Wearable robot assistance parameter adjustment method and system based on biomechanical model

Abstract

This invention discloses a method and system for adjusting assist parameters of wearable robots based on a biomechanical model, relating to the field of wearable robots. It addresses the current problem that wearable robots cannot intelligently adjust assist torque and damping coefficient. The method involves: constructing the standard deviation of the single-step cycle and the standard plantar pressure range for the wearer's legs; selecting the initial assist torque and initial damping coefficient when the wearer uses the wearable robot based on the difference values; analyzing the plantar pressure at different time points after the wearer uses the robot; analyzing whether the wearable robot needs adjustment based on the actual plantar pressure and the actual single-step cycle standard deviation; and adjusting the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate. This invention achieves adaptive adjustment of the wearable robot's assist parameters based on plantar pressure deviation data and single-step cycle deviation data.

Description

Technical Field

[0001] This invention belongs to the field of wearable robot technology, specifically a method and system for adjusting assistive parameters of wearable robots based on a biomechanical model. Background Technology

[0002] Wearable robots, also known as exoskeleton robots, are intelligent devices worn on the outside of the human body that enable human-machine collaboration through mechanical structures and sensing and control technologies. Based on the core principles of human kinematics and dynamics, they assist or enhance human movement capabilities, reduce muscle load, improve work efficiency, or promote limb function rehabilitation. They are widely used in fields such as medical care, industry, daily mobility assistance, and special operations.

[0003] In existing technologies, most methods for adjusting the assist parameters of wearable robots are achieved by adjusting the assist level of the wearable robot. They cannot intelligently adjust the assist torque of the wearable robot based on the pressure exerted on the wearer's legs. At the same time, existing technologies cannot effectively analyze the wearer's stride before and after using the wearable robot for assistance, thus making it impossible to effectively adjust the damping coefficient of the wearable robot.

[0004] Therefore, this invention proposes a method and system for adjusting the assist parameters of wearable robots based on a biomechanical model. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for adjusting the assist parameters of wearable robots based on a biomechanical model, so as to solve the problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for adjusting assistive parameters of wearable robots based on biomechanical models, including:

[0008] Step S1: Based on the plantar pressure of the wearer's legs at different time points, construct the standard deviation of the single-step test cycle and the standard plantar pressure range of the wearer's legs;

[0009] Step S2: Based on the difference values ​​between the current wearer and different wearers in the biomechanical model, select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot.

[0010] Step S3: Analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and obtain the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs during the reference time period.

[0011] Step S4: Analyze whether the wearable robot needs adjustment based on the actual plantar pressure and the standard deviation of the actual single-step cycle, and obtain the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time node.

[0012] Step S5: Adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

[0013] Furthermore, the construction process in step S1 includes the following sub-steps:

[0014] Step S101: Collect the plantar pressure of the wearer's legs at different time points and record it as the test plantar pressure. Take the first time point when the test plantar pressure is equal to zero as the initial swing time point, and take the first time point after the initial swing time point when the test plantar pressure is not equal to zero as the initial support time point.

[0015] Step S102: The first time node after the initial support time node where the test plantar pressure is equal to zero is recorded as the second swing time node, and the first time node after the second swing time node where the test plantar pressure is not equal to zero is recorded as the second support time node. This process is repeated to obtain multiple second swing time nodes and multiple second support time nodes.

[0016] Step S103: Sort the initial swing time node and different subsequent swing time nodes in ascending order of time to obtain a swing time array. Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to obtain multiple test single-step cycles of the wearer.

[0017] Step S104: Sum the different test single-step cycles, take the average value, and calculate the wearer's average test single-step cycle. Calculate the standard deviation of the wearer's test single-step cycle using the standard deviation formula.

[0018] Furthermore, the construction process in step S1 also includes the following sub-steps:

[0019] Step S105: Sort the different time nodes in ascending order of time to obtain a test time array. Take the initial support time node in the test time array as the left endpoint and the first swing time node after the initial support time node as the right endpoint to construct the support time period. Repeat this process to construct multiple sets of support time periods.

[0020] Step S106: Iterate through and compare the test plantar pressure of the wearer's legs at different time points within the support period, obtain the maximum value of the test plantar pressure, and record the maximum value of the test plantar pressure as the standard plantar pressure of the wearer's legs within the corresponding support period, thereby obtaining the standard plantar pressure of the wearer's legs within different support periods.

[0021] Step S107: Sum the standard plantar pressures corresponding to different support time periods and take the average value to obtain the average standard plantar pressure of the wearer's legs during the support time period. Calculate the standard deviation of the standard plantar pressure of the wearer's legs during the support time period using the standard deviation formula.

[0022] Step S108: Subtract the standard deviation of the standard plantar pressure from the average standard plantar pressure to obtain the first value; add the average standard plantar pressure and the standard deviation of the standard plantar pressure to obtain the second value; and construct the standard plantar pressure range of the wearer's legs with the first value as the left endpoint and the second value as the right endpoint.

[0023] Furthermore, the selection process in step S2 includes the following sub-steps:

[0024] Step S201: Collect the assist damping coefficient and assist torque of different wearers with different heights, weights and average single-step cycles when using the wearable robot, summarize all assist damping coefficients and assist torques into the database, and record the corresponding database as a biomechanical model.

[0025] Step S202: Collect the actual height and weight of the current wearer, and record the average test single-step cycle of the current wearer as the actual average single-step cycle, and obtain the height, weight and average test single-step cycle of different wearers in the biomechanical model;

[0026] Step S203: Calculate the numerical difference between the current wearer and different wearers in the biomechanical model;

[0027] Step S204: Iterate through and compare the difference values ​​between the current wearer and different wearers in the biomechanical model to obtain the minimum difference value, and obtain the assist damping coefficient and assist torque corresponding to the wearer using the wearable robot. The corresponding assist damping coefficient is used as the initial damping coefficient when the current wearer uses the wearable robot, and the corresponding assist torque is used as the initial assist torque when the current wearer uses the wearable robot.

[0028] Furthermore, the analysis process in step S3 includes the following sub-steps:

[0029] Step S301: Collect the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and repeat steps S101-S105 to construct the swing time array and multiple support time periods;

[0030] Step S302: Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to calculate the multiple actual single-step cycles after the wearer uses the wearable robot for assistance, thereby obtaining the standard deviation of the wearer's actual single-step cycle at the current time node.

[0031] Furthermore, the analysis process in step S3 also includes the following sub-steps:

[0032] Step S303: Obtain the right endpoint of different support time periods, and subtract the time of the right endpoint of different support time periods from the time corresponding to the current time node to obtain the time difference between different support time periods and the current time node.

[0033] Step S304: Iterate through and compare the time intervals between different support time periods and the current time node to obtain the minimum time interval, and use the support time period corresponding to the minimum time interval as the reference time period.

[0034] Step S305: Iterate through and compare the plantar pressure of the wearer's legs at different time points within the reference time period to obtain the maximum value of the plantar pressure, and record the maximum value of the plantar pressure as the actual plantar pressure of the wearer's legs within the corresponding reference time period.

[0035] Furthermore, the analysis process in step S4 includes the following sub-steps:

[0036] Step S401: Compare the actual plantar pressure of the wearer's legs with the standard plantar pressure range during the reference time period;

[0037] If the actual plantar pressure of the wearer's legs during the reference time period is within the standard plantar pressure range, no operation will be performed.

[0038] If the actual plantar pressure of the wearer's legs during the reference time period is not within the standard plantar pressure range, proceed to step S402.

[0039] Step S402: Compare the actual plantar pressure with the endpoint values ​​of the standard plantar pressure range;

[0040] If the actual plantar pressure is greater than the right endpoint of the standard plantar pressure range, then subtract the right endpoint of the standard plantar pressure range from the actual plantar pressure and divide by the right endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period.

[0041] If the actual plantar pressure is less than the left endpoint of the standard plantar pressure range, then subtract the actual plantar pressure from the left endpoint of the standard plantar pressure range and divide by the left endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period.

[0042] Step S403: Obtain the test single-step cycle standard deviation of the wearer and compare the actual single-step cycle standard deviation with the test single-step cycle standard deviation;

[0043] If the actual single-step cycle standard deviation is less than or equal to the test single-step cycle standard deviation, no operation is performed;

[0044] If the actual single-step cycle standard deviation is greater than the test single-step cycle standard deviation, proceed to step S404;

[0045] Step S404: Subtract the average test single-step cycle from the wearer's actual single-step cycle at the current time point, and then divide by the average test single-sided cycle to obtain the wearer's single-step cycle deviation rate at the current time point.

[0046] Furthermore, the adjustment process in step S5 includes the following sub-steps:

[0047] Step S501: Obtain the initial assist torque and initial damping coefficient of the wearable robot, multiply the initial assist torque by the deviation rate of the foot pressure of the wearer's leg during the reference time period, and obtain the adjustment amount of the assist torque at the next time node.

[0048] Step S502: Multiply the initial damping coefficient by the wearer's single-step cycle deviation rate at the current time point to obtain the adjustment amount of the damping coefficient at the next time point.

[0049] Furthermore, the adjustment process in step S5 also includes the following sub-steps:

[0050] Step S503: Compare the deviation rate of the foot pressure on the wearer's legs during the reference time period with zero;

[0051] If the deviation rate of the plantar pressure on the wearer's legs is greater than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be increased.

[0052] If the deviation rate of the plantar pressure on the wearer's legs is less than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be reduced.

[0053] Step S504: Compare the wearer's single-step cycle deviation rate at the current time point with zero;

[0054] If the wearer's single-step cycle deviation rate is greater than or equal to zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be increased.

[0055] If the wearer's single-step cycle deviation rate is less than zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be reduced.

[0056] This invention also provides a wearable robot assist parameter adjustment system based on a biomechanical model, comprising:

[0057] The data acquisition module is used to collect the plantar pressure of the wearer's legs at different time points and send it to the data analysis module and the adjustment judgment module;

[0058] The data analysis module is used to analyze the plantar pressure of the wearer's legs at different time points. The analysis yields the standard plantar pressure range of the wearer's legs and the standard deviation of the wearer's test single-step cycle, which is then sent to the adjustment judgment module. The analysis also yields the average test single-step cycle, which is then sent to the difference calculation module.

[0059] The data acquisition module is also used to collect the wearer's height and weight and send them to the difference calculation module;

[0060] The difference calculation module is used to calculate the difference between the current wearer and different wearers in the biomechanical model and send it to the parameter selection module.

[0061] The parameter selection module is used to select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot and send them to the intelligent adjustment module.

[0062] The data analysis module is also used to analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, obtain the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs in the reference time period, and send it to the adjustment and judgment module.

[0063] The adjustment judgment module is used to determine whether the wearable robot needs to be adjusted based on the actual plantar pressure and the actual single-step cycle standard deviation. It obtains the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time node and sends it to the intelligent adjustment module.

[0064] The intelligent adjustment module is used to adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

[0065] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0066] 1. The present invention first constructs the standard deviation of the test single step cycle and the standard plantar pressure range of the wearer's leg based on the plantar pressure when the wearer does not use the wearable robot for assisted walking. Then, it analyzes the differences between the current wearer and different wearers in the biomechanical model, thereby selecting the initial assist torque and initial damping coefficient suitable for the current wearer to use the wearable robot.

[0067] 2. This invention also analyzes the plantar pressure of the wearer at different time points after using the wearable robot for assistance, obtaining the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs during the reference time period. This allows for determination of whether the initial assist torque and initial damping coefficient of the wearable robot need to be adjusted. Simultaneously, the invention analyzes the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time point, thereby enabling effective adjustment of the corresponding initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate. Attached Figure Description

[0068] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0069] Figure 1 This is a flowchart of the method of the present invention;

[0070] Figure 2 This is a schematic diagram of different swing time nodes and support time nodes in this invention;

[0071] Figure 3 This is a schematic diagram illustrating the time period supported in this invention;

[0072] Figure 4 This is an overall system block diagram of the present invention. Detailed Implementation

[0073] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Example 1: Please refer to Figures 1-3As shown, the technical solution provided by this invention is: a method for adjusting the assistive parameters of a wearable robot based on a biomechanical model. This method first analyzes the plantar pressure and single-step cycle of the wearer when walking without the wearable robot, obtaining the standard plantar pressure range of the wearer's legs. Then, based on the differences in biometric characteristics between the current wearer and different wearers in the biomechanical model, the initial parameters for the current wearable robot are selected. Then, when the wearer uses the wearable robot for assisted walking, the method determines whether the initial parameters of the wearable robot need adjustment based on the real-time plantar pressure and single-step cycle, and calculates the adjustment amount of the initial parameters. The method includes the following steps:

[0075] Step S1: Based on the plantar pressure of the wearer's legs at different time points, construct the standard deviation of the single-step test cycle and the standard plantar pressure range of the wearer's legs;

[0076] In this embodiment, the construction process in step S1 includes the following sub-steps:

[0077] Step S101: Collect the plantar pressure of the wearer's legs at different time points and record it as the test plantar pressure. Take the first time point when the test plantar pressure is equal to zero as the initial swing time point, and take the first time point after the initial swing time point when the test plantar pressure is not equal to zero as the initial support time point.

[0078] For step S102, please refer to... Figure 2 As shown, the first test foot pressure after the initial support time node is equal to zero is recorded as the second swing time node, and the first test foot pressure after the second swing time node is not equal to zero is recorded as the second support time node. This process is repeated to obtain multiple second swing time nodes and multiple second support time nodes.

[0079] In this embodiment, the plantar pressure data was collected on a flat road surface. When the wearer starts the wearable robot, the robot initially does not provide assistance. Figure 2 Point A is the initial swing time node, at which point the wearer's foot leaves the ground and the foot pressure is zero. Time node B corresponds to a test foot pressure of zero, at which point the wearer's foot is still in the air and has not landed. Time node C corresponds to a test foot pressure that is not zero, which is the initial support time node, indicating that the wearer's foot is just in contact with the ground. Time nodes D and E correspond to test foot pressures that are not zero, indicating that the wearer's foot is still in contact with the ground. Time node F corresponds to a test foot pressure of zero, at which point the wearer's foot leaves the ground again, and time node F is the second swing time node. Time node H corresponds to a test foot pressure of zero, and time node I corresponds to a test foot pressure that is not zero, so time node I is the second support time node.

[0080] Step S103: Sort the initial swing time node and different subsequent swing time nodes in ascending order of time to obtain a swing time array. Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to obtain multiple test single-step cycles of the wearer.

[0081] Step S104: Add up the different single-step test cycles, take the average value, and calculate the wearer's average single-step test cycle. Calculate the standard deviation of the wearer's single-step test cycle using the standard deviation formula.

[0082] It should be noted that the average test single-step cycle represents the average time it takes for the wearer to take each step;

[0083] For step S105, please refer to... Figure 3 As shown, different time nodes are arranged in ascending order of time to obtain a test time array. The initial support time node in the test time array is taken as the left endpoint, and the first swing time node after the initial support time node is taken as the right endpoint to construct the support time period. In this way, multiple sets of support time periods are constructed.

[0084] It should be explained that the support time period refers to the time from when the wearer's foot first makes contact with the ground until the wearer's foot is completely off the ground;

[0085] Step S106: Iterate through and compare the test plantar pressure of the wearer's legs at different time points within the support period, obtain the maximum value of the test plantar pressure, and record the maximum value of the test plantar pressure as the standard plantar pressure of the wearer's legs within the corresponding support period, thereby obtaining the standard plantar pressure of the wearer's legs within different support periods.

[0086] Step S107: Sum the standard plantar pressures corresponding to different support time periods and take the average value to obtain the average standard plantar pressure of the wearer's legs during the support time period. Calculate the standard deviation of the standard plantar pressure of the wearer's legs during the support time period using the standard deviation formula.

[0087] Step S108: Subtract the standard deviation of the standard plantar pressure from the average standard plantar pressure to obtain the first value; add the average standard plantar pressure and the standard deviation of the standard plantar pressure to obtain the second value; and construct the standard plantar pressure range of the wearer's legs with the first value as the left endpoint and the second value as the right endpoint.

[0088] Step S2: Based on the difference values ​​between the current wearer and different wearers in the biomechanical model, select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot.

[0089] In this embodiment, the selection process in step S2 includes the following sub-steps:

[0090] Step S201: Collect the assist damping coefficient and assist torque of different wearers with different heights, weights and average single-step cycles when using the wearable robot, summarize all assist damping coefficients and assist torques into the database, and record the corresponding database as a biomechanical model.

[0091] Step S202: Collect the actual height SL and actual weight SM of the current wearer, and record the average test single-step cycle of the current wearer as the actual average single-step cycle ST. Record the height of different wearers in the biomechanical model as L, the weight as M, and the average test single-step cycle as T.

[0092] Step S203: The difference value CY between the current wearer and different wearers in the biomechanical model is calculated using the formula CY=|SL-L| / (LB)×k1+(SM-M) / (MB)×k2+(ST-T) / (TB)×k3; where k1, k2 and k3 are weighting coefficients, k1+k2+k3=1, LB is the standard value of height, MB is the standard value of weight, and TB is the standard value of average single-step cycle.

[0093] In this embodiment, k1 is 0.2, k2 is 0.4, k3 is 0.4, and LB, MB, and TB are all 1.

[0094] Step S204: Iterate through and compare the difference values ​​between the current wearer and different wearers in the biomechanical model to obtain the minimum difference value, and obtain the assist damping coefficient and assist torque corresponding to the wearer using the wearable robot. The corresponding assist damping coefficient is used as the initial damping coefficient when the current wearer uses the wearable robot, and the corresponding assist torque is used as the initial assist torque when the current wearer uses the wearable robot.

[0095] It should be explained that the assist damping coefficient reflects the stiffness and flexibility of the wearer when walking with the wearable robot. Once the initial damping coefficient and initial assist torque are obtained, the wearable robot begins to assist the wearer's legs.

[0096] Step S3: Analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and obtain the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs during the reference time period.

[0097] In this embodiment, the analysis process in step S3 includes the following sub-steps:

[0098] Step S301: Collect the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and repeat steps S101-S105 to construct the swing time array and multiple support time periods;

[0099] Step S302: Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to calculate the multiple actual single-step cycles after the wearer uses the wearable robot for assistance, thereby obtaining the standard deviation of the wearer's actual single-step cycle at the current time node.

[0100] Step S303: Obtain the right endpoint of different support time periods, and subtract the time of the right endpoint of different support time periods from the time corresponding to the current time node to obtain the time difference between different support time periods and the current time node.

[0101] Step S304: Iterate through and compare the time intervals between different support time periods and the current time node to obtain the minimum time interval, and use the support time period corresponding to the minimum time interval as the reference time period.

[0102] Step S305: Iterate through and compare the plantar pressure of the wearer's legs at different time points within the reference time period to obtain the maximum value of the plantar pressure, and record the maximum value of the plantar pressure as the actual plantar pressure of the wearer's legs within the corresponding reference time period.

[0103] Step S4: Analyze whether the wearable robot needs adjustment based on the actual plantar pressure and the standard deviation of the actual single-step cycle, and obtain the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time node.

[0104] In this embodiment, the analysis process in step S4 includes the following sub-steps:

[0105] Step S401: Compare the actual plantar pressure of the wearer's legs with the standard plantar pressure range during the reference time period;

[0106] If the actual plantar pressure of the wearer's legs during the reference time period is within the standard plantar pressure range, no operation will be performed.

[0107] If the actual plantar pressure of the wearer's legs during the reference time period is not within the standard plantar pressure range, proceed to step S402.

[0108] Step S402: Compare the actual plantar pressure with the endpoint values ​​of the standard plantar pressure range;

[0109] If the actual plantar pressure is greater than the right endpoint of the standard plantar pressure range, then subtract the right endpoint of the standard plantar pressure range from the actual plantar pressure and divide by the right endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period.

[0110] If the actual plantar pressure is less than the left endpoint of the standard plantar pressure range, then subtract the actual plantar pressure from the left endpoint of the standard plantar pressure range and divide by the left endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period.

[0111] Step S403: Obtain the test single-step cycle standard deviation of the wearer and compare the actual single-step cycle standard deviation with the test single-step cycle standard deviation;

[0112] If the actual single-step cycle standard deviation is less than or equal to the test single-step cycle standard deviation, no operation is performed;

[0113] If the actual single-step cycle standard deviation is greater than the test single-step cycle standard deviation, proceed to step S404;

[0114] It needs to be explained that standard deviation is used to reflect the dispersion of a set of data. The larger the standard deviation, the more dispersed the data; the smaller the standard deviation, the more concentrated the data. When the standard deviation of the actual single-step cycle is less than or equal to the standard deviation of the test single-step cycle, it means that the wearer's stride is more stable after using the wearable robot for assistance compared to before assistance. When the standard deviation of the actual single-step cycle is greater than the standard deviation of the test single-step cycle, it means that the wearer's stride differs significantly after using the wearable robot for assistance compared to before assistance. In this case, the damping coefficient of the wearable robot needs to be adjusted. For example, when the stride difference before and after assistance is 0.3 meters, the damping coefficient of the wearable robot needs to be adjusted. The value of 0.3 meters for the stride difference is obtained by summing the stride differences of multiple wearers before and after using the wearable robot for assistance and taking the average value.

[0115] Step S404: Subtract the average test single-step cycle from the wearer's actual single-step cycle at the current time point, and then divide by the average test single-sided cycle to obtain the wearer's single-step cycle deviation rate at the current time point.

[0116] Step S5: Adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

[0117] In this embodiment, the adjustment process in step S5 includes the following sub-steps:

[0118] Step S501: Obtain the initial assist torque and initial damping coefficient of the wearable robot, multiply the initial assist torque by the deviation rate of the foot pressure of the wearer's leg during the reference time period, and obtain the adjustment amount of the assist torque at the next time node.

[0119] Step S502: Multiply the initial damping coefficient by the wearer's single-step cycle deviation rate at the current time point to obtain the adjustment amount of the damping coefficient at the next time point.

[0120] Step S503: Compare the deviation rate of the foot pressure on the wearer's legs during the reference time period with zero;

[0121] If the deviation rate of the plantar pressure on the wearer's legs is greater than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be increased.

[0122] If the deviation rate of the plantar pressure on the wearer's legs is less than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be reduced.

[0123] Step S504: Compare the wearer's single-step cycle deviation rate at the current time point with zero;

[0124] If the wearer's single-step cycle deviation rate is greater than or equal to zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be increased.

[0125] If the wearer's single-step cycle deviation rate is less than zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be reduced.

[0126] It should be explained that if the wearer's single-step cycle deviation rate is less than zero at the current time point, it means that the wearer's stride length is reduced after assistance compared to before assistance, and the wearable robot hinders the wearer's walking. In this case, the damping coefficient needs to be lowered to improve smoothness. If the wearer's single-step cycle deviation rate is greater than or equal to zero at the current time point, it means that the wearer's stride length is increased after assistance compared to before assistance. In this case, the damping coefficient needs to be increased to buffer the impact of the wearable robot on the wearer.

[0127] Example 2: Please refer to Figure 4 As shown, based on another concept of the same invention, a wearable robot assist parameter adjustment system based on a biomechanical model is proposed, including a data acquisition module, a data analysis module, a difference calculation module, a parameter selection module, an adjustment judgment module, and an intelligent adjustment module:

[0128] The data acquisition module is used to collect the plantar pressure of the wearer's legs at different time points and send it to the data analysis module and the adjustment judgment module; the data analysis module is used to analyze the plantar pressure of the wearer's legs at different time points, analyze and obtain the standard plantar pressure range of the wearer's legs and the standard deviation of the wearer's test single-step cycle and send it to the adjustment judgment module, analyze and obtain the average test single-step cycle and send it to the difference calculation module.

[0129] The data acquisition module is also used to collect the wearer's height and weight and send them to the difference calculation module; the difference calculation module is used to send the difference values ​​between the current wearer and different wearers in the biomechanical model to the parameter selection module; the parameter selection module is used to select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot and send them to the intelligent adjustment module.

[0130] The data analysis module is also used to analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, obtain the actual single-step cycle standard deviation of the wearer at the current time point and the actual plantar pressure of the wearer's legs during the reference time period, and send them to the adjustment judgment module; the adjustment judgment module is used to determine whether the wearable robot needs to be adjusted based on the actual plantar pressure and the actual single-step cycle standard deviation, obtain the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time point, and send them to the intelligent adjustment module; the intelligent adjustment module is used to adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

[0131] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for adjusting assistive parameters of wearable robots based on biomechanical models, characterized in that, The methods include: Step S1: Based on the plantar pressure of the wearer's legs at different time points, construct the standard deviation of the single-step test cycle and the standard plantar pressure range of the wearer's legs; Step S2: Based on the difference values ​​between the current wearer and different wearers in the biomechanical model, select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot. The selection process in step S2 includes the following sub-steps: Step S201: Collect the assist damping coefficient and assist torque of different wearers with different heights, weights and average single-step cycles when using the wearable robot, summarize all assist damping coefficients and assist torques into the database, and record the corresponding database as a biomechanical model. Step S202: Collect the actual height and weight of the current wearer, and record the average test single-step cycle of the current wearer as the actual average single-step cycle, and obtain the height, weight and average test single-step cycle of different wearers in the biomechanical model; Step S203: Calculate the numerical difference between the current wearer and different wearers in the biomechanical model; Step S204: Iterate through and compare the difference values ​​between the current wearer and different wearers in the biomechanical model to obtain the minimum difference value, and obtain the assist damping coefficient and assist torque corresponding to the wearer using the wearable robot. The corresponding assist damping coefficient is used as the initial damping coefficient when the current wearer uses the wearable robot, and the corresponding assist torque is used as the initial assist torque when the current wearer uses the wearable robot. Step S3: Analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and obtain the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs during the reference time period. Step S4: Analyze whether the wearable robot needs adjustment based on the actual plantar pressure and the standard deviation of the actual single-step cycle, and obtain the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time node. The analysis process in step S4 includes the following sub-steps: Step S401: Compare the actual plantar pressure of the wearer's legs with the standard plantar pressure range during the reference time period; If the actual plantar pressure of the wearer's legs during the reference time period is within the standard plantar pressure range, no operation will be performed. If the actual plantar pressure of the wearer's legs during the reference time period is not within the standard plantar pressure range, proceed to step S402. Step S402: Compare the actual plantar pressure with the endpoint values ​​of the standard plantar pressure range; If the actual plantar pressure is greater than the right endpoint of the standard plantar pressure range, then subtract the right endpoint of the standard plantar pressure range from the actual plantar pressure and divide by the right endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period. If the actual plantar pressure is less than the left endpoint of the standard plantar pressure range, then subtract the actual plantar pressure from the left endpoint of the standard plantar pressure range and divide by the left endpoint of the standard plantar pressure range to obtain the plantar pressure deviation rate of the wearer's legs within the reference time period. Step S403: Obtain the test single-step cycle standard deviation of the wearer and compare the actual single-step cycle standard deviation with the test single-step cycle standard deviation; If the actual single-step cycle standard deviation is less than or equal to the test single-step cycle standard deviation, no operation is performed; If the actual single-step cycle standard deviation is greater than the test single-step cycle standard deviation, proceed to step S404; Step S404: Subtract the average test single-step cycle from the wearer's actual single-step cycle at the current time point, and then divide by the average test single-step cycle to obtain the wearer's single-step cycle deviation rate at the current time point. Step S5: Adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

2. The method for adjusting assistive parameters of a wearable robot based on a biomechanical model according to claim 1, characterized in that, The construction process in step S1 includes the following sub-steps: Step S101: Collect the plantar pressure of the wearer's legs at different time points and record it as the test plantar pressure. Take the first time point when the test plantar pressure is equal to zero as the initial swing time point, and take the first time point after the initial swing time point when the test plantar pressure is not equal to zero as the initial support time point. Step S102: The first time node after the initial support time node where the test plantar pressure is equal to zero is recorded as the second swing time node, and the first time node after the second swing time node where the test plantar pressure is not equal to zero is recorded as the second support time node. This process is repeated to obtain multiple second swing time nodes and multiple second support time nodes. Step S103: Sort the initial swing time node and different subsequent swing time nodes in ascending order of time to obtain a swing time array. Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to obtain multiple test single-step cycles of the wearer. Step S104: Sum the different test single-step cycles, take the average value, and calculate the wearer's average test single-step cycle. Calculate the standard deviation of the wearer's test single-step cycle using the standard deviation formula.

3. The method for adjusting assistive parameters of a wearable robot based on a biomechanical model according to claim 2, characterized in that, The construction process in step S1 also includes the following sub-steps: Step S105: Sort the different time nodes in ascending order of time to obtain a test time array. Take the initial support time node in the test time array as the left endpoint and the first swing time node after the initial support time node as the right endpoint to construct the support time period. Repeat this process to construct multiple sets of support time periods. Step S106: Iterate through and compare the test plantar pressure of the wearer's legs at different time points within the support period, obtain the maximum value of the test plantar pressure, and record the maximum value of the test plantar pressure as the standard plantar pressure of the wearer's legs within the corresponding support period, thereby obtaining the standard plantar pressure of the wearer's legs within different support periods. Step S107: Sum the standard plantar pressures corresponding to different support time periods and take the average value to obtain the average standard plantar pressure of the wearer's legs during the support time period. Calculate the standard deviation of the standard plantar pressure of the wearer's legs during the support time period using the standard deviation formula. Step S108: Subtract the standard deviation of the standard plantar pressure from the average standard plantar pressure to obtain the first value; add the average standard plantar pressure and the standard deviation of the standard plantar pressure to obtain the second value; and construct the standard plantar pressure range of the wearer's legs with the first value as the left endpoint and the second value as the right endpoint.

4. The method for adjusting assist parameters of a wearable robot based on a biomechanical model according to claim 3, characterized in that, The analysis process in step S3 includes the following sub-steps: Step S301: Collect the plantar pressure of the wearer at different time points after using the wearable robot for assistance, and repeat steps S101-S105 to construct the swing time array and multiple support time periods; Step S302: Subtract the time corresponding to the previous element from the time corresponding to any element in the swing time array to calculate the multiple actual single-step cycles after the wearer uses the wearable robot for assistance, thereby obtaining the standard deviation of the wearer's actual single-step cycle at the current time node.

5. The method for adjusting assistive parameters of a wearable robot based on a biomechanical model according to claim 4, characterized in that, The analysis process in step S3 also includes the following sub-steps: Step S303: Obtain the right endpoint of different support time periods, and subtract the time of the right endpoint of different support time periods from the time corresponding to the current time node to obtain the time difference between different support time periods and the current time node. Step S304: Iterate through and compare the time intervals between different support time periods and the current time node to obtain the minimum time interval, and use the support time period corresponding to the minimum time interval as the reference time period. Step S305: Iterate through and compare the plantar pressure of the wearer's legs at different time points within the reference time period to obtain the maximum value of the plantar pressure, and record the maximum value of the plantar pressure as the actual plantar pressure of the wearer's legs within the corresponding reference time period.

6. The method for adjusting assistive parameters of a wearable robot based on a biomechanical model according to claim 5, characterized in that, The adjustment process in step S5 includes the following sub-steps: Step S501: Obtain the initial assist torque and initial damping coefficient of the wearable robot, multiply the initial assist torque by the deviation rate of the foot pressure of the wearer's leg during the reference time period, and obtain the adjustment amount of the assist torque at the next time node. Step S502: Multiply the initial damping coefficient by the wearer's single-step cycle deviation rate at the current time point to obtain the adjustment amount of the damping coefficient at the next time point.

7. The method for adjusting assist parameters of a wearable robot based on a biomechanical model according to claim 6, characterized in that, The adjustment process in step S5 further includes the following sub-steps: Step S503: Compare the deviation rate of the foot pressure on the wearer's legs during the reference time period with zero; If the deviation rate of the plantar pressure on the wearer's legs is greater than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be increased. If the deviation rate of the plantar pressure on the wearer's legs is less than zero during the reference time period, the initial assist torque of the wearable robot at the next time point will be reduced. Step S504: Compare the wearer's single-step cycle deviation rate at the current time point with zero; If the wearer's single-step cycle deviation rate is greater than or equal to zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be increased. If the wearer's single-step cycle deviation rate is less than zero at the current time point, then the initial damping coefficient of the wearable robot at the next time point will be reduced.

8. A wearable robot assist parameter adjustment system based on a biomechanical model, characterized in that, The method for adjusting the assist parameters of a wearable robot based on a biomechanical model according to any one of claims 1-7 includes: The data acquisition module is used to collect the plantar pressure of the wearer's legs at different time points and send it to the data analysis module and the adjustment judgment module; The data analysis module is used to analyze the plantar pressure of the wearer's legs at different time points. The analysis yields the standard plantar pressure range of the wearer's legs and the standard deviation of the wearer's test single-step cycle, which is then sent to the adjustment judgment module. The analysis also yields the average test single-step cycle, which is then sent to the difference calculation module. The data acquisition module is also used to collect the wearer's height and weight and send them to the difference calculation module; The difference calculation module is used to calculate the difference between the current wearer and different wearers in the biomechanical model and send it to the parameter selection module. The parameter selection module is used to select the initial assist torque and initial damping coefficient when the current wearer uses the wearable robot and send them to the intelligent adjustment module. The data analysis module is also used to analyze the plantar pressure of the wearer at different time points after using the wearable robot for assistance, obtain the standard deviation of the wearer's actual single-step cycle at the current time point and the actual plantar pressure of the wearer's legs in the reference time period, and send it to the adjustment and judgment module. The adjustment judgment module is used to determine whether the wearable robot needs to be adjusted based on the actual plantar pressure and the actual single-step cycle standard deviation. It obtains the plantar pressure deviation rate of the wearer's legs during the reference time period and the single-step cycle deviation rate of the wearer at the current time node and sends it to the intelligent adjustment module. The intelligent adjustment module is used to adjust the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate.

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