Abnormal gait intervention treatment scheme optimization method and device, equipment and medium

Abstract

The invention discloses an optimization method and device for an abnormal gait intervention treatment scheme, equipment and a medium, and relates to the technical field of intelligent medical treatment. The specific implementation mode comprises the steps that in the process of conducting abnormal gait intervention treatment on a target object, multi-modal physiological data and gait data of the target object are collected in real time; performing time sequence analysis on the multi-modal physiological data and the gait data within a preset duration, and dynamically and quantitatively evaluating the training state conversion condition of the target object; and if the training state conversion condition is that a training state type changes, optimizing related parameters of a current abnormal gait and rhythm intervention scheme, and generating a new abnormal gait and rhythm intervention scheme adaptive to the gait state and the physiological state of the target object to perform abnormal gait intervention treatment. According to the scheme, the individuation and adaptability of a new abnormal gait rhythm intervention scheme can be improved, and then 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 optimization methods, devices, equipment, and media for abnormal gait intervention treatment plans. Background Art

[0002] Gait disorders are a common problem for patients with many neurological diseases (such as Parkinson's disease, stroke, multiple sclerosis, etc.), affecting their daily activities and quality of life. Studies have shown that gait rhythm intervention therapy (such as music- or beat-based gait training) can significantly improve patients' gait stability, stride length, and step frequency. However, current gait rhythm intervention treatment programs mainly use preset beats or music to guide patients in walking rehabilitation training, and patients need to walk according to the rhythm; the walking rhythm is detected by simple sensors (such as accelerometers) and the metronome speed is manually adjusted; some studies have explored biofeedback methods, such as adjusting the music rhythm according to changes in the gait cycle, but the adjustment method is relatively simple, making it impossible to make personalized adjustments based on the patient's real-time status, resulting in limited intervention effects. Summary of the Invention

[0003] Aiming at the technical problems in the existing technology of gait rhythm intervention treatment programs such as single adjustment and poor intervention effect, an optimization method, device, equipment and medium for abnormal gait intervention treatment programs are provided.

[0004] According to a first aspect, a method for optimizing an abnormal gait intervention treatment plan is provided, comprising: During the abnormal gait intervention treatment of the target subject, real-time collection of multimodal physiological data and gait data of the target subject; Performing time series analysis on the multimodal physiological data and the gait data within a preset time period to dynamically and quantitatively evaluate the training state transition of the target subject; If the training state conversion situation is a change in the training state type, the relevant parameters of the current abnormal gait rhythm intervention plan are optimized, and a new abnormal gait rhythm intervention plan adapted to the gait state and physiological state of the target object is generated to perform abnormal gait intervention treatment. According to a second aspect, a device for optimizing an abnormal gait intervention treatment plan is provided, comprising: a data acquisition unit, configured to acquire multimodal physiological data and gait data of the target subject in real time during the abnormal gait intervention treatment of the target subject; a state quantification unit, configured to perform time series analysis on the multimodal physiological data and the gait data within a preset time period, and dynamically quantify and evaluate the training state transition of the target subject; A program optimization unit is used to optimize the relevant parameters of the current abnormal gait rhythm intervention program if the training state conversion situation is a change in the training state type, and generate a new abnormal gait rhythm intervention program adapted to the gait state and physiological state of the target object to perform abnormal gait intervention treatment.

[0005] 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 as described in any embodiment of the method for optimizing an abnormal gait intervention treatment plan.

[0006] 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 optimizing an abnormal gait intervention treatment plan is implemented.

[0007] According to the solution of the present application, it is proposed that during the process of abnormal gait intervention treatment (intervention correction) for the target subject, the multimodal physiological data and gait data of the target subject be collected in real time, and then a time series analysis be performed based on the multimodal physiological data and the gait data, and the training state conversion of the target subject be dynamically and quantitatively evaluated. Finally, if the training state conversion is a change in the training state type, the relevant parameters of the current abnormal gait rhythm intervention program are optimized in real time and dynamically, so that a new abnormal gait rhythm intervention program adapted to the gait state and physiological state of the target subject can be generated for abnormal gait intervention treatment (intervention correction, rehabilitation treatment). The optimization of the abnormal gait rhythm intervention program fully and comprehensively considers the multimodal physiological data and gait data, so that the optimization of the intervention program with personalized and multi-dimensional considerations can be achieved, avoiding the single adjustment method, which is conducive to improving the personalization and adaptability of the new abnormal gait rhythm intervention program, and thus is conducive to improving the effect of abnormal gait intervention rehabilitation treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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 optimizing an abnormal gait intervention treatment plan according to the present application; Figure 2 is a schematic diagram of an application scenario of the optimization method for abnormal gait intervention treatment plan according to the present application; Figure 3 is a structural schematic diagram of an embodiment of an optimization device for abnormal gait intervention treatment plan according to the present application; Figure 44 is a block diagram of an electronic device used to implement the optimization method of the abnormal gait intervention treatment plan in an embodiment of the present application. DETAILED DESCRIPTION

[0009] 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.

[0010] 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.

[0011] An exemplary system architecture of an embodiment of the present invention's method for optimizing an abnormal gait intervention treatment plan or apparatus for optimizing an abnormal gait intervention treatment plan 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.

[0012] Users can use multiple terminal devices to interact with the server over the network to receive or send messages. Terminal devices can be various physiological data and gait data collection devices, such as IMU (inertial sensor) gait data collection devices, myoelectric signal collection devices, and heart rate collection devices.

[0013] 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 feedback the processing results (such as relevant parameters of the abnormal gait rhythm intervention plan) to the terminal device.

[0014] It should be noted that the optimization method of the abnormal gait intervention treatment plan provided in the embodiment of the present application can be executed by a server or a terminal device, and accordingly, the optimization device of the abnormal gait intervention treatment plan can be set in the server or the terminal device.

[0015] It should be understood that there may be any number of terminal devices, networks, and servers depending on implementation requirements.

[0016] refer to Figure 1 , shows a process of an embodiment of an optimization method for an abnormal gait intervention treatment program according to the present application. The optimization method for an abnormal gait intervention treatment program includes the following steps: Step 101 : During the abnormal gait intervention treatment of a target subject, multimodal physiological data and gait data of the target subject are collected in real time.

[0017] Step 102 : performing a time series analysis on the multimodal physiological data and the gait data within a preset time period, and dynamically and quantitatively evaluating the training state transition of the target subject.

[0018] Step 103: If the training state conversion condition is a change in the training state type, relevant parameters of the current abnormal gait rhythm intervention program are optimized to generate a new abnormal gait rhythm intervention program adapted to the gait state and physiological state of the target subject for abnormal gait intervention treatment.

[0019] In some optional implementations of this embodiment, in order to formulate an abnormal gait rhythm intervention plan that is personalized for each target subject and strongly adapted to the physiological state, gait, etc., it is proposed that the initial relevant parameters of the abnormal gait rhythm intervention plan can be calculated based on gait data and multimodal physiological data (the gait data and multimodal physiological data can be obtained based on motion assessment or recent historical data of the target subject), for example, The initial training period is calculated based on gait data and physiological load using the following formula:

[0020] in, is the duration of the initial training cycle; is the duration of a baseline training cycle (e.g., a conventional training cycle or empirical value in the prior art); is the gait stability index of the target object; The heart rate load of the target object; is the muscle fatigue index of the target object; 、 、 They are the gait stability index, heart rate load, and muscle fatigue index of normal people; 、 、 All are adjusted weights.

[0021] In some optional implementations of this embodiment, the gait stability index S can be expressed as stride variability. For example, The stride length can be calculated using data collected by the IMU (inertial sensor), such as, stride length : Calculated by integrating the velocity collected by the IMU over the time between two consecutive steps:

[0022] in, is the instantaneous speed, The time of the previous step, Time for the next step; Gait stability index S:

[0023] in, For the Sub-stride, is the average step length, and M is the total number of steps.

[0024] In some optional implementations of this embodiment, the heart rate load It can be calculated by the heartbeat interval, for example,

[0025] in, is the interval between the first two consecutive heartbeats, is the interval between the next two consecutive heartbeats, and F is the total number of intervals between each two consecutive heartbeats.

[0026] In some optional implementations of this embodiment, the muscle fatigue index It can be calculated from the electromyographic signal value, for example,

[0027] in, is the electromyographic signal value of the muscle, The duration of the evaluation period.

[0028] In some optional implementations of this embodiment, the initial training intensity is calculated based on the gait data and physiological data using the following formula:

[0029] in, is the initial training intensity value, is a baseline training intensity value (for example, a conventional training intensity value or an experience value in the prior art); is the muscle fatigue index of the target object, The heart rate load of the target object; They are muscle fatigue index and heart rate load of normal people; To adjust the weight.

[0030] The initial stride training cadence is calculated from the multimodal physiological data and gait data using the following formula:

[0031] in, Training rhythm for initial strides; Training cadence for baseline strides; is the muscle fatigue index of the target object, is the gait stability index of the target object; They are the muscle fatigue index and gait stability index of normal people; To adjust the weight.

[0032] In some optional implementations of this embodiment, after obtaining the duration of the initial training cycle, the initial training intensity value, and the initial step training rhythm, an initial abnormal gait rhythm intervention plan can be obtained. Abnormal gait intervention treatment can then be performed based on the initial abnormal gait rhythm intervention plan. During the abnormal gait intervention treatment process, the initial abnormal gait rhythm intervention plan can be optimized based on real-time multimodal physiological data and gait data. The optimized abnormal gait rhythm intervention plan can then be repeatedly optimized based on the real-time, dynamic multimodal physiological data and gait data.

[0033] In some optional implementations of this embodiment, during the process of the target subject undergoing abnormal gait intervention treatment according to the initial abnormal gait rhythm intervention plan or the existing abnormal gait rhythm intervention plan, the target subject's multimodal physiological data and gait data are collected in real time, and the target subject's multimodal physiological data and gait data are subjected to time series analysis to dynamically and in real time quantitatively evaluate the target subject's training state transition (such as whether the training state will change, what type of training state it will change to, etc.), so as to discover training adaptability problems in advance. For example, the target subject's training state may include different state types such as adaptation state, fatigue state, recovery state, etc. By evaluating the target subject's training state transition, it is accurately determined whether the relevant parameters of the current abnormal gait rhythm intervention plan need to be optimized. Compared with a fixed training plan, this method can gradually adjust the intervention strategy according to the individual's real-time state, improve the training effect and reduce the risk of sports injuries.

[0034] For example, performing a time series analysis on the multimodal physiological data and the gait data within a preset time period to dynamically and quantitatively evaluate the training state transition of the target subject includes: The following time series analysis model is used to dynamically quantify and evaluate the probability of the target object entering different types of training states in the next stage, and the training state with the highest probability is output as the training state for the next stage:

[0035] in, Represents the predicted training state that the target object will enter in the next stage; is the current training status; The current multimodal physiological data and the gait data; is the state transition probability, which indicates the probability of entering different types of training states from the current training state in the next stage. The types of training states include adaptation, transition, and recovery; is the probability distribution of the multimodal physiological data and the gait data.

[0036] For example, the above time series analysis model infers that the next stage will enter different types of training states (i.e., S t+1 represents the probability of the training state to be entered in the next stage) (e.g., P(S t+1 = adaptation) = 0.6, P(S t+1 = overtraining) = 0.3, P(S t+1 = recovery) = 0.1), the training state with the highest probability (i.e., adaptation) is selected as the training state for the next stage. By comparing the training state for the next stage with the current training state, it can be determined whether the type of training state has changed compared to the current one.

[0037] In some optional implementations of this embodiment, the above-mentioned timing analysis model can be implemented based on a hidden Markov model (HMM), and then the training adaptability is predicted through the above-mentioned timing analysis model, and the training cycle and training intensity are adjusted based on the state transition probability. In some optional implementations of this embodiment, the present application has found through research that different physiological data or gait data can directly and in real time reflect the physiological state or gait condition of the target subject, and then based on the physiological data or gait data, the optimization direction of the relevant parameters of the abnormal gait rhythm intervention program can be qualitatively determined, for example, as shown in Table 1 below: The higher the gait stability index (S), the more precise the rhythm adjustment is required, and the amplitude of the intervention rhythm mutation is reduced; Increased Muscle Fatigue Index (MFI) → Indicates increased muscle tension and requires adjustments to cadence increase in training regimen. Heart rate load (HRV) decreases → Fatigue may occur, it is recommended to reduce training intensity or increase rest time.

[0038] Table 1

[0039] For example, cadence It can be calculated using the following data:

[0040] in, is the number of steps detected per unit time (calculated by heel strike events), The total duration of the unit time.

[0041] Therefore, this embodiment proposes to optimize the relevant parameters of the current abnormal gait rhythm intervention program according to the training state transition, including: If the heart rate load (HRV) continues to decrease, it means that the training load may be too heavy and the training period should be extended. Alternatively, if the gait stability index continues to increase, it means that the subject is gradually adapting to the training and the training period can be shortened to increase the training frequency. If the Muscle Fatigue Index (MFI) increases, it indicates that muscle coordination is reduced and abnormal muscle compensation may occur, so the training intensity should be reduced (for example, the training intensity needs to be reduced or recovery training content needs to be increased). Alternatively, if the heart rate load increases, it indicates that cardiorespiratory fitness has improved and the training intensity can be increased (for example, the training duration or difficulty can be appropriately increased). When the gait stability index (S) decreases, it means that the gait rhythm is gradually stabilizing. In this case, the stepping training rhythm should be increased to enhance gait control. Alternatively, when the muscle fatigue index increases, it indicates that the muscle control ability is declining. In this case, the stepping training rhythm should be reduced to reduce the risk of gait instability.

[0042] In some optional implementations of this embodiment, the training cycle determines the frequency and time span of the intervention. In order to dynamically adjust the training cycle and prevent over- or under-training, this embodiment uses time-weighted regression (TWR) to calculate the optimized training cycle: For example, the time-weighted regression method is used to calculate the optimized training cycle at the current moment using the following formula:

[0043] in, is the duration of the optimized training cycle; The first The duration of the training cycle; is the time weight, and the time weight of the training cycle closer to the current moment is greater; N is the total number of training cycles within the preset duration; and / or; The dynamic weight model is used to calculate the optimized training intensity at the current moment using the following formula to ensure that the target subject is training under a reasonable exercise load:

[0044] in, is the optimized training intensity value; is the training intensity value at the current moment; The adjustment factor for the e-th training intensity adjustment determined based on the fatigue level in the current training cycle (the adjustment factor can be determined based on the muscle fatigue index and gait variability), the value of the adjustment factor being dynamically determined based on changes in the gait stability index, the muscle fatigue index, and the heart rate load; is the training intensity adjustment amount for the e-th training intensity adjustment determined according to the change in training state during abnormal gait training (the training intensity adjustment amount can be determined based on the difference in training intensity between two adjacent training states), and E is the total number of training intensity adjustments in the current training cycle, where the training intensity is adjusted once for each change in training state; In specific implementation, it can be dynamically determined based on the changes in the gait stability index, the muscle fatigue index, and the heart rate load in various ways, for example, by calculation and confirmation through a trained neural network. For example, it can also be calculated and determined using the following formula:

[0045] in, are the threshold values of gait stability index, heart rate load, and muscle fatigue index under high load or critical fatigue state, are the weight coefficients of gait stability index, heart rate load, and muscle fatigue index, respectively, satisfying: .

[0046] and / or; The adjustment value of the walking training rhythm is calculated using the exponential smoothing method using the following formula. The adjustment value is added to the walking training rhythm at the current moment to obtain the optimized walking training rhythm at the current moment to improve gait stability:

[0047] in, is the adjustment value; is the current cadence; is the target cadence; is a smoothing factor, and its value range can be [0, 1], and its value varies depending on the adaptability of different individuals.

[0048] In some optional implementations of this embodiment, the training cycle may be the duration of the entire training process (including all required training frequencies). The training intensity value may be measured and calculated based on any one or any combination of the duration of a single training session, the training rhythm, and the difficulty of the training content.

[0049] In specific implementations, after determining the optimized training intensity value, the duration of a single training session, the training rhythm, the difficulty of the training content, etc. can be adjusted based on the optimized training intensity value to achieve corresponding training intensity adjustments. After determining the optimized step training rhythm, the beat or rhythm of the music, or the frequency of rhythm guidance such as physical stimulation (such as electrical stimulation, vibration stimulation, etc.) and voice prompts can be adjusted based on the optimized step training rhythm to achieve rehabilitation treatment for abnormal gait based on the optimized step training rhythm.

[0050] Continue to see Figure 2 , Figure 2 FIG. 1 is a schematic diagram of an application scenario of the optimization method for abnormal gait intervention treatment according to this embodiment. Figure 2 In the application scenario of FIG, the execution subject 201 collects multimodal physiological data and gait data 202 of the target subject in real time during the abnormal gait intervention treatment of the target subject. The execution subject 201 performs time series analysis on the multimodal physiological data and gait data within a preset time period, and dynamically and quantitatively evaluates the training state transition of the target subject 203. The execution subject 201 optimizes the relevant parameters of the current abnormal gait rhythm intervention plan 204 based on the training state transition, and generates a new abnormal gait rhythm intervention plan adapted to the gait state and physiological state of the target subject to perform abnormal gait intervention treatment.

[0051] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of an optimization device for abnormal gait intervention treatment program. 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.

[0052] like Figure 3As shown, the optimization device 300 for the abnormal gait intervention treatment program of this embodiment includes: a data acquisition unit 301, a state quantification unit 302, and a program optimization unit 303. Among them, the data acquisition unit 301 is configured to collect the target subject's multimodal physiological data and gait data in real time during the abnormal gait intervention treatment process; the state quantification unit 302 is configured to perform time series analysis on the multimodal physiological data and gait data within a preset time period, and dynamically quantify and evaluate the training state conversion of the target subject; the program optimization unit 303 is configured to optimize the relevant parameters of the current abnormal gait rhythm intervention program if the training state conversion is a change in the training state type, and generate a new abnormal gait rhythm intervention program adapted to the gait state and physiological state of the target subject for abnormal gait intervention treatment.

[0053] In this embodiment, the specific processing of the data acquisition unit 301, the state quantization unit 302 and the program optimization unit 303 of the abnormal gait intervention treatment program optimization device 300 and the technical effects thereof can be referred to respectively. Figure 1 The relevant descriptions of step 101, step 102 and step 103 in the corresponding embodiment are not repeated here.

[0054] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0055] like Figure 4 , is a block diagram of an electronic device according to an optimization method for an abnormal gait intervention treatment program 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.

[0056] like Figure 4As 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.

[0057] 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 optimizing the abnormal gait intervention treatment plan 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 optimizing the abnormal gait intervention treatment plan provided in this application.

[0058] 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 optimization method of the abnormal gait intervention treatment program in the embodiment of the present application (for example, the attached Figure 3 The 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 optimization method of the abnormal gait intervention treatment plan in the above-mentioned method embodiment.

[0059] 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 by the use of an electronic device according to the optimization method for an abnormal gait intervention treatment plan, 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 the optimization method for an abnormal gait intervention treatment plan 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.

[0060] The electronic device of the abnormal gait intervention treatment program optimization method 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.

[0061] Input device 403 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device processing the optimization method for abnormal gait intervention treatment plans. Input devices such as a touch screen, keypad, mouse, trackpad, touchpad, pointer, one or more mouse buttons, trackball, joystick, etc. can be used. Output device 404 can include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0062] 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.

[0063] 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.

[0064] 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).

[0065] 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.

[0066] 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.

[0067] 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.

[0068] The units involved in the embodiments described in this application can be implemented by software or hardware. The units described can also be set in a processor. For example, it can be described as: a processor includes a data acquisition unit, a state quantization unit, and a solution optimization unit. In some cases, the names of these units do not constitute a limitation on the units themselves. For example, the data acquisition unit can also be described as a "unit for collecting data."

[0069] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the device, the device: collects the multimodal physiological data and gait data of the target object in real time during the abnormal gait intervention treatment of the target object; performs time series analysis on the multimodal physiological data and the gait data within a preset time length, and dynamically quantifies and evaluates the training state conversion of the target object; if the training state conversion is a change in the training state type, optimizes the relevant parameters of the current abnormal gait rhythm intervention plan, and generates a new abnormal gait rhythm intervention plan adapted to the gait state and physiological state of the target object for abnormal gait intervention treatment.

[0070] 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 optimizing an abnormal gait intervention treatment plan, the method comprising: During the abnormal gait intervention treatment of the target subject, real-time collection of multimodal physiological data and gait data of the target subject; Performing time series analysis on the multimodal physiological data and the gait data within a preset time period to dynamically and quantitatively evaluate the training state transition of the target subject; If the training state conversion situation is a change in the training state type, the relevant parameters of the current abnormal gait rhythm intervention plan are optimized, and a new abnormal gait rhythm intervention plan adapted to the gait state and physiological state of the target object is generated to perform abnormal gait intervention treatment.

2. The method according to claim 1, wherein Performing time series analysis on the multimodal physiological data and the gait data within a preset time period to dynamically and quantitatively evaluate the training state transition of the target subject includes: The following time series analysis model is used to dynamically quantify and evaluate the probability of the target object entering different types of training states in the next stage, and the training state with the highest probability is output as the training state for the next stage: in, Represents the predicted training state that the target object will enter in the next stage; is the current training status; The current multimodal physiological data and the gait data; is the state transition probability, which indicates the probability of entering different types of training states from the current training state in the next stage. The types of training states include adaptation, transition, and recovery; is the probability distribution of the multimodal physiological data and the gait data.

3. The method according to claim 1, wherein Optimize relevant parameters of current abnormal gait rhythm intervention programs, including: If the heart rate load continues to decrease, extend the training period, or if the gait stability index continues to increase, shorten the training period; If muscle fatigue index increases, reduce training intensity, or if heart rate load increases, increase training intensity; If the gait stability index decreases, the step training cadence is increased, or if the muscle fatigue index increases, the step training cadence is decreased.

4. The method according to claim 3, wherein: Optimize relevant parameters of current abnormal gait rhythm intervention programs, including: The optimized training cycle at the current moment is calculated using the following formula: in, is the duration of the optimized training cycle; The first The duration of the training cycle; is the time weight, and the time weight of the training cycle closer to the current moment is greater; N is the total number of training cycles within the preset duration; and / or; The optimized training intensity at the current moment is calculated using the following formula: in, is the optimized training intensity value; is the training intensity value at the current moment; is the adjustment factor for adjusting the training intensity for the eth time determined based on the fatigue level in the current training cycle, the value of the adjustment factor being dynamically determined according to changes in the gait stability index, the muscle fatigue index, and the heart rate load; is the training intensity adjustment amount for the e-th training intensity adjustment determined according to the training state change during the abnormal gait training process, where E is the total number of training intensity adjustments in the current training cycle; and / or; The adjustment value of the walking training rhythm is calculated using the following formula, and the adjustment value is added to the walking training rhythm at the current moment to obtain the optimized walking training rhythm at the current moment: in, is the adjustment value; is the current cadence; is the target cadence; is the smoothing factor.

5. The method according to any one of claims 1 to 4, wherein Also includes: The duration of the initial training cycle was calculated based on the multimodal physiological data and gait data using the following formula: in, is the duration of the initial training cycle; is the duration of the baseline training period; is the gait stability index of the target object; The heart rate load of the target object; is the muscle fatigue index of the target object; 、 、 They are the gait stability index, heart rate load, and muscle fatigue index of normal people; 、 、 All are adjusted weights.

6. The method according to any one of claims 1 to 4, wherein Also includes: The initial training intensity value is calculated based on the multimodal physiological data and gait data using the following formula: in, is the initial training intensity value, is the baseline training intensity value; is the muscle fatigue index of the target object, The heart rate load of the target object; They are muscle fatigue index and heart rate load of normal people; To adjust the weight.

7. The method according to any one of claims 1 to 4, wherein Also includes: The initial stride training cadence is calculated from the multimodal physiological data and gait data using the following formula: in, Training rhythm for initial strides; Training cadence for baseline strides; is the muscle fatigue index of the target object, is the gait stability index of the target object; They are the muscle fatigue index and gait stability index of normal people; To adjust the weight.

8. A device for optimizing an abnormal gait intervention treatment plan, the device comprising: a data acquisition unit, configured to acquire multimodal physiological data and gait data of the target subject in real time during the abnormal gait intervention treatment of the target subject; a state quantification unit, configured to perform time series analysis on the multimodal physiological data and the gait data within a preset time period, and dynamically quantify and evaluate the training state transition of the target subject; A program optimization unit is used to optimize the relevant parameters of the current abnormal gait rhythm intervention program if the training state conversion situation is a change in the training state type, and generate a new abnormal gait rhythm intervention program adapted to the gait state and physiological state of the target object to perform abnormal gait intervention 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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