Recommendation method and device of abnormal gait intervention correction scheme based on disease characteristics, equipment and medium

Abstract

The invention discloses a recommendation method and device for an abnormal gait intervention correction scheme based on disease features, equipment and a medium, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring multi-modal gait motion data of a target object suffering from dyskinesia in an abnormal gait rhythm intervention correction process; determining the dyskinesia disease type of the target object according to the multi-modal gait motion data; aiming at the dyskinesia disease type, determining corresponding disease movement characteristics according to the multi-modal gait movement data; and determining related parameters of an abnormal gait intervention correction scheme adapted to the target object according to the disease motion characteristics corresponding to the dyskinesia disease type so as to perform abnormal gait intervention rehabilitation treatment. According to the scheme, the recommended abnormal gait intervention correction scheme is more personalized, accurate and adaptive to each target object, and the effect of abnormal gait intervention rehabilitation treatment can be improved.

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 methods, devices, equipment, and media for recommending abnormal gait intervention treatment plans based on disease characteristics. Background Art

[0002] Gait disorders are common in patients with neurological diseases such as Parkinson's disease (PD), stroke, and multiple sclerosis (MS), impacting their ability to walk independently and their quality of life. Rhythmic Auditory Stimulation (RAS) is a correction and rehabilitation method that uses external rhythms (such as music or beats) to guide the patient's gait.

[0003] Current gait rhythm interventions are mainly performed using fixed rhythms or manual adjustments, and there are mainly the following methods: Fixed-rhythm intervention: Using a metronome or music with a fixed BPM (beats per minute) to guide the patient's walking is suitable for general gait training, but it cannot be personalized according to the patient's gait characteristics.

[0004] Manual rhythm adjustment: Rehabilitation therapists manually select the appropriate rhythm based on the patient's gait assessment results. However, this method relies on experience and cannot achieve real-time optimization, resulting in poor long-term effects.

[0005] Feedback regulation system: Some studies use sensors to monitor gait parameters and adjust cadence based on feedback data, but usually only adjust cadence and are not optimized for different disease characteristics. Summary of the Invention

[0006] In response to the technical problems of poor personalization of intervention plans and poor long-term intervention effects in the existing gait rhythm intervention technology, a recommended method, device, equipment and medium for an abnormal gait intervention correction plan based on disease characteristics are provided.

[0007] According to a first aspect, a method for recommending an abnormal gait correction intervention plan based on disease characteristics is provided, comprising: Acquiring multimodal gait motion data of target subjects with movement disorders during intervention and correction of abnormal gait rhythm; determining a movement disorder type of the target subject based on the multimodal gait motion data; For the movement disorder disease type, determining corresponding disease motion characteristics according to the multimodal gait motion data; Relevant parameters of an abnormal gait intervention correction program adapted to the target object are determined according to the disease movement characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0008] According to a second aspect, a device for recommending an abnormal gait correction intervention plan based on disease characteristics is provided, comprising: A data acquisition unit, used to obtain multimodal gait motion data of a target subject suffering from a movement disorder during an intervention and correction process for abnormal gait rhythm; a disease type determining unit, configured to determine a movement disorder disease type of the target subject based on the multimodal gait motion data; a motion feature determination unit, configured to determine, for the movement disorder disease type, a corresponding disease motion feature based on the multimodal gait motion data; A program recommendation unit is used to determine relevant parameters of an abnormal gait intervention correction program adapted to the target object based on the disease movement characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0009] 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 of any embodiment of the recommended method for an abnormal gait intervention correction plan based on disease characteristics.

[0010] 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 method for recommending an abnormal gait intervention correction plan based on disease characteristics is implemented.

[0011] According to the solution of the present application, multimodal gait motion data of a target subject during an abnormal gait rhythm intervention and correction (or treatment) is collected in real time, and the target subject's movement disorder disease type is accurately determined based on the multimodal gait motion data. Then, based on the movement disorder disease type, corresponding disease motion features are dynamically and in real time determined from the multimodal gait motion data to obtain a disease motion feature set corresponding to the movement disorder disease type. Finally, based on all disease motion features in the disease motion feature set corresponding to the movement disorder disease type, relevant parameters of an abnormal gait intervention and correction plan adapted to the target subject are determined for abnormal gait intervention and correction treatment (intervention correction). The recommendation of the abnormal gait rhythm intervention and correction plan fully considers the different gait motion characteristics of different movement disorders, and further determines the relevant parameters of the intervention plan for each movement disorder disease type based on the respective disease motion features corresponding to each movement disorder disease type. This makes the recommended abnormal gait intervention and correction plan more consistent with and tailored to the gait condition of the target subject's movement disorder disease, thereby making the recommended abnormal gait intervention and correction plan more personalized, accurate, and adaptable to each target subject, which is conducive to improving the effectiveness of abnormal gait intervention and rehabilitation treatment and optimizing gait stability and coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] 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: Figure 1 is a flow chart of an embodiment of a method for recommending an abnormal gait intervention and correction program based on disease characteristics according to the present application; Figure 2 is a schematic diagram of an application scenario of the recommended method for the abnormal gait intervention and correction scheme based on disease characteristics according to the present application; Figure 3 1 is a schematic structural diagram of an embodiment of a device for recommending an abnormal gait intervention and correction solution based on disease characteristics according to the present application; Figure 4 This is a block diagram of an electronic device used to implement the method for recommending an abnormal gait intervention and correction solution based on disease characteristics in an embodiment of the present application. DETAILED DESCRIPTION

[0013] 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.

[0014] 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.

[0015] An exemplary system architecture for embodiments of the method or apparatus for recommending an abnormal gait intervention and correction solution based on disease characteristics of the present application may include multiple terminal devices, a network, and a server. The network is a medium for providing a communication link between the terminal device and the server. The network may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0016] Users can use multiple terminal devices to interact with the server over the network to receive or send messages, etc. The terminal devices can be various gait motion data collection devices, for example, IMU (inertial sensor), plantar pressure sensor and other gait data collection devices.

[0017] The server can be a server that provides various services, such as a backend server that supports terminal devices. The backend server can analyze and process the received data and provide feedback (such as relevant parameters of an abnormal gait rhythm intervention and correction plan) to the terminal device.

[0018] It should be noted that the method for recommending an abnormal gait intervention and correction plan based on disease characteristics provided in the embodiment of the present application can be executed by a server or a terminal device. Accordingly, the device for recommending an abnormal gait intervention and correction plan based on disease characteristics can be set in a server or a terminal device.

[0019] It should be understood that there may be any number of terminal devices, networks, and servers depending on implementation requirements.

[0020] refer to Figure 1 , shows a process of an embodiment of a method for recommending an abnormal gait intervention and correction scheme based on disease characteristics according to the present application. The method for recommending an abnormal gait intervention and correction scheme based on disease characteristics includes the following steps: Step 101 : Acquire multimodal gait motion data of a target subject suffering from a movement disorder during an abnormal gait rhythm intervention and correction process.

[0021] Step 102: determining the movement disorder type of the target subject based on the multimodal gait motion data; Step 103: determining corresponding disease motion features according to the multimodal gait motion data for the movement disorder disease type; Step 104 : determining relevant parameters of an abnormal gait intervention correction program adapted to the target subject based on the disease movement characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0022] In some optional implementations of this embodiment, in order to recommend an abnormal gait rhythm intervention correction plan that is personalized for each target subject and strongly adapted to the gait motion conditions of the movement disorder, it is proposed that the movement disorder type of the target subject can be determined based on the multimodal gait motion data. For example, the movement disorder type can be determined by the correspondence between the gait motion data and the movement disorder type; the movement disorder type can also be accurately and conveniently determined based on a trained network model, for example, by using sensors such as IMU and plantar pressure to collect the target subject's multimodal gait motion data (including basic parameters such as cadence, stride, and joint angles), and constructing a standardized feature vector. .

[0023] , in, are the stride lengths of the left and right feet respectively (in meters), are the stance phases of the left and right feet respectively (in %), is the angle of the shoulder over time (in degrees), is the angle of the hip over time (in degrees).

[0024] Build lightweight disease classification models such as decision trees or XGBoost , the feature vector Input disease classification model , you can output the label of the predicted movement disorder type:

[0025] In some optional implementations of this embodiment, in order to accurately and individually determine the relevant parameters of intervention plans for different types of movement disorders based on the respective disease motion characteristics corresponding to different types of movement disorders, this embodiment proposes a method for determining the corresponding disease motion characteristics for different types of movement disorders, for example, If the movement disorder is Parkinson's disease (PD), the bradykinesia index is determined based on the maximum angular velocity of upper limb swing; the tremor index is determined based on the main frequency of hand tremor; and the freezing gait ratio is determined based on the stride length.

[0026] Specifically, the method for determining the bradykinesia index based on the maximum angular velocity of upper limb swing can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0027] in, is the bradykinesia index, For the The maximum angular velocity of the upper limb swing, is the total number of repeated upper limb swings.

[0028] Specifically, the method of determining the tremor index according to the main frequency of hand tremor can be implemented by using a trained network model, or by converting the main frequency of hand tremor to the main frequency of hand tremor. Directly determined as tremor index , tremor index quantification and precise determination, .

[0029] Specifically, the method for determining the freezing gait ratio based on the stride length can be implemented by using a trained network model, or can be quantified and accurately calculated using the following formula:

[0030] in, is the freezing gait ratio, is the standard deviation of stride length, is the mean stride length.

[0031] If the movement disorder is a stroke, the stride symmetry index is determined based on the stride lengths of the left and right feet; the stance phase symmetry index is determined based on the stance phases of the left and right feet; and the upper and lower limb coordination index is determined based on the angles of the shoulder and hip that change over time.

[0032] Specifically, the method for determining the stride symmetry index based on the stride lengths of the left and right feet can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0033] in, is the stride symmetry index, are the stride lengths of the left and right feet respectively (in meters).

[0034] Specifically, the method for determining the stance symmetry index based on the left and right stance phases can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0035] in, is the standing symmetry index, The stance phases of the left and right feet, respectively (in %).

[0036] Specifically, the method for determining the upper and lower limb coordination index based on the change in shoulder angle and hip angle over time can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0037] in, The upper and lower limb coordination index, is the angle of the shoulder over time (in degrees), is the angle of the hip changing over time (in degrees), and Cov is the covariance function.

[0038] If the movement disorder is a sports injury ( ), the joint movement stability index is determined according to the joint angle; the bipedal weight-bearing symmetry index is determined according to the plantar pressure of the left and right feet.

[0039] Specifically, the method for determining the joint motion stability index (or joint motion range variability) based on the joint angle can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0040] in, is the joint movement stability index, is the standard deviation of joint angles, is the average joint angle, and θ is the joint angle changing over time (in degrees).

[0041] Specifically, the method for determining the bipedal weight-bearing symmetry index based on the plantar pressure of the left and right feet can be implemented using a trained network model, or can be quantified and accurately calculated using the following formula:

[0042] in, is the bipedal weight-bearing symmetry index, The sum of the plantar pressures of the left and right feet respectively (in Newtons).

[0043] In some optional implementations of this embodiment, after determining the disease motion characteristics corresponding to different types of movement disorders, in order to achieve accurate and personalized relevant parameters of the abnormal gait intervention and correction plan, it is proposed to determine the relevant parameters of the abnormal gait intervention and correction plan that are adapted and consistent with different types of movement disorders based on all the disease motion characteristics corresponding to each of the different types of movement disorders, so as to ensure the stability and interpretability of the relevant parameters of the abnormal gait intervention and correction plan and avoid feature generalization or cross-contamination.

[0044] For example, determining relevant parameters of an abnormal gait intervention correction scheme adapted to the target subject based on the disease motion characteristics corresponding to the movement disorder disease type includes: The duration of the intervention period is determined according to the movement characteristics of the disease corresponding to the movement disorder disease type using the following formula:

[0045] in, is the duration of the intervention period, The movement disorder type The corresponding The motor characteristics of the disease, For the The weight corresponding to each of the disease motion characteristics, is the lower limit of the intervention period, is the amplitude coefficient for adjusting the upper limit of the intervention period, and m is the type of movement disorder The total number of corresponding disease motion features.

[0046] For example, taking stroke as an example of a movement disorder, the duration of the intervention cycle is:

[0047] If the parameters of the target object reflect good coordination (i.e. 、 、 If the value is low, it means good coordination), the recommended intervention period is short; if it is highly uncoordinated (i.e. 、 、 If the value is large, it indicates abnormal coordination), the duration of the intervention cycle will be automatically extended to enhance the intervention stimulus.

[0048] For example, determining relevant parameters of an abnormal gait intervention correction scheme adapted to the target subject based on the disease motion characteristics corresponding to the movement disorder disease type includes: The intervention rhythm is determined according to the movement characteristics of the movement disorder type using the following formula:

[0049] in, is the gait rhythm frequency used for abnormal gait intervention correction, The movement disorder type The corresponding The motor characteristics of the disease, is a constant (the value range can be 60-90), For the The mapping coefficient corresponding to the disease motion characteristics, m is the type of movement disorder disease The total number of corresponding disease motion features.

[0050] For example, if the movement disorder type is Parkinson's disease, the intervention rhythm is:

[0051] For example, determining relevant parameters of an abnormal gait intervention correction scheme adapted to the target subject based on the disease motion characteristics corresponding to the movement disorder disease type includes: By inputting all the disease movement features corresponding to the movement disorder disease type into a classification model, the classification model outputs a rhythm pattern and an intervention mode pattern, wherein the rhythm pattern includes a fixed rhythm or a dynamic rhythm, and the intervention mode pattern includes any one or any combination of audio prompts, visual guidance and tactile feedback.

[0052] For example, the classification model above It can be in the form of a classifier that classifies movement disorder disease types Corresponding motion characteristics of each of the diseases Input classification model After that, the recommended and predicted rhythm patterns and intervention mode patterns can be output:

[0053] For intervention mode, It includes rhythm patterns and intervention mode patterns. The rhythm patterns include fixed rhythm or dynamic rhythm (constant or progressive). The intervention mode patterns include any one or any combination of audio prompts, visual guidance and tactile feedback (such as vibration feedback, electrical stimulation).

[0054] Continue to see Figure 2 , Figure 2 This is a schematic diagram of an application scenario of the method for recommending an abnormal gait intervention and correction solution based on disease characteristics according to this embodiment. Figure 2 In an application scenario, the execution entity 201 obtains multimodal gait motion data 202 of a target subject suffering from a movement disorder during an abnormal gait rhythm intervention correction process. The execution entity 201 determines the movement disorder disease type 203 of the target subject based on the multimodal gait motion data. The execution entity 201 determines the corresponding disease motion features 204 based on the multimodal gait motion data for the movement disorder disease type. The execution entity 201 determines relevant parameters 205 of the abnormal gait intervention correction plan adapted to the target subject based on the disease motion features corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0055] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a device for recommending an abnormal gait intervention correction plan based on disease characteristics. Figure 1 Corresponding to the method embodiment shown, in addition to the features described below, the device embodiment may also include Figure 1 The device can be applied to various electronic devices.

[0056] like Figure 3 As shown, the device 300 for recommending an abnormal gait intervention correction scheme based on disease characteristics of this embodiment includes: a data acquisition unit 301, a disease type determination unit 302, a motion feature determination unit 303, and a scheme recommendation unit 304. The data acquisition unit 301 is configured to obtain multimodal gait motion data of a target subject suffering from a movement disorder during the abnormal gait rhythm intervention correction process; the disease type determination unit 302 is configured to determine the movement disorder disease type of the target subject based on the multimodal gait motion data; and the motion feature determination unit 303 is configured to determine the corresponding disease motion features based on the multimodal gait motion data for the movement disorder disease type. The scheme recommendation unit 304 is configured to determine the relevant parameters of the abnormal gait intervention correction scheme adapted to the target subject based on the disease motion features corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0057] In this embodiment, the specific processing of the data acquisition unit 301, the disease type determination unit 302, the motion feature determination unit 303 and the solution recommendation unit 304 of the device for recommending abnormal gait intervention correction solutions based on disease characteristics and the technical effects thereof can be referred to respectively. Figure 1 The relevant descriptions of step 101, step 102, step 103 and step 104 in the corresponding embodiment are not repeated here.

[0058] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0059] like Figure 4 , is a block diagram of an electronic device for a recommended method of an abnormal gait intervention correction scheme based on disease characteristics 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.

[0060] like Figure 4 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 4 A processor 401 is taken as an example.

[0061] 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 execute the method for recommending an abnormal gait intervention and correction solution based on disease characteristics provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to execute the method for recommending an abnormal gait intervention and correction solution based on disease characteristics provided in this application.

[0062] The memory 402 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the program instructions / modules corresponding to the method for recommending an abnormal gait intervention correction solution based on disease characteristics in the embodiment of the present application (for example, the attached Figure 3The processor 401 executes the non-transient software programs, instructions, and modules stored in the memory 402 to execute various functional applications and data processing of the server, thereby implementing the method for recommending abnormal gait intervention and correction solutions based on disease characteristics in the above-mentioned method embodiment.

[0063] 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 recommending a method for correcting abnormal gait intervention based on disease characteristics, 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 recommending a method for correcting abnormal gait intervention based on disease characteristics 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.

[0064] The electronic device for recommending a method for correcting abnormal gait intervention based on disease characteristics 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 4 The bus connection is taken as an example.

[0065] Input device 403 can receive input digital or character information, as well as key signal input related to user settings and function control of electronic devices that process recommendations for corrective interventions for abnormal gait based on disease characteristics. Input devices such as a touch screen, keypad, mouse, trackpad, touchpad, indicator stick, one or more mouse buttons, trackball, joystick, and the like can be used. Output device 404 can include a display device, auxiliary lighting (e.g., LED), and tactile feedback (e.g., vibration motor). Display devices may include, but are not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0066] Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpreted on a programmable system comprising 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.

[0067] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors 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.

[0068] 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).

[0069] 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 with 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.

[0070] 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.

[0071] 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.

[0072] The units involved in the embodiments described in this application can be implemented by software or by hardware. The units described can also be set in a processor. For example, it can be described as: a processor includes a data acquisition unit, a disease type determination unit, a motion feature determination unit, and a scheme recommendation unit. Among them, the names of these units do not constitute a limitation of the units themselves under certain circumstances. For example, the data acquisition unit can also be described as a "unit for collecting gait motion data."

[0073] 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-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the device, the device enables the device to: obtain multimodal gait motion data of a target subject suffering from a movement disorder during the abnormal gait rhythm intervention correction process; determine the movement disorder disease type of the target subject based on the multimodal gait motion data; for the movement disorder disease type, determine the corresponding disease motion characteristics based on the multimodal gait motion data; determine the relevant parameters of the abnormal gait intervention correction plan adapted to the target subject based on the disease motion characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

[0074] 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 herein is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for recommending an intervention and correction plan for abnormal gait based on disease characteristics, the method comprising: Acquiring multimodal gait motion data of target subjects with movement disorders during intervention and correction of abnormal gait rhythm; determining a movement disorder type of the target subject based on the multimodal gait motion data; For the movement disorder disease type, determining corresponding disease motion characteristics according to the multimodal gait motion data; Relevant parameters of an abnormal gait intervention correction program adapted to the target object are determined according to the disease movement characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

2. The method according to claim 1, wherein For the movement disorder disease type, determining corresponding disease motion characteristics based on the multimodal gait motion data includes: If the movement disorder is Parkinson's disease, the bradykinesia index is determined according to the maximum angular velocity of the upper limb swing; the tremor index is determined according to the main frequency of the hand tremor; and the freezing gait ratio is determined according to the stride length.

3. The method according to claim 1, wherein For the movement disorder disease type, determining corresponding disease motion characteristics based on the multimodal gait motion data includes: If the movement disorder is a stroke, the stride symmetry index is determined based on the stride lengths of the left and right feet; the stance phase symmetry index is determined based on the stance phases of the left and right feet; and the upper and lower limb coordination index is determined based on the angles of the shoulder and hip that change over time.

4. The method according to claim 1, wherein For the movement disorder disease type, determining corresponding disease motion characteristics based on the multimodal gait motion data includes: If the movement disorder is a sports injury, the joint movement stability index is determined based on the joint angle; and the bipedal weight-bearing symmetry index is determined based on the plantar pressures of the left and right feet.

5. The method according to claim 1, wherein Determining relevant parameters of an abnormal gait intervention correction program adapted to the target subject according to the disease motion characteristics corresponding to the movement disorder disease type includes: The duration of the intervention period is determined according to the movement characteristics of the disease corresponding to the movement disorder disease type using the following formula: in, is the duration of the intervention period, The movement disorder type The corresponding The motor characteristics of the disease, For the The weight corresponding to each of the disease motion characteristics, is the lower limit of the intervention period, is the amplitude coefficient for adjusting the upper limit of the intervention period, and m is the type of movement disorder The total number of corresponding disease motion features.

6. The method according to claim 1, wherein Determining relevant parameters of an abnormal gait intervention correction program adapted to the target subject according to the disease motion characteristics corresponding to the movement disorder disease type includes: The intervention rhythm is determined according to the movement characteristics of the movement disorder type using the following formula: in, is the gait rhythm frequency used for abnormal gait intervention correction, The movement disorder type The corresponding The motor characteristics of the disease, is a constant, is the mapping coefficient corresponding to each disease motion feature, and m is the type of movement disorder disease The total number of corresponding disease motion features.

7. The method according to claim 1, wherein Determining relevant parameters of an abnormal gait intervention correction program adapted to the target subject according to the disease motion characteristics corresponding to the movement disorder disease type includes: By inputting all the disease movement features corresponding to the movement disorder disease type into a classification model, the classification model outputs a rhythm pattern and an intervention mode pattern, wherein the rhythm pattern includes a fixed rhythm or a dynamic rhythm, and the intervention mode pattern includes any one or any combination of audio prompts, visual guidance and tactile feedback.

8. A device for recommending an intervention and correction plan for abnormal gait based on disease characteristics, the device comprising: A data acquisition unit, used to obtain multimodal gait motion data of a target subject suffering from a movement disorder during an intervention and correction process for abnormal gait rhythm; a disease type determining unit, configured to determine a movement disorder disease type of the target subject based on the multimodal gait motion data; a motion feature determination unit, configured to determine, for the movement disorder disease type, a corresponding disease motion feature based on the multimodal gait motion data; A program recommendation unit is used to determine relevant parameters of an abnormal gait intervention correction program adapted to the target object based on the disease movement characteristics corresponding to the movement disorder disease type to perform abnormal gait intervention rehabilitation treatment.

9. 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 7.

10. 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 7 is implemented.

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