Walking detection method based on intelligent walking aid and computer equipment

Abstract

The embodiment of the invention relates to the field of walking detection, in particular to a walking detection method based on an intelligent walking aid and computer equipment. A specific embodiment of the method comprises the following steps: respectively performing signal conversion processing on an initial walking pressure signal, an initial walking electromyographic signal and an initial walking acceleration signal of a target user, which are acquired in advance, within a preset duration to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence; carrying out integration processing to obtain walking muscle activity data; inputting to a pre-trained muscle feature extraction model to obtain walking muscle feature information; determining a strength difference value and muscle abnormality information; and in response to determining that the preset abnormal condition is met, switching the operation mode of the intelligent walking aid. According to the embodiment, the use safety of the intelligent walking aid is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of walking detection, and specifically to a walking detection method and computer equipment based on an intelligent walker. Background Art

[0002] In the elderly patient group, there are often many health problems after surgery, among which lower limb thrombosis and lung infection are more common, and muscle weakness or muscle loss is not uncommon. In fact, muscle loss in elderly patients is often systemic. This is because there is a close relationship between muscle, protein and the body's immune system, so the probability of sarcopenia after surgery is quite high. Compared with young people, elderly patients have obvious differences in tolerance to the body, tolerance to pain, and recovery ability after surgery. For this reason, the various postoperative problems mentioned above are more common in elderly patients, and the subsequent effects brought about by this should not be underestimated. In view of the high incidence of sarcopenia and the many adverse consequences it may cause, it is particularly important to prevent its occurrence. As far as the current prevention plan is concerned, it is mainly to encourage elderly patients to exercise their limbs as soon as possible and try to get out of the ground. However, it is extremely inconvenient for elderly patients to move after surgery, especially after undergoing major surgery, the rate of muscle loss will be significantly accelerated.

[0003] In response to this situation, the current solution is to use walkers to assist elderly patients in postoperative exercise. At the same time, a series of tests need to be carried out on patients during the exercise. The instruments used in these tests include body composition analyzers, which can measure the composition of the human body in detail. Various health indexes such as body fat, weight, BMI (body mass index), and non-fat mass can be accurately analyzed; there are also handgrips (grip dynamometers) that can be used to detect the patient's grip strength; and gait analysis detection instruments, which can evaluate patients from aspects such as walking posture. Through the comprehensive use of these instruments, the degree of muscle loss of patients can be monitored more systematically. This monitoring is usually started when the patient can start activities on the third day after surgery, and the patient will also be monitored once before surgery in order to collect relevant data, which will be compared and analyzed with the postoperative data later, so as to have a more comprehensive understanding of the changes in the patient's muscle status.

[0004] However, the existing rehabilitation prevention and treatment programs have many shortcomings. On the one hand, its operation is not convenient enough. Whether it is the use of the walker or the operation of various testing instruments, it requires a certain amount of manpower and material resources, which brings a lot of burden to medical staff and patients; on the other hand, the program lacks systematicity and cannot timely and completely monitor the patient's muscle state in all directions, which may lead to the omission of some important information, thereby affecting the accurate judgment and effective intervention of the patient's condition. As a result, the safety of the smart walker is reduced when in use. Summary of the invention

[0005] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present application propose a walking detection method, apparatus, computer device and computer-readable storage medium based on an intelligent walker to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present application provide a walking detection method based on an intelligent walker, the method comprising: performing signal conversion processing on the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, wherein the above-mentioned initial walking pressure signal is collected by a control pressure sensor, the above-mentioned initial walking electromyographic signal is collected by a control electromyographic sensor, and the above-mentioned initial walking acceleration signal is collected by a control acceleration sensor; integrating the above-mentioned walking muscle pressure value sequence, the above-mentioned walking muscle activity state information sequence and the above-mentioned walking motion parameter information sequence to obtain walking muscle activity data; inputting the above-mentioned walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information; determining the force difference value and muscle abnormality information corresponding to the above-mentioned walking muscle feature information and the pre-acquired initial muscle feature information; in response to determining that the above-mentioned force difference value is greater than the target threshold or the above-mentioned muscle abnormality information meets the preset abnormal condition, sending a preset mode switching signal to the mobile controller to switch the operation mode of the intelligent walker.

[0008] In a second aspect, some embodiments of the present disclosure provide a walking detection device based on an intelligent walker, the device comprising: a signal conversion unit, configured to perform signal conversion processing on the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, wherein the above-mentioned initial walking pressure signal is collected by a control pressure sensor, the above-mentioned initial walking electromyographic signal is collected by a control electromyographic sensor, and the above-mentioned initial walking acceleration signal is collected by a control acceleration sensor; an integration unit, configured to perform signal conversion processing on the above-mentioned walking muscle The muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence are integrated and processed to obtain walking muscle activity data; the input unit is configured to input the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information; the determination unit is configured to determine the force difference value and muscle abnormality information corresponding to the walking muscle feature information and the pre-acquired initial muscle feature information; the sending unit is configured to send a preset mode switching signal to the mobile controller to switch the intelligent walker operation mode in response to determining that the force difference value is greater than a target threshold or the muscle abnormality information meets a preset abnormal condition.

[0009] In a third aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the method described in any implementation of the first aspect is implemented.

[0010] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0011] The above-mentioned embodiments of the present application have the following beneficial effects: through the walking detection method based on the intelligent walker of some embodiments of the present application, the safety of the intelligent walker when in use can be improved. Specifically, the reason for the reduced safety of the intelligent walker when in use is that: on the one hand, its operation is not convenient enough. Whether it is the use of the walker or the operation of various detection instruments, it requires a certain amount of manpower and material resources, which brings a lot of burden to medical staff and patients; on the other hand, the system of the scheme is also lacking, and it is impossible to monitor the patient's muscle state in a timely and complete manner, resulting in the possibility of missing some important information, which in turn affects the accurate judgment and effective intervention of the patient's condition. Based on this, the walking detection method based on the intelligent walker of some embodiments of the present application, first, first, the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within the preset time length are respectively converted and processed to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence. Thus, the signal representing the user's muscle state can be collected and the collected electrical signal can be converted into analyzable data information. Then, the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence are integrated and processed to obtain walking muscle activity data. Thus, the information after each signal conversion can be integrated to extract muscle feature information. Next, the walking muscle activity data is input into a pre-trained muscle feature extraction model to obtain walking muscle feature information. Thus, the user's current muscle state characteristics can be obtained. Then, the force difference value and muscle abnormality information corresponding to the walking muscle feature information and the pre-acquired initial muscle feature information are determined. Thus, the user's muscle state change information can be obtained. Finally, in response to determining that the force difference value is greater than the target threshold or the muscle abnormality information meets the preset abnormal condition, a preset mode switching signal is sent to the mobile controller to switch the intelligent walker operation mode. Thus, the operation state of the intelligent walker can be adjusted according to the user's muscle state change information. Therefore, some walking detection methods based on smart walkers in the present application can embed the smart walker with the monitoring test data acquisition software. During the rehabilitation training process, the walker structure is equipped with sensors and other electronic equipment to conduct real-time testing of the patient's condition, promptly detect abnormal muscle loss in the patient, and reduce the number of times the patient falls due to muscle weakness, thereby improving the safety of the smart walker when in use. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of the walking detection method based on the intelligent walker according to the present application; Figure 2 is a schematic diagram of application scenarios of some embodiments of the walking detection method based on an intelligent walker according to the present application; Figure 3 is a schematic diagram of application scenarios of other embodiments of the walking detection method based on an intelligent walker according to the present application; Figure 4 is a schematic structural diagram of some embodiments of a walking detection device based on an intelligent walker according to the present application; Figure 5 is a schematic diagram of a computer device suitable for implementing some embodiments of the present application; Figure 6 It is a schematic diagram of the structure of some embodiments of the intelligent walking aid according to the present application. DETAILED DESCRIPTION

[0014] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0015] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0016] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0019] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0020] Figure 1 The process 100 of some embodiments of the walking detection method based on the intelligent walker according to the present application is shown. The walking detection method based on the intelligent walker is applied to the intelligent walker, and the intelligent walker includes: a pressure sensor, an electromyography sensor, an acceleration sensor, a mobile controller and a camera component, and includes the following steps: Step 101, performing signal conversion processing on the target user's initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal collected in advance within a preset time period, and obtaining a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence.

[0021] In some embodiments, the execution subject may perform signal conversion processing on the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence. Among them, the initial walking pressure signal may be collected by a control pressure sensor. The initial walking electromyographic signal may be collected by a control electromyographic sensor. The initial walking acceleration signal may be collected by a control acceleration sensor. Specifically, the target user may be a user who is walking with an intelligent walker. The pressure sensor may be a sensor for collecting the pressure signal of the target user. The electromyographic sensor may be a sensor for collecting the electromyographic signal of the target user. The acceleration sensor may be a sensor for measuring the acceleration of the target user. The mobile controller may be a controller for controlling the movement of the intelligent walker. The camera in the camera assembly may be a camera for capturing scene images. The camera assembly may be divided into a first camera assembly and a second camera assembly. Here, the first camera in the first camera assembly may be a camera located on a foot in the forward direction of the intelligent walker. The second camera assembly may be any camera in the camera assembly except the first camera assembly.

[0022] As an example, the preset time length may be, but is not limited to, at least one of the following: 1 second, 30 milliseconds, 10 milliseconds. The execution subject may be a central processing unit. The specific structure of the intelligent walking aid may refer to Figure 6 FIG. 6 is a schematic diagram 600 of the structure of some embodiments of the intelligent walking aid according to the present application. Figure 6As shown, the intelligent walking aid 600 may include but is not limited to: a pressure sensor 601, an electromyographic sensor 602, an acceleration sensor 603, a central processor 604, a mobile controller 605, a camera assembly 606, an armrest 607, a foot assembly 608, a seat 609 and a wheel assembly 610. The pressure sensor 601 and the electromyographic sensor 602 may be installed on the armrest 607. The acceleration sensor 603 and the camera assembly 606 may be installed on the foot 608. The central processor 604 and the mobile controller 605 may be installed in the seat 609.

[0023] In some optional implementations of some embodiments, the execution subject performs signal conversion processing on the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, which may include the following steps: In the first step, the initial walking pressure signal, the initial walking electromyography signal and the initial walking acceleration signal of the target user collected in advance within the preset time length are preprocessed respectively to obtain the walking pressure filter signal, the walking electromyography filter signal and the walking acceleration filter signal. Among them, the initial walking pressure signal, the initial walking electromyography signal and the initial walking acceleration signal of the target user collected in advance within the preset time length can be preprocessed by a preset signal processing algorithm to obtain the walking pressure filter signal, the walking electromyography filter signal and the walking acceleration filter signal.

[0024] As an example, the above-mentioned preset signal processing algorithm may include but is not limited to at least one of the following: a median filter algorithm, a wavelet transform algorithm, a Fourier transform algorithm and a polyphase filter algorithm.

[0025] The second step is to perform pressure conversion processing on the walking pressure filter signal to obtain a walking muscle pressure value sequence. The walking pressure filter signal can be subjected to pressure conversion processing by a preset pressure conversion algorithm to obtain a walking muscle pressure value sequence.

[0026] As an example, the preset pressure conversion algorithm may be, but is not limited to, at least one of the following: a linear regression algorithm or a polynomial regression algorithm.

[0027] The third step is to perform feature extraction processing on the walking electromyography filter signal to obtain a walking muscle activity state information sequence. The walking electromyography filter signal can be subjected to feature extraction processing by a preset feature extraction algorithm to obtain a walking muscle activity state information sequence.

[0028] As an example, the above-mentioned preset feature extraction algorithm may be but is not limited to at least one of the following: a principal component analysis algorithm, a genetic algorithm, and a heterogeneous feature mapping algorithm.

[0029] The fourth step is to split the walking acceleration filter signal to obtain a walking acceleration sub-signal sequence. The walking acceleration filter signal can be evenly split at preset time intervals to obtain a walking acceleration sub-signal sequence.

[0030] As an example, the preset time interval may be 5 milliseconds.

[0031] Step 5: For each walking acceleration sub-signal sequence in the walking acceleration sub-signal sequence, perform the following detection sub-steps: The first sub-step is to perform gait detection processing on the walking acceleration sub-signal to obtain a walking speed value, a walking frequency value, and a walking stride value. The walking acceleration sub-signal can be subjected to gait detection processing by a preset gait detection algorithm to obtain a walking speed value, a walking frequency value, and a walking stride value.

[0032] As an example, the above-mentioned preset gait detection algorithm may include but is not limited to at least one of the following: an acceleration amplitude formula, a step frequency formula, and a Weinberg nonlinear stride estimation model algorithm.

[0033] The second sub-step is to fuse the walking speed value, the walking frequency value and the walking stride value to obtain walking motion parameter information. The above-mentioned fusing the walking speed value, the walking frequency value and the walking stride value to obtain the walking motion parameter information may be to determine the walking speed value, the walking frequency value and the walking stride value as the walking speed value, the walking frequency value and the walking stride value included in the walking motion parameter information.

[0034] The sixth step is to determine the generated walking motion parameter information as a walking motion parameter information sequence.

[0035] Step 102, integrating the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence to obtain walking muscle activity data.

[0036] In some embodiments, the execution subject may integrate the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence to obtain the walking muscle activity data. The walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence may be integrated by a preset integration algorithm to obtain the walking muscle activity data.

[0037] As an example, the above-mentioned preset integration algorithm can be but is not limited to at least one of the following: a Kalman filter algorithm, a sliding window algorithm or a dynamic time warping algorithm.

[0038] Step 103, inputting the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information.

[0039] In some embodiments, the execution entity may input the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information. The pre-trained muscle feature extraction model may be a pre-trained neural network model that takes walking muscle activity data as input and takes walking muscle feature information as output. The walking muscle feature information may include: walking muscle strength value and walking muscle content information. The walking muscle strength value may represent the muscle strength of the target user at the current moment. The walking muscle content information may represent the muscle content of the target user at the current moment.

[0040] Optionally, before inputting the walking muscle activity data into the pre-trained muscle feature extraction model to obtain the walking muscle feature information, the execution subject may further perform the following steps: The first step is to obtain a sample walking muscle activity data set. The sample walking muscle activity data set can be obtained from a storage terminal. The sample walking muscle activity data in the sample walking muscle activity data set can be pre-collected data characterizing muscle characteristics.

[0041] The second step is to select target sample walking muscle activity data from the above sample walking muscle activity data set and perform the following first training sub-step: In the first sub-step, the target sample walking muscle activity data is input into the three-dimensional feature extraction sub-model included in the initial muscle feature extraction model to obtain the initial muscle three-dimensional feature information. The initial muscle feature extraction model also includes: a three-dimensional attention sub-model, an aggregation sub-model, a comprehensive feature extraction sub-model, a feature fusion sub-model and a feature mapping sub-model. The sample walking muscle activity data can be randomly selected from the above-mentioned sample walking muscle activity data set as the target sample walking muscle activity data. The above-mentioned initial muscle feature extraction model can be an untrained neural network model that takes the target sample walking muscle activity data as input and the initial muscle feature information as output.

[0042] Specifically, the three-dimensional feature extraction sub-model may be a neural network model that takes the target sample walking muscle activity data as input and takes the initial muscle three-dimensional feature information as output. For example, the three-dimensional feature extraction sub-model may be a three-dimensional convolution model.

[0043] The above-mentioned 3D attention sub-model may be a neural network model that takes the initial muscle 3D feature information as input and takes the initial muscle 3D attention information as output. For example, the above-mentioned 3D attention sub-model may be a 3D-CBAM (3D-Convolutional Block Attention Module, 3D lightweight attention module) model.

[0044] The above-mentioned aggregation sub-model may be a neural network model that takes the initial muscle three-dimensional attention information as input and takes the initial muscle aggregation information as output. For example, the above-mentioned aggregation sub-model may be an LTA (Local Temporal Aggregation) model.

[0045] The above-mentioned comprehensive feature extraction sub-model may be a neural network model that takes initial muscle aggregation information as input and takes initial muscle comprehensive feature information as output. For example, the above-mentioned comprehensive feature extraction sub-model may be an EGLFE (enhanced global and local feature extractor) model.

[0046] The feature fusion sub-model may be a neural network model that takes the initial muscle comprehensive feature information as input and takes the initial muscle feature fusion information as output. For example, the feature fusion sub-model may be a residual convolution model.

[0047] The feature mapping sub-model may be a neural network model that takes the initial muscle feature fusion information as input and takes the initial muscle feature fusion information as output. For example, the feature mapping sub-model may be a maximum pooling layer.

[0048] In the second sub-step, the initial muscle three-dimensional feature information is input into the three-dimensional attention sub-model included in the initial muscle feature extraction model to obtain the initial muscle three-dimensional attention information.

[0049] In the third sub-step, the initial muscle three-dimensional attention information is input into the aggregation sub-model included in the initial muscle feature extraction model to obtain the initial muscle aggregation information.

[0050] The fourth sub-step is to input the initial muscle aggregation information into the comprehensive feature extraction sub-model included in the initial muscle feature extraction model to obtain the initial muscle comprehensive feature information.

[0051] The fifth sub-step is to input the initial muscle comprehensive feature information into the feature fusion sub-model included in the initial muscle feature extraction model to obtain the initial muscle feature fusion information.

[0052] In the sixth sub-step, the initial muscle feature fusion information is input into the feature mapping sub-model included in the initial muscle feature extraction model to obtain the initial muscle feature information.

[0053] The seventh sub-step is to determine the feature extraction loss value corresponding to the initial muscle feature information based on at least one preset feature extraction loss function.

[0054] As an example, the at least one preset feature extraction loss function may include but is not limited to at least one of the following: a triplet loss function and an upper loss function.

[0055] An eighth sub-step, in response to determining that the feature extraction loss value is less than the first target threshold, determines the initial muscle feature extraction model as the muscle feature extraction model.

[0056] As an example, the first target threshold mentioned above may be 0.01.

[0057] Optionally, the execution subject may also adjust relevant parameters in the initial muscle feature extraction model in response to determining that the feature extraction loss value is greater than or equal to the first target threshold, determine the adjusted initial muscle feature extraction model as the initial muscle feature extraction model, and select target sample walking muscle activity data from each sample walking muscle activity data set that has not been selected, for re-execution of the first training step. The relevant parameters in the initial muscle feature extraction model may be adjusted by a preset adjustment algorithm.

[0058] As an example, the preset adjustment algorithm may be, but is not limited to, at least one of the following: a back propagation algorithm or a stochastic gradient algorithm.

[0059] Therefore, the spatial features and temporal features of walking muscle activity data can be extracted through the three-dimensional feature extraction model and the three-dimensional attention model. Then, by fusing the multi-scale spatial features and temporal features, the dimension and accuracy of feature extraction are improved. As a result, the accuracy of muscle feature information extracted by the model can be improved (for example, the accuracy of the patient's current muscle strength value), and then, abnormal muscle loss of patients can be discovered in a timely manner.

[0060] Step 104, determining the strength difference value and muscle abnormality information corresponding to the walking muscle characteristic information and the pre-acquired initial muscle characteristic information.

[0061] In some embodiments, the execution subject may determine the strength difference value and muscle abnormality information corresponding to the walking muscle characteristic information and the pre-acquired initial muscle characteristic information. The initial muscle characteristic information may be obtained from a storage terminal. The storage terminal may be a terminal for storing initial muscle characteristic information. The initial muscle characteristic information may characterize the muscle characteristics of the target user at a moment before the preset time interval of the current moment. The initial muscle characteristic information may include: initial walking muscle strength value and initial walking muscle content information. The initial walking muscle strength value may characterize the muscle strength of the target user at a moment before the preset time interval of the current moment. The initial walking muscle content information may characterize the muscle content of the target user at a moment before the preset time interval of the current moment. Specifically, the determination of the strength difference value and muscle abnormality information corresponding to the walking muscle characteristic information and the initial muscle characteristic information may be: first, the difference between the walking muscle strength value included in the walking muscle characteristic information and the initial walking muscle strength value included in the initial muscle characteristic information is determined as the strength difference value. Then, the muscle abnormality information corresponding to the walking muscle content information included in the walking muscle characteristic information and the initial walking muscle content information included in the initial muscle characteristic information is determined by a preset abnormality recognition algorithm. The muscle abnormality information may be information indicating "abnormal muscle loss" or information indicating "no abnormal muscle loss".

[0062] As an example, the above-mentioned preset anomaly recognition algorithm can be but is not limited to at least one of the following: LSTM (Long Short-Term Memory, long short-term memory network model) algorithm, dynamic clustering algorithm or exponential smoothing algorithm.

[0063] Step 105, in response to determining that the force difference value is greater than the target threshold or the muscle abnormality information meets the preset abnormal condition, a preset mode switching signal is sent to the mobile controller to switch the intelligent walker operation mode.

[0064] In some embodiments, the execution subject may send a preset mode switching signal to the mobile controller to switch the operation mode of the intelligent walker in response to determining that the force difference value is greater than the target threshold or the muscle abnormality information satisfies a preset abnormality condition. The preset abnormality condition may be that the muscle abnormality information is information indicating "abnormal muscle loss".

[0065] As an example, the target threshold may be 10. The preset mode switching signal may be a signal indicating “increasing the walking assistance level of the mobile controller”.

[0066] Optionally, the above execution entity may further perform the following steps: In the first step, each walking scene image in the walking scene image set of the target user shot in advance is synthesized to obtain the target walking scene image. The walking scene image set may be shot by controlling the camera assembly. Here, each camera in the camera assembly may be controlled to shoot at least one walking scene image to obtain the walking scene image set. The walking scene images in the walking scene image set may represent the characteristics of the surrounding scene when the target user is using the intelligent walker.

[0067] The second step is to perform scene recognition processing on the target walking scene image to obtain walking scene feature information. The target walking scene image can be subjected to scene recognition processing by a preset scene recognition algorithm to obtain walking scene feature information.

[0068] As an example, the preset scene recognition algorithm may be, but is not limited to, at least one of the following: a naive Bayes algorithm, a support vector machine algorithm, or a random forest algorithm. The walking scene feature information may be, but is not limited to, at least one of the following: information representing "moving steadily forward", information representing "staying still", or information representing "falling down".

[0069] The third step is to send a preset mode switching signal to the mobile controller to switch the operation mode of the intelligent walker in response to determining that the walking scene characteristic information meets the preset dangerous scene conditions.

[0070] As an example, the preset dangerous scene condition may be that the walking scene characteristic information is information indicating "falling down". The preset mode switching signal may be a signal indicating that "the mobile controller turns on the balance mode".

[0071] In some optional implementations of some embodiments, the execution subject synthesizes each walking scene image in a set of walking scene images of a target user captured in advance to obtain a target walking scene image, which may include the following steps: In the first step, the walking scene image set of the target user taken in advance is grouped to obtain a first scene image set and a second scene image set. Among them, each walking scene image taken by the first camera component in the above camera components can be determined as the first scene image set, and each walking scene image taken by the second camera component in the above camera components can be determined as the second scene image set.

[0072] The second step is to construct an image generation model based on the first scene image set. The image generation model can be constructed based on the first scene image set by a preset construction algorithm.

[0073] As an example, the above-mentioned preset construction algorithm may be a NeRF (Neural Radiance Fields) algorithm.

[0074] The third step is to generate a first scene generated image set and a second scene generated image set corresponding to the first scene image set and the second scene image set respectively based on the image generation model.

[0075] In the fourth step, each first scene generated image in the first scene generated image set is input into a pre-trained image mapping model to generate a first scene mapping image, thereby obtaining a first scene mapping image set. The pre-trained image mapping model may be a pre-trained neural network model that takes the first scene generated image as input and the first scene mapping image as output.

[0076] In the fifth step, each second scene generated image in the above second scene generated image set is input into a pre-trained image mapping model to generate a second scene mapping image, thereby obtaining a second scene mapping image set.

[0077] The sixth step is to perform three-dimensional reconstruction processing on the first scene mapping image set and the second scene mapping image set to obtain a target walking scene image. The first scene mapping image set and the second scene mapping image set can be subjected to three-dimensional reconstruction processing by a preset three-dimensional reconstruction algorithm to obtain a target walking scene image.

[0078] As an example, the above-mentioned preset 3D reconstruction algorithm can be but is not limited to at least one of the following: SfM (Structure from Motion) algorithm, MVS (Multi-View Stereo) algorithm or Monodepth2 (monocular depth estimation) algorithm.

[0079] In some optional implementations of some embodiments, the execution subject generates the first scene generated image set and the second scene generated image set corresponding to the first scene image set and the second scene image set respectively based on the image generation model, which may include the following steps: In the first step, each first scene image in the first scene image set is subjected to posture extraction processing to generate first scene posture information, thereby obtaining a first scene posture information set. In particular, a preset posture extraction algorithm can be used to perform posture extraction processing on each first scene image in the first scene image set to generate first scene posture information, thereby obtaining a first scene posture information set.

[0080] As an example, the above-mentioned preset pose extraction algorithm may be but is not limited to at least one of the following: an epipolar geometry algorithm, a direct linear transformation algorithm or a PoseNet (pose network) model algorithm.

[0081] In the second step, each first scene pose information in the first scene pose information set is input into the image generation model to generate a first scene generated image, thereby obtaining a first scene generated image set.

[0082] The third step is to perform posture extraction processing on each second scene image in the second scene image set to generate second scene posture information and obtain a second scene posture information set. The second scene posture information set can be obtained by performing posture extraction processing on each second scene image in the second scene image set using the preset posture extraction algorithm.

[0083] The fourth step is to input each second scene pose information in the second scene pose information set into the image generation model to generate a second scene generated image, thereby obtaining a second scene generated image set.

[0084] Optionally, before inputting each first scene generation image in the first scene generation image set into a pre-trained image mapping model to generate a first scene mapping image, the execution subject may further perform the following steps: The first step is to obtain a sample acquisition image set. The sample acquisition image set can be obtained from a storage terminal. The sample acquisition images in the sample acquisition image set can be pre-captured scene images.

[0085] The second step is to generate a sample generated image corresponding to each sample collected image in the sample collected image set based on the image generation model to obtain a sample generated image set. The specific implementation method of generating the sample generated image and the technical effect brought about by it can refer to step 105 in the above embodiment, which will not be repeated here.

[0086] The third step is to select a target sample generated image from the above sample generated image set and perform the following second training sub-step: In the first sub-step, the target sample generated image is input into the initial image mapping model to obtain the initial mapping image. A sample generated image can be randomly selected from the sample generated image set as the target sample generated image. The initial image mapping model can be an untrained neural network model that takes the target sample generated image as input and the initial mapping image as output.

[0087] As an example, the above initial image mapping model may be a codec network model.

[0088] In the second sub-step, based on a preset mapping loss function, a mapping loss value between the sample acquisition image corresponding to the initial mapping image in the sample acquisition image set and the initial mapping image is determined.

[0089] As an example, the preset mapping loss function may be, but is not limited to, one of the following: a cross entropy loss function, a least squares function, or a classification cross entropy loss function.

[0090] A third sub-step, in response to determining that the mapping loss value is less than a second target threshold, determines the initial image mapping model as the image mapping model.

[0091] As an example, the second target threshold may be 0.01.

[0092] Optionally, the execution subject may also adjust relevant parameters in the initial image mapping model in response to determining that the mapping loss value is greater than or equal to the second target threshold, determine the adjusted initial image mapping model as the initial image mapping model, and select a target sample generation image from each sample generation image that has not been selected in the sample generation image set for performing the second training step again. The relevant parameters in the initial image mapping model may be adjusted by the preset adjustment algorithm.

[0093] Therefore, the image generation model can be used to initially generate a three-dimensional feature image of the image, and then the image mapping model can be used to improve the image quality (peak signal-to-noise ratio). This can improve the quality of the synthesized image, and further improve the accuracy of scene recognition. In this way, it can be discovered in time whether the patient is in a dangerous scene (about to fall), thereby improving the safety of the intelligent walker when in use.

[0094] The above-mentioned embodiments of the present application have the following beneficial effects: through the walking detection method based on the intelligent walker of some embodiments of the present application, the safety of the intelligent walker when in use can be improved. Specifically, the reason for the reduced safety of the intelligent walker when in use is that: on the one hand, its operation is not convenient enough. Whether it is the use of the walker or the operation of various detection instruments, it requires a certain amount of manpower and material resources, which brings a lot of burden to medical staff and patients; on the other hand, the system of the scheme is also lacking, and it is impossible to monitor the patient's muscle state in a timely and complete manner, resulting in the possibility of missing some important information, which in turn affects the accurate judgment and effective intervention of the patient's condition. Based on this, the walking detection method based on the intelligent walker of some embodiments of the present application, first, first, the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within the preset time length are respectively converted and processed to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence. Thus, the signal representing the user's muscle state can be collected and the collected electrical signal can be converted into analyzable data information. Then, the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence are integrated and processed to obtain walking muscle activity data. Thus, the information after each signal conversion can be integrated to extract muscle feature information. Next, the walking muscle activity data is input into a pre-trained muscle feature extraction model to obtain walking muscle feature information. Thus, the user's current muscle state characteristics can be obtained. Then, the force difference value and muscle abnormality information corresponding to the walking muscle feature information and the pre-acquired initial muscle feature information are determined. Thus, the user's muscle state change information can be obtained. Finally, in response to determining that the force difference value is greater than the target threshold or the muscle abnormality information meets the preset abnormal condition, a preset mode switching signal is sent to the mobile controller to switch the intelligent walker operation mode. Thus, the operation state of the intelligent walker can be adjusted according to the user's muscle state change information. Therefore, some walking detection methods based on smart walkers in the present application can embed the smart walker with the monitoring test data acquisition software. During the rehabilitation training process, the walker structure is equipped with sensors and other electronic equipment to conduct real-time testing of the patient's condition, promptly detect abnormal muscle loss in the patient, and reduce the number of times the patient falls due to muscle weakness, thereby improving the safety of the smart walker when in use. Figure 2 A schematic diagram showing an application scenario of some embodiments of walking detection based on an intelligent walker according to the present application is shown. Figure 3 A schematic diagram showing an application scenario of other embodiments of walking detection based on an intelligent walker according to the present application.

[0095] like Figure 2As shown, first, the execution subject of the walking detection method based on the intelligent walking aid performs signal conversion processing on the initial walking pressure signal 202, the initial walking electromyographic signal 204 and the initial walking acceleration signal 206 of the target user collected in advance within the preset time period, and obtains the walking muscle pressure value sequence 207, the walking muscle activity state information sequence 208 and the walking motion parameter information sequence 209, wherein the above-mentioned initial walking pressure signal 202 is collected by the control pressure sensor 201, the above-mentioned initial walking electromyographic signal 204 is collected by the control electromyographic sensor 203, and the above-mentioned initial walking acceleration signal 206 is collected by the control acceleration sensor 205. Here, the initial walking pressure signal 202 can be "pressure signal 1". The initial walking electromyographic signal 204 can be "electromyographic signal 1". The initial walking acceleration signal 206 can be "acceleration signal 1". The walking muscle pressure value in the above-mentioned walking muscle pressure value sequence 207 can be "muscle pressure value: 19.5 kg", "muscle pressure value: 19.8 kg" or "muscle pressure value: 20 kg". The walking muscle activity state information in the walking muscle activity state information sequence 208 may be "muscle fatigue information: fatigue occurs", "muscle fatigue information: fatigue occurs" or "muscle fatigue information: fatigue occurs". The walking motion parameter information in the walking motion parameter information sequence 209 may be "walking speed: 1 meter per second, step frequency: 60, step size: 40 centimeters", "walking speed: 1 meter per second, step frequency: 58, step size: 38 centimeters" or "walking speed: 1 meter per second, step frequency: 59, step size: 41 centimeters". Then, the walking muscle pressure value sequence 207, the walking muscle activity state information sequence 208 and the walking motion parameter information sequence 209 are integrated and processed to obtain walking muscle activity data 210. Here, the walking muscle activity data 210 may be "muscle pressure value: 20 kilograms, muscle fatigue information: fatigue occurs, walking speed: 1 meter per second, step frequency: 60, step size: 40 centimeters". Then, the walking muscle activity data 210 is input into the pre-trained muscle feature extraction model to obtain walking muscle feature information 211. Here, the walking muscle characteristic information 211 may be "muscle characteristic B". Then, determine the strength difference value 213 and muscle abnormality information 214 corresponding to the above-mentioned walking muscle characteristic information 211 and the pre-acquired initial muscle characteristic information 212. Here, the initial muscle characteristic information 212 may be "initial muscle characteristic A". The strength difference value 213 may be "10". The muscle abnormality information 214 may be "muscle loss phenomenon". Finally, in response to determining that the above-mentioned strength difference value 213 is greater than the target threshold or the above-mentioned muscle abnormality information 214 meets the preset abnormal condition, a preset mode switching signal 215 is sent to the mobile controller to switch the intelligent walker operation mode. Here, the preset mode switching signal 215 may be "increase the walking assistance level of the mobile controller".

[0096] Optionally, the execution subject may also, first, synthesize and process each walking scene image in the walking scene image set of the target user captured in advance to obtain a target walking scene image 307, wherein the walking scene image set is captured by controlling the camera assembly. Here, the camera assembly may include: "camera A" 301, "camera B" 302, and "camera C" 303. The walking scene images in the walking scene image set may be "image A" 304, "image B" 305, and "image C" 306. The target walking scene image 307 may be a "synthesized image". Then, the target walking scene image 307 is subjected to scene recognition processing to obtain walking scene feature information 308. Here, the walking scene feature information 308 may be "recognition scene: the user is falling". Finally, in response to determining that the walking scene feature information meets the preset dangerous scene condition, a preset mode switching signal 309 is sent to the mobile controller to switch the intelligent walker operation mode. Here, the preset mode switching signal 309 may be "the mobile controller turns on the balance mode". Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a walking detection device based on an intelligent walking aid. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the walking detection device based on the intelligent walker can be specifically applied to various electronic devices.

[0097] like Figure 4As shown, some embodiments of the walking detection device 400 based on the intelligent walking aid include: a signal conversion unit 401, an integration unit 402, an input unit 403, a determination unit 404 and a sending unit 405. The signal conversion unit 401 is configured to perform signal conversion processing on the initial walking pressure signal, the initial walking electromyographic signal and the initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, wherein the above-mentioned initial walking pressure signal is collected by a control pressure sensor, the above-mentioned initial walking electromyographic signal is collected by a control electromyographic sensor, and the above-mentioned initial walking acceleration signal is collected by a control acceleration sensor; the integration unit 402 is configured to perform signal conversion processing on the above-mentioned walking muscle pressure value sequence, the above-mentioned walking muscle activity state information sequence The column and the walking motion parameter information sequence are integrated and processed to obtain walking muscle activity data; the input unit 403 is configured to input the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information; the determination unit 404 is configured to determine the strength difference value and muscle abnormality information corresponding to the walking muscle feature information and the pre-acquired initial muscle feature information; the sending unit 405 is configured to respond to determining that the strength difference value is greater than the target threshold or the muscle abnormality information meets the preset abnormal condition, and send a preset mode switching signal to the mobile controller to switch the intelligent walker operation mode.

[0098] It can be understood that the units recorded in the walking detection device 400 based on the intelligent walker are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the walking detection device 400 based on the intelligent walker and the units contained therein, and will not be described in detail here. The present application also provides a computer device 500. Figure 5 As shown, the computer device 500 includes: a bus 501, a processor 502, a memory 503 and a communication interface 504. The processor 502, the memory 503 and the communication interface 504 communicate with each other through the bus 501. The computer device 500 can be a server or a terminal device. It should be understood that the present application does not limit the number of processors and memories in the computer device 500.

[0099] The bus 501 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus 501 is represented by only one line, but it does not mean that there is only one bus or one type of bus. The bus 501 may include a path for transmitting information between various components of the computer device 500 (for example, the memory 503, the processor 502, and the communication interface 504).

[0100] The processor 502 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0101] The memory 503 may include a volatile memory, such as a random access memory (RAM). The memory 503 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0102] The memory 503 stores executable program codes, and the processor 502 executes the executable program codes to respectively implement the functions of the aforementioned signal conversion unit, integration unit, input unit, determination unit, and sending unit, thereby implementing the aforementioned walking detection method based on the intelligent walker. That is, the memory 503 stores instructions for executing the aforementioned walking detection method based on the intelligent walker.

[0103] The communication interface 504 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computer device 500 and other devices or a communication network.

[0104] An embodiment of the present application also provides a chip, which includes a processor and a data interface. The processor reads instructions stored in a memory through the data interface to execute the above-mentioned walking detection method based on an intelligent walker.

[0105] The embodiment of the present application also provides a computer-readable storage medium. The above-mentioned computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The above-mentioned available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state hard disk). The computer-readable storage medium includes instructions, and the above-mentioned instructions instruct the computing device to execute the above-mentioned walking detection method based on the intelligent walker.

[0106] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A walking detection method based on an intelligent walker, applied to the intelligent walker, the intelligent walker comprising: Pressure sensor, electromyographic sensor, acceleration sensor, mobile controller, the method comprises: The initial walking pressure signal, the initial walking electromyographic signal and the initial walking acceleration signal of the target user collected in advance within a preset time period are respectively converted to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, wherein the initial walking pressure signal is collected by a control pressure sensor, the initial walking electromyographic signal is collected by a control electromyographic sensor, and the initial walking acceleration signal is collected by a control acceleration sensor; Integrate the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence to obtain walking muscle activity data; Inputting the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information; Determining the strength difference value and muscle abnormality information corresponding to the walking muscle characteristic information and the pre-acquired initial muscle characteristic information; In response to determining that the force difference value is greater than a target threshold or the muscle abnormality information satisfies a preset abnormal condition, a preset mode switching signal is sent to the mobile controller to switch the intelligent walker operating mode.

2. The walking detection method based on the intelligent walking aid according to claim 1, wherein: The intelligent walking aid further includes: a camera component, and the method further includes: Performing synthesis processing on each walking scene image in a walking scene image set of a target user shot in advance to obtain a target walking scene image, wherein the walking scene image set is shot by controlling the camera component; Performing scene recognition processing on the target walking scene image to obtain walking scene feature information; In response to determining that the walking scene characteristic information meets the preset dangerous scene condition, a preset mode switching signal is sent to the mobile controller to switch the intelligent walking aid operation mode.

3. The walking detection method based on the intelligent walking aid according to claim 1, wherein: The initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period are respectively converted to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, including: Preprocessing the initial walking pressure signal, initial walking electromyography signal and initial walking acceleration signal of the target user collected in advance within a preset time period respectively to obtain a walking pressure filter signal, a walking electromyography filter signal and a walking acceleration filter signal; Performing pressure conversion processing on the walking pressure filter signal to obtain a walking muscle pressure value sequence; Performing feature extraction processing on the walking electromyography filter signal to obtain a walking muscle activity state information sequence; The walking acceleration filter signal is split and processed to obtain a walking acceleration sub-signal sequence; For each walking acceleration sub-signal sequence in the walking acceleration sub-signal sequence, the following detection steps are performed: Performing gait detection processing on the walking acceleration sub-signal to obtain a walking speed value, a walking frequency value, and a walking stride value; The walking speed value, the walking frequency value and the walking stride value are fused to obtain walking motion parameter information; The generated pieces of walking motion parameter information are determined as a walking motion parameter information sequence.

4. The walking detection method based on the intelligent walking aid according to claim 1, wherein: Before inputting the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information, the method further includes: Get a sample walking muscle activity dataset; Select target sample walking muscle activity data from the sample walking muscle activity data set, and perform the following first training step: Inputting the target sample walking muscle activity data into the three-dimensional feature extraction sub-model included in the initial muscle feature extraction model to obtain the initial muscle three-dimensional feature information, wherein the initial muscle feature extraction model also includes: a three-dimensional attention sub-model, an aggregation sub-model, a comprehensive feature extraction sub-model, a feature fusion sub-model and a feature mapping sub-model; Inputting the initial muscle three-dimensional feature information into the three-dimensional attention sub-model included in the initial muscle feature extraction model to obtain the initial muscle three-dimensional attention information; Inputting the initial muscle three-dimensional attention information into the aggregation sub-model included in the initial muscle feature extraction model to obtain initial muscle aggregation information; Inputting the initial muscle aggregation information into the comprehensive feature extraction sub-model included in the initial muscle feature extraction model to obtain the initial muscle comprehensive feature information; Inputting the initial muscle comprehensive feature information into the feature fusion sub-model included in the initial muscle feature extraction model to obtain initial muscle feature fusion information; Inputting the initial muscle feature fusion information into the feature mapping sub-model included in the initial muscle feature extraction model to obtain the initial muscle feature information; Based on at least one preset feature extraction loss function, determining a feature extraction loss value corresponding to the initial muscle feature information; In response to determining that the feature extraction loss value is less than the first target threshold, the initial muscle feature extraction model is determined as the muscle feature extraction model.

5. The walking detection method based on the intelligent walking aid according to claim 4, wherein: The method further comprises: In response to determining that the feature extraction loss value is greater than or equal to the first target threshold, the relevant parameters in the initial muscle feature extraction model are adjusted, the adjusted initial muscle feature extraction model is determined as the initial muscle feature extraction model, and the target sample walking muscle activity data is selected from the sample walking muscle activity data set and the unselected sample walking muscle activity data for re-executing the first training step.

6. A walking detection device based on an intelligent walker, applied to the intelligent walker, the intelligent walker comprising: Pressure sensor, electromyographic sensor, acceleration sensor, mobile controller, the device comprises: A signal conversion unit is configured to perform signal conversion processing on the initial walking pressure signal, initial walking electromyographic signal and initial walking acceleration signal of the target user collected in advance within a preset time period, respectively, to obtain a walking muscle pressure value sequence, a walking muscle activity state information sequence and a walking motion parameter information sequence, wherein the initial walking pressure signal is collected by a control pressure sensor, the initial walking electromyographic signal is collected by a control electromyographic sensor, and the initial walking acceleration signal is collected by a control acceleration sensor; an integration unit configured to integrate the walking muscle pressure value sequence, the walking muscle activity state information sequence and the walking motion parameter information sequence to obtain walking muscle activity data; An input unit, configured to input the walking muscle activity data into a pre-trained muscle feature extraction model to obtain walking muscle feature information; a determination unit configured to determine a force difference value and muscle abnormality information corresponding to the walking muscle characteristic information and the pre-acquired initial muscle characteristic information; The sending unit is configured to send a preset mode switching signal to the mobile controller to switch the operation mode of the intelligent walker in response to determining that the force difference value is greater than a target threshold or the muscle abnormality information meets a preset abnormal condition.

7. A computer device, wherein: The computer device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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