Assistance regulation and control method and system of walker and storage medium

Abstract

The invention relates to the field of mechanical power assisting devices, in particular to a power assisting regulation and control method and system of a walker, a barrier-free walking process under the condition that a user wears the walker is detected, walking gait characteristics and muscle activity characteristics of the user are obtained, and a leg muscle activity state model of the user is constructed; detecting an obstacle avoidance walking process when the user wears the walker, and obtaining leg pressure characteristics of the user, so as to predict leg action information about to occur by the user; and based on the leg muscle activity state model and the leg action information, determining assistance demand information of the legs of the user so as to adjust the assistance effect provided by the walker to the legs of the user. According to the walking device, the leg assisting requirement can be accurately determined, multi-directional assisting support is provided for leg muscles, walking rehabilitation training is strengthened, and the assisting accuracy and reliability of the walking device are improved.

Description

Technical Field

[0001] The present invention relates to the field of mechanical assistance devices, and particularly to a method and system for assisting and regulating a walker, and a storage medium. Background Art

[0002] As a walking training assistance device, a walker can help users complete walking tasks. For example, patients who have undergone surgeries such as knee joint replacement, hip joint replacement, etc., or those with lower limb fractures in recovery usually need walking exercises to re-adapt to the replaced new joints or restore the motor sensitivity of the lower limbs. During the actual walking training process, patients do not have any intuitive feeling about their original load. If relying solely on simple tools such as crutches, it will not be able to provide stable support for patients, nor can it provide appropriate assistance for the actual walking movements of patients, and cannot reduce the muscle load during walking and provide assistance support when the patient is in a state of muscle weakness. Therefore, the walker acts as a rehabilitation training device, which matches the lower limbs of the patient, and can provide assistance support for the lower limbs of the patient when the patient wears the walker.

[0003] Existing walkers are only limited to providing assistance to the lower limb joint parts such as the hip joint, knee joint, and ankle joint. However, the above-mentioned assistance providing method can only assist several position points of the human lower limbs, and the assistance providing dimension is relatively single. It cannot provide assistance support for the human lower limbs in a large range, and it is easy to cause uneven application of assistance and rollover. In addition, existing walkers can only detect whether the human body is unbalanced during walking, and provide reverse assistance support when the human body is in an unbalanced situation, and cannot perform reliable muscle strength training for the human walking state. It can be seen that how to provide multi-directional assistance support during human walking according to the muscle activity state of the human lower limbs is of great significance for strengthening human walking rehabilitation training and improving the accuracy and reliability of the walker assistance. Summary of the Invention

[0004] In order to avoid problems such as uneven application of assistance and inability to effectively exercise the lower limb muscles when using a walker for human walking training, by detecting the human walking state to construct a leg muscle activity state model, predicting the upcoming leg movement information, determining the leg assistance requirement, and providing multi-directional assistance support for the leg muscles to strengthen walking rehabilitation training and improve the accuracy and reliability of the walker assistance, the present invention provides a method for assisting and regulating a walker, and the method includes the following steps: Detect the unobstructed walking process of the user wearing the walker to obtain the walking gait characteristics and muscle activity characteristics of the user; based on the walking gait characteristics and the muscle activity characteristics, construct the leg muscle activity state model of the user; Detect the obstacle avoidance walking process of the user while wearing the walker to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user will perform. Based on the leg muscle activity state model and the leg movement information, determine the assistance requirement information of the user's legs; based on the assistance requirement information, adjust the assistance effect provided by the walker to the user's legs.

[0005] Preferably, the process of detecting the obstacle-free walking of the user while wearing the walker to obtain the walking gait characteristics and muscle activity characteristics of the user is specifically as follows: During the obstacle-free walking process of the user while wearing the walker, perform leg movement and bio-signal detection to obtain the leg movement posture data and leg surface electromyogram signal data of the user. Perform multi-degree-of-freedom analysis on the leg movement posture data to obtain the walking gait characteristics of the user; wherein, the walking gait characteristics include the changes in the leg movement postures during the user's walking process. Analyze the leg surface electromyogram signal data to obtain the muscle activity characteristics of the user; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process.

[0006] Preferably, the process of constructing the leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics is specifically as follows: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the changes in the leg posture angles of the user and the changes in the muscle activity stability of the user's legs. Based on the relationship information, construct the leg muscle activity state model of the user; wherein, the leg muscle activity state model represents the change amount of the corresponding posture angle of the user's legs when all muscle groups in the user's legs change from a strong state to a weak state.

[0007] Preferably, the process of detecting the obstacle avoidance walking process of the user while wearing the walker to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user will perform is specifically as follows: During the obstacle avoidance walking process of the user while wearing the walker, perform leg contact pressure detection to obtain the contact pressure distribution characteristics between the user's legs and the outside world; wherein, the contact pressure distribution characteristics include the distribution of the magnitudes of the contact forces exerted on the outside world during the contact process between the global range of the user's legs and the outside world. Analyze the leg pressure characteristics to predict the user's action intention, and thereby obtain the leg muscle parts where the user will perform actions and their action posture angles.

[0008] Preferably, based on the leg muscle activity state model and the leg movement information, determine the assistance requirement information of the user's legs; based on the assistance requirement information, adjust the assistance effect provided by the walker to the user's legs, specifically: Compare the leg muscle parts where the user will perform actions and their action posture angles included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts with weakness states during the actions that the user's legs will perform, thereby determining the assistance requirement information of the user's legs; wherein, the assistance requirement information of the user's legs includes the area range of the leg muscle parts with weakness states and the external force compensation value; Based on the assistance requirement information, adjust the range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts with weakness states.

[0009] On the other hand, the present invention provides an assistance control system for a walker, and the system includes the following modules: A first detection module for detecting the unobstructed walking process of the user while wearing the walker to obtain the walking gait characteristics and muscle activity characteristics of the user; An activity state model construction module for constructing a leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics; A second detection module for detecting the obstacle avoidance walking process of the user while wearing the walker to obtain the leg pressure characteristics of the user; A leg movement prediction module for predicting the leg movement information that the user will perform based on the leg pressure characteristics; An assistance requirement determination module for determining the assistance requirement information of the user's legs based on the leg muscle activity state model and the leg movement information; An assistance effect adjustment module for adjusting the assistance effect provided by the walker to the user's legs based on the assistance requirement information.

[0010] Preferably, the first detection module is used to detect the unobstructed walking process of the user while wearing the walker to obtain the walking gait characteristics and muscle activity characteristics of the user, specifically: Detect the leg movements and biological signals during the unobstructed walking process of the user while wearing the walker to obtain the leg movement posture data and leg surface electromyogram signal data of the user; Perform multi-degree-of-freedom analysis on the leg movement posture data to obtain the walking gait characteristics of the user; wherein, the walking gait characteristics include the changes in the leg movement posture during the user's walking process. Analyze the leg surface electromyogram signal data to obtain the muscle activity characteristics of the user; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process. The activity state model construction module is used to construct the leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics, specifically: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the leg posture angle changes and the muscle activity stability changes of the user's legs. Based on the relationship information, construct the leg muscle activity state model of the user; wherein, the leg muscle activity state model represents the amount of leg posture angle change corresponding to the user's legs when all muscle groups in the user's legs change from a strong state to a weak state.

[0011] Preferably, the second detection module is used to detect the obstacle avoidance walking process of the user when wearing a walker to obtain the leg pressure characteristics of the user, specifically: Perform leg contact pressure detection during the obstacle avoidance walking process of the user when wearing a walker to obtain the contact pressure distribution characteristics between the user's legs and the outside world; wherein, the contact pressure distribution characteristics include the distribution of the magnitude of the contact force applied to the outside world during the contact process between the global range of the user's legs and the outside world. The leg movement prediction module is used to predict the leg movement information that the user is about to perform based on the leg pressure characteristics, specifically: Analyze the leg pressure characteristics to predict the user's action intention, and thereby obtain the leg muscle parts and action posture angles of the user's upcoming actions.

[0012] Preferably, the assistance requirement determination module is used to determine the assistance requirement information of the user's legs based on the leg muscle activity state model and the leg movement information, specifically: Compare the leg muscle parts and action posture angles of the user's upcoming actions included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts with a weak state during the upcoming actions of the user's legs, and thereby determine the assistance requirement information of the user's legs; wherein, the assistance requirement information of the user's legs includes the regional range of the leg muscle parts with a weak state and the external force value to be compensated. The assisting effect adjustment module is configured to adjust the assisting effect provided by the walker to the user's leg based on the assisting requirement information. Specifically: Based on the assisting requirement information, adjust the range and magnitude of the compensatory driving torque applied by the walker to the leg muscle parts in a state of weakness.

[0013] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. The computer program, when executed by a processor, implements the method described above.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Detect the obstacle-free walking process of the user when wearing the walker to 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 assisting effects according to the actual weakness / asthenia conditions of the patient's lower limb muscle groups. It is necessary to comprehensively detect and characterize the possible weakness / asthenia conditions of all muscle groups in different parts of the patient's lower limbs under different leg movements to determine at what posture and amplitude the patient's leg movements can trigger the weakness / asthenia of the lower limb muscle groups. By constructing the leg muscle activity state model of the patient, the maximum posture angle change that the muscle group strength can support for leg movement under the current leg tissue structure state of the patient can be comprehensively and accurately characterized, providing a reliable basis for accurately implementing assisting assistance for predicting possible muscle group asthenia during the patient's rehabilitation training process.

[0015] Detect the obstacle avoidance walking process of the user when wearing the walker to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user is about to make. Considering the complex and variable movement conditions during the obstacle avoidance walking process of the patient, it is necessary to pre-determine the leg movement conditions during the obstacle avoidance walking process of the patient to ensure that the walker provides targeted assistance. Considering that pressure effects are formed when the sole and the side of the leg of the patient come into contact with the outside world during walking, the distribution of the above pressure effects depends on the walking movement conditions of the patient's leg. Therefore, by detecting and analyzing the distribution of the pressure effects formed during the contact between the patient's leg and the outside world, the intention of the patient's leg movement can be predicted, and the leg muscle part and its movement posture angle of the action that the user is about to make can be obtained, thus providing an accurate reference for subsequent controlling the walker to apply an assisting effect.

[0016] Based on the leg muscle activity state model and leg movement information, determine the assistance requirement information of the user's leg; based on the assistance requirement information, adjust the assistance provided by the walker to the user's leg. Considering the complex and changeable movement conditions during the obstacle avoidance walking of the patient, the patient's leg may need to make a large posture angle change to step over the obstacle to be avoided. At this time, due to the limitation of the self-tissue strength of the patient's leg muscle group, the leg muscle group cannot generate force independently to support the leg to complete the leg movement with the above large posture angle change. At this time, the walker needs to provide assistance to the corresponding leg muscle group to assist in completing the leg movement. Through the above process, determine the leg muscle parts in a weak state during the upcoming movement of the user's leg, and obtain the area range of the leg muscle parts in a weak state and the external force value to be compensated, so as to facilitate the walker to accurately apply an appropriate amount of assistance to the corresponding muscle group of the patient's leg. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them: Figure 1 is a flowchart of the assistance regulation method of the walker provided by the present invention.

[0018] Figure 2 is a schematic diagram of the sensor setting positions corresponding to the detection of the leg movement posture change in the assistance regulation method of the walker provided by the present invention; Figure 3 is a schematic circuit diagram corresponding to the detection of the leg movement posture change in the assistance regulation method of the walker provided by the present invention; Figure 4 is a schematic diagram of the muscle groups corresponding to the detection of the leg surface electromyogram signal in the assistance regulation method of the walker provided by the present invention; Figure 5 is a schematic circuit diagram corresponding to the detection of the leg surface electromyogram signal in the assistance regulation method of the walker provided by the present invention; Figure 6 is a structural diagram of the assistance regulation system of the walker provided by the present invention.

[0019] Reference 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 OF THE EMBODIMENTS

[0020] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following will describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Additionally, it should be noted that for the convenience of description, only the parts related to the present invention rather than all the structures are shown in the drawings. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0021] The terms "comprising" and "having" in the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0022] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0023] Please refer to Figure 1 As shown, the present invention provides a method for assisting and regulating a walker, and the method includes the following steps: S100, detecting the unobstructed walking process of a user wearing a walker to obtain the walking gait characteristics and muscle activity characteristics of the user; based on the walking gait characteristics and muscle activity characteristics, constructing a leg muscle activity state model of the user.

[0024] Further, detecting the unobstructed walking process of a user wearing a walker to obtain the walking gait characteristics and muscle activity characteristics of the user specifically includes: Detecting the leg movements and biological signals during the unobstructed walking process of a user wearing a walker to obtain the leg movement posture data and leg surface electromyogram signal data of the user; Performing multi-degree-of-freedom analysis on the leg movement posture data to obtain the walking gait characteristics of the user; wherein, the walking gait characteristics include the changes in the leg movement postures during the user's walking process; Analyzing the leg surface electromyogram signal data to obtain the muscle activity characteristics of the user; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process.

[0025] As a power-assisted support device for lower limb walking rehabilitation training, a walker mainly includes a number of support components corresponding to different parts of the human body, such as the hip, thigh, calf, and foot. When a patient who has undergone knee replacement surgery, hip replacement surgery, or lower limb fracture recovery wears the walker, the patient's hip, thigh, calf, and foot respectively come into contact with the corresponding support components of the walker. Each support component can provide power-assisted support to the lower limb part in contact with it, and the above power-assisted support can be implemented for the muscle groups of the corresponding lower limb part, so as to ensure that the power-assisted support acts evenly and balancedly on the lower limb part. This not only avoids applying power-assisted support only to the lower limb joint part and being unable to ensure the walking balance of the human body, but also can effectively train the lower limb muscles to exert force and restore the movement sensitivity and force 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 lower limb muscles of the patient temporarily lose their original activity and sensitivity due to lack of activity for a long time, resulting in muscle weakness or fatigue in the patient during walking. For example, when a patient makes a small-amplitude posture movement in the thigh or calf or other leg parts during walking, the muscles in the thigh or calf can exert force accordingly to maintain the above small-amplitude movement; once the thigh or calf or other leg parts make a large-amplitude posture movement, the current tissue strength of the muscles in the thigh or calf will not be able to exert force to maintain the above large-amplitude movement. It can be seen that the patient does not have muscle weakness or fatigue in any movement during walking, but only when the amplitude of the walking movement posture exceeds a certain threshold will the muscles be unable to form continuous and stable force due to their own tissue structure.

[0026] In order to enable the walker to accurately provide targeted power assistance according to the actual muscle weakness or fatigue of the patient's lower limb muscle groups after the patient wears the walker, it is necessary to comprehensively detect and characterize the possible muscle weakness or fatigue of the muscle groups in all parts of the patient's lower limbs under different leg movements, and determine at what amplitude of the posture the patient's leg can trigger muscle weakness or fatigue in the lower limb muscle groups. Specifically, a number of three-axis acceleration sensors can be distributed in the areas corresponding to the hip, thigh, calf, and foot of the patient's lower limb. During the initial stage of the patient's joint replacement surgery or lower limb fracture recovery, the walking movement of the patient is detected to accurately determine the original leg dynamic posture of the patient. Specifically, the movement of the patient's lower limb is mainly achieved by the relative movement of the joints and the connected bones in the lower limb. Please refer to Figure 2 As shown in the figure, the lower limb mainly includes the hip joint, femur, knee joint, tibia, and ankle joint. In the actual detection of the change of the leg movement posture, detection bands with three-axis acceleration sensors can be wound around the hip joint, femur, knee joint, tibia, and ankle joint corresponding to the lower limb respectively, so as to comprehensively and accurately detect the movement postures of the above joints and bones during the leg movement. Please refer to Figure 3As shown, all three-axis acceleration sensors are connected to the A / D conversion circuit. The analog signals detected and generated by each three-axis acceleration sensor are converted into digital signals through the A / D conversion circuit, and then the processor processes the digital signals to obtain the changes in the leg movement postures. Among them, the three-axis acceleration sensors, A / D conversion circuit, and processor are all commonly used electronic devices in the field and will not be introduced in detail here.

[0027] In addition, the activity and force characteristics of leg muscles can be characterized by detecting the surface electromyogram signals of leg muscles. Generally speaking, during the walking process of a patient, the muscle groups in different parts of the leg will exert corresponding degrees of force to complete the traction action on the whole leg. The muscle groups will generate surface electromyogram signals during the force exertion process, and there is a specific correlation between the intensity of the surface electromyogram signals and the force intensity of the muscle groups, that is, the greater the force intensity of the muscle groups, the greater the intensity of the surface electromyogram signals, and the longer the force stability of the muscle groups is maintained, the more stable the intensity of the surface electromyogram signals is and the less likely it is to drift. Therefore, several electromyogram sensors can be distributed and set in the corresponding hip, thigh, calf, foot and other areas of the patient's lower limb. During the walking process of the patient, the surface electromyogram signals of all muscle groups in the global range of the leg can be detected in a non-invasive manner, so as to accurately detect the force conditions of all leg muscle groups during activities. Specifically, as Figure 4 shown, the force of the patient's lower limb muscles mainly depends on the rectus femoris, vastus lateralis, vastus medialis, tibialis anterior, biceps femoris, gastrocnemius, and soleus muscles of the lower limb. In the actual detection of the activity and force characteristics of muscles, electromyogram sensors with surface electrodes can be distributed and attached corresponding to the rectus femoris, vastus lateralis, vastus medialis, tibialis anterior, biceps femoris, gastrocnemius, and soleus muscles of the lower limb, so that the surface electromyogram signals of the above muscle groups during the leg movement process can be comprehensively and accurately detected. Please refer to Figure 5 shown, the surface electrodes of all electromyogram sensors are connected to a preamplifier, an amplifier filter, and an A / D conversion circuit. The analog signals detected and generated by each surface electrode are converted into digital signals through the amplification processing of the preamplifier, the filtering processing of the amplifier filter, and the A / D conversion circuit, and then the processor processes the digital signals to obtain the surface electromyogram signals of the leg. Among them, the preamplifier, amplifier filter, A / D conversion circuit, and processor are all commonly used electronic devices in the field and will not be introduced in detail here.

[0028] In order to detect the leg movement posture data and leg surface electromyogram signal data of a patient in a non-interfering normal scenario, a number of triaxial acceleration sensors and a number of electromyogram sensors can be distributed globally on the patient's legs. And when the patient wears a walker, the patient is instructed to walk on a flat road without obstacles, while triggering all triaxial acceleration sensors and all electromyogram sensors to work. Each triaxial acceleration sensor and each electromyogram sensor can comprehensively and synchronously detect the leg movement posture and the surface electromyogram signal generated by the leg muscle groups during the walking process of the patient without external assistance, so as to obtain the leg movement posture data and the leg surface electromyogram signal data, providing a data basis for subsequent determination of the leg movement posture changes and muscle activity stability during the patient's walking process.

[0029] Perform multi-degree-of-freedom analysis on the leg movement posture data detected by all triaxial acceleration sensors to obtain the walking gait characteristics of the patient during walking on the current flat road without obstacles, so as to comprehensively and accurately characterize the leg movement posture changes of the patient himself during the walking process. And analyze the leg surface electromyogram signal data detected by all electromyogram sensors to obtain the muscle activity characteristics of the patient. When the surface electromyogram signal intensity is greater and the duration of the drift rate of the surface electromyogram signal being less than the preset drift rate threshold is greater, the activity stability of the leg muscle groups corresponding to the above surface electromyogram signal is higher. Through the relationship between the surface electromyogram signal intensity and its change drift and the activity stability of the muscle groups, it is possible to analyze the change in the signal intensity of the surface electromyogram signal, obtain the activity stability of each leg muscle group of the patient during the walking process, and quantitatively characterize the activity state of the leg muscles.

[0030] Furthermore, based on the walking gait characteristics and muscle activity characteristics, construct a leg muscle activity state model for the user, specifically: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the leg posture angle changes of the user and the changes in the muscle activity stability of the user's legs; Based on the relationship information, construct a leg muscle activity state model for the user; wherein, the leg muscle activity state model characterizes the amount of change in the corresponding posture angle of the user's leg when all the muscle groups in the user's leg change from a strong state to a weak state.

[0031] Considering that all the triaxial acceleration sensors and all the electromyography sensors set on the patient's leg work synchronously at the same frequency, when the patient makes a walking motion with the leg during unobstructed walking, the triaxial acceleration sensor and the electromyography sensor will respectively generate leg motion posture data and leg surface electromyography signal data synchronously. In this way, there is a synchronous correlation between the leg motion posture data and the leg surface electromyography signal data in the time domain. In addition, a triaxial acceleration sensor and an electromyography sensor are simultaneously set on a certain muscle group (such as the thigh muscle group or the calf muscle group) of the patient's leg. There is a correlation between the leg motion posture data and the leg surface electromyography signal data generated by the triaxial acceleration sensor and the electromyography sensor set on the same muscle group at the airspace level of the human leg. Based on the correlation between the leg motion posture data and the leg surface electromyography signal data generated by the triaxial acceleration sensor and the electromyography sensor respectively, there is also a corresponding correlation between the leg posture change and the muscle activity stability obtained from the leg motion posture data and the leg surface electromyography signal data respectively. Therefore, the leg posture change during the patient's walking process and the muscle activity stability during the user's walking process are matched and compared in space-time to obtain the relationship information between the leg posture angle change of the patient and the change in the muscle activity stability of the user's leg. The above relationship information can be, but is not limited to, that whenever the leg posture angle of the patient changes by a preset unit difference amount, the corresponding change in the activity stability of the patient's corresponding leg muscle group due to exerting force in cooperation with the leg motion, and calculate whether the patient's leg muscle group can actively exert a corresponding amount of force during the process of cooperating with the leg motion.

[0032] Modeling is also performed based on the relationship information between the leg posture angle changes of the above-mentioned patient and the changes in the stability of the user's leg muscle activities, to obtain a leg muscle activity state model of the patient. In this way, the above-mentioned leg muscle activity state model can comprehensively and accurately represent the corresponding posture angle change amount of 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 amount can be understood as follows: for a certain muscle group in the patient's leg, when the posture angle of the leg in a certain dimension changes from zero to the first posture angle value during the patient's walking process, the above-mentioned muscle group can always actively exert force to provide sufficient force to support the above-mentioned leg movement. When the posture angle of the leg in a certain dimension continues to increase from the first posture angle value, the above-mentioned muscle group cannot continue to provide greater force to support the above-mentioned leg movement only by relying on its own active force. At this time, it can be considered that the above-mentioned muscle group changes from a strong state to a weak state. Correspondingly, the above-mentioned first posture angle value is the posture angle change amount corresponding to the patient's leg when the above-mentioned muscle group changes from a strong state to a weak state. Through the above analysis, it can be seen that the above-mentioned posture angle change amount is the maximum posture angle change amount of the leg movement that can be maintained by a certain muscle group in the patient's leg only by relying on its own active force. Once the patient's leg movement exceeds the above-mentioned posture angle change amount, the normal leg movement cannot be maintained. The size of the above-mentioned posture angle change amount 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 posture angle change amount. Leg rehabilitation training increases the posture angle change amount of leg movement by training the tissue strength of the leg muscle group. By constructing a leg muscle activity state model of the patient, it is possible to comprehensively and accurately represent the maximum posture angle change amount that the muscle group strength can support the leg movement under the current leg tissue structure state of the patient, providing a reliable basis for accurately implementing assistive assistance in predicting possible muscle group weakness during the patient's rehabilitation training process.

[0033] S200, detect the obstacle avoidance walking process of the user when wearing the walker, to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user is about to perform.

[0034] Furthermore, detect the obstacle avoidance walking process of the user when wearing the walker, to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user is about to perform, specifically: Perform leg contact pressure detection during the obstacle avoidance walking process of the user when wearing the walker, to obtain the contact pressure distribution characteristics between the user's leg and the outside world; among them, the contact pressure distribution characteristics include the distribution of the magnitude of the contact force applied to the outside world during the contact process between the global range of the user's leg and the outside world. Analyze the leg pressure characteristics, predict the action intention of the user, and thereby obtain the leg muscle part where the user is about to perform the action and its action posture angle.

[0035] The function of the walker is to actively provide assistance to the patient in a timely manner during the walking rehabilitation training process, that is, when a certain leg muscle group becomes weak or fatigued during the patient's walking process, to apply an assisting force to the above-mentioned leg muscle group. 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 obstacle conditions and walk flexibly, the patient can be required to walk on a road surface with pre-set obstacles while wearing the walker, so that the patient can improve the strength of the leg muscle group by avoiding obstacles during the walking process on the above-mentioned road surface. When the patient walks while wearing the walker and avoids obstacles, the walking actions made during the obstacle avoidance process are more complex and have a larger range of motion posture angle changes compared to the walking process without obstacles. In this way, it is easier for the patient to cause weakness or fatigue in the corresponding muscle group.

[0036] In view of the complex and variable nature of the patient's movement during the obstacle avoidance walking process, it is necessary to provide an assisting force to the corresponding leg muscle group of the patient in a timely and accurate manner to improve the effect of the patient's walking rehabilitation training. Therefore, it is necessary to pre-determine the leg movement situation of the patient during the obstacle avoidance walking process to ensure that the walker applies the assisting force in a targeted manner. Considering that the sole of the foot and the side of the leg will come into contact with the outside world during the walking process to form a pressure effect (in the current situation, the sole of the foot and the side of the leg will come into contact with the walker to form a pressure effect on the walker), the distribution of the above pressure effect depends on the leg walking movement situation of the patient. Therefore, by detecting and analyzing the distribution of the pressure effect formed during the contact between the patient's leg and the outside world, the intention of the patient's leg movement (that is, the movement that the patient's leg will make at the next moment) can be predicted. Specifically, pressure sensors can be used on the side of the walker in contact with the patient's leg to detect the leg contact pressure during the obstacle avoidance walking process of the patient while wearing the walker, and obtain the distribution of the magnitude of the contact force applied to the outside world during the contact between the entire range of the patient's leg and the outside world. Then, analyze the distribution of the magnitude of the above contact force to predict the movement intention of the patient's leg, and obtain the leg muscle part where the user will make a movement and its movement posture angle; among them, the analysis of the distribution of the magnitude of the contact force can be obtained through the existing large human walking analysis model, which belongs to the conventional technical means in this field and will not be introduced in detail here.

[0037] S300, based on the leg muscle activity state model and leg movement information, determine the assisting force requirement information of the user's leg; based on the assisting force requirement information, adjust the assisting force provided by the walker to the user's leg.

[0038] Furthermore, based on the leg muscle activity state model and leg movement information, determine the assisting force requirement information of the user's leg; based on the assisting force requirement information, adjust the assisting force provided by the walker to the user's leg, specifically as follows: Compare the leg muscle parts and their movement posture angles of the actions that the user is about to perform included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts with weakness during the actions that the user's legs are about to perform, so as to determine the assistance requirement information of the user's legs; wherein, the assistance requirement information of the user's legs includes the regional range of the leg muscle parts with weakness and the external force value to be compensated. Based on the assistance requirement information, adjust the acting range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts with weakness.

[0039] Considering the complex and variable movement conditions during the obstacle avoidance walking of the patient, the patient's legs may need to make relatively large posture angle changes to step over the obstacles to be avoided. At this time, due to the limitation of the self-tissue strength of the patient's leg muscle group, the leg muscle group cannot generate force independently to support the leg to complete the leg movement with the above relatively large posture angle changes. At this time, the walker needs to provide assistance to the corresponding leg muscle group to assist in completing the leg movement. Through the above analysis, it can be seen that the leg muscle activity state model represents the maximum posture angle change amount that the muscle group strength can support the leg movement under the current leg muscle tissue structure state of the patient. Therefore, compare the leg muscle parts and their movement posture angles of the actions that the user is about to perform included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts with weakness during the actions that the user's legs are about to perform, and obtain the regional range of the leg muscle parts with weakness and the external force value to be compensated, so as to facilitate the walker to accurately apply an appropriate magnitude of assistance to the corresponding muscle group of the patient's legs.

[0040] In addition, a sub-airbag array can be arranged on the side of the walker in contact with the patient's legs. The above sub-airbag array includes a number of sub-airbags arranged evenly. Each sub-airbag can perform inflation and deflation operations independently. When the sub-airbag is in the inflated state, it can provide an assistance effect to the leg muscle area in contact with it. The greater the inflation pressure inside the sub-airbag, the greater the assistance effect applied to the corresponding leg muscle area; when several sub-airbags are inflated simultaneously, it can apply a corresponding assistance torque to the leg muscle area in contact with them, and by changing the inflation pressure magnitudes of different sub-airbags, the magnitude and direction of the assistance torque applied to the above leg muscle area can be adjusted. Therefore, based on the regional range of the leg muscle parts with weakness and the external force value to be compensated determined above, adjust the inflation pressure magnitudes of at least some of the sub-airbags in the sub-airbag array of the walker, so as to adjust the acting range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts with weakness, ensure that the patient always obtains accurate and timely assistance support during walking, provide multi-directional assistance support to the leg muscles, strengthen the walking rehabilitation training, and improve the assistance accuracy and reliability of the walker.

[0041] Please refer to Figure 6As shown, the present invention provides an assist control system for a walker, which system includes the following modules: A first detection module, configured to detect the unobstructed walking process of a user while wearing the walker, and obtain the user's walking gait characteristics and muscle activity characteristics; An activity state model construction module, configured to construct a leg muscle activity state model of the user based on the walking gait characteristics and muscle activity characteristics; A second detection module, configured to detect the obstacle avoidance walking process of the user while wearing the walker, and obtain the leg pressure characteristics of the user; A leg movement prediction module, configured to predict the leg movement information to be occurred by the user based on the leg pressure characteristics; An assist demand determination module, configured to determine the assist demand information of the user's legs based on the leg muscle activity state model and the leg movement information; An assist action adjustment module, configured to adjust the assist action provided by the walker to the user's legs based on the assist demand information.

[0042] Further, the first detection module is configured to detect the unobstructed walking process of the user while wearing the walker, and obtain the user's walking gait characteristics and muscle activity characteristics, specifically: Detect the leg movements and biological signals during the unobstructed walking process of the user while wearing the walker, and obtain the leg movement posture data and leg surface electromyogram signal data of the user; Perform multi-degree-of-freedom analysis on the leg movement posture data to obtain the user's walking gait characteristics; wherein, the walking gait characteristics include the leg movement posture changes during the user's walking process; Analyze the leg surface electromyogram signal data to obtain the user's muscle activity characteristics; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process; The activity state model construction module is configured to construct a leg muscle activity state model of the user based on the walking gait characteristics and muscle activity characteristics, specifically: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the leg posture angle changes of the user and the changes in the muscle activity stability of the user's legs; Construct a leg muscle activity state model of the user based on the relationship information; wherein, the leg muscle activity state model represents the amount of change in the corresponding posture angle of the user's legs when all muscle groups in the user's legs change from a strong state to a weak state.

[0043] Further, the second detection module is configured to detect the obstacle avoidance walking process of the user while wearing the walker, and obtain the leg pressure characteristics of the user, specifically: During the obstacle avoidance walking process of the user wearing the walker, the leg contact pressure is detected to obtain the contact pressure distribution characteristics between the user's legs and the outside world; among them, the contact pressure distribution characteristics include the distribution of the magnitude of the contact force exerted on the outside world during the contact process of the global range of the user's legs with the outside world. The leg movement prediction module is used to predict the leg movement information that the user is about to make based on the leg pressure characteristics. Specifically: Analyze the leg pressure characteristics, predict the user's action intention, and thus obtain the leg muscle parts and their action posture angles where the user is about to make a movement.

[0044] Furthermore, the assistance demand determination module is used to determine the assistance demand information of the user's legs based on the leg muscle activity state model and the leg movement information. Specifically: Compare the leg muscle parts and their action posture angles where the user is about to make a movement included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts with weak states during the upcoming movement of the user's legs, and thus determine the assistance demand information of the user's legs; among them, the assistance demand information of the user's legs includes the regional range of the leg muscle parts with weak states and the external force compensation value. The assistance effect adjustment module is used to adjust the assistance effect provided by the walker to the user's legs based on the assistance demand information. Specifically: Based on the assistance demand information, adjust the range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts with weak states.

[0045] The assistance control system of the walker of the present invention corresponds to the operation and effect of the above-mentioned assistance control method of the walker, and the assistance control system of this walker will not be repeated here.

[0046] In an embodiment of the present invention, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program realizes the above-mentioned method when executed by a processor.

[0047] In an embodiment of the present invention, the present invention also provides a computer device, which at least includes a memory and a processor, and a computer program is stored on the memory, and the computer program realizes the above-mentioned method when executed by the processor.

[0048] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of adding a necessary general hardware platform, and of course, it can also be implemented by a combination of hardware and software. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a computer product. The present invention can be implemented in the form of a computer program product on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes.

[0049] 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 them. Other embodiments can also be adopted; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assisting and regulating a walker, characterized in that The method includes the following steps: Detect the unobstructed walking process of the user while wearing a walker to obtain the walking gait characteristics and muscle activity characteristics of the user; based on the walking gait characteristics and the muscle activity characteristics, construct a leg muscle activity state model of the user; Detect the obstacle avoidance walking process of the user while wearing a walker to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user is about to perform; Based on the leg muscle activity state model and the leg movement information, determine the assistive demand information of the user's legs; based on the assistive demand information, adjust the assistive effect provided by the walker to the user's legs.

2. The method according to claim 1, wherein the step of detecting the unobstructed walking process of the user while wearing a walker to obtain the walking gait characteristics and muscle activity characteristics of the user is specifically: During the unobstructed walking process of the user while wearing a walker, perform leg movement and bio-signal detection to obtain the leg movement posture data and leg surface electromyogram signal data of the user; Perform multi-degree-of-freedom analysis on the leg movement posture data to obtain the walking gait characteristics of the user; wherein, the walking gait characteristics include the leg movement posture changes during the user's walking process; Analyze the leg surface electromyogram signal data to obtain the muscle activity characteristics of the user; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process.

3. The method according to claim 2, wherein the step of constructing a leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics is specifically: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the leg posture angle changes of the user and the changes in the muscle activity stability of the user's legs; Based on the relationship information, construct a leg muscle activity state model of the user; wherein, the leg muscle activity state model represents the amount of change in the corresponding posture angle of the user's legs when all muscle groups in the user's legs change from a strong state to a weak state.

4. The method according to claim 1, wherein the step of detecting the obstacle avoidance walking process of the user while wearing a walker to obtain the leg pressure characteristics of the user; based on the leg pressure characteristics, predict the leg movement information that the user is about to perform is specifically: During the obstacle avoidance walking process of the user while wearing a walker, perform leg contact pressure detection to obtain the contact pressure distribution characteristics between the user's legs and the outside world; wherein, the contact pressure distribution characteristics include the magnitude distribution of the contact force applied to the outside world during the contact process between the global range of the user's legs and the outside world; Analyze the leg pressure characteristics to predict the action intention of the user, thereby obtaining the leg muscle parts where the user is about to perform an action and their action posture angles.

5. The method according to claim 1, wherein: Based on the leg muscle activity state model and the leg movement information, determining the assistance requirement information of the user's legs; and based on the assistance requirement information, adjusting the assistance effect provided by the walker to the user's legs, specifically: Comparing the leg muscle parts and their movement posture angles of the actions that the user is about to perform included in the leg movement information with the leg muscle activity state model to determine the leg muscle parts in a weak state during the actions that the user's legs are about to perform, thereby determining the assistance requirement information of the user's legs; wherein, the assistance requirement information of the user's legs includes the regional range of the leg muscle parts in a weak state and the external force value to be compensated; Based on the assistance requirement information, adjusting the range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts in a weak state.

6. The power assist control system of the walker, characterized in that, The system includes the following modules: A first detection module, configured to detect the unobstructed walking process of the user when wearing the walker, and obtain the walking gait characteristics and muscle activity characteristics of the user; An activity state model construction module, configured to construct the leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics; A second detection module, configured to detect the obstacle avoidance walking process of the user when wearing the walker, and obtain the leg pressure characteristics of the user; A leg movement prediction module, configured to predict the leg movement information that the user is about to perform based on the leg pressure characteristics; An assistance requirement determination module, configured to determine the assistance requirement information of the user's legs based on the leg muscle activity state model and the leg movement information; An assistance effect adjustment module, configured to adjust the assistance effect provided by the walker to the user's legs based on the assistance requirement information.

7. The system according to claim 6, wherein: The first detection module is configured to detect the unobstructed walking process of the user when wearing the walker, and obtain the walking gait characteristics and muscle activity characteristics of the user, specifically: Detecting the leg movements and biological signals during the unobstructed walking process of the user when wearing the walker, and obtaining the leg movement posture data and leg surface electromyogram signal data of the user; Performing multi-degree-of-freedom analysis on the leg movement posture data to obtain the walking gait characteristics of the user; wherein, the walking gait characteristics include the changes in the leg movement postures during the user's walking process; Analyzing the leg surface electromyogram signal data to obtain the muscle activity characteristics of the user; wherein, the muscle activity characteristics include the muscle activity stability during the user's walking process; The activity state model construction module is configured to construct the leg muscle activity state model of the user based on the walking gait characteristics and the muscle activity characteristics, specifically: Match and compare the leg posture changes during the user's walking process with the muscle activity stability during the user's walking process to obtain the relationship information between the leg posture angle changes of the user and the changes in the leg muscle activity stability of the user; Based on the relationship information, construct a leg muscle activity state model of the user; wherein, the leg muscle activity state model characterizes the amount of change in the corresponding leg posture angle of the user when all muscle groups in the user's legs change from a strong state to a weak state.

8. The system according to claim 6, wherein: The second detection module is used to detect the obstacle avoidance walking process of the user when wearing the walker, and obtain the leg pressure characteristics of the user, specifically: Perform leg contact pressure detection on the user during the obstacle avoidance walking process when wearing the walker to obtain the contact pressure distribution characteristics between the user's legs and the outside world; wherein, the contact pressure distribution characteristics include the magnitude distribution of the contact force applied to the outside world during the contact process between the global range of the user's legs and the outside world; The leg motion prediction module is used to predict the leg motion information that the user is about to perform based on the leg pressure characteristics, specifically: Analyze the leg pressure characteristics to predict the user's motion intention, so as to obtain the leg muscle parts where the user is about to perform the motion and their motion posture angles.

9. The system according to claim 6, wherein: The assistance requirement determination module is used to determine the assistance requirement information of the user's legs based on the leg muscle activity state model and the leg motion information, specifically: Compare the leg muscle parts where the user is about to perform the motion and their motion posture angles included in the leg motion information with the leg muscle activity state model to determine the leg muscle parts in a weak state during the motion that the user is about to perform, so as to determine the assistance requirement information of the user's legs; wherein, the assistance requirement information of the user's legs includes the regional range of the leg muscle parts in a weak state and the external force value to be compensated; The assistance effect adjustment module is used to adjust the assistance effect provided by the walker to the user's legs based on the assistance requirement information, specifically: Based on the assistance requirement information, adjust the range and magnitude of the compensation driving torque applied by the walker to the leg muscle parts in a weak state.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-5.

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