Power-assistance control method, system and storage medium for walking device

Abstract

The present invention relates to the field of mechanical power-assisting devices, specifically a power-assistance control method and system for a walker. The method detects the barrier-free walking process of a user wearing a walker, obtains the user's walking gait characteristics and muscle activity characteristics, and thereby constructs a model of the user's leg muscle activity state; detects the obstacle-avoiding walking process of the user wearing the walker, obtains the user's leg pressure characteristics, and thereby predicts the user's upcoming leg movement information; and determines the power-assistance demand information of the user's legs based on the leg muscle activity state model and leg movement information, thereby adjusting the power-assistance provided by the walker to the user's legs. The present invention can accurately determine the leg power-assistance demand, provide multi-directional power-assistance support to the leg muscles, strengthen walking rehabilitation training, and improve the accuracy and reliability of the power-assistance provided by the walker.

Description

Technical Field

[0001] The present invention relates to the field of mechanical power-assisting devices, and in particular to a power-assisting control method, system and storage medium for a walking device. Background Art

[0002] As a walking training assist device, the walker can help users complete walking tasks. For example, patients who have completed knee replacement, hip replacement or lower limb fracture recovery usually need walking exercises to readapt to the new joints or restore lower limb motor sensitivity. In the actual walking training process, patients do not have any intuitive feeling of their original weight-bearing. If they only rely on simple tools such as crutches, they will not be able to provide stable support for the patients. At the same time, they will not be able to provide appropriate assistive force for the patients' actual walking movements, reduce the patients' sense of muscle weight during walking, and provide assistive support when the patients are in a state of muscle weakness. For this reason, the walker acts as a rehabilitation training device, which matches the patient's lower limbs. When the patient wears the walker, it can provide assistive support for the patient's lower limbs.

[0003] Existing walkers are limited to providing assistance to the hip, knee, ankle and other joints of the lower limbs. However, the above-mentioned assistance providing method can only provide assistance to a few points on the lower limbs of the human body. The dimension of assistance provision is relatively single, and it cannot provide assistance support to the lower limbs of the human body in a larger range, which easily causes uneven application of assistance and causes sideways tilt. In addition, existing walkers can only detect whether there is imbalance in the walking process of the human body, and provide reverse assistance support when the human body is unbalanced. It is impossible to perform reliable muscle force training according to the walking state of the human body. It can be seen that how to provide multi-directional assistance support according to the activity state of the lower limb muscles of the human body during the walking process of the human body is of great significance for strengthening the rehabilitation training of human walking and improving the accuracy and reliability of the assistance of the walker. Summary of the Invention

[0004] In order to avoid problems such as uneven power application and ineffective training of lower limb muscles caused by providing power only to the lower limb joints during walking training using a walker, the present invention provides a power-assistance control method for a walker, which comprises the following steps:

[0005] Detecting an obstacle-free walking process of a user wearing a walking device to obtain walking gait characteristics and muscle activity characteristics of the user; and constructing a leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics;

[0006] detecting an obstacle-avoiding walking process of the user while wearing the walker to obtain a leg pressure characteristic of the user; and predicting upcoming leg movement information of the user based on the leg pressure characteristic;

[0007] Based on the leg muscle activity state model and the leg movement information, the power assistance requirement information of the user's legs is determined; based on the power assistance requirement information, the power assistance effect provided by the walker to the user's legs is adjusted.

[0008] Preferably, the detecting of the barrier-free walking process of the user wearing the walking device to obtain the walking gait characteristics and muscle activity characteristics of the user is specifically as follows:

[0009] Detecting leg movements and biosignals of a user while wearing the walker during unimpeded walking, to obtain leg movement posture data and leg surface electromyography signal data of the user;

[0010] Performing multi-degree-of-freedom analysis on the leg motion posture data to obtain the walking gait characteristics of the user; wherein the walking gait characteristics include changes in the leg motion posture of the user during walking;

[0011] The surface electromyographic signal data of the legs is analyzed to obtain muscle activity characteristics of the user; wherein the muscle activity characteristics include the stability of the muscle activity of the user during walking.

[0012] Preferably, the step of constructing the user's leg muscle activity state model based on the walking gait characteristics and the muscle activity characteristics is as follows:

[0013] Matching and comparing the leg posture changes of the user during walking with the stability of the user's muscle activity during walking to obtain relationship information between the changes in the user's leg posture angle and the changes in the stability of the user's leg muscle activity;

[0014] Based on the relationship information, a leg muscle activity state model of the user is constructed; wherein the leg muscle activity state model represents the change in posture angle of the user's leg when all muscle groups in the user's leg change from a strong state to a weak state.

[0015] Preferably, the detecting the obstacle-avoiding walking process of the user while wearing the walker to obtain the leg pressure characteristics of the user; and predicting the user's upcoming leg movement information based on the leg pressure characteristics, specifically:

[0016] Performing leg contact pressure detection on the user while wearing the walker and avoiding obstacles while walking, and obtaining a contact pressure distribution characteristic between the user's legs and the outside world; wherein the contact pressure distribution characteristic includes a distribution of the magnitude of the contact force exerted by the user's legs on the outside world during the process of global contact with the outside world;

[0017] The leg pressure characteristics are analyzed to predict the user's action intention, thereby obtaining the leg muscle position where the user is about to move and the action posture angle.

[0018] Preferably, determining the power assistance requirement information of the user's legs based on the leg muscle activity state model and the leg motion information; and adjusting the power assistance provided by the walker to the user's legs based on the power assistance requirement information, specifically:

[0019] Comparing the leg muscle parts and movement posture angles of the user's upcoming movement, as included in the leg movement information, with the leg muscle activity state model to determine the leg muscle parts of the user's leg that are in a state of weakness during the upcoming movement, thereby determining the power assistance requirement information of the user's leg; wherein the power assistance requirement information of the user's leg includes the area range of the leg muscle parts in a state of weakness and the value of the external force to be compensated;

[0020] Based on the power assistance requirement information, the range and magnitude of the compensatory driving torque applied by the walker to the leg muscle portion in the powerless state are adjusted.

[0021] In another aspect, the present invention provides a power-assistance control system for a walking device, the system comprising the following modules:

[0022] The first detection module is used to detect the barrier-free walking process of the user wearing the walking device, and obtain the walking gait characteristics and muscle activity characteristics of the user;

[0023] an activity state model building module, configured to build a leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics;

[0024] a second detection module, configured to detect the obstacle-avoiding walking process of the user wearing the walker, and obtain the leg pressure characteristics of the user;

[0025] a leg movement prediction module, configured to predict the user's upcoming leg movement information based on the leg pressure characteristics;

[0026] a power assistance requirement determination module, configured to determine power assistance requirement information of the user's legs based on the leg muscle activity state model and the leg motion information;

[0027] The power-assistance adjustment module is used to adjust the power-assistance provided by the walker to the user's legs based on the power-assistance requirement information.

[0028] Preferably, the first detection module is used to detect the barrier-free walking process of the user wearing the walker, and obtain the walking gait characteristics and muscle activity characteristics of the user, specifically:

[0029] Detecting leg movements and biosignals of a user while wearing the walker during unimpeded walking, to obtain leg movement posture data and leg surface electromyography signal data of the user;

[0030] Performing multi-degree-of-freedom analysis on the leg motion posture data to obtain the walking gait characteristics of the user; wherein the walking gait characteristics include changes in the leg motion posture of the user during walking;

[0031] Analyzing the surface electromyographic signal data of the legs to obtain muscle activity characteristics of the user; wherein the muscle activity characteristics include the stability of the muscle activity of the user during walking;

[0032] The activity state model construction module is used to construct the user's leg muscle activity state model based on the walking gait characteristics and the muscle activity characteristics, specifically:

[0033] Matching and comparing the leg posture changes of the user during walking with the stability of the user's muscle activity during walking to obtain relationship information between the changes in the user's leg posture angle and the changes in the stability of the user's leg muscle activity;

[0034] Based on the relationship information, a leg muscle activity state model of the user is constructed; wherein the leg muscle activity state model represents the change in posture angle of the user's leg when all muscle groups in the user's leg change from a strong state to a weak state.

[0035] Preferably, the second detection module is used to detect the obstacle avoidance walking process of the user wearing the walker, and obtain the leg pressure characteristics of the user, specifically:

[0036] Performing leg contact pressure detection on the user while wearing the walker and avoiding obstacles while walking, and obtaining a contact pressure distribution characteristic between the user's legs and the outside world; wherein the contact pressure distribution characteristic includes a distribution of the magnitude of the contact force exerted by the user's legs on the outside world during the process of global contact with the outside world;

[0037] The leg movement prediction module is used to predict the user's upcoming leg movement information based on the leg pressure characteristics, specifically:

[0038] The leg pressure characteristics are analyzed to predict the user's action intention, thereby obtaining the leg muscle position where the user is about to move and the action posture angle.

[0039] Preferably, the power assistance requirement determination module is used to determine the power assistance requirement information of the user's legs based on the leg muscle activity state model and the leg motion information, specifically:

[0040] Comparing the leg muscle parts and movement posture angles of the user's upcoming movement, as included in the leg movement information, with the leg muscle activity state model to determine the leg muscle parts of the user's leg that are in a state of weakness during the upcoming movement, thereby determining the power assistance requirement information of the user's leg; wherein the power assistance requirement information of the user's leg includes the area range of the leg muscle parts in a state of weakness and the value of the external force to be compensated;

[0041] The power-assistance adjustment module is used to adjust the power-assistance provided by the walker to the user's legs based on the power-assistance requirement information, specifically:

[0042] Based on the power assistance requirement information, the range and magnitude of the compensatory driving torque applied by the walker to the leg muscle portion in the powerless state are adjusted.

[0043] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] Detect the user's barrier-free walking process while wearing a walker, and obtain the user's walking gait characteristics and muscle activity characteristics; based on the walking gait characteristics and muscle activity characteristics, construct a leg muscle activity state model of the user. The walker can accurately provide targeted assistance based on the actual weakness / fatigue of the patient's lower limb muscle groups. It is necessary to comprehensively detect and characterize the weakness / fatigue that may occur in the muscle groups of all parts of the patient's lower limbs under different leg movements, and determine what posture and amplitude of movement the patient's legs must make to trigger weakness / fatigue in the lower limb muscle groups. By constructing a leg muscle activity state model for the patient, the patient's muscle group strength under the current leg tissue structure state can be fully and accurately characterized to support the maximum posture angle change that can be achieved by leg movement, providing a reliable basis for subsequent prediction of muscle group weakness that may occur in the patient during rehabilitation training and accurate implementation of assistance.

[0046] The obstacle avoidance walking process of the user while wearing the walker is detected to obtain the user's leg pressure characteristics; based on the leg pressure characteristics, the user's upcoming leg movement information is predicted. In view of the complex and variable movement conditions of the patient during obstacle avoidance walking, it is necessary to predetermine the patient's leg movement conditions during obstacle avoidance walking to ensure that the walker can apply targeted assistance. Considering that the soles of the patient's feet and the sides of the legs will contact the outside world during walking, forming pressure, and the distribution of the above pressure depends on the patient's leg walking movement, by detecting and analyzing the pressure distribution formed during the patient's legs contacting the outside world, the patient's leg movement intention can be predicted, and the leg muscle parts and movement posture angles of the user that are about to move can be obtained, thereby providing an accurate reference for the subsequent control of the walker to apply assistance.

[0047] Based on the leg muscle activity state model and leg movement information, the user's leg assistance requirement information is determined; based on the assistance requirement information, the assistance provided by the walker to the user's legs is adjusted. Taking into account the complexity and variability of the patient's movement during obstacle avoidance walking, the patient's legs may need to make large posture angle changes in order to cross and avoid obstacles. At this time, due to the strength limitations of the patient's leg muscle groups themselves, the leg muscle groups are unable to independently exert force to support the legs to complete the leg movement with the above-mentioned large posture angle changes. At this time, the walker is required to provide assistance to the corresponding leg muscle groups to assist in completing the leg movement. Through the above process, the leg muscle parts of the user's legs that are in a state of weakness during the upcoming movement are determined, and the regional range of the leg muscle parts in the state of weakness and the external force value that needs to be compensated are obtained, so that the walker can accurately apply the appropriate amount of assistance to the corresponding muscle groups in the patient's legs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. Among them:

[0049] Figure 1 This is a flow chart of the power-assistance control method for a walking device provided by the present invention.

[0050] Figure 2 This is a schematic diagram of the sensor placement corresponding to detecting leg movement posture changes in the power-assistance control method for a walking device provided by the present invention;

[0051] Figure 3 This is a schematic diagram of a circuit corresponding to detecting changes in leg movement posture in the power-assistance control method for a walking device provided by the present invention;

[0052] Figure 4 Schematic diagram of muscle groups corresponding to leg surface electromyographic signals detected in the power-assistance control method for a walking device provided by the present invention;

[0053] Figure 5 This is a schematic diagram of a circuit corresponding to detecting surface electromyographic signals of the legs in the power-assistance control method of the walking device provided by the present invention;

[0054] Figure 6 It is a structural diagram of the power-assistance control system of the walking device provided by the present invention.

[0055] Figure numerals: 1, hip joint; 2, femur; 3, knee joint; 4, tibia; 5, ankle joint; 6, rectus femoris; 7, vastus lateralis; 8, vastus medialis; 9, tibialis anterior; 10, biceps femoris; 11, gastrocnemius; 12, soleus. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It will be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] The terms "comprise," "comprising," and "having," and any variations thereof, as used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0058] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] See also Figure 1 As shown, the present invention provides a method for controlling the power of a walking device, the method comprising the following steps:

[0060] S100, detecting the barrier-free walking process of the user wearing the walker, obtaining the user's walking gait characteristics and muscle activity characteristics; and constructing the user's leg muscle activity state model based on the walking gait characteristics and muscle activity characteristics.

[0061] Furthermore, the obstacle-free walking process of the user wearing the walker is detected to obtain the user's walking gait characteristics and muscle activity characteristics, specifically:

[0062] Detect leg movements and biosignals of the user while wearing the walker and walking without obstacles, and obtain leg movement posture data and leg surface electromyography signal data of the user;

[0063] Performing multi-degree-of-freedom analysis on the leg motion posture data to obtain the user's walking gait characteristics; wherein the walking gait characteristics include changes in the user's leg motion posture during walking;

[0064] The surface electromyographic signal data of the legs is analyzed to obtain the user's muscle activity characteristics; wherein the muscle activity characteristics include the stability of the user's muscle activity during walking.

[0065] As a power-assisting support device for lower limb walking rehabilitation training, the walker mainly includes several support components corresponding to different parts of the human body, such as the hip, thigh, calf, and foot. When a patient who has completed knee replacement surgery, hip replacement surgery, or lower limb fracture recovery wears the walker, the patient's hip, thigh, calf, and foot are in one-to-one contact with the several support components of the walker. Each support component can provide power-assisting support to the lower limb part in contact with it, and the above-mentioned power-assisting support can be implemented for the muscle groups of the corresponding lower limb part, thereby ensuring that the power-assisting support acts evenly and balancedly on the lower limb part, not only avoiding the failure to ensure the balance of human walking by only applying power-assisting support to the lower limb joint part, but also effectively training the lower limb muscle force and restoring the movement sensitivity and power activity of the lower limb muscles. Generally speaking, at the beginning of a patient's knee replacement surgery, hip replacement surgery, or lower limb fracture recovery, the patient's lower limb muscles temporarily lose their original activity and sensitivity due to a long period of lack of activity, resulting in muscle weakness / fatigue during walking. For example, when a patient makes small movements in their thighs or calves while walking, the muscles in those thighs or calves can exert force to maintain these small movements. However, once the thighs or calves make larger movements, the muscles in those thighs or calves are unable to exert force to maintain these larger movements. This shows that patients do not experience weakness or fatigue in all walking movements. Only when the walking movement exceeds a certain threshold will the muscles themselves be unable to generate sustained and stable force.

[0066] In order for the walker to accurately provide targeted assistance based on the actual weakness / fatigue of the patient's lower limb muscle groups after the patient wears it, it is necessary to comprehensively detect and characterize the weakness / fatigue that may occur in the muscle groups of all parts of the patient's lower limbs under different leg movements, and determine exactly what posture and amplitude of movement the patient's legs must make to trigger weakness / fatigue in the lower limb muscle groups. Specifically, a number of three-axis acceleration sensors can be distributed in the patient's lower limbs corresponding to the hip, thigh, calf, foot and other areas, and the patient's walking movements can be detected after the patient completes joint replacement surgery or the initial stage of lower limb fracture recovery to accurately determine the patient's original leg dynamic posture. Specifically, the patient's lower limb movement is mainly achieved by the relative movement of the joints in the lower limbs and the bones connected thereto. Please refer to Figure 2 As shown in the figure, the lower limbs mainly include the hip joint, femur, knee joint, tibia, and ankle joint. In the actual leg movement posture change detection, a detection belt with a three-axis acceleration sensor can be wrapped around the corresponding hip joint, femur, knee joint, tibia, and ankle joint of the lower limb. This can fully and accurately detect the movement posture of the above joints and bones during leg movement. Figure 3 As shown, all three-axis acceleration sensors are connected to the A / D conversion circuit. The analog signal generated by each three-axis acceleration sensor is converted into a digital signal through the A / D conversion circuit. The processor then processes the digital signal to obtain the change in leg movement posture. Among them, the three-axis acceleration sensor, A / D conversion circuit, and processor are all commonly used electronic devices in this field and will not be introduced in detail here.

[0067] In addition, the active force characteristics of the leg muscles can be characterized by detecting the surface electromyographic signals of the leg muscles. Generally speaking, when the patient walks, the muscle groups in different parts of the legs will exert force to a corresponding degree, thereby completing the traction action of the leg as a whole. The muscle groups will generate surface electromyographic signals during the force generation process, and the intensity of the surface electromyographic signals has a specific correlation with the force generation strength of the muscle groups, that is, the greater the force generation strength of the muscle groups, the greater the surface electromyographic signal intensity. The longer the force generation stability of the muscle groups is maintained, the more stable the surface electromyographic signal intensity is, and the less likely it is to drift. For this purpose, a number of electromyographic sensors can be distributed in the patient's lower limbs corresponding to the hips, thighs, calves, feet and other areas. When the patient walks, the surface electromyographic signals of all muscle groups in the global range of the legs can be detected in a non-invasive manner, thereby accurately detecting the force generation conditions of all muscle groups in the legs during activity. Specifically, if Figure 4As shown in the figure, the patient's lower limb muscles mainly rely on the rectus femoris, vastus lateralis, vastus medialis, tibialis anterior, biceps femoris, gastrocnemius, and soleus muscles to generate force. In the actual muscle activity force characteristic detection, EMG sensors with surface electrodes can be attached to the corresponding rectus femoris, vastus lateralis, vastus medialis, tibialis anterior, biceps femoris, gastrocnemius, and soleus muscles in the lower limbs. This can fully and accurately detect the surface EMG signals of the above muscle groups during leg movement. Figure 5 As shown, the surface electrodes of all electromyographic sensors are connected to a preamplifier, an amplifying filter, and an A / D conversion circuit. The analog signal generated by each surface electrode detection is amplified by the preamplifier, filtered by the amplifying filter, and converted into a digital signal by the A / D conversion circuit. The processor processes the digital signal to obtain the surface electromyographic signal of the leg. Among them, the preamplifier, the amplifying filter, the A / D conversion circuit, and the processor are all commonly used electronic devices in this field and will not be introduced in detail here.

[0068] In order to detect the patient's leg movement posture data and leg surface electromyography signal data in a normal scenario without interference, several three-axis acceleration sensors and several electromyography sensors can be distributed around the patient's legs, and the patient can be instructed to walk on a straight road without obstacles when wearing a walker, and all three-axis acceleration sensors and all electromyography sensors can be triggered to work at the same time. Each three-axis acceleration sensor and each electromyography sensor can synchronously and comprehensively detect the patient's leg movement posture and the surface electromyography signals generated by the leg muscle groups during the patient's autonomous walking without external assistance, and obtain leg movement posture data and leg surface electromyography signal data, providing a data basis for subsequent determination of the patient's leg movement posture changes and muscle activity stability during walking.

[0069] Multi-degree-of-freedom analysis is performed on the leg motion and posture data detected by all three-axis acceleration sensors to obtain the patient's walking gait characteristics while walking on a straight, obstacle-free road, thereby comprehensively and accurately characterizing the patient's own leg motion and posture changes during walking. Furthermore, analysis is performed on the leg surface electromyography (SEM) signal data detected by all electromyography (EMG) sensors to obtain the patient's muscle activity characteristics. The greater the SEM signal intensity and the longer the drift rate of the SEM signal intensity is less than a preset drift rate threshold, the higher the activity stability of the leg muscle group corresponding to the SEM signal. By analyzing the relationship between the SEM signal intensity and its drift and the muscle group activity stability, the SEM signal intensity can be analyzed to obtain the activity stability of all leg muscle groups during the patient's walking, thereby quantitatively characterizing the activity state of the leg muscles.

[0070] Furthermore, based on the walking gait characteristics and muscle activity characteristics, a user's leg muscle activity state model is constructed, specifically:

[0071] Matching and comparing the changes in the user's leg posture during walking with the stability of the user's muscle activity during walking to obtain relationship information between the changes in the user's leg posture angle and the changes in the stability of the user's leg muscle activity;

[0072] Based on the relationship information, a user's leg muscle activity state model is constructed; wherein the leg muscle activity state model represents the change in posture angle of the user's leg when all muscle groups in the user's leg change from a strong state to a weak state.

[0073] Considering that all triaxial accelerometers and myoelectric sensors installed on the patient's legs operate synchronously at the same frequency, the triaxial accelerometers and myoelectric sensors synchronously generate leg posture data and leg surface electromyographic signal data with each walking movement during unimpeded walking. This results in a synchronous correlation between the leg posture data and the leg surface electromyographic signal data in the time domain. Furthermore, if a muscle group in the patient's leg (such as the thigh or calf muscles) is equipped with both triaxial accelerometers and myoelectric sensors, the leg posture data and leg surface electromyographic signal data generated by the triaxial accelerometers and myoelectric sensors for the same muscle group are correlated at the spatial level of the human leg. Based on this temporal and spatial correlation between the leg posture data and leg surface electromyographic signal data generated by the triaxial accelerometers and myoelectric sensors, the leg posture changes and muscle activity stability derived from the leg posture data and leg surface electromyographic signal data, respectively, also show a corresponding correlation. To this end, the changes in the patient's leg posture during walking and the muscle activity stability of the user during walking are temporally and spatially matched and compared to obtain the relationship information between the changes in the patient's leg posture angle and the changes in the user's leg muscle activity stability. The above relationship information may be, but is not limited to, the change in activity stability corresponding to the patient's corresponding leg muscle group exerting force in coordination with the leg movement whenever the patient's leg posture angle changes by a preset unit difference, and calculates whether the patient's leg muscle group can actively exert a force of corresponding magnitude in coordination with the leg movement.

[0074] Modeling is also performed based on the relationship between the patient's leg posture angle change and the user's leg muscle activity stability change to obtain the patient's leg muscle activity state model. In this way, the leg muscle activity state model can comprehensively and accurately characterize the posture angle change corresponding to the patient's leg when all muscle groups in the patient's leg change from a strong state to a weak state. The above-mentioned posture angle change can be understood as, for a muscle group in the patient's leg, when the posture angle of the patient's leg in a certain dimension changes from zero to a first posture angle value during walking, the muscle group can always actively exert force to provide sufficient force to support the leg movement. When the posture angle of the leg in a certain dimension continues to increase from the first posture angle value, the muscle group cannot continue to provide greater force to support the leg movement by relying solely on its own active force. At this time, it can be considered that the muscle group has changed from a strong state to a weak state. Accordingly, the first posture angle value is the posture angle change corresponding to the user's leg when the muscle group changes from a strong state to a weak state. Through the above analysis, it can be seen that the above-mentioned attitude angle change is the maximum attitude angle change that can be maintained by a certain muscle group in the patient's leg by relying solely on its own active force. Once the patient's leg movement exceeds the above-mentioned attitude angle change, the normal leg movement will not be maintained. The size of the above-mentioned attitude angle change is positively correlated with the tissue strength of the muscle group itself. The greater the tissue strength of the muscle group itself, the greater the attitude angle change. Leg rehabilitation training increases the attitude angle change of leg movement by training the tissue strength of the leg muscle group. By constructing a model of the patient's leg muscle activity state, it is possible to comprehensively and accurately characterize the maximum attitude angle change that can be achieved by the patient's muscle group strength under the current leg tissue structure state to support leg movement, providing a reliable basis for subsequent prediction of muscle group fatigue that may occur in patients during rehabilitation training and accurate implementation of power assistance.

[0075] S200, detecting the obstacle-avoiding walking process of the user wearing the walker to obtain the user's leg pressure characteristics; based on the leg pressure characteristics, predicting the user's upcoming leg movement information.

[0076] Furthermore, the obstacle avoidance walking process of the user wearing the walker is detected to obtain the user's leg pressure characteristics; based on the leg pressure characteristics, the user's upcoming leg movement information is predicted, specifically:

[0077] The contact pressure of the user's legs is detected during obstacle avoidance walking while wearing the walker, and the contact pressure distribution characteristics between the user's legs and the outside world are obtained. The contact pressure distribution characteristics include the distribution of the contact force applied by the user's legs to the outside world during the process of global contact with the outside world.

[0078] The leg pressure characteristics are analyzed to predict the user's movement intention, thereby obtaining the leg muscle position where the user will move and its movement posture angle.

[0079] The function of the walker is to provide timely and proactive assistance to the patient during the patient's walking rehabilitation training, that is, to provide assistance to the above-mentioned leg muscle group when a certain leg muscle group becomes weak / fatigued during the patient's walking. At the same time, in order to improve the efficiency of the patient's walking rehabilitation training and enable the patient to actively adapt to different terrain obstacles and walk flexibly, the patient may be required to walk on a road with obstacles pre-set while wearing a walker, so that the patient can improve the strength of the leg muscle group by avoiding obstacles while walking on the above-mentioned road. When the patient is wearing a walker and performing obstacle avoidance walking, the patient will make more complex walking movements with a larger range of movement posture angle changes than when walking without obstacles, so the patient is more likely to cause weakness / fatigue in the corresponding muscle group.

[0080] Given the complex and variable nature of a patient's movements during obstacle-avoiding walking, timely and accurate assistance to the corresponding leg muscle groups is crucial for improving the effectiveness of walking rehabilitation training. To achieve this, it's necessary to predetermine the patient's leg movements during obstacle-avoiding walking to ensure targeted assistance from the walker. Considering that the soles of the patient's feet and the sides of their legs come into contact with the outside world during walking, exerting pressure (in this case, the soles of the feet and the sides of the legs come into contact with the walker, exerting pressure on the walker), the distribution of which depends on the patient's leg movements. Therefore, by detecting and analyzing the pressure distribution generated during the patient's leg contact with the outside world, it's possible to predict the patient's intended leg movement (i.e., the movement the patient's leg will make in the next moment). Specifically, a pressure sensor positioned on the side of the walker that contacts the patient's legs can be used to measure leg contact pressure during obstacle-avoiding walking while the patient is wearing the walker, thereby obtaining the distribution of the contact force applied by the patient's legs to the outside world. The above-mentioned contact force distribution is then analyzed to predict the patient's leg movement intention and obtain the leg muscle position and movement posture angle where the user is about to move; among them, the analysis of the contact force distribution can be obtained through the existing large model of human walking analysis, which is a conventional technical means in this field and will not be introduced in detail here.

[0081] S300, determining the power assistance requirement information of the user's legs based on the leg muscle activity state model and leg movement information; and adjusting the power assistance provided by the walker to the user's legs based on the power assistance requirement information.

[0082] Furthermore, based on the leg muscle activity state model and leg motion information, the power assistance requirement information of the user's legs is determined; based on the power assistance requirement information, the power assistance provided by the walker to the user's legs is adjusted, specifically:

[0083] Comparing the leg muscle parts and posture angles of the user's upcoming movement, as included in the leg movement information, with the leg muscle activity state model to determine the leg muscle parts of the user that are in a state of weakness during the upcoming movement, thereby determining the user's leg assistance requirement information; wherein the user's leg assistance requirement information includes the area of the leg muscle parts that are in a state of weakness and the amount of external force that needs to be compensated;

[0084] Based on the assistance requirement information, the range and size of the compensatory driving torque applied by the walker to the leg muscle parts in the powerless state are adjusted.

[0085] Considering the complex and variable nature of the patient's movement during obstacle avoidance walking, the patient's legs may need to make large posture angle changes to cross the obstacle. At this time, due to the strength limitations of the patient's leg muscle groups, the leg muscles are unable to independently exert force to support the legs to complete the leg movement with such large posture angle changes. At this time, the walker is required to provide assistance to the corresponding leg muscle groups to assist in completing the leg movement. Through the above analysis, it can be seen that the leg muscle activity state model is a representation of the maximum posture angle change that can be achieved by the patient's muscle group strength under the current leg muscle tissue structure state. To this end, the leg muscle parts and their posture angles included in the leg movement information of the user are about to perform the movement and are compared with the leg muscle activity state model to determine the leg muscle parts that are in a state of weakness during the user's upcoming movement. The regional range of the leg muscle parts in a state of weakness and the amount of external force required to be compensated are obtained, so that the walker can accurately apply the appropriate amount of assistance to the corresponding muscle groups in the patient's legs.

[0086] In addition, a sub-airbag array may be provided on the side of the walker that contacts the patient's legs. The sub-airbag array includes several evenly arranged sub-airbags, each of which can be independently inflated and deflated. When the sub-airbag is in an inflated state, it can provide assistance to the leg muscle area in contact with it. The greater the inflation pressure inside the sub-airbag, the greater the assistance applied to the corresponding leg muscle area; when several sub-airbags are inflated at the same time, corresponding assistance torque can be applied to the leg muscle area in contact with them, and by changing the size of the inflation pressure of different sub-airbags, the size and direction of the assistance torque applied to the above-mentioned leg muscle area can be adjusted. To this end, based on the above-determined regional range of the leg muscle parts in a state of weakness and the value of the external force that needs to be compensated, the inflation pressure of at least some of the sub-airbags in the sub-airbag array of the walker is adjusted, thereby adjusting the range and size of the compensatory driving torque applied by the walker to the leg muscle parts in a state of weakness, ensuring that the patient always obtains accurate and timely power support during walking, providing multi-directional power support for the leg muscles, strengthening walking rehabilitation training, and improving the accuracy and reliability of the walker's power support.

[0087] See also Figure 6 As shown, the present invention provides a power-assistance control system for a walking device, which includes the following modules:

[0088] The first detection module is used to detect the barrier-free walking process of the user wearing the walker, and obtain the user's walking gait characteristics and muscle activity characteristics;

[0089] An activity state model building module is used to build a user's leg muscle activity state model based on walking gait characteristics and muscle activity characteristics;

[0090] The second detection module is used to detect the obstacle avoidance walking process of the user wearing the walker and obtain the user's leg pressure characteristics;

[0091] The leg movement prediction module is used to predict the user's upcoming leg movement information based on leg pressure characteristics;

[0092] A power assistance requirement determination module is used to determine the power assistance requirement information of the user's legs based on the leg muscle activity state model and leg movement information;

[0093] The power assist adjustment module is used to adjust the power assist provided by the walker to the user's legs based on the power assist demand information.

[0094] Furthermore, the first detection module is used to detect the barrier-free walking process of the user wearing the walker, and obtain the user's walking gait characteristics and muscle activity characteristics, specifically:

[0095] Detect leg movements and biosignals of the user while wearing the walker and walking without obstacles, and obtain leg movement posture data and leg surface electromyography signal data of the user;

[0096] Performing multi-degree-of-freedom analysis on the leg motion posture data to obtain the user's walking gait characteristics; wherein the walking gait characteristics include changes in the user's leg motion posture during walking;

[0097] Analyze the surface electromyographic signal data of the legs to obtain the user's muscle activity characteristics; wherein the muscle activity characteristics include the stability of the user's muscle activity during walking;

[0098] The activity state model building module is used to build a user's leg muscle activity state model based on walking gait characteristics and muscle activity characteristics. Specifically:

[0099] Matching and comparing the changes in the user's leg posture during walking with the stability of the user's muscle activity during walking to obtain relationship information between the changes in the user's leg posture angle and the changes in the stability of the user's leg muscle activity;

[0100] Based on the relationship information, a user's leg muscle activity state model is constructed; wherein the leg muscle activity state model represents the change in posture angle of the user's leg when all muscle groups in the user's leg change from a strong state to a weak state.

[0101] Furthermore, the second detection module is used to detect the obstacle avoidance walking process of the user wearing the walker, and obtain the user's leg pressure characteristics, specifically:

[0102] The contact pressure of the user's legs is detected during obstacle avoidance walking while wearing the walker, and the contact pressure distribution characteristics between the user's legs and the outside world are obtained. The contact pressure distribution characteristics include the distribution of the contact force applied by the user's legs to the outside world during the process of global contact with the outside world.

[0103] The leg movement prediction module is used to predict the user's upcoming leg movement information based on leg pressure characteristics, specifically:

[0104] The leg pressure characteristics are analyzed to predict the user's movement intention, thereby obtaining the leg muscle position where the user will move and its movement posture angle.

[0105] Furthermore, the power assistance requirement determination module is used to determine the power assistance requirement information of the user's legs based on the leg muscle activity state model and leg movement information, specifically:

[0106] Comparing the leg muscle parts and posture angles of the user's upcoming movement, as included in the leg movement information, with the leg muscle activity state model to determine the leg muscle parts of the user that are in a state of weakness during the upcoming movement, thereby determining the user's leg assistance requirement information; wherein the user's leg assistance requirement information includes the area of the leg muscle parts that are in a state of weakness and the amount of external force that needs to be compensated;

[0107] The power assist adjustment module is used to adjust the power assist provided by the walker to the user's legs based on the power assist demand information, specifically:

[0108] Based on the assistance requirement information, the range and size of the compensatory driving torque applied by the walker to the leg muscle parts in the powerless state are adjusted.

[0109] The operation and effects of the power-assistance control system of the walker of the present invention correspond to and are consistent with the power-assistance control method of the walker described above, and the power-assistance control system of the walker will not be described again here.

[0110] In one embodiment of the present invention, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0111] In one embodiment of the present invention, the present invention further provides a computer device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described above is implemented.

[0112] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding the necessary general-purpose hardware platform, or of course, by combining hardware and software. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a computer product. The present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it, and other embodiments may also be used. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A power assist control system for a walking device, characterized in that: The system includes the following modules: The first detection module is used to detect the barrier-free walking process of the user wearing the walker, and obtain the walking gait characteristics and muscle activity characteristics of the user, specifically: Detecting leg movements and biosignals of a user while wearing the walker during unimpeded walking, to obtain leg movement posture data and leg surface electromyography signal data of the user; Performing multi-degree-of-freedom analysis on the leg motion posture data to obtain the walking gait characteristics of the user; wherein the walking gait characteristics include changes in the leg motion posture of the user during walking; Analyzing the surface electromyographic signal data of the legs to obtain muscle activity characteristics of the user; wherein the muscle activity characteristics include the stability of the muscle activity of the user during walking; The activity state model construction module is used to construct the user's leg muscle activity state model based on the walking gait characteristics and the muscle activity characteristics, specifically: Matching and comparing the leg posture changes of the user during walking with the stability of the user's muscle activity during walking to obtain relationship information between the changes in the user's leg posture angle and the changes in the stability of the user's leg muscle activity; Based on the relationship information, a leg muscle activity state model of the user is constructed; wherein the leg muscle activity state model represents the change in posture angle of the user's leg when all muscle groups in the user's leg change from a strong state to a weak state; a second detection module, configured to detect the obstacle-avoiding walking process of the user wearing the walker, and obtain the leg pressure characteristics of the user; a leg movement prediction module, configured to predict the user's upcoming leg movement information based on the leg pressure characteristics; a power assistance requirement determination module, configured to determine power assistance requirement information of the user's legs based on the leg muscle activity state model and the leg motion information; The power-assistance adjustment module is used to adjust the power-assistance provided by the walker to the user's legs based on the power-assistance requirement information.

2. The system according to claim 1, wherein: The second detection module is used to detect the obstacle avoidance walking process of the user wearing the walker, and obtain the leg pressure characteristics of the user, specifically: Performing leg contact pressure detection on the user while wearing the walker and avoiding obstacles while walking, and obtaining a contact pressure distribution characteristic between the user's legs and the outside world; wherein the contact pressure distribution characteristic includes a distribution of the magnitude of the contact force exerted by the user's legs on the outside world during the process of global contact with the outside world; The leg movement prediction module is used to predict the user's upcoming leg movement information based on the leg pressure characteristics, specifically: The leg pressure characteristics are analyzed to predict the user's action intention, thereby obtaining the leg muscle position where the user is about to move and the action posture angle.

3. The system according to claim 1, wherein: The power assistance requirement determination module is used to determine the power assistance requirement information of the user's legs based on the leg muscle activity state model and the leg movement information, specifically: Comparing the leg muscle parts and movement posture angles of the user's upcoming movement, as included in the leg movement information, with the leg muscle activity state model to determine the leg muscle parts of the user's leg that are in a state of weakness during the upcoming movement, thereby determining the power assistance requirement information of the user's leg; wherein the power assistance requirement information of the user's leg includes the area range of the leg muscle parts in a state of weakness and the value of the external force to be compensated; The power-assistance adjustment module is used to adjust the power-assistance provided by the walker to the user's legs based on the power-assistance requirement information, specifically: Based on the power assistance requirement information, the range and magnitude of the compensatory driving torque applied by the walker to the leg muscle portion in the powerless state are adjusted.

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