Post-stroke ultra-early-stage bedside hand rehabilitation training method, system, equipment and medium

Abstract

The invention provides a post-stroke ultra-early-stage bedside hand rehabilitation training method, system and device and a medium, and relates to the technical field of intelligent medical treatment. Comprising the steps that in the process that a target patient conducts hand rehabilitation training according to rhythm audio, at least one preset hand action completed by the patient on each rhythm according to the preset hand training rhythm is collected and recognized, and action parameters corresponding to the hand actions are extracted; according to the action parameters, the hand training completion degree of the patient is analyzed; according to the completion degree, a preset hand training rhythm is dynamically adjusted, and hand rehabilitation training of the target patient is carried out based on the adjusted hand training rhythm. In the training of rhythmic auditory stimulation, a self-adaptive rhythm regulation and control mechanism fused with the action execution quality of the patient is introduced, so that a non-invasive rehabilitation scheme for hand fine exercise training is provided for the bedridden patient in the post-stroke ultra-early stage.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of smart medical technology, and in particular to a method, system, device and medium for ultra-early bedside hand rehabilitation training after stroke based on rhythmic auditory stimulation. Background Art

[0002] Stroke is a common disease that causes central nervous system dysfunction. Effective rehabilitation intervention, especially in the very early stages (24-72 hours after onset), is believed to play a key role in promoting neuroplasticity and functional recovery. Currently, clinical interventions often involve exercise rehabilitation and occupational therapy. However, these interventions typically rely on professional therapists and require patients to possess a certain level of physical strength and cooperation. Consequently, traditional hand rehabilitation training often relies on complex equipment or physical environments, cannot be performed while bedridden, and is therefore unsuitable for bedridden patients in the early stages of stroke.

[0003] Rhythmic Auditory Stimulation (RAS), as an important component of neuromusic therapy, has been widely used in lower limb motor rehabilitation, including gait training. Its basic principle is to stimulate the patient's nervous system through regular rhythmic signals, inducing synchronized rhythmic movement responses. However, existing RAS systems are mostly used for lower limb rehabilitation and lack targeted training designs for rhythmic guidance of the hands. Currently, there is no rhythmic training system specifically designed for fine hand function reconstruction that can be used at the bedside in the very early stages after stroke. Furthermore, current training systems mostly use preset fixed rhythms and instructions, lacking a closed-loop intelligent training method that dynamically adjusts the rhythm and prompt content based on the patient's movement quality (such as accuracy and amplitude). Summary of the Invention

[0004] Aiming at the problems that existing hand rehabilitation training lacks rhythmic guidance of the hands and cannot dynamically adjust the training program according to the patient's movement quality, a method, system, equipment and medium for ultra-early bedside hand rehabilitation training after stroke are provided.

[0005] According to a first aspect, a method for ultra-early bedside hand rehabilitation training after stroke is provided, comprising:

[0006] During the hand rehabilitation training of the target patient according to the rhythmic audio, collecting and identifying at least one preset hand movement performed by the target patient on each beat according to the preset hand training rhythm, and extracting movement parameters corresponding to the hand movement;

[0007] analyzing the hand training completion degree of the target patient according to the motion parameters corresponding to the hand motion;

[0008] According to the completion degree of the hand training of the target patient, the preset hand training rhythm is dynamically adjusted, and the hand rehabilitation training of the target patient is performed based on the adjusted hand training rhythm.

[0009] According to a second aspect, a post-stroke ultra-early bedside hand rehabilitation training system is provided, comprising:

[0010] A hand movement recognition module is used to collect and recognize at least one preset hand movement performed by the target patient at each beat according to a preset hand training rhythm during the target patient's hand rehabilitation training according to rhythmic audio, and to extract movement parameters corresponding to the hand movement;

[0011] an analysis module, configured to analyze the degree of completion of the hand training of the target patient based on the motion parameters corresponding to the hand motion;

[0012] The rhythm adjustment module is used to dynamically adjust the preset hand training rhythm according to the hand training completion degree of the target patient, and perform hand rehabilitation training for the target patient based on the adjusted hand training rhythm.

[0013] According to a third aspect, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method such as any embodiment of the ultra-early bedside hand rehabilitation training method after stroke.

[0014] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method of any embodiment of the ultra-early bedside hand rehabilitation training method after stroke is implemented.

[0015] According to the scheme of the present application, in the rehabilitation training process driven by rhythmic auditory stimulation, the present application introduces an adaptive rhythm control mechanism that integrates the patient's movement execution quality. By continuously monitoring the hand movements under each beat and extracting multiple movement parameters, the rhythmic auditory stimulation is precisely coupled with the specific hand movements. Then, the rhythm and movement difficulty are dynamically adjusted according to the completion quality of the patient's hand movements, ensuring that the patient continues to receive adaptive stimulation in the early stages of rehabilitation, and realizing personalized rhythm training and quantitative evaluation of bedridden stroke patients in the ultra-early stage, thereby providing bedridden patients with a non-invasive rehabilitation program for fine motor training of the hands in the ultra-early stage after stroke. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0017] Figure 1 is an exemplary system architecture diagram to which some embodiments of the present application may be applied;

[0018] Figure 2 is a flow chart of an embodiment of the ultra-early bedside hand rehabilitation training method after stroke according to the present application;

[0019] Figure 3 1 is a structural diagram of an embodiment of an ultra-early bedside hand rehabilitation training system after stroke according to the present application;

[0020] Figure 4 1 is a structural diagram of another embodiment of the ultra-early bedside hand rehabilitation training system after stroke according to the present application;

[0021] Figure 5 This is a block diagram of an electronic device used to implement the ultra-early bedside hand rehabilitation training method after stroke according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following description of exemplary embodiments of the present application is made in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the ultra-early bedside hand rehabilitation training method or ultra-early bedside hand rehabilitation training system after stroke of the present application can be applied.

[0025] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0026] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as video applications, live broadcast applications, instant messaging tools, email clients, social platform software, etc.

[0027] The terminal devices 101, 102, and 103 here can be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with display screens, including but not limited to smartphones, tablet computers, e-book readers, laptop computers, and desktop computers, etc. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, multiple software or software modules for providing distributed services), or they can be implemented as a single software or software module. No specific limitation is made here.

[0028] The server 105 may be a server that provides various services, such as a backend server that provides support for the terminal devices 101, 102, and 103. The backend server may analyze and process the collected data, such as motion parameters corresponding to the patient's hand motions, and feed back the processing results (e.g., the degree of completion of the patient's hand training) to the terminal device.

[0029] It should be noted that the ultra-early bedside hand rehabilitation training method after stroke provided in the embodiment of the present application can be executed by the server 105 or the terminal devices 101, 102, 103. Accordingly, the ultra-early bedside hand rehabilitation training system after stroke can be set in the server 105 or the terminal devices 101, 102, 103.

[0030] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0031] Continue to refer Figure 2 , shows a process 200 of an embodiment of a post-stroke ultra-early bedside hand rehabilitation training method according to the present application. The post-stroke ultra-early bedside hand rehabilitation training method comprises the following steps:

[0032] Step 201: During hand rehabilitation training of a target patient according to rhythmic audio, at least one preset hand movement performed by the target patient at each beat according to a preset hand training rhythm is collected and identified, and movement parameters corresponding to the hand movement are extracted;

[0033] Step 202: analyzing the hand training completion degree of the target patient according to the motion parameters corresponding to the hand motion;

[0034] Step 203: dynamically adjust the preset hand training rhythm according to the hand training completion degree of the target patient, and perform hand rehabilitation training for the target patient based on the adjusted hand training rhythm.

[0035] Since traditional hand rehabilitation training mostly relies on complex equipment or physical environment settings, it is not suitable for bedridden patients in the early stage of stroke. In addition, the training movement form is disconnected from the rhythmic stimulation, and there is a lack of rhythmic guidance for the patient's hand and personalized training feedback mechanism. Therefore, in this embodiment, the execution subject (such as Figure 1 The server or terminal device shown in the figure) takes rhythmic auditory stimulation as the core driving factor, and accurately couples rhythmic auditory stimulation with specific hand movements (such as clenching a fist and pinching) by building a closed-loop feedback system of "perception-analysis-regulation", providing a dynamic rhythm adjustment mechanism based on comprehensive feedback of data such as multiple motion parameters of the patient's hand movements, and constructs a comprehensive training feedback index for feedback on the quality of the patient's movement execution by continuously monitoring multiple motion parameters of the hand movements in each beat, supporting bedridden training, adapting to the ultra-early needs of stroke patients, and realizing precise rehabilitation training and quantitative evaluation of fine hand movements of bedridden stroke patients in the ultra-early stage.

[0036] After collecting one or more preset hand movements performed by the patient on each beat according to the preset hand training rhythm, it is necessary to extract and analyze the motion parameters of the hand movements, establish a rhythm dynamic adjustment mechanism based on the motion quality, and achieve closed-loop intervention. In some optional implementations of this embodiment, the degree of completion of the patient's hand training is analyzed based on the motion parameters corresponding to the hand movements, including: performing a weighted synthesis of multiple motion parameters corresponding to the hand movements to calculate the patient's hand training comprehensive score, so as to represent the degree of completion of the hand training through the hand training comprehensive score; wherein the motion parameters may include motion amplitude, motion accuracy, and rhythm synchronization score, etc.

[0037] During implementation, the patient's hand training movements are first collected. The patient lies in a supine or semi-recumbent position and wears the device. The device system plays a preset rhythmic sound and synchronizes voice prompts such as "clench a fist" and "relax" or "pinch" and "relax" to guide the patient in performing the corresponding movements. Through optical tracking, inertial sensors, or electromyography detection, the patient's hand movements are collected and identified, and parameters such as movement amplitude and accuracy are extracted.

[0038] For example, the patient's fist-clenching action is collected and the motion parameters of the fist-clenching action are extracted. Specifically, it includes:

[0039] Range of motion A fist : The degree of closure of a fist can be indicated by measuring the joint angle using a micro inertial measurement unit (IMU) or by optical motion capture using an infrared high-speed camera.

[0040] Action accuracy E fist : Refers to the degree of fit between the accuracy of the patient's execution of the action and the standard action. Specifically, the degree of deviation between the patient's action and the preset ideal action is quantified, including the degree of fit in dimensions such as spatial trajectory, force, and speed. For example, when the patient makes a fist, whether the bending angle of each knuckle meets the standard, and whether the fingertips accurately touch the target area. Scoring can be performed based on the resolution of the sensor data and the degree of match with the standard action. For example, if the accuracy exceeds the set threshold (for example, 0.8), the action is considered completed.

[0041] Rhythm synchronization score S fist : Indicates the degree to which the fist-clenching action is synchronized with the rhythm within each beat of a rhythmic sound. The maximum error is controlled within ±300ms to be effective. Specifically, synchronization is evaluated by quantifying the time deviation between the action trigger point and the rhythm beat, such as: embedding a pulse marker (such as a 5ms square wave) in the RAS audio, marking the starting point of each beat (t_beat), and marking the rhythm track; using an inertial sensor to detect the starting point of the patient's fist, or using a high-speed camera (1000fps) to record the precise timestamp of the patient's fingertips touching the target object, and then calculating the time deviation. The time deviation is compared with the set threshold to determine the rhythm synchronization score. In addition, in order to reduce the error, a microphone can be used to synchronously record the rhythm sound actually heard by the patient to eliminate the device playback delay (calibration error <2ms).

[0042] Collect the patient's kneading movements and extract the movement parameters from the kneading movements. Specifically including:

[0043] Range of motion A pinch : Indicates the change in distance between the thumb and index finger, which is measured in the same way as when making a fist.

[0044] Action accuracy E pinch : Action recognition accuracy. If it exceeds the set threshold, the action is considered valid. The specific measurement method is the same as that of the fist-clenching action.

[0045] Rhythm synchronization score S pinch : Indicates the degree to which the pinching action is synchronized with the rhythm within each beat of a rhythmic sound. The specific measurement method is the same as the fist-clenching action.

[0046] Next, in order to achieve dynamic rhythm adjustment, the various indicators corresponding to the patient's hand movements (amplitude, synchronization, accuracy) are weighted and integrated to calculate the patient's hand training comprehensive score R n , and adjust the rhythm frequency according to the scoring results.

[0047] In specific implementation, the scoring formula for fist-clenching and pinching actions is as follows:

[0048]

[0049] in, are the comprehensive scores of the nth fist-clenching action and the nth pinching action, respectively. fist,n 、A pinch,n They represent the actual amplitude of the nth fist clenching action and the actual amplitude of the nth pinching action, respectively. They represent the reference amplitude of fist clenching and pinching respectively, S fist,n 、S pinch,n They represent the rhythm synchronization score of the n-th fist clenching action and the rhythm synchronization score of the n-th pinching action, respectively, and E fist,n 、E pinch,n The nth clenching and pinching movements are respectively represented by their accuracy. w1, w2, and w3 are weight coefficients, satisfying w1 + w2 + w3 = 1, representing the importance of movement amplitude, synchronization, and movement accuracy in the scoring. This embodiment introduces a quantified model for movement amplitude and accuracy, achieving highly sensitive rehabilitation assessment.

[0050] Get a comprehensive score of R n In some optional implementations of this embodiment, dynamically adjusting the preset hand training rhythm and / or the difficulty level of the movements includes matching the hand training comprehensive score with a preset score interval threshold, and dynamically adjusting the preset hand training rhythm and / or the difficulty level of the movements based on the matching result.

[0051] In specific implementation, the following formula can be used for dynamic rhythm adjustment:

[0052]

[0053] Where: f BPM is the current rhythm frequency, δ is the step size of rhythm adjustment, R min and R max is the preset scoring interval threshold.

[0054] When the patient completes the correct action, the system will automatically record the success or failure of the action and adjust the rhythm or difficulty of subsequent training. Specifically, when the patient's action is completed with high quality, the system automatically increases the rhythm frequency to increase the challenge of training; when it detects that the movement amplitude is insufficient or the recognition is inaccurate, the system can slow down the rhythm, or prompt the patient to switch to a basic movement instruction that is easier to execute, to ensure that the patient continues to receive adaptive stimulation in the early stages of rehabilitation. In this embodiment, the number of movements of the patient in a rhythmic sound, the rhythm synchronization or the length of time the training is completed are recorded to achieve early low-intensity, high-frequency fine motor stimulation. This method can be repeated multiple times a day, combined with the clinical rehabilitation process to gradually improve the training rhythm and movement complexity, forming a closed-loop intervention system.

[0055] In some optional implementations of this embodiment, the method further includes: obtaining all hand movements performed by the target patient in the rhythmic audio of a preset duration, and multiple movement parameters corresponding to each hand movement, and calculating the average value of each movement parameter respectively; obtaining the movement completion rate of the target patient in the rhythmic audio of a preset duration based on all hand movements performed by the target patient in the rhythmic audio of a preset duration; constructing a comprehensive scoring function based on the average value of each movement parameter and the movement completion rate to obtain a comprehensive score within the preset duration; dynamically adjusting the complexity of the target patient's training movements based on the comprehensive score within the preset duration, and performing hand rehabilitation training for the target patient based on the adjusted training movements.

[0056] This embodiment designs a dynamic training content adjustment mechanism. This mechanism uses the real-time collected movement parameters during training as input, and combines dynamic change trends with comprehensive scoring results to make multi-dimensional adjustments to the training rhythm, training content, music tempo, and movement intensity, ensuring that the patient is always in the optimal rehabilitation load state "slightly above the capacity limit."

[0057] In specific implementation, the process of action parameter trend analysis and comprehensive score calculation is as follows:

[0058] (1) This embodiment uses a time window sliding average method to analyze the following key parameters:

[0059] a. Range of motion A t

[0060] b. Action accuracy P t

[0061] c. Rhythm synchronization score S t

[0062] d. Action completion rate C t

[0063] Use the following sliding average formula to obtain the average value of the past N actions:

[0064]

[0065] Among them, X∈A,P,S,C represents parameters of different dimensions.

[0066] Construct a comprehensive scoring function based on the above parameters:

[0067]

[0068] in:

[0069] α, β, γ, δ∈[0,1], are configurable weight parameters, satisfying α+β+γ+δ=1

[0070] (2) Adaptive upgrade of action complexity and prompt content

[0071] The comprehensive score R within the preset time period t and the set threshold range (R low , R high ) to match, according to R t Contents of graded adjustment actions in the interval:

[0072] If R t <R low : Maintain basic movements (such as making a fist);

[0073] If R low ≤R t <R high : Introducing intermediate movements (such as pinching and alternating);

[0074] If R t ≥R high : Introduce advanced compound movements (such as rapid alternation + visual feedback).

[0075] In some optional implementations of this embodiment, the method further includes: calculating a decreasing trend in the continuous movement amplitude based on the movement amplitudes of all hand movements performed by the target patient in the rhythmic audio of a preset duration; and determining whether to provide a movement intensity adjustment prompt to the target patient based on the decreasing trend in the continuous movement amplitude.

[0076] In specific implementation, the system determines whether to add guidance words based on the decreasing trend of the continuous action amplitude (as shown in the following formula):

[0077]

[0078] If ΔA t<-∈(set threshold), the system adds instructions such as "harder" and "larger amplitude" in the voice prompts.

[0079] In order to ensure that the patient can follow the rhythm and complete the movements during the execution of the movements, some optional implementations of this embodiment also include: providing movement prompts to the patient through voice and / or visual means to guide the patient to complete at least one preset hand movement on each beat according to the preset hand training rhythm, or providing the target patient with movement intensity adjustment prompts.

[0080] In practice, voice prompts are played synchronously with the rhythmic signal through an audio output device, guiding the patient to perform the predetermined movement on each beat. The voice prompts include the name of the movement, the execution time, and relaxation instructions, ensuring that the patient can complete the movement to the rhythm. Optionally, visual prompts are used. In some cases, the system can be combined with visual prompts (such as displaying movement diagrams on the screen) to help patients better understand the movement requirements.

[0081] In actual application, the action prompt content can be dynamically adjusted according to the patient's execution status. In some optional implementations of this embodiment, if the patient's action accuracy E is set for a continuous number of times in the hand training rhythm, n If the accuracy is less than the set threshold ∈, the type of the current hand movement is changed by means of action prompts, that is, it is switched to a simpler action prompt, such as "clenching a fist" instead of "pinching". If the patient's rhythm synchronization score S in the hand training rhythm is n If the score exceeds the set scoring threshold and the movement amplitude meets the set amplitude standard, the current hand movement type is changed through action prompts, and more complex movements (such as "alternating fist clenching and pinching") can be added. This embodiment realizes the integration of rhythm and action prompts, further improving the rhythm and compliance of training.

[0082] In some optional implementations of this embodiment, the method further includes: if the target patient's action completion rate in the rhythmic audio of the preset duration is less than a set threshold (such as C t <0.5), or if the movement accuracy / rhythm synchronization score continues to decline within the preset time, the fatigue recognition and rhythm interval optimization mechanism will be automatically triggered; the fatigue recognition and rhythm interval optimization mechanism includes any one or more of reducing the rhythm frequency (slowing down the BPM), extending the relaxation beat duration, inserting rest guidance voice, and pausing training to enter the recovery stage.

[0083] In some optional implementations of this embodiment, the method further includes: recording the patient's hand movement training data, and generating the patient's current training quality score based on the hand movement training data; wherein the patient's hand movement training data includes patient ID, training time, type of hand movement and movement parameters corresponding to the hand movement, and the patient's hand training completion degree.

[0084] During implementation, all recorded patient data can be stored in a structured format, linked to patient ID, training time, movement type, and parameter tags, allowing upload to a cloud platform for long-term tracking and personalized modeling. Optionally, a quality score for the current training can be generated based on parameters such as movement completion rate, movement amplitude compliance rate, and rhythm synchronization score. Based on the patient's current training quality score, the hand training rhythm and / or movement complexity can be dynamically adjusted, providing a basis for clinical feedback and patient motivation, thereby generating a new hand rehabilitation training plan for the patient.

[0085] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a bedside hand rehabilitation training system for post-stroke patients in the early stage. Figure 2 Corresponding to the method embodiment shown, in addition to the features described below, the system embodiment may also include Figure 2 The system can be applied to various electronic devices.

[0086] like Figure 3 As shown, a super-early bedside hand rehabilitation training system 300 after stroke in this embodiment includes: a hand movement recognition module 301, which is used to collect and identify at least one preset hand movement performed by the target patient on each beat according to a preset hand training rhythm during the process of the target patient performing hand rehabilitation training according to rhythmic audio, and extract the movement parameters corresponding to the hand movement; an analysis module 302, which is used to analyze the degree of completion of the hand training of the target patient according to the movement parameters corresponding to the hand movement; a rhythm adjustment module 303, which is used to dynamically adjust the preset hand training rhythm according to the degree of completion of the hand training of the target patient, and perform hand rehabilitation training of the target patient based on the adjusted hand training rhythm.

[0087] In this embodiment, the specific processing of the hand movement recognition module 301, the analysis module 302 and the rhythm adjustment module 303 of the ultra-early bedside hand rehabilitation training system 300 after stroke and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of step 201, step 202 and step 203 in the corresponding embodiment are not repeated here.

[0088] In some optional implementations of this embodiment, the analysis module 302 performs weighted synthesis on multiple motion parameters corresponding to the hand motion to calculate a comprehensive hand training score for the patient, so as to characterize the degree of completion of the hand training through the comprehensive hand training score; wherein the motion parameters include motion amplitude, motion accuracy and rhythm synchronization score.

[0089] The analysis module 302 also supports analysis of the execution time, frequency, and accuracy of each action, providing quantitative data for subsequent rehabilitation plans.

[0090] In some optional implementations of this embodiment, the rhythm adjustment module 303 matches the hand training comprehensive score with a preset score interval threshold, and dynamically adjusts the preset hand training rhythm according to the matching result.

[0091] In some optional implementations of this embodiment, such as Figure 4 As shown, the ultra-early bedside hand rehabilitation training system 300 after stroke also includes a training content dynamic adjustment module 304, which is used to obtain all hand movements completed by the target patient in the rhythmic audio of a preset duration, and multiple movement parameters corresponding to each hand movement, and calculate the average value of each movement parameter respectively; based on all hand movements completed by the target patient in the rhythmic audio of a preset duration, obtain the movement completion rate of the target patient in the rhythmic audio of a preset duration; based on the average value of each movement parameter and the movement completion rate, construct a comprehensive scoring function to obtain a comprehensive score within the preset duration; based on the comprehensive score within the preset duration, dynamically adjust the complexity of the target patient's training movements, and perform hand rehabilitation training on the target patient based on the adjusted training movements.

[0092] The training content dynamic adjustment module 304 is also used to calculate the downward trend of continuous movement amplitude based on the movement amplitude of all hand movements completed by the target patient in the rhythmic audio of a preset duration; and determine whether to provide the target patient with a movement intensity adjustment prompt based on the downward trend of continuous movement amplitude.

[0093] The training content dynamic adjustment module 304 is also used to automatically trigger the fatigue recognition and rhythm interval optimization mechanism if the target patient's action completion rate in the rhythmic audio of the preset duration is less than a set threshold, or the action accuracy / rhythm synchronization score within the preset duration continues to decline; the fatigue recognition and rhythm interval optimization mechanism includes any one or more of reducing the rhythm frequency, extending the relaxation beat duration, inserting rest guiding voice, and pausing training to enter the recovery stage.

[0094] In some optional implementations of this embodiment, such as Figure 4 As shown, the ultra-early bedside hand rehabilitation training system 300 after stroke also includes a motion prompt module 305, which is used to provide motion prompts to the patient through voice and / or visual means to guide the patient to complete at least one preset hand movement on each beat according to the preset hand training rhythm, or to provide the target patient with the motion intensity adjustment prompt. Optionally, if the patient's motion accuracy for a set number of consecutive times in the hand training rhythm is less than a set accuracy threshold, the motion prompt module 305 changes the type of the current hand movement; if the patient's rhythm synchronization score in the hand training rhythm exceeds a set score threshold, and the motion amplitude meets the set amplitude standard, the motion prompt module 305 changes the type of the current hand movement.

[0095] In some optional implementations of this embodiment, such as Figure 4 As shown, the ultra-early post-stroke bedside hand rehabilitation training system 300 also includes a training data recording module 306. The training data recording module 306 is used to record the patient's hand movement training data and generate the patient's current training quality score based on the hand movement training data. The patient's hand movement training data includes the patient ID, training time, hand movement type and corresponding movement parameters, and the patient's hand training completion level. The training data recording module 306 is a functional component in this system for collecting, storing, and analyzing key data during the patient's rehabilitation movement execution. Its main goal is to achieve quantitative evaluation of the training process and personalized intervention optimization.

[0096] The present embodiment provides a bedside hand rehabilitation training system 300 for ultra-early post-stroke patients. Based on the RAS principle, it enables ultra-early post-stroke patients to complete hand movements, such as clenching a fist and pinching fingers, at the bedside through auditory rhythm guidance, thereby activating the brain's motor pathways and improving hand motor ability. The system includes a hand movement recognition module 301, an analysis module 302, a rhythm adjustment module 303, a training content dynamic adjustment module 304, an action prompt module 305, and a training data recording module 306, and supports voice guidance, beat adjustment, and training data collection. The patient can complete a preset action once on each beat according to the rhythm, and the system records the response frequency, execution time, and quantitative evaluation parameters of finger activity, providing a basis for subsequent evaluation and training prescriptions. The system constitutes a closed-loop, individualized rehabilitation regulation strategy, which is suitable for ultra-early post-stroke neurological function reconstruction scenarios.

[0097] Optionally, the system of this embodiment also has an expandable motion recognition module and data recording interface to facilitate the integration of AI analysis and training prescription recommendation systems.

[0098] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0099] like Figure 5 , is a block diagram of an electronic device for an ultra-early bedside hand rehabilitation training method after stroke according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0100] like Figure 5 As shown, the electronic device includes: one or more processors 401, a memory 402, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 401 is taken as an example.

[0101] Memory 402 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor, causing the at least one processor to perform the ultra-early bedside hand rehabilitation training method for post-stroke provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the ultra-early bedside hand rehabilitation training method for post-stroke provided in this application.

[0102] The memory 402 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the ultra-early bedside hand rehabilitation training method after stroke in the embodiment of the present application (for example, the attached Figure 4The processor 401 executes the non-transient software programs, instructions, and modules stored in the memory 402 to execute various server functional applications and data processing, thereby implementing the ultra-early bedside hand rehabilitation training method after stroke in the above-mentioned method embodiment.

[0103] The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the electronic device for ultra-early bedside hand rehabilitation training after stroke, etc. In addition, the memory 402 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 402 may optionally include a memory remotely located relative to the processor 401, and these remote memories may be connected to the electronic device for ultra-early bedside hand rehabilitation training after stroke via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The electronic device of the ultra-early bedside hand rehabilitation training method after stroke may further include: an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403 and the output device 404 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0105] The input device 403 can receive input digital or character information, and generate key signal input related to user settings and function control of the electronic device for ultra-early bedside hand rehabilitation training after stroke, such as input devices such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, and a joystick. The output device 404 may include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0106] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0111] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0112] The units involved in the embodiments described in this application can be implemented by software or hardware. The units described can also be set in a processor. For example, it can be described as: a processor includes a hand movement recognition module, an analysis module, and a rhythm adjustment module. Among them, the names of these units do not constitute a limitation of the units themselves in some cases. For example, the analysis module can also be described as a "module for calculating the degree of completion of the patient's hand training."

[0113] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the device, the device: collects and identifies at least one preset hand movement performed by the patient on each beat according to the preset hand training rhythm, and extracts the motion parameters corresponding to the hand movement; analyzes the degree of completion of the patient's hand training based on the motion parameters corresponding to the hand movement; dynamically adjusts the preset hand training rhythm and / or the complexity of the training movement based on the degree of completion of the patient's hand training, thereby achieving precise rehabilitation training and quantitative evaluation of fine hand movements of bedridden stroke patients in the ultra-early stage.

[0114] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A bedside hand rehabilitation training method for ultra-early post-stroke patients, characterized in that: The method comprises: During the hand rehabilitation training of the target patient according to the rhythmic audio, collecting and identifying at least one preset hand movement performed by the target patient on each beat according to the preset hand training rhythm, and extracting movement parameters corresponding to the hand movement; analyzing the hand training completion degree of the target patient according to the motion parameters corresponding to the hand motion; According to the completion degree of the hand training of the target patient, the preset hand training rhythm is dynamically adjusted, and the hand rehabilitation training of the target patient is performed based on the adjusted hand training rhythm.

2. The method according to claim 1, characterized in that Analyzing the hand training completion degree of the target patient according to the motion parameters corresponding to the hand motion, including: The multiple motion parameters corresponding to the hand motion are weighted and integrated to calculate the comprehensive hand training score of the target patient, so as to represent the degree of completion of the hand training through the comprehensive hand training score; wherein, the motion parameters include motion amplitude, motion accuracy and rhythm synchronization score.

3. The method according to claim 2, characterized in that Dynamically adjusting the preset hand training rhythm according to the target patient's hand training completion level includes: The hand training comprehensive score is matched with a preset score interval threshold, and the preset hand training rhythm is dynamically adjusted according to the matching result.

4. The method according to claim 2, characterized in that The method further comprises: Obtaining all hand movements performed by the target patient during the rhythmic audio of a preset duration, and a plurality of movement parameters corresponding to each hand movement, and calculating the average value of each movement parameter; Obtaining a movement completion rate of the target patient in the rhythmic audio of the preset duration based on all hand movements completed by the target patient in the rhythmic audio of the preset duration; Based on the average value of each action parameter and the action completion rate, a comprehensive scoring function is constructed to obtain a comprehensive score within the preset time period; According to the comprehensive score within the preset time period, the complexity of the training movements of the target patient is dynamically adjusted, and the hand rehabilitation training of the target patient is performed based on the adjusted training movements.

5. The method according to claim 4, characterized in that The method further comprises: Calculating a decreasing trend of continuous movement amplitude based on the movement amplitudes of all hand movements performed by the target patient during the rhythmic audio of a preset duration; Based on the downward trend of the continuous movement amplitude, it is determined whether to provide a movement intensity adjustment prompt to the target patient.

6. The method according to claim 5, characterized in that The method also includes: providing movement prompts to the target patient through voice and / or visual means to guide the patient to complete at least one preset hand movement on each beat according to a preset hand training rhythm, or providing movement intensity adjustment prompts to the target patient.

7. The method according to claim 6, characterized in that The method further comprises: If the target patient's movement accuracy for a set number of consecutive times in the hand training rhythm is less than a set accuracy threshold, the type of the current hand movement is changed by means of movement prompts; If the rhythm synchronization score of the target patient in the hand training rhythm exceeds a set score threshold and the movement amplitude meets the set amplitude standard, the type of the current hand movement is changed through movement prompts.

8. The method according to claim 4, characterized in that The method further comprises: If the target patient's action completion rate in the rhythmic audio of the preset duration is less than the set threshold, or the action accuracy / rhythm synchronization score continues to decline within the preset duration, the fatigue recognition and rhythm interval optimization mechanism will be automatically triggered; the fatigue recognition and rhythm interval optimization mechanism includes any one or more of reducing the rhythm frequency, extending the relaxation beat duration, inserting rest guidance voice, and pausing training to enter the recovery stage.

9. The method according to claim 1, characterized in that The method also includes: recording the hand movement training data of the target patient and generating a current training quality score of the patient based on the hand movement training data; wherein the hand movement training data of the target patient includes the patient ID, training time, the type of hand movement and the movement parameters corresponding to the hand movement, and the degree of completion of the hand training of the target patient.

10. The method according to claim 9, characterized in that The method further includes: dynamically adjusting the hand training rhythm and / or training movement complexity according to the current training quality score of the target patient, and generating a new hand rehabilitation training program for the target patient.

11. A bedside hand rehabilitation training system for post-stroke patients in the early stage, characterized by: include: A hand movement recognition module is used to collect and recognize at least one preset hand movement performed by the target patient at each beat according to a preset hand training rhythm during the target patient's hand rehabilitation training according to rhythmic audio, and to extract movement parameters corresponding to the hand movement; an analysis module, configured to analyze the degree of completion of the hand training of the target patient based on the motion parameters corresponding to the hand motion; The rhythm adjustment module is used to dynamically adjust the preset hand training rhythm according to the hand training completion degree of the target patient, and perform hand rehabilitation training for the target patient based on the adjusted hand training rhythm.

12. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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