At this time, the patient can form a combined force with the robot to accelerate the movement to the target point; If the patient cannot recall, the robot can also drive the patient to the target point; When S - S0 < 5mm, it is considered that the current training task is completed at this time. The next round of memory and training process can be started; Finally, according to the degree of the patient's active participation, the number of times and duration of the activation of the assistance mode, the training process of the patient this time can be evaluated.

[0049] Figure 3 Fig. shows a human-computer interaction system of a mobile rehabilitation robot, and the system includes:

[0050] A motion sensor module, which is used to monitor the position and motion state of the mobile rehabilitation robot in real time, and detect whether the mobile rehabilitation robot has moved to a predetermined target area;

[0051] A display prompt interface, which is used for visual prompts, including dividing the planar motion space into several predefined independent areas, displaying the target area and instructing the mobile rehabilitation robot to perform specified motions;

[0052] A display sequence module, which is used to randomly select the target area and update and light up a new target area after the robot reaches each target area, and at the same time record the order of each lit target area to form a display sequence;

[0053] An assistance auxiliary module, which is used to control the mobile rehabilitation robot to provide corresponding assistance.

[0054] It is not difficult to find that the embodiment of the present application is a system embodiment corresponding to the method embodiment. The implementation details of the embodiment of the present application have been explained in the method embodiment. To avoid repetition, they will not be repeated here.

[0055] In addition, the present application also provides a near-ultraviolet laser frequency stabilization device, the structure of which is as follows: Figure 4 As shown, the device includes a memory 90 for storing computer-readable instructions and a processor 100 for executing computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor is triggered to execute the human-computer interaction method of the mobile rehabilitation robot.

[0056] The methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the processing unit, the above functions defined in the method of the present application are executed.

[0057] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0058] In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0059] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0060] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0061] As another aspect, the embodiments of the present application further provide a computer-readable medium, which may be included in the device described in the above embodiments; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions, which may be executed by a processor to implement the steps of the methods and / or technical solutions of the above multiple embodiments of the present application.

[0062] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0063] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0064] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0065] In addition, an embodiment of the present application further provides a computer program, which is stored in a computer device, so that the computer device executes the method for controlling code execution.

[0066] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0067] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A human-computer interaction method for a mobile rehabilitation robot, characterized in that: The mobile rehabilitation robot is equipped with a motion sensor and a display prompt interface, and the method includes: Using the display prompt interface, the planar motion space is divided into a number of predefined independent areas, each of which can be identified and used to display the motion target; Randomly selecting a predefined area as a target area through the display prompt interface, and clearly indicating the target area through a visual prompt on the display prompt interface to guide the mobile rehabilitation robot to perform a specified movement; When the mobile rehabilitation robot detects that it has moved to the target area, it automatically updates and lights up the new target area, and records the order of each of the lit target areas, forming a display sequence in a cycle; Based on the display sequence, specific prompt information is generated and issued to guide the mobile rehabilitation robot to perform a series of predetermined motion paths in a specific order to complete the rehabilitation training task; the generating and issuing of specific prompt information includes: generating prompt information requiring the target area reaching action to be repeated in a specific order; The mobile rehabilitation robot monitors and analyzes its own motion trajectory and speed and its positional relationship relative to the target area in real time through the motion sensor to obtain motion data; When the mobile rehabilitation robot determines, based on the motion data, that it has failed to reach the target area along a predetermined trajectory or within a specified time, it activates a power-assistance mode to provide power-assistance in the direction of the target area; based on the mobile rehabilitation robot's response to and execution of the prompt information, it automatically evaluates the training individual's memory and execution of the training, and adjusts the implementation of the power-assistance mode according to the training situation; The power assist modes include: The inertia, damping and stiffness of the mobile rehabilitation robot are defined as M1, B1 and K1 respectively; Obtain the external force F and the assist force F of the mobile rehabilitation robot d , displacement Δx0, velocity and acceleration The impedance model of the mobile rehabilitation robot has the following relationship:

2. The human-computer interaction method according to claim 1, characterized in that: The boost F d A plurality of power assistance levels are set; and the power assistance output by the mobile rehabilitation robot is dynamically adjusted according to the motion data.

3. The human-computer interaction method according to claim 1 or 2, characterized in that: The method further comprises: When the mobile rehabilitation robot detects that it has reached the target area, or its movement trend meets the target movement trajectory, the power-assisting mode is automatically released.

4. A human-machine interaction system for a mobile rehabilitation robot, characterized in that: The system comprises: A motion sensor module is used to monitor the position and motion state of the mobile rehabilitation robot in real time, and to detect whether the mobile rehabilitation robot has moved to a predetermined target area; Displaying a prompt interface for providing visual prompts, including dividing the planar motion space into a number of predefined independent areas, displaying a target area and instructing the mobile rehabilitation robot to perform a specified motion; A display sequence module is used to randomly select target areas and update and light up new target areas after the robot reaches each target area, and record the order of each illuminated target area to form a display sequence in a loop; based on the display sequence, generate and issue specific prompt information to guide the mobile rehabilitation robot to perform a series of predetermined motion paths in a specific order to complete the rehabilitation training task; the generating and issuing of specific prompt information includes: generating prompt information requiring the repetition of the target area reaching action in a previous specific order; The power assist module is used to control the mobile rehabilitation robot to provide corresponding power assist; the mobile rehabilitation robot monitors and analyzes its own motion trajectory and speed and its positional relationship relative to the target area in real time through the motion sensor module to obtain motion data; when the mobile rehabilitation robot determines that it has failed to reach the target area according to the predetermined trajectory or within the specified time based on the motion data, it starts the power assist mode and provides power assist in the direction of the target area; according to the response and execution of the mobile rehabilitation robot to the prompt information, the training individual's memory and execution are automatically evaluated, and the power assist mode is adjusted according to the training situation; The power assist modes include: The inertia, damping and stiffness of the mobile rehabilitation robot are defined as M1, B1 and K1 respectively; Obtain the external force F and the assist force F of the mobile rehabilitation robot d , displacement Δx0, velocity and acceleration The impedance model of the mobile rehabilitation robot has the following relationship:

5. A human-machine interaction device for a mobile rehabilitation robot, characterized in that: The device comprises: one or more processors; and A memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the method according to any one of claims 1 to 3.

6. A computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 3.

Images