Virtual reality-based hemiplegic lower limb active rehabilitation training method, device and equipment

Abstract

Embodiments of the present disclosure disclose a virtual reality-based hemiplegic lower limb active rehabilitation training method, device and equipment. A specific embodiment of the method comprises: acquiring an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction of rehabilitation training, and the input information set comprises: movement information of a patient's hand, movement information of a patient's limb, and position coordinate sampling point information of a patient's eye fixation point; in response to determining that an adjusted input information set is received, sending an action corresponding to the adjusted input information set to a user, and controlling a vibration module and an exoskeleton module to realize sensory coordination stimulation, wherein the user wears a head-mounted audio-visual feedback device. The embodiment adopts multiple interaction methods to adapt to various patients and rehabilitation scenes, provides an active rehabilitation training method for patients, and provides real-time interaction feedback for patients through hardware devices.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, and device for active rehabilitation training of hemiplegic lower limbs based on virtual reality. Background Technology

[0002] Rehabilitation training can be divided into active and passive rehabilitation based on the mode of participation. Many patients in the disabled population have severe motor impairments, making it difficult for them to participate in active rehabilitation training. Therefore, they mostly use passive rehabilitation methods, such as passive critical activity training, acupuncture, and electrical stimulation. These passive rehabilitation methods lack voluntary motor commands and fail to effectively activate the plasticity of the nervous system, thus having limited effectiveness in rehabilitating motor function. For example, the early stage after stroke is a critical period for the recovery, compensation, and remodeling of motor function, and early rehabilitation intervention plays an important role in improving function. However, many early-stage stroke patients often have severe hemiplegia and, even with the help of medical assistive devices, cannot actively participate in rehabilitation training. They can only use passive rehabilitation methods while bedridden, causing them to miss the golden period of rehabilitation and seriously affecting the effectiveness of rehabilitation treatment.

[0003] Virtual Reality (VR) possesses three key characteristics: imagination, interactivity, and immersion, making it a powerful technological tool. VR offers various interaction methods, such as controllers, eye tracking, and data gloves, providing users with an immersive experience and has been widely applied in education, industry, entertainment, and healthcare. VR technology can further explore active rehabilitation methods for patients through diverse interactive means. For example, using interactive devices in VR, patients can participate in motion interactions within virtual scenes, grasping, moving, and manipulating virtual objects, thereby promoting muscle movement and control. VR can also provide real-time feedback and guidance, helping patients accurately execute rehabilitation movements and recording and evaluating their rehabilitation progress.

[0004] In traditional rehabilitation therapy, patients often have motor impairments, making it difficult for them to actively engage in rehabilitation training. Therefore, passive rehabilitation methods are frequently employed. However, due to the lack of active patient participation, the stimulation of motor nerve remodeling is insufficient, resulting in limited motor function rehabilitation outcomes. This disclosure presents an interactive mechanism that uses the healthy limb to control the virtual limb on the affected side. It also employs multiple interactive methods to adapt to diverse patients and rehabilitation scenarios, providing patients with an active rehabilitation training method and offering real-time interactive feedback through hardware devices.

[0005] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure propose a method, apparatus, and device for active rehabilitation training of hemiplegic lower limbs based on virtual reality, in order to solve one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a method for active rehabilitation training of hemiplegic lower limbs based on virtual reality. The method includes: acquiring an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction in rehabilitation training, and the input information set includes: motion information of the patient's hand, motion information of the patient's limbs, and position coordinate sampling point information of the patient's eye gaze point; adaptively adjusting the input information set within a preset first period range and a preset second period range to obtain an adjusted input information set; in response to determining that the adjusted input information set has been received, sending the action corresponding to the adjusted input information set to the user, and controlling a vibration module and an exoskeleton module to achieve sensory synergistic stimulation, wherein the user wears a head-mounted audiovisual feedback device. The exoskeleton module is mapped to its range of motion to achieve active rehabilitation training of the hemiplegic lower limbs.

[0009] Secondly, some embodiments of this disclosure provide a virtual reality-based active rehabilitation training device for hemiplegic lower limbs. The device includes: an acquisition unit configured to acquire an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction in rehabilitation training, and the input information set includes: motion information of the patient's hand, motion information of the patient's limbs, and position coordinate sampling point information of the patient's eye gaze point; an adjustment unit configured to adaptively adjust the input information set within a preset first period range and a preset second period range to obtain an adjusted input information set; a control unit configured to, in response to determining that the adjusted input information set has been received, send the action corresponding to the adjusted input information set to the user, and control a vibration module and an exoskeleton module to achieve sensory synergistic stimulation, wherein the user wears a head-mounted audiovisual feedback device; and a mapping unit configured to map the range of motion of the exoskeleton module to achieve active rehabilitation training for hemiplegic lower limbs.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] This disclosure firstly integrates multiple interaction technologies into the motion interaction module, including optical capture, inertial capture, and eye tracking. It flexibly selects the most suitable interaction method based on the patient's specific condition and the actual situation of the rehabilitation environment, accurately capturing the patient's movement intentions and details. After complex signal processing and adaptive algorithm processing, the motion data is transmitted to the virtual scene in real time, providing data for subsequent rehabilitation training. The virtual scene module adopts a first-person perspective, allowing the patient to intuitively observe the movement state of the three-dimensional human body model, enhancing immersion. For foot and ankle rehabilitation, gamified training tasks such as pulling a treasure chest are designed to ensure that the patient concentrates on key motor imagery and observation. This module receives motion data, drives virtual limb movement, coordinates visual and auditory feedback, and forwards data to the physical feedback module, ensuring the coordinated operation of the entire system. The physical feedback module combines vibration feedback and exoskeleton force feedback technology to provide precise and appropriate stimulation to the patient's affected limb, strengthening the activation of motor nerves and the reconstruction of muscle memory. The vibration intensity and exoskeleton range of motion are dynamically adjusted according to the patient's actual condition, ensuring both safety and effectiveness. The monitoring and analysis module utilizes various bioindicators, such as medical scales, surface electromyography (EMG) signals, and electroencephalography (EEG) signals, to comprehensively assess the patient's rehabilitation progress. Based on the data analysis results, doctors can dynamically adjust training programs and parameter settings, forming a closed-loop training system from customized training to effect monitoring and program optimization, ensuring the continuity and relevance of rehabilitation training. The active rehabilitation training method mentioned in this disclosure is simple to operate, has a low workload, and through real-time monitoring and analysis, assesses the patient's training status, allowing therapists to personalize training programs and ultimately form a progressive closed-loop rehabilitation process integrating customization, training, monitoring, and assessment. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs according to this disclosure;

[0014] Figure 2This is a schematic diagram of the structure of some embodiments of the virtual reality-based active rehabilitation training device for hemiplegic lower limbs according to the present disclosure;

[0015] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure.

[0016] Figure 4 This is a framework diagram of some embodiments of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs disclosed herein.

[0017] Figure 5 These are virtual training scene diagrams based on some embodiments of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs disclosed herein.

[0018] Figure 6 This is a diagram of an exoskeleton force feedback device according to some embodiments of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs disclosed herein. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Figure 1 This is a flowchart 100 of some embodiments of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs disclosed herein. The virtual reality-based active rehabilitation training method for hemiplegic lower limbs includes the following steps:

[0026] Step 101: Obtain the input information set of the virtual scene.

[0027] In some embodiments, the execution subject (e.g., a computing device) of the virtual reality-based active rehabilitation training method for hemiplegic lower limbs can acquire an input information set of the virtual scene via a wired or wireless connection. This input information set is used for virtual-real interaction in rehabilitation training and includes: motion information of the patient's hand, motion information of the patient's limbs, and position coordinate sampling point information of the patient's eye gaze point. A virtual training scene diagram of the aforementioned virtual scene is shown below. Figure 5 .

[0028] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0029] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. For example, the computing device can be the aforementioned target terminal. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0030] Optionally, the aforementioned executing entity can obtain the input information set of the virtual scene through the following steps:

[0031] The first step is to use a motion-sensing interactive device to collect motion information of the patient's hands.

[0032] Here, the aforementioned motion-sensing interactive device can refer to Leap Motion (motion controller). The aforementioned hand motion information can refer to the activity information of the hand joints. For example, the aforementioned hand motion information can refer to the activity angle information of the wrist joint. The aforementioned motion-sensing interactive device uses optical gesture tracking technology. When a hand enters the recognition area, the motion-sensing interactive device will automatically track it and output a series of data frames, each containing all the relevant hand motion information.

[0033] The second step is to wear a nine-axis Bluetooth posture sensor on the patient's interactive limb to obtain the relevant information set of the patient's interactive limb in real time. The relevant information set includes: joint motion angle information and acceleration information.

[0034] Here, the aforementioned nine-axis Bluetooth attitude sensor supports 360-degree x-axis and 180-degree y-axis angle tracking with a resolution of 0.0055. It also supports the measurement and tracking of multiple parameters, including acceleration, magnetic force, and gyroscope readings. Data communication is via Bluetooth with a communication distance of 10 meters. The sensor uses a 10Hz data return rate.

[0035] The third step involves using an eye-tracking device to detect the user's gaze point coordinates and sampling points according to a preset call frequency.

[0036] Here, the aforementioned eye-tracking device can refer to the PICO 4Pro (VR all-in-one device). The preset call frequency mentioned above is the call frequency of the Update function in the Unity 3D engine, which is 60 frames / second.

[0037] The fourth step is to determine the above-mentioned patient hand movement information, the above-mentioned related information set, and the above-mentioned fixation point position coordinate sampling points as the input information set.

[0038] Optionally, after detecting the user's gaze point location coordinate sampling points using an eye-tracking device at a preset calling frequency, the method further includes:

[0039] The first step is to determine the difference between the two detected gaze point location coordinate sampling points to obtain the direction and distance of the user's eye movement.

[0040] The second step is to determine the direction of the patient's interactive limb movements as the direction of the user's eye movements.

[0041] The third step is to linearly map the distance of the user's eye movement to the movement distance of the patient's interactive limbs.

[0042] Step 102: Adaptively adjust the above input information set within a preset first cycle range and a preset second cycle range to obtain the adjusted input information set.

[0043] In some embodiments, the execution entity may adaptively adjust the input information set within a preset first period range and a preset second period range to obtain an adjusted input information set.

[0044] Optionally, the aforementioned execution entity can adaptively adjust the input information set within a preset first period and a preset second period using the following steps to obtain the adjusted input information set:

[0045] The first step is to perform the following first processing step for each cycle within the aforementioned preset first cycle range:

[0046] The first sub-step involves linearly mapping the range of motion of the patient's interactive limb to the actual range of motion of the patient's interactive limb, thus obtaining the first actual range of motion.

[0047] The second sub-step involves mapping the first actual activity range to the first virtual activity range to update the mapping parameters.

[0048] The third sub-step involves applying the above mapping parameters to the range of motion of the patient's interactive limbs and continuing to execute the above first processing step.

[0049] The second step is to adjust the input information set corresponding to the patient's interactive limbs in response to the determination that the first actual activity range is similar to the second actual activity range obtained by continuing to perform the first processing step above, so as to obtain the first adjusted input information set.

[0050] Here, the first processing step can be implemented using an interval mapping algorithm, as shown in the following formula: in Let represent the estimated value, x represent the data obtained after transforming the motion information, and a and b represent the mapping parameters. This indicates the size of the estimated value interval for the previous period. max -z min This indicates the size of the target interval for the previous period.

[0051] Third, for each cycle within the aforementioned preset second cycle range, perform the following second processing step:

[0052] The first sub-step involves linearly mapping the movable range within the preset first cycle range to the actual activity range of the patient's interactive limbs to obtain the actual activity range of the first cycle.

[0053] The second sub-step is to map the actual activity range of the first cycle to the virtual activity range of the first cycle in order to update the mapping parameters of the first cycle.

[0054] The third sub-step involves applying the first cycle mapping parameters to the range of motion of the patient's interactive limbs and continuing to execute the second processing step.

[0055] Fourth step: In response to the determination that the actual activity range of the first cycle is similar to the actual activity range of the second cycle obtained by continuing to execute the second processing step above, the input information set corresponding to the patient's interactive limbs is adjusted to obtain the second adjusted input information set;

[0056] Here, the second processing step can be implemented using the average interval mapping algorithm, as shown in the following formula: here, Here, N is a numerical value; for example, the value of N is 5.

[0057] The fifth step is to determine the first adjusted input information set and the second adjusted input information set as the adjusted input information set.

[0058] To address the technical challenges of inconsistent rehabilitation outcomes due to significant individual patient variability and poor physical condition, steps one through five integrate interval mapping and average interval mapping algorithms. This aims to achieve efficient and precise adaptive management of patients' motion information ranges during rehabilitation training. In the initial stage, given the potential for significant differences in joint range of motion among patients, a responsive interval mapping adaptive algorithm is introduced. This algorithm can rapidly adjust to varying motion amplitudes between individuals within a short timeframe, effectively adapting to initial range of motion changes within just a few cycles. This ensures appropriate stimulation and feedback are provided from the outset of training, stimulating patients' rehabilitation potential.

[0059] Step 103: In response to confirming that the above-mentioned adjusted input information set has been received, the action corresponding to the above-mentioned adjusted input information set is sent to the user, and the vibration module and exoskeleton module are controlled to achieve sensory synergistic stimulation.

[0060] In some embodiments, the execution entity may, in response to determining that the adjusted input information set has been received, send the action corresponding to the adjusted input information set to the user, and control the vibration module and the exoskeleton module to achieve sensory synergistic stimulation, wherein the user wears a head-mounted audiovisual feedback device.

[0061] Optionally, the aforementioned execution entity may, in response to determining that the adjusted input information set has been received, send the action corresponding to the adjusted input information set to the user, and control the vibration module and exoskeleton module to achieve sensory synergistic stimulation through the following steps:

[0062] The first step is to send the actions corresponding to the adjusted input information set to the user through a head-mounted audiovisual feedback device, so that the user can receive visual and auditory feedback.

[0063] The second step is to set the initial vibration intensity of the vibration module, wherein the vibration module is fixed around a specific muscle group of the patient's interactive limb, and the initial vibration intensity is within the acceptable range of the vibration response of the patient's interactive limb muscles.

[0064] The third step is to control the intensity of the initial vibration based on muscle contraction in order to achieve tactile feedback.

[0065] As an example, the aforementioned actuator can utilize multiple vibration motors, driven by a MOSFET amplifier circuit, and employ PWM (Pulse Width Modulation) technology to control the initial vibration intensity, thereby achieving vibration tactile feedback. When the patient performs an action with their virtual limb and muscle contraction, the patient can instantly feel the vibration response in the corresponding muscle area, and the vibration intensity can be gradually increased as the virtual limb muscles contract.

[0066] The fourth step is to set the torque for the exoskeleton module in response to determining the virtual foot's action.

[0067] Here, the torque mentioned above can refer to 0.65 Nm.

[0068] The fifth step is to drive and control the torque based on the foot movements to achieve force tactile feedback.

[0069] As an example, the aforementioned execution entity can first respond to determining the virtual foot's execution action, with the exoskeleton module responding in real time. By adjusting torque, it assists the patient's physical foot and ankle in synchronously performing corresponding movements, thereby achieving force-tactile feedback. See the exoskeleton force feedback device corresponding to force-tactile feedback. Figure 6 .

[0070] Step 104: Map the range of motion of the exoskeleton module to enable active rehabilitation training of the hemiplegic lower limb.

[0071] In some embodiments, the aforementioned execution entity may map the range of motion of the aforementioned exoskeleton module to achieve active rehabilitation training for hemiplegic lower limbs.

[0072] As an example, the aforementioned execution entity can use an interval mapping algorithm to map the movable range of the aforementioned exoskeleton module in order to achieve active rehabilitation training for hemiplegic lower limbs.

[0073] To address the issue of inconsistent rehabilitation outcomes due to significant individual patient variability and poor physical condition, this invention innovatively integrates interval mapping and average interval mapping algorithms to achieve efficient and precise adaptive management of patient movement information intervals during rehabilitation training. In the initial stage, given the potential for significant differences in joint range of motion among patients, a responsive interval mapping adaptive algorithm is introduced. This algorithm can quickly adjust to varying movement amplitudes between individuals within a short timeframe, effectively adapting to initial range of motion changes in just a few cycles. This ensures appropriate stimulation and feedback are provided from the outset of training, stimulating the patient's rehabilitation potential. Firstly, the motion interaction module integrates multiple interaction technologies, including optical capture, inertial capture, and eye tracking. It flexibly selects the most suitable interaction method based on the patient's specific condition and the actual rehabilitation environment, accurately capturing the patient's movement intentions and details. After complex signal processing and adaptive algorithm processing, the motion data is transmitted in real-time to the virtual scene, providing data for subsequent rehabilitation training. The virtual scene module uses a first-person perspective, allowing patients to intuitively observe the movement state of the three-dimensional human body model, enhancing immersion. For foot and ankle rehabilitation, gamified training tasks such as pulling a treasure chest were designed to ensure patients focus on key motor imagery and observation. This module receives motion data, drives virtual limb movements, coordinates visual and auditory feedback, and forwards data to the physical feedback module, ensuring the coordinated operation of the entire system. The physical feedback module combines vibration feedback and exoskeleton force feedback technology to provide precise and appropriate stimulation to the patient's affected limb, enhancing the activation of motor nerves and the reconstruction of muscle memory. Vibration intensity and exoskeleton range of motion are dynamically adjusted according to the patient's actual condition, ensuring both safety and effectiveness. The monitoring and analysis module uses various bioindicators such as medical scales, surface electromyography (EMG) signals, and electroencephalography (EEG) signals to comprehensively assess the patient's rehabilitation progress. Based on the data analysis results, doctors can dynamically adjust training programs and parameter settings, forming a closed-loop training system from customized training to effect monitoring and program optimization, ensuring the continuity and targeting of rehabilitation training. The active rehabilitation training method mentioned in this disclosure is simple to operate, has a low workload, and assesses the patient's training status through real-time monitoring and analysis, enabling therapists to personalize the training plan and ultimately form a progressive closed-loop rehabilitation process that integrates customization, training, monitoring, and assessment.

[0074] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an active rehabilitation training method for hemiplegic lower limbs based on virtual reality. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0075] like Figure 2As shown, some embodiments of the virtual reality-based active rehabilitation training device 200 for hemiplegic lower limbs include: an acquisition unit 201, an adjustment unit 202, a control unit 203, and a mapping unit 204. The acquisition unit 201 is configured to acquire an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction in rehabilitation training, and the input information set includes: motion information of the patient's hand, motion information of the patient's limbs, and position coordinate sampling point information of the patient's eye gaze point. The adjustment unit 202 is configured to adaptively adjust the input information set within a preset first cycle range and a preset second cycle range to obtain an adjusted input information set. The control unit 203 is configured to, in response to determining that the adjusted input information set has been received, send the action corresponding to the adjusted input information set to the user, and control the vibration module and the exoskeleton module to achieve sensory synergistic stimulation, wherein the user wears a head-mounted audiovisual feedback device. The mapping unit 204 is configured to map the range of motion of the exoskeleton module to achieve active rehabilitation training for hemiplegic lower limbs.

[0076] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0077] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0078] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 304. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 304 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0079] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0080] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0081] Figure 4 A framework diagram of some embodiments of a virtual reality-based active rehabilitation training method for hemiplegic lower limbs is shown.

[0082] Figure 5 The diagram shows a virtual training scene of some embodiments of an active rehabilitation training method for hemiplegic lower limbs based on virtual reality.

[0083] Figure 6 A diagram of an exoskeleton force feedback device is shown, illustrating some embodiments of an active rehabilitation training method for hemiplegic lower limbs based on virtual reality.

[0084] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0085] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0086] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire a set of input information from a virtual scene, wherein the input information set is used for virtual-real interaction in rehabilitation training, and the input information set includes: motion information of the patient's hand, motion information of the patient's limbs, and position coordinate sampling point information of the patient's eye gaze point; adaptively adjust the input information set within a preset first period range and a preset second period range to obtain an adjusted input information set; in response to determining that the adjusted input information set has been received, send the action corresponding to the adjusted input information set to the user, and control the vibration module and exoskeleton module to achieve sensory synergistic stimulation, wherein the user wears a head-mounted audiovisual feedback device; and map the range of motion of the exoskeleton module to achieve active rehabilitation training for hemiplegic lower limbs.

[0087] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including: an acquisition unit, an adjustment unit, a control unit, and a mapping unit. The names of these units do not necessarily limit the unit itself; for example, an adjustment unit can also be described as "a unit that adaptively adjusts the input information set within a preset first period range and a preset second period range to obtain an adjusted input information set."

[0090] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0091] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A virtual reality-based active rehabilitation training method for hemiplegic lower limbs, comprising: obtaining an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction of rehabilitation training, and the input information set comprises motion information of a patient's hand, motion information of a patient's limb, and position coordinate sampling point information of a patient's eye fixation point; performing adaptive adjustment on the input information set within a preset first cycle range and a preset second cycle range to obtain an adjusted input information set, comprising: for each cycle within the preset first cycle range, performing the following first processing step: linearly mapping a movable range interval of a patient's interactive limb to an actual activity interval of the patient's interactive limb to obtain a first actual activity interval; mapping the first actual activity interval to a first virtual activity interval to update a mapping parameter; applying the mapping parameter to the movable range of the patient's interactive limb to continue performing the first processing step; in response to determining that the first actual activity interval is similar to a second actual activity interval obtained by continuing to perform the first processing step, adjusting the input information set corresponding to the patient's interactive limb to obtain a first adjusted input information set, comprising: implementing the first processing step by an interval mapping algorithm; for each cycle within the preset second cycle range, performing the following second processing step: linearly mapping a movable range interval within the preset first cycle range to an actual activity interval of the patient's interactive limb to obtain a first cycle actual activity interval; mapping the first cycle actual activity interval to a first cycle virtual activity interval to update a first cycle mapping parameter; applying the first cycle mapping parameter to the movable range of the patient's interactive limb to continue performing the second processing step; in response to determining that the first cycle actual activity interval is similar to a second cycle actual activity interval obtained by continuing to perform the second processing step, adjusting the input information set corresponding to the patient's interactive limb to obtain a second adjusted input information set, comprising: implementing the second processing step by an average interval mapping algorithm; determining the first adjusted input information set and the second adjusted input information set as the adjusted input information set; in response to determining that the adjusted input information set is received, sending a motion corresponding to the adjusted input information set to a user, and controlling a vibration module and an exoskeleton module to realize sensory coordination stimulation, wherein the user wears a head-mounted audio-visual feedback device; performing movable range mapping on the exoskeleton module to realize active rehabilitation training for hemiplegic lower limbs.

2. The method of claim 1, wherein, The obtaining of the input information set of the virtual scene comprises: collecting motion information of a patient's hand by using a somatosensory interactor; wearing a nine-axis Bluetooth posture sensor to a patient's interactive limb to obtain a related information set of the patient's interactive limb in real time, wherein the related information set comprises joint activity angle information and acceleration information; detecting a fixation point position coordinate sampling point of a user according to a preset calling frequency by using an eye movement tracking device; determining the motion information of the patient's hand, the related information set, and the fixation point position coordinate sampling point as the input information set.

3. The method of claim 2, wherein, After the eye tracking device detects the gaze point position coordinate sample points of the user according to the preset calling frequency, the method further comprises: determining the direction of the user's eye movement and the distance of the user's eye movement by differentiating the two detected gaze point position coordinate sample points; determining the direction of the patient's interactive limb movement as the direction of the user's eye movement; linearly mapping the distance of the user's eye movement to the movement distance of the patient's interactive limb.

4. The method of claim 1, wherein, In response to determining that the adjusted input information set is received, the method further comprises: sending the action corresponding to the adjusted input information set to the user, and controlling the vibration module and the exoskeleton module to realize the sensory synergistic stimulation, including: sending the action corresponding to the adjusted input information set to the user through the head-mounted audio-visual feedback device, so that the user realizes visual feedback and auditory feedback; initial vibration intensity setting of the vibration module, wherein the vibration module is fixed around a specific muscle group of the patient's interactive limb, and the initial vibration intensity is within the acceptable intensity range of the muscle vibration response of the patient's interactive limb; intensity control of the initial vibration intensity according to muscle contraction to realize vibration haptic feedback; torque setting of the exoskeleton module in response to determining that the virtual foot performs an action; drive control of the torque according to the foot movement to realize force haptic feedback.

5. A virtual reality-based active rehabilitation training device for hemiplegic lower limbs, comprising: an acquisition unit configured to acquire an input information set of a virtual scene, wherein the input information set is used for virtual-real interaction of rehabilitation training, and the input information set includes movement information of a patient's hand, movement information of a patient's limb, and position coordinate sample point information of a patient's eye gaze point; an adjustment unit configured to adaptively adjust the input information set within a preset first cycle range and a preset second cycle range to obtain an adjusted input information set, including: for each cycle within the preset first cycle range, the following first processing steps are performed: linearly mapping the movable range interval of the patient's interactive limb to the actual activity interval of the patient's interactive limb to obtain a first actual activity interval; mapping the first actual activity interval to a first virtual activity interval to update a mapping parameter; applying the mapping parameter to the movable range of the patient's interactive limb to continue the first processing step; in response to determining that the first actual activity interval is similar to a second actual activity interval obtained by continuing the first processing step, adjusting the input information set corresponding to the patient's interactive limb to obtain a first adjusted input information set, including: implementing the first processing step through an interval mapping algorithm; for each cycle within the preset second cycle range, the following second processing steps are performed: linearly mapping the movable range interval within the preset first cycle range to the actual activity interval of the patient's interactive limb to obtain a first cycle actual activity interval; mapping the first cycle actual activity interval to a first cycle virtual activity interval to update a first cycle mapping parameter; applying the first cycle mapping parameter to the movable range of the patient's interactive limb to continue the second processing step; in response to determining that the first period actual activity interval is similar to a second period actual activity interval obtained by continuing to perform the second processing step, adjusting the input information set corresponding to the patient interaction limb to obtain a second adjusted input information set, including: implementing the second processing step by an average interval mapping algorithm; determining the first adjusted input information set and the second adjusted input information set as an adjusted input information set; a control unit configured to, in response to determining that the adjusted input information set is received, send a motion corresponding to the adjusted input information set to a user, and control a vibration module and an exoskeleton module to implement a sensory synergistic stimulation, wherein the user wears a head-mounted audio-visual feedback device; a mapping unit configured to perform a movable range mapping on the exoskeleton module to implement a hemiplegic lower limb active rehabilitation training. 6.An electronic device, comprising: one or more processors; a storage having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-4.

7. A computer readable medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method of any one of claims 1-4.

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