Method, device and equipment for recommending abnormal gait intervention correction scheme based on disease characteristics and medium

Abstract

The application discloses a recommendation method and device for abnormal gait intervention correction scheme based on disease characteristics, equipment and medium, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring multi-modal gait movement data of a target object suffering from a movement disorder disease in an abnormal gait rhythm intervention correction process; determining the type of the movement disorder disease of the target object according to the multi-modal gait movement data; determining corresponding disease movement characteristics according to the multi-modal gait movement data for the type of the movement disorder disease; and determining relevant parameters of an abnormal gait intervention correction scheme adapted to the target object according to the disease movement characteristics corresponding to the type of the movement disorder disease to perform abnormal gait intervention rehabilitation treatment. The scheme makes the recommended abnormal gait intervention correction scheme more personalized, accurate and adaptive for each target object, and is beneficial to improving the effect of abnormal gait intervention rehabilitation treatment.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to the field of smart healthcare technology, and in particular to recommended methods, devices, equipment, and media for abnormal gait intervention and treatment based on disease characteristics. Background Technology

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

[0003] Current gait rhythm interventions mainly employ fixed rhythms or manual adjustments, and primarily involve the following methods:

[0004] Fixed-rhythm intervention: Using a metronome or music with a fixed BPM (beats per minute) to guide the patient's walking. This is suitable for general gait training, but cannot be personalized according to the patient's gait characteristics.

[0005] Manually adjusting the rhythm: The therapist manually selects an appropriate rhythm based on the patient's gait assessment results, but this method relies on experience and cannot achieve real-time optimization, resulting in poor long-term effects.

[0006] Feedback adjustment system: Some studies use sensors to monitor gait parameters and adjust the rhythm based on feedback data, but usually only the cadence is adjusted, without optimization for different disease characteristics. Summary of the Invention

[0007] To address the technical problems of poor personalization of intervention programs and unsatisfactory long-term intervention effects in existing gait rhythm intervention technologies, this paper provides a method, device, equipment, and medium for recommending abnormal gait intervention and correction programs based on disease characteristics.

[0008] According to the first aspect, a recommended method for abnormal gait intervention and correction programs based on disease characteristics is provided, including:

[0009] Acquire multimodal gait data of target subjects with movement disorders during the intervention and correction of abnormal gait rhythms;

[0010] The type of movement disorder of the target object is determined based on the multimodal gait data;

[0011] For the aforementioned movement disorder type, the corresponding disease movement characteristics are determined based on the multimodal gait movement data;

[0012] Based on the movement characteristics corresponding to the movement disorder type, relevant parameters for an abnormal gait intervention and correction plan suitable for the target individual are determined to carry out abnormal gait intervention and rehabilitation treatment.

[0013] According to the second aspect, a device for recommending abnormal gait intervention and correction programs based on disease characteristics is provided, comprising:

[0014] The data acquisition unit is used to acquire multimodal gait motion data of target subjects with movement disorders during the intervention and correction of abnormal gait rhythms.

[0015] The disease type determination unit is used to determine the type of movement disorder of the target object based on the multimodal gait data;

[0016] A motion feature determination unit is used to determine the corresponding disease motion features based on the multimodal gait motion data for the type of movement disorder.

[0017] The program recommendation unit is used to determine the relevant parameters of the abnormal gait intervention and correction program that is suitable for the target object based on the movement characteristics of the movement disorder type, so as to carry out abnormal gait intervention and rehabilitation treatment.

[0018] According to a third aspect, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement a method such as any embodiment of a recommended method for abnormal gait intervention and correction schemes based on disease characteristics.

[0019] According to a fourth aspect, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method of any embodiment of a recommended method for abnormal gait intervention and correction schemes based on disease characteristics.

[0020] According to the scheme of this application, multimodal gait motion data of the target subject is collected in real time during the abnormal gait rhythm intervention and correction (or intervention treatment) process. Based on the multimodal gait motion data, the type of movement disorder of the target subject is accurately determined. Then, based on the movement disorder type, the corresponding disease movement characteristics are determined dynamically and in real time according to the multimodal gait motion data, resulting in a disease movement feature set corresponding to the movement disorder type. Finally, based on all the disease movement features in the disease movement feature set corresponding to the movement disorder type, the relevant parameters of the abnormal gait intervention and correction scheme suitable for the target subject are determined for abnormal gait intervention treatment (intervention correction). The recommended abnormal gait rhythm intervention and correction scheme fully considers the different gait characteristics of different movement disorders. Based on the respective disease movement characteristics of different movement disorder types, the relevant parameters of the intervention scheme for different movement disorder types are determined, making the recommended abnormal gait intervention and correction scheme more consistent with and appropriate for the gait condition of the target subject's movement disorder. This makes the recommended abnormal gait intervention and correction scheme more personalized, accurate, and adaptable for each target subject, which is conducive to improving the effect of abnormal gait intervention and rehabilitation treatment and optimizing gait stability and coordination. Attached Figure Description

[0021] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0022] Figure 1 This is a flowchart of an embodiment of a recommended method for abnormal gait intervention and correction based on disease characteristics according to this application;

[0023] Figure 2 This is a schematic diagram of an application scenario of the recommended method for abnormal gait intervention and correction schemes based on disease characteristics according to this application;

[0024] Figure 3 This is a schematic diagram of a structure of an embodiment of a recommended device for an abnormal gait intervention and correction scheme based on disease characteristics according to this application;

[0025] Figure 4 This is a block diagram of an electronic device used to implement the recommended method for abnormal gait intervention and correction schemes based on disease characteristics according to embodiments of this application. Detailed Implementation

[0026] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] An exemplary system architecture of the embodiments of the method or apparatus for recommending intervention and correction schemes for abnormal gait based on disease characteristics in this application may include multiple terminal devices, a network, and a server. The network serves as the medium for providing communication links between the terminal devices and the server. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0029] Users can use multiple terminal devices to interact with the server via the network to receive or send messages, etc. The terminal devices can be various gait data acquisition devices, such as IMU (inertial measurement unit), plantar pressure sensors, and other gait data acquisition devices.

[0030] A 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 feed back the processing results (such as relevant parameters of the abnormal gait rhythm intervention and correction program) to the terminal device.

[0031] It should be noted that the method for recommending abnormal gait intervention and correction schemes based on disease characteristics provided in this application embodiment can be executed by a server or a terminal device. Correspondingly, the device for recommending abnormal gait intervention and correction schemes based on disease characteristics can be set in the server or terminal device.

[0032] It should be understood that, depending on the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0033] refer to Figure 1 This document illustrates a flowchart of an embodiment of a method for recommending disease-characteristic-based abnormal gait intervention and correction schemes according to this application. The method includes the following steps:

[0034] Step 101: Obtain multimodal gait motion data of the target subject with movement disorder during the abnormal gait rhythm intervention and correction process.

[0035] Step 102: Determine the type of movement disorder of the target object based on the multimodal gait data;

[0036] Step 103: For the type of movement disorder, determine the corresponding movement characteristics based on the multimodal gait data;

[0037] Step 104: Determine the relevant parameters of the abnormal gait intervention and correction scheme suitable for the target object based on the movement characteristics corresponding to the movement disorder type, so as to carry out abnormal gait intervention and rehabilitation treatment.

[0038] In some optional implementations of this embodiment, in order to recommend personalized abnormal gait rhythm intervention and correction schemes that are highly adapted to the gait movement of each target object and conform to the movement disorder, it is proposed that the movement disorder type of the target object can be determined based on the multimodal gait movement data. For example, the movement disorder type can be determined by the correspondence between gait movement data and movement disorder types; alternatively, the movement disorder type can be accurately and conveniently determined based on a trained network model. For example, multimodal gait movement data (including basic parameters such as cadence, stride length, and joint angles) of the target object can be collected using sensors such as IMU and plantar pressure, and a standardized feature vector can be constructed. .

[0039] ,

[0040] in, The stride lengths (in meters) are for the left and right feet, respectively. Standing postures for the left and right feet (unit: %) The angle of the shoulder changes over time (in degrees). The angle of the hip changes over time (in degrees).

[0041] Build lightweight disease classification models such as decision trees or XGBoost. , the feature vector Input disease classification model This will output the predicted labels for the movement disorder types:

[0042]

[0043] In some optional implementations of this embodiment, in order to accurately and personally determine the relevant parameters of intervention programs for different movement disorder types based on their respective disease movement characteristics, this embodiment proposes a method for determining the corresponding disease movement characteristics for different movement disorder types. For example,

[0044] If the movement disorder is Parkinson's disease (PD), then the bradykinesia index is determined based on the maximum angular velocity of the upper limb swing; the tremor index is determined based on the dominant frequency of hand tremor; and the frozen gait ratio is determined based on stride length.

[0045] Specifically, the method for determining the bradykinesia index based on the maximum angular velocity of the upper limb swing can be implemented using a trained network model, or it can be quantified and calculated precisely using the following formula:

[0046]

[0047] in, The bradykinesia index, For the first The maximum angular velocity of the upper limb swing. This represents the total number of repetitive upper limb swings.

[0048] Specifically, the method for determining the tremor index based on the dominant frequency of hand tremors can be implemented using a trained network model, or by using the dominant frequency of hand tremors... Directly determined as the tremor index , with tremor index Quantification and precise determination .

[0049] Specifically, the method for determining the frozen gait ratio based on stride length can be implemented using a trained network model, or it can be quantified and precisely calculated using the following formula:

[0050]

[0051] in, To freeze gait ratio, The standard deviation of stride, This represents the average stride length.

[0052] If the movement disorder is stroke, then the stride symmetry index is determined based on the stride length of the left and right feet; the standing symmetry index is determined based on the standing position of the left and right feet; and the upper and lower limb coordination index is determined based on the angle of the shoulder and the angle of the hip over time.

[0053] Specifically, the method for determining the stride symmetry index based on the stride length of the left and right feet can be implemented using a trained network model, or it can be quantified and calculated precisely using the following formula:

[0054]

[0055] in, The stride symmetry index, The stride lengths (in meters) are for the left and right feet, respectively.

[0056] Specifically, the method for determining the standing symmetry index based on the left and right foot stance can be implemented using a trained network model, or it can be quantified and precisely calculated using the following formula:

[0057]

[0058] in, The index of standing symmetry. Standing postures for the left and right feet respectively (unit: %).

[0059] Specifically, the method for determining the upper and lower limb coordination index based on the changes in shoulder and hip angles over time can be implemented using a trained network model, or it can be quantified and precisely calculated using the following formula:

[0060]

[0061] in, The upper and lower limb coordination index, The angle of the shoulder changes over time (in degrees). Let be the angle of the hip as a function of time (in degrees), and Cov be the covariance function.

[0062] If the type of movement disorder is a sports injury ( If the joint angle is used to determine the joint stability index, then the weight-bearing symmetry index of the two feet is determined based on the pressure on the soles of the left and right feet.

[0063] Specifically, the method for determining the joint stability index (or joint range of motion variability) based on joint angles can be implemented using a trained network model, or it can be quantified and precisely calculated using the following formula:

[0064]

[0065] in, It is a joint stability index. The standard deviation of the joint angle. θ represents the average joint angle, where θ is the joint angle that changes over time (in degrees).

[0066] 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 it can be quantified and calculated precisely using the following formula:

[0067]

[0068] in, The index of bipedal weight-bearing symmetry. These are the total plantar pressures of the left and right feet, respectively (in Newtons).

[0069] In some optional implementations of this embodiment, after determining the disease movement characteristics corresponding to different movement disorder types, in order to achieve the accuracy and personalization of the relevant parameters of the abnormal gait intervention and correction scheme, it is proposed to determine the relevant parameters of the abnormal gait intervention and correction scheme that are suitable for and conform to the different movement disorder types based on all the disease movement characteristics corresponding to each of the different movement disorder types, so as to ensure the stability and interpretability of the relevant parameters of the abnormal gait intervention and correction scheme and avoid feature generalization or cross-contamination.

[0070] For example, based on the movement characteristics corresponding to the movement disorder type, relevant parameters for determining an abnormal gait intervention and correction plan suitable for the target subject are included:

[0071] The duration of the intervention period is determined using the following formula based on the movement characteristics corresponding to the type of movement disorder:

[0072]

[0073] in, The duration of the intervention period, For the aforementioned movement disorder type The corresponding number The aforementioned disease movement characteristics, For the first The weights corresponding to the disease motion features. This represents the lower limit of the intervention period duration. The amplitude coefficient for adjusting the upper limit of the intervention cycle duration, where m represents the type of movement disorder. The total number of all the disease motion features mentioned above.

[0074] For example, taking stroke as an example of a movement disorder, the duration of the intervention cycle would be:

[0075]

[0076] If the parameters of the target object reflect good coordination (i.e.) , , If the value is low (indicating good coordination), then a shorter intervention period is recommended; if it shows high incoordination (i.e., low coordination), then a shorter intervention period is recommended. , , A large value indicates abnormal coordination, and the intervention cycle will be automatically lengthened to enhance the intervention stimulus.

[0077] For example, based on the movement characteristics corresponding to the movement disorder type, relevant parameters for determining an abnormal gait intervention and correction plan suitable for the target subject are included:

[0078] The intervention pace is determined based on the movement characteristics corresponding to the type of movement disorder using the following formula:

[0079]

[0080] in, The gait rhythm frequency used for intervention and correction of abnormal gait. For the aforementioned movement disorder type The corresponding number The aforementioned disease movement characteristics, It is a constant (its value can be between 60 and 90). For the first The mapping coefficients corresponding to the movement characteristics of the disease, where m is the type of movement disorder. The total number of all the disease motion features mentioned above.

[0081] For example, taking Parkinson's disease as an example of a movement disorder, the intervention rhythm would be:

[0082]

[0083] For example, based on the movement characteristics corresponding to the movement disorder type, relevant parameters for determining an abnormal gait intervention and correction plan suitable for the target subject are included:

[0084] By inputting all the movement characteristics of the movement disorder type into the classification model, the classification model outputs a rhythm pattern and an intervention mode pattern. 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.

[0085] For example, the classification model mentioned above It can take the form of a classifier to categorize movement disorder types. Corresponding to each of the aforementioned disease movement characteristics Input classification model Then, the recommended and predicted rhythm patterns and intervention method patterns can be output:

[0086]

[0087] As an intervention model, It includes rhythm patterns and intervention mode patterns. Rhythm patterns include fixed rhythms or dynamic rhythms (constant or progressive), and intervention mode patterns include any one or any combination of audio cues, visual guidance, and tactile feedback (such as vibration feedback, electrical stimulation).

[0088] See also Figure 2 , Figure 2 This is a schematic diagram illustrating an application scenario of the recommended method for abnormal gait intervention and correction schemes based on disease characteristics according to this embodiment. Figure 2 In the application scenario, the execution entity 201 acquires multimodal gait motion data 202 of a target subject suffering from a movement disorder during the abnormal gait rhythm intervention and correction process. The execution entity 201 determines the type of movement disorder of the target subject based on the multimodal gait motion data 203. For the specific movement disorder type, the execution entity 201 determines the corresponding disease movement characteristics based on the multimodal gait motion data 204. Based on the disease movement characteristics corresponding to the movement disorder type, the execution entity 201 determines relevant parameters 205 to adapt the abnormal gait intervention and correction plan for the target subject for abnormal gait intervention and rehabilitation treatment.

[0089] Further reference Figure 3 As an implementation of the methods shown in the above figures, this application provides an embodiment of a device for recommending abnormal gait intervention and correction schemes based on disease characteristics. This device embodiment is similar to... Figure 1 Corresponding to the method embodiment shown, in addition to the features described below, the device embodiment may also include [features related to...]. Figure 1 The method embodiments shown have the same or corresponding features or effects. This device can be specifically applied to various electronic devices.

[0090] like Figure 3As shown, the disease-feature-based abnormal gait intervention and correction scheme recommendation device 300 of this embodiment includes: a data acquisition unit 301, a disease type determination unit 302, a movement feature determination unit 303, and a scheme recommendation unit 304. The data acquisition unit 301 is configured to acquire multimodal gait movement data of a target subject suffering from a movement disorder during the abnormal gait rhythm intervention and correction process; the disease type determination unit 302 is configured to determine the type of movement disorder of the target subject based on the multimodal gait movement data; the movement feature determination unit 303 is configured to determine the corresponding disease movement features for the type of movement disorder based on the multimodal gait movement data. The scheme recommendation unit 304 is configured to determine relevant parameters for an abnormal gait intervention and correction scheme suitable for the target subject based on the disease movement features corresponding to the type of movement disorder, for abnormal gait intervention and rehabilitation treatment.

[0091] In this embodiment, the specific processing of the data acquisition unit 301, disease type determination unit 302, motion feature determination unit 303, and scheme recommendation unit 304 of the disease-characteristic abnormal gait intervention and correction scheme recommendation device 300, and the resulting technical effects, can be referred to respectively. Figure 1 The relevant descriptions of steps 101, 102, 103 and 104 in the corresponding embodiments will not be repeated here.

[0092] According to embodiments of this application, this application also provides an electronic device and a readable storage medium.

[0093] like Figure 4 The diagram shown is a block diagram of an electronic device for a recommended method of abnormal gait intervention and correction scheme based on disease characteristics according to an embodiment of this 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0094] like Figure 4As shown, the electronic device includes one or more processors 401, a memory 402, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on 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, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 401 as an example.

[0095] The memory 402 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the recommended method of the disease-characteristic-based abnormal gait intervention and correction scheme provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the recommended method of the disease-characteristic-based abnormal gait intervention and correction scheme provided in this application.

[0096] Memory 402, as a non-transitory computer-readable storage medium, 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 recommended method for abnormal gait intervention and correction schemes based on disease characteristics in the embodiments of this application (e.g., appendix). Figure 3 The data acquisition unit 301, disease type determination unit 302, motion characteristic determination unit 303, and scheme recommendation unit 304 are shown. The processor 401 executes various server functions and data processing by running non-transient software programs, instructions, and modules stored in the memory 402, thereby implementing the method for recommending abnormal gait intervention and correction schemes based on disease characteristics in the above method embodiments.

[0097] Memory 402 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function. The data storage area may store data created by the use of the electronic device according to the recommended method of the disease-based abnormal gait intervention and correction program. Furthermore, memory 402 may include high-speed random access memory and may also include non-transient memory, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, which can be connected via a network to the electronic device of the recommended method of the disease-based abnormal gait intervention and correction program. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The electronic device for recommending intervention and correction programs for abnormal gait based on disease characteristics may further include: an input device 403 and an output device 404. The processor 401, memory 402, input device 403, and output device 404 may be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0099] Input device 403 can receive input numerical or character information, as well as key signal input related to user settings and function control of processing electronic devices that generate recommended methods for abnormal gait intervention and correction programs based on disease characteristics. Examples of input devices include touchscreens, keypads, mice, trackpads, touchpads, joysticks, one or more mouse buttons, trackballs, and joysticks. Output device 404 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touchscreen.

[0100] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] These computational 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, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0104] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The units described in the embodiments of this application can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a data acquisition unit, a disease type determination unit, a motion characteristic determination unit, and a plan recommendation unit. The names of these units do not necessarily limit the specific unit; for example, the data acquisition unit may also be described as a "unit for acquiring gait motion data."

[0107] In another aspect, this application also provides a computer-readable medium, which may be included in the apparatus described in the above embodiments; or it may exist independently and not assembled into the apparatus. The computer-readable medium carries one or more programs, which, when executed by the apparatus, cause the apparatus to: acquire multimodal gait motion data of a target object suffering from a movement disorder during an abnormal gait rhythm intervention and correction process; determine the type of movement disorder of the target object based on the multimodal gait motion data; determine corresponding disease movement characteristics for the type of movement disorder based on the multimodal gait motion data; and determine relevant parameters for an abnormal gait intervention and correction scheme suitable for the target object based on the disease movement characteristics corresponding to the type of movement disorder for abnormal gait intervention and rehabilitation treatment.

[0108] The above description is merely a preferred embodiment of this application and an explanation 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 technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for recommending intervention and correction programs for abnormal gait based on disease characteristics, the method comprising: Acquire multimodal gait data of target subjects with movement disorders during the intervention and correction of abnormal gait rhythms. The multimodal gait data includes cadence, stride length, and joint angles. The type of movement disorder of the target object is determined based on the multimodal gait data; For the aforementioned movement disorder type, the corresponding disease movement characteristics are determined based on the multimodal gait movement data; Based on the movement characteristics corresponding to the movement disorder type, relevant parameters for an abnormal gait intervention and correction plan suitable for the target object are determined for abnormal gait intervention and rehabilitation treatment. The relevant parameters include the duration of the intervention cycle, the intervention rhythm, the rhythm pattern, and the intervention method pattern. The rhythm pattern includes a fixed rhythm or a dynamic rhythm, and the intervention method pattern includes any one or any combination of audio prompts, visual guidance, and tactile feedback. For the aforementioned movement disorder type, the corresponding movement characteristics are determined based on the multimodal gait data, including: If the motor disorder is Parkinson's disease, the bradykinesia index is determined based on the maximum angular velocity of the upper limb swing; the tremor index is determined based on the dominant frequency of hand tremor; and the frozen gait ratio is determined based on stride length. The bradykinesia index is then calculated using the following formula: in, The bradykinesia index, For the first The maximum angular velocity of the upper limb swing. The total number of repetitive upper limb swings; the dominant frequency of hand tremors. The tremor index was determined to be... The frozen gait ratio is calculated using the following formula: in, To freeze gait ratio, The standard deviation of stride, This represents the average stride length. For the aforementioned movement disorder type, the corresponding movement characteristics are determined based on the multimodal gait data, including: If the motor disorder is stroke, the stride symmetry index is determined based on the stride length of the left and right feet; the standing symmetry index is determined based on the standing position of the left and right feet; and the upper and lower limb coordination index is determined based on the shoulder angle and hip angle over time. ,in, The upper and lower limb coordination index, The angle of the shoulder changes over time. Let be the angle of the hip as a function of time, and Cov be the covariance function. The stride symmetry index is calculated using the following formula: wherein, is the stride symmetry index, are the strides of the left and right foot, respectively; The standing relative symmetry index is calculated using the following formula: wherein, is the standing symmetry index, is the standing symmetry index for the left and right foot, respectively; For the aforementioned movement disorder type, the corresponding movement characteristics are determined based on the multimodal gait data, including: If the movement disorder is a sports injury, the joint stability index is determined based on the joint angle; the bipedal weight-bearing symmetry index is determined based on the plantar pressure of the left and right feet, and the joint stability index is calculated using the following formula: in, It is a joint stability index. The standard deviation of the joint angle. Let θ be the average joint angle, and θ be the joint angle as a function of time. The bipedal weight-bearing symmetry index is calculated using the following formula: wherein, is a bipedal weight-bearing symmetry index, is the total plantar pressure of the left and right foot, respectively; Based on the movement characteristics corresponding to the movement disorder type, relevant parameters for an appropriate abnormal gait intervention and correction plan for the target subject are determined, including: The duration of the intervention period is determined using the following formula based on the movement characteristics corresponding to the type of movement disorder: in, The duration of the intervention period, The first corresponding to the movement disorder type The aforementioned disease movement characteristics, For the first The weights corresponding to the disease motion features. This represents the lower limit of the intervention period duration. The amplitude coefficient for adjusting the upper limit of the intervention period duration is m, which is the total number of all the movement features corresponding to the movement disorder type.

2. The method of claim 1, wherein, Based on the movement characteristics corresponding to the movement disorder type, relevant parameters for an appropriate abnormal gait intervention and correction plan for the target subject are determined, including: The intervention pace is determined based on the movement characteristics corresponding to the type of movement disorder using the following formula: in, The gait rhythm frequency used for intervention and correction of abnormal gait. The first corresponding to the movement disorder type The aforementioned disease movement characteristics, It is a constant. The mapping coefficient is given for each of the disease motion features, and m is the total number of all the disease motion features corresponding to the movement disorder disease type.

3. The method according to claim 1, wherein, Based on the movement characteristics corresponding to the movement disorder type, relevant parameters for an appropriate abnormal gait intervention and correction plan for the target subject are determined, including: By inputting all the movement characteristics corresponding to the movement disorder type into the classification model, the classification model outputs rhythm pattern and intervention mode pattern.

4. A device for recommending an abnormal gait intervention and correction program based on disease characteristics, the device comprising: The data acquisition unit is used to acquire multimodal gait motion data of target subjects with movement disorders during the intervention and correction process of abnormal gait rhythm. The multimodal gait motion data includes cadence, stride length, and joint angles. The disease type determination unit is used to determine the type of movement disorder of the target object based on the multimodal gait data; A motion feature determination unit is used to determine the corresponding disease motion features based on the multimodal gait motion data for the type of movement disorder. The program recommendation unit is used to determine relevant parameters of the abnormal gait intervention and correction program suitable for the target object based on the movement characteristics of the movement disorder type to carry out abnormal gait intervention and rehabilitation treatment. The relevant parameters include the duration of the intervention cycle, the intervention rhythm, the rhythm pattern and the intervention method mode. The rhythm pattern includes a fixed rhythm or a dynamic rhythm, and the intervention method mode includes any one or any combination of audio prompts, visual guidance and tactile feedback. The motor characteristic determination unit is used to determine the bradykinesia index based on the maximum angular velocity of upper limb swing if the motor disorder is Parkinson's disease; determine the tremor index based on the dominant frequency of hand tremor; determine the frozen gait ratio based on stride length; and calculate the bradykinesia index using the following formula: ,in, The bradykinesia index, For the first The maximum angular velocity of the upper limb swing. The total number of repetitive upper limb swings; the dominant frequency of hand tremors. The tremor index was determined to be... The frozen gait ratio is calculated using the following formula: ,in, To freeze gait ratio, The standard deviation of stride, The stride length is the average; if the motor disorder is stroke, the stride symmetry index is determined based on the stride length of the left and right feet; the standing symmetry index is determined based on the standing position of the left and right feet; and the upper and lower limb coordination index is determined based on the shoulder angle and hip angle over time. ,in, The upper and lower limb coordination index, The angle of the shoulder changes over time. Let be the angle of the hip as a function of time, and Cov be the covariance function. The stride symmetry index is calculated using the following formula: in, The stride symmetry index, The stride lengths of the left and right feet are respectively; the standing symmetry index is calculated using the following formula: in, The index of standing symmetry. The standing postures are for the left and right feet, respectively. If the movement disorder is a sports injury, the joint stability index is determined based on the joint angle. The weight-bearing symmetry index is determined based on the plantar pressure of the left and right feet. The joint stability index is calculated using the following formula: ,in, It is an index of joint mobility stability. The standard deviation of the joint angle. Let θ be the average joint angle, and θ be the joint angle as a function of time. The bipedal weight-bearing symmetry index is calculated using the following formula: ,in, The index of bipedal weight-bearing symmetry. These are the total plantar pressures of the left and right feet, respectively. The program recommendation unit is used to determine the duration of the intervention cycle based on the movement characteristics corresponding to the movement disorder type using the following formula: in, The duration of the intervention period, The first corresponding to the movement disorder type The aforementioned disease movement characteristics, For the first The weights corresponding to the disease motion features. This represents the lower limit of the intervention period duration. The amplitude coefficient for adjusting the upper limit of the intervention period duration is m, which is the total number of all the movement features corresponding to the movement disorder type.

5. An electronic device, comprising: One or more processors; 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 as described in any one of claims 1-3.

6. A computer readable storage medium having stored thereon a computer program, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

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