A method and system for motion gait analysis

Abstract

The application provides a motion gait analysis method and system, and relates to the technical field of gait analysis, which comprises the following steps: obtaining an opening trigger signal of a preset sensor; obtaining device detection parameters of a preset fixing device, sensor parameters of the sensor and skin deformation parameters based on the opening trigger signal; analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine a fixing state recognition result of the sensor; judging whether the fixing state recognition result meets a preset fixing state loosening result requirement; if not, continuing to obtain the device detection parameters of the fixing device, the sensor parameters of the sensor and the skin deformation parameters for cyclic judgment; if yes, obtaining device driving parameters; and controlling the fixing device to adjust the tightness degree to stabilize the sensor according to the device driving parameters. The application has the effect of improving the accuracy of gait analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gait analysis, and particularly to a motion gait analysis method and system. BACKGROUND

[0002] Gait analysis is a scientific method to study the walking patterns of humans or animals, which is widely applied in multiple fields, including medicine, sports science, rehabilitation engineering, and safety monitoring, etc.

[0003] In related technologies, gait analysis mainly fixes an inertial measurement unit at the ankle, thigh, waist and other parts of the subject through adjustable straps, so as to measure the acceleration, angular velocity and direction change of these parts. Through these data, researchers can analyze the kinematic characteristics in the walking process.

[0004] In view of the related technologies in the above, the inertial measurement unit is fixed at the ankle, thigh, waist and other parts of the subject through adjustable straps. In the process of walking of the subject, the body and the strap will gradually loosen, thereby causing the inertial measurement unit to produce additional shaking, which is not accurate for measuring the motion parameters of the subject, and leads to low accuracy of gait analysis, which still has room for improvement. SUMMARY

[0005] In order to improve the accuracy of gait analysis, the present application provides a motion gait analysis method and system.

[0006] In a first aspect, the present application provides a motion gait analysis method, which adopts the following technical solution:

[0007] A motion gait analysis method, comprising:

[0008] obtaining an opening trigger signal of a preset sensor;

[0009] obtaining device detection parameters of a preset fixing device, sensor parameters of the sensor and skin deformation parameters based on the opening trigger signal;

[0010] analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine a fixing state recognition result of the sensor;

[0011] judging whether the fixing state recognition result meets a requirement of a preset fixing state loosening result;

[0012] if not, continuing to obtain the device detection parameters of the fixing device, the sensor parameters of the sensor and the skin deformation parameters for cyclic judgment;

[0013] if yes, obtaining device driving parameters;

[0014] The fixing device adjusts the tightness degree according to the device driving parameter to stabilize the sensor.

[0015] According to the technical solution, after detecting the opening trigger signal of the sensor, the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter are detected, and the fixing state recognition result of the sensor is analyzed. When it is determined that the fixing state recognition result meets the requirement of the fixing state loosening result, the fixing device adjusts the tightness degree according to the device driving parameter, so that the sensor is stabilized, and the inaccuracy caused by the loosening of the sensor is avoided, and the accuracy of the gait analysis is improved.

[0016] Optionally, the step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine the fixing state recognition result of the sensor comprises:

[0017] The device detection parameter, the sensor parameter and the skin deformation parameter are analyzed to determine the loosening detection coefficient of the sensor.

[0018] The real-time loosening coefficient threshold of the sensor is obtained.

[0019] It is judged whether the loosening detection coefficient meets the requirement of the real-time loosening coefficient threshold.

[0020] If yes, the preset sensor stabilization result is defined as the fixing state recognition result of the sensor.

[0021] If no, the preset sensor loosening result is defined as the fixing state recognition result of the sensor.

[0022] According to the technical solution, the loosening detection coefficient of the sensor is determined according to the device detection parameter, the sensor parameter and the skin deformation parameter, and the real-time loosening coefficient threshold at the current time is detected. When the loosening detection coefficient exceeds the real-time loosening coefficient threshold, the sensor loosening result is defined as the fixing state recognition result, and when the loosening detection coefficient does not exceed the real-time loosening coefficient threshold, the sensor stabilization result is defined as the fixing state recognition result, so that the efficiency and accuracy of determining the fixing state recognition result are improved.

[0023] Optionally, the step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine the loosening detection coefficient of the sensor comprises:

[0024] The sensor parameter, the skin deformation parameter and the preset maximum acceleration are analyzed to determine the normalized acceleration coefficient.

[0025] The device detection parameter and the preset reference pressure value are analyzed to determine the normalized pressure coefficient.

[0026] analyzing the device detection parameter and the preset maximum allowable deformation variable to determine a normalized deformation coefficient;

[0027] obtaining a loosening coefficient weight;

[0028] analyzing the normalized acceleration coefficient, the normalized pressure coefficient, the normalized deformation coefficient and the loosening coefficient weight to determine a loosening detection coefficient.

[0029] By using the above technical solution, the loosening detection coefficient is calculated by comprehensively using the normalized acceleration coefficient, the normalized pressure coefficient, the normalized deformation coefficient and the corresponding loosening coefficient weight, thereby improving the accuracy of the loosening detection coefficient.

[0030] Optionally, the step of obtaining the real-time loosening coefficient threshold of the sensor comprises:

[0031] obtaining a single working duration of the sensor;

[0032] analyzing the single working duration and a preset threshold decay coefficient to determine a threshold adjustment coefficient;

[0033] analyzing the threshold adjustment coefficient, a preset adjustable threshold and a preset minimum fixed threshold to determine the real-time loosening coefficient threshold.

[0034] By using the above technical solution, the threshold adjustment coefficient is calculated according to the single working duration and the threshold decay coefficient, and the real-time loosening coefficient threshold is calculated according to the threshold adjustment coefficient, the adjustable threshold and the minimum fixed threshold, so as to simulate the state that the actual threshold decays over time, ensure a higher threshold at the initial stage to prevent misjudgment, and ensure a lower threshold at the later stage to ensure the sensitivity of the judgment.

[0035] Optionally, the step of obtaining the device driving parameter comprises:

[0036] analyzing the loosening detection coefficient and the real-time loosening coefficient threshold to determine a driving proportional term, a driving differential term and a driving integral term;

[0037] obtaining a proportional gain coefficient, a differential gain coefficient and an integral gain coefficient;

[0038] analyzing the driving proportional term, the driving differential term, the driving integral term, the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient to determine a device driving signal;

[0039] analyzing the device driving signal to determine a basic fixed pressure parameter;

[0040] optimizing the basic fixed pressure parameter according to a preset pressure optimization algorithm to generate the device driving parameter.

[0041] According to the technical scheme, the device driving signal is calculated according to the driving proportional term, the driving differential term, the driving integral term, the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient, and the device driving signal is mapped to the basic fixed pressure parameter, so that the device driving parameter is obtained after the basic fixed pressure parameter is optimized according to the pressure optimization algorithm, and the accuracy of the device driving parameter is improved.

[0042] Optionally, the step of obtaining the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient comprises:

[0043] The body mass index of the person is obtained.

[0044] The body mass index of the person and the preset gain calibration parameter are analyzed to determine the integral gain coefficient.

[0045] The sensor parameter, the preset maximum acceleration threshold and the preset basic differential coefficient are analyzed to determine the differential gain coefficient.

[0046] The sensor parameter is analyzed to determine the gait support phase or the gait swing phase.

[0047] If the gait support phase is determined, the preset enhancement gain coefficient is defined as the proportional gain coefficient.

[0048] If the gait swing phase is determined, the preset reduction gain coefficient is defined as the proportional gain coefficient.

[0049] According to the technical scheme, the integral gain coefficient is calculated according to the body mass index of the person and the gain calibration parameter, so that the integral gain coefficient can compensate for the cumulative error caused by inertia or resistance, the differential gain coefficient is calculated according to the sensor parameter, the maximum acceleration threshold and the basic differential coefficient, so that the differential gain coefficient can inhibit the change speed of acceleration, the enhancement gain coefficient is defined as the proportional gain coefficient when the gait support phase is determined, and the reduction gain coefficient is defined as the proportional gain coefficient when the gait swing phase is determined, so that the stability is enhanced and the energy consumption and interference are reduced.

[0050] Optionally, the step of analyzing the device driving signal to determine the basic fixed pressure parameter comprises:

[0051] The device driving signal and the preset driving pressure coefficient are analyzed to determine the pressure change amount.

[0052] The initial detection pressure of the fixing device is obtained.

[0053] The pressure change amount and the initial detection pressure are analyzed to determine the basic fixed pressure parameter.

[0054] According to the technical scheme, the pressure change amount is calculated according to the device driving signal and the driving pressure coefficient, and the basic fixed pressure parameter is obtained by adding the pressure change amount to the initial detection pressure, thereby improving the accuracy of the basic fixed pressure parameter.

[0055] Optionally, the step of optimizing the basic fixed pressure parameter according to the preset pressure optimization algorithm to generate the device driving parameter comprises:

[0056] obtaining the total pressure value and the maximum safety pressure value;

[0057] analyzing the total pressure value and the maximum safety pressure value to determine the optimization constraint condition;

[0058] analyzing the basic fixed pressure parameter, the preset physiological adaptation pressure parameter and the preset uniform adaptation coefficient to determine the optimization objective function;

[0059] iteratively optimizing the optimization objective function according to the optimization constraint condition to generate the device driving parameter.

[0060] According to the technical scheme, the optimization constraint condition is determined according to the total pressure value and the maximum safety pressure value, the optimization objective function is determined according to the basic fixed pressure parameter, the physiological adaptation pressure parameter and the uniform adaptation coefficient, and the device driving parameter is obtained by iteratively optimizing the optimization objective function according to the optimization constraint condition, thereby improving the efficiency and accuracy of determining the device driving parameter.

[0061] In a second aspect, the application provides a motion gait analysis system, which adopts the following technical scheme:

[0062] A motion gait analysis system comprises:

[0063] An acquisition module is configured to acquire an opening trigger signal, a device detection parameter, a sensor parameter, a skin deformation parameter and a device driving parameter.

[0064] A memory is configured to store a program of the motion gait analysis method according to any one of the above aspects.

[0065] A processor is configured to load and execute the program in the memory, and implement the motion gait analysis method according to any one of the above aspects.

[0066] By adopting the technical scheme, the processor loads and executes a program of a motion gait analysis method stored in the memory, and the acquisition module acquires a series of data related to motion gait analysis, so that after detecting the opening trigger signal of the sensor, the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter are detected, and the fixing state recognition result of the sensor is analyzed, and when it is determined that the fixing state recognition result meets the requirement of the fixing state loosening result, the device driving parameter is used to control the fixing device to adjust the tightness, so that the sensor is stable, and the inaccuracy caused by the loosening of the sensor is avoided, and the accuracy of gait analysis is improved.

[0067] In summary, the present application includes at least one of the following beneficial technical effects:

[0068] 1. By detecting the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter after detecting the opening trigger signal of the sensor, and analyzing the fixing state recognition result of the sensor, when it is determined that the fixing state recognition result meets the requirement of the fixing state loosening result, the device driving parameter is used to control the fixing device to adjust the tightness, so that the sensor is stable, and the inaccuracy caused by the loosening of the sensor is avoided, and the accuracy of gait analysis is improved;

[0069] 2. By determining the loosening detection coefficient of the sensor according to the device detection parameter, the sensor parameter and the skin deformation parameter, and detecting the real-time loosening coefficient threshold at the current time, when the loosening detection coefficient exceeds the real-time loosening coefficient threshold, the sensor loosening result is defined as the fixing state recognition result, and when it does not exceed, the sensor stable result is defined as the fixing state recognition result, and the efficiency and accuracy of determining the fixing state recognition result are improved;

[0070] 3. By determining the optimization constraint condition according to the total pressure value and the maximum safe pressure value, and determining the optimization objective function according to the basic fixing pressure parameter, the physiological adaptation pressure parameter and the uniform adaptation coefficient, the device driving parameter is obtained by iteratively optimizing the optimization objective function according to the optimization constraint condition, and the efficiency and accuracy of determining the device driving parameter are improved. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is a flowchart of a motion gait analysis method in an embodiment of the present application.

[0072] Figure 2 is a flowchart of the step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine the fixing state recognition result of the sensor in an embodiment of the present application.

[0073] Figure 3is a flow chart of a step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter in the embodiment of the present application to determine the loosening detection coefficient of the sensor.

[0074] Figure 4 is a flow chart of a step of obtaining the real-time loosening coefficient threshold of the sensor in the embodiment of the present application.

[0075] Figure 5 is a flow chart of a step of obtaining the device driving parameter in the embodiment of the present application.

[0076] Figure 6 is a flow chart of a step of obtaining the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient in the embodiment of the present application.

[0077] Figure 7 is a flow chart of a step of analyzing the device driving signal to determine the basic fixed pressure parameter in the embodiment of the present application.

[0078] Figure 8 is a flow chart of a step of optimizing the basic fixed pressure parameter according to the preset pressure optimization algorithm to generate the device driving parameter in the embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the objects, technical solutions and advantages of the present application clearer, the following will further describe the present application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. Figures 1 to 8

[0080] The embodiment of the present application discloses a motion gait analysis method, and specifically discloses a sensor, a fixing device and a processing terminal. The processing terminal is in communication connection with the sensor and the fixing device respectively to realize data interaction and control. After the sensor sends an opening trigger signal to the processing terminal, the processing terminal responds to the opening trigger signal to issue an instruction to call the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter, so as to analyze the device detection parameter, the sensor parameter and the skin deformation parameter to determine the fixed state recognition result of the sensor. When the processing terminal determines that the fixed state recognition result does not meet the requirement of the fixed state loosening result, the processing terminal continues to issue an instruction to call the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter, so as to continuously pay attention to the fixed state of the sensor. When the fixed state recognition result meets the requirement of the fixed state loosening result, it indicates that the sensor is fixed and loosened. Therefore, the device driving parameter is detected, and the fixing device is controlled to adjust the tightness degree with the device driving parameter, so as to maintain the stability of the sensor and further improve the accuracy of gait analysis.

[0081] Reference Figure 1 ​The embodiment of the application discloses a motion gait analysis method, comprising the following steps:

[0082] Step S100: obtaining an opening trigger signal of a preset sensor.

[0083] The sensor refers to a sensor for detecting gait analysis data, and in the embodiment of the application, an inertial measurement unit is taken as an example. The opening trigger signal refers to a signal for starting the sensor, which is stored in the sensor by an operator, and when the operator opens the sensor, the sensor sends the opening trigger signal to a processing terminal.

[0084] Step S101: obtaining device detection parameters of a preset fixing device, sensor parameters of the sensor and skin deformation parameters based on the opening trigger signal.

[0085] After the processing terminal receives the opening trigger signal, the processing terminal responds to the opening trigger signal, thereby issuing an instruction to control the fixing device and the sensor to send the device detection parameters, the sensor parameters and the skin deformation parameters to the processing terminal, so as to provide data support for subsequent determination of the fixing state of the sensor.

[0086] The fixing device refers to a device for fixing the sensor on the body of a person, comprising a flexible air bag belt, a micro air pump and an electromagnetic valve. The micro air pump and the electromagnetic valve are controlled and matched to quickly charge and discharge air in the flexible air bag belt, so as to adjust the tightness of the flexible air bag belt and ensure the stability of the sensor on the body of the person.

[0087] The device detection parameters refer to related detection parameters of the fixing device, comprising air bag pressure and belt deformation, which are sent to the processing terminal by the pressure sensor and the strain sensor after detecting the air bag and the belt respectively.

[0088] The sensor parameters refer to related parameters detected by the sensor, and in the embodiment of the application, three-axis acceleration data is taken as an example to reflect the dynamic change of the motion state, which is sent to the processing terminal by the sensor.

[0089] The skin deformation parameters refer to acceleration derived from skin deformation, which reflects the local dynamic response of the skin and the contact surface of the equipment. The skin surface strain sensor measures the skin deformation, and through a transfer function, the strain signal is converted into an acceleration signal by using Laplace transform or frequency domain analysis, and then the skin deformation parameters are obtained by inverse transform.

[0090] Step S102: analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine the fixing state recognition result of the sensor.

[0091] The fixed state recognition result refers to a result obtained by recognizing the fixed state of the sensor, including loose and stable results, and is obtained by the processing terminal according to the device detection parameter, the sensor parameter and the skin deformation parameter. For details, refer to the steps of Figure 2 .

[0092] Step S103: Determine whether the fixed state recognition result meets the requirement of the fixed state loose result.

[0093] The fixed state loose result refers to the state recognition result of the sensor being loose, and the requirement of the fixed state loose result refers to being consistent with the fixed state loose result.

[0094] The processing terminal determines whether the fixed state recognition result is consistent with the fixed state loose result, so as to determine whether the fixing device needs to be adjusted to stabilize the sensor.

[0095] Step S1031: If not, continue to obtain the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter for cyclic determination.

[0096] If the processing terminal determines that the fixed state recognition result is inconsistent with the fixed state loose result, it indicates that the sensor is in a stable state, so there is no need to adjust the fixing device, and therefore the device detection parameter of the fixing device, the sensor parameter of the sensor and the skin deformation parameter are continuously detected, so as to continuously monitor the state change of the sensor.

[0097] Step S1032: If yes, obtain the device driving parameter.

[0098] If the processing terminal determines that the fixed state recognition result is consistent with the fixed state loose result, it indicates that the sensor is in a loose state, so the fixing device needs to be adjusted to stabilize the sensor, and therefore the device driving parameter is detected to provide data support for subsequent control of the fixing device to adjust the tightness.

[0099] The device driving parameter refers to the pressure of the air bag of the device adjusting the band, and for details, refer to the steps of Figure 5 .

[0100] Step S104: Control the fixing device to adjust the tightness according to the device driving parameter to stabilize the sensor.

[0101] After the processing terminal determines the device driving parameter, the current pressure parameter of the air bag in the fixing device is called, so as to calculate the difference between the current pressure parameter and the device driving parameter to obtain the adjustment air pressure difference, and then the PID algorithm is used to calculate the adjustment air pressure difference to obtain a specific control signal, so that the fixing device is controlled to adjust the tightness, thereby keeping the sensor stable.

[0102] Referring to Figure 2 The step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine the fixed state recognition result of the sensor includes:

[0103] Step S200: Analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine the looseness detection coefficient of the sensor.

[0104] The looseness detection coefficient refers to a coefficient representing the looseness degree of the sensor, and the greater the looseness detection coefficient, the looser the sensor. The looseness detection coefficient is obtained by the processing terminal according to the device detection parameter, the sensor parameter and the skin deformation parameter, and the specific method refers to the step of Figure 3 .

[0105] Step S201: Obtaining the real-time looseness coefficient threshold of the sensor.

[0106] The real-time looseness coefficient threshold refers to the threshold of the looseness coefficient of the sensor at the current time, and the specific obtaining method refers to the step of Figure 4 .

[0107] Step S202: Determining whether the looseness detection coefficient meets the requirement of the real-time looseness coefficient threshold.

[0108] The requirement of the real-time looseness coefficient threshold refers to not greater than the real-time looseness coefficient threshold. The processing terminal determines whether the looseness detection coefficient is not greater than the real-time looseness coefficient threshold, so as to determine whether the sensor is in a stable or loose state.

[0109] Step S2021: If it meets, defining the preset sensor stable result as the fixed state recognition result of the sensor.

[0110] If the processing terminal determines that the looseness detection coefficient is not greater than the real-time looseness coefficient threshold, it indicates that the sensor is in a stable state, and therefore the sensor stable result is defined as the fixed state recognition result of the sensor.

[0111] The sensor stable result refers to the result that the sensor is in a stable state, which is stored in the processing terminal by the operator.

[0112] Step S2022: If it does not meet, defining the preset sensor looseness result as the fixed state recognition result of the sensor.

[0113] If the processing terminal determines that the looseness detection coefficient is greater than the real-time looseness coefficient threshold, it indicates that the sensor is in a loose state, and therefore the sensor looseness result is defined as the fixed state recognition result of the sensor.

[0114] The sensor looseness result refers to the result that the sensor is in a loose state, which is stored in the processing terminal by the operator.

[0115] Referring to Figure 3 The step of analyzing the device detection parameter, the sensor parameter and the skin deformation parameter to determine a looseness detection coefficient of the sensor includes:

[0116] Step S300: analyzing the sensor parameter, the skin deformation parameter and a preset maximum acceleration to determine a normalized acceleration coefficient.

[0117] The maximum acceleration refers to the maximum acceleration of the motion state, and the specific value is determined by an operator according to the actual situation.

[0118] The normalized acceleration coefficient refers to the influence coefficient of the normalized acceleration on the looseness. The processing terminal calculates the difference between the three-axis acceleration corresponding to the sensor parameter and the acceleration corresponding to the skin deformation parameter, and then calculates the quotient of the acceleration difference and the maximum acceleration to obtain the normalized acceleration coefficient. The greater the difference is, the more likely the looseness between the device and the skin is.

[0119] Step S301: analyzing the device detection parameter and a preset reference pressure value to determine a normalized pressure coefficient.

[0120] The reference pressure value refers to the initial pressure value used to determine the degree of the air bag pressure, which is usually the initial inflation pressure of the air bag.

[0121] The normalized pressure coefficient refers to the influence coefficient of the air bag pressure on the looseness. The processing terminal calculates the quotient of the pressure corresponding to the device detection parameter and the reference pressure value, and then subtracts 1 from the calculated quotient to obtain the normalized pressure coefficient.

[0122] Step S302: analyzing the device detection parameter and a preset maximum allowed deformation variable to determine a normalized deformation coefficient.

[0123] The maximum allowed deformation variable refers to the maximum deformation variable allowed by the strap, which is determined by an operator according to the material tensile test of the strap and stored in the processing terminal.

[0124] The normalized deformation coefficient refers to the influence coefficient of the strap deformation on the looseness. The processing terminal calculates the quotient of the strap deformation variable corresponding to the device detection parameter and the maximum allowed deformation variable to obtain the normalized deformation coefficient. The greater the deformation variable is, the more likely the looseness is.

[0125] Step S303: obtaining a looseness coefficient weight.

[0126] The looseness coefficient weight refers to the influence degree of the acceleration, the pressure and the deformation on the looseness coefficient, including the acceleration weight, the pressure weight and the deformation weight. In the embodiments of the present application, 0.6, 0.3 and 0.1 are taken as examples.

[0127] Step S304: Analyzing the normalized acceleration coefficient, the normalized pressure coefficient, the normalized deformation coefficient, and the loosening coefficient weight to determine the loosening detection coefficient.

[0128] In this step, the loosening detection coefficient is consistent with the loosening detection coefficient in step S200, and the acceleration weight, the pressure weight, and the deformation weight in the normalized acceleration coefficient, the normalized pressure coefficient, the normalized deformation coefficient, and the loosening coefficient weight are weighted and summed by the processing terminal.

[0129] Referring to Figure 4 , the step of obtaining the real-time loosening coefficient threshold of the sensor includes:

[0130] Step S400: Obtaining the single working duration of the sensor.

[0131] The single working duration refers to the total duration of the sensor from the start of this time to the current time, which is obtained by counting by a timer.

[0132] Step S401: Analyzing the single working duration and the preset threshold decay coefficient to determine the threshold adjustment coefficient.

[0133] The threshold decay coefficient refers to a coefficient for controlling the change speed of the threshold adjustment coefficient, which is taken as 0.1 in the embodiments of the present application. The threshold adjustment coefficient refers to a coefficient for adjusting the threshold, which is calculated by the processing terminal as the product of the single working duration and the threshold decay coefficient, and the reciprocal of the exponential function of the product, so as to obtain the threshold adjustment coefficient, which ensures that the initial threshold is high, and the threshold gradually decreases with time.

[0134] Step S402: Analyzing the threshold adjustment coefficient, the preset adjustable threshold, and the preset minimum fixed threshold to determine the real-time loosening coefficient threshold.

[0135] The adjustable threshold refers to a threshold that is adjusted over time, which is taken as 0.25 in the embodiments of the present application. The minimum fixed threshold refers to a loosening coefficient threshold that remains unchanged after a long time of movement, which is taken as 0.15 in the embodiments of the present application.

[0136] The real-time loosening coefficient threshold in this step is consistent with the real-time loosening coefficient threshold in step S201, which is calculated by the processing terminal as the product of the threshold adjustment coefficient and the adjustable threshold, and the sum of the product and the minimum fixed threshold.

[0137] Referring to Figure 5 , the step of obtaining the device driving parameter includes:

[0138] Step S500: Analyzing the loosening detection coefficient and the real-time loosening coefficient threshold to determine the driving proportional term, the driving differential term, and the driving integral term.

[0139] The driving proportional term refers to a proportional term used to determine the driving signal, is used to quickly respond to the looseness coefficient deviation, and is obtained by the processing terminal using a Sigmoid function to calculate the looseness detection coefficient and the real-time looseness coefficient threshold value, with the real-time looseness coefficient threshold value as the middle point of the Sigmoid function, and the steepness coefficient of the Sigmoid function being determined by an operator.

[0140] The driving differential term refers to a differential term used to determine the driving signal, is used to suppress the looseness coefficient change speed from being too fast, and is obtained by the processing terminal taking the derivative of the looseness detection coefficient with respect to time.

[0141] The driving integral term refers to an integral term used to determine the driving signal, is used to eliminate the looseness coefficient deviation after a long time of walking, and is obtained by the processing terminal calculating the difference between the looseness detection coefficient and the real-time looseness coefficient threshold value and calculating the integral of the difference from the start to the current time.

[0142] Step S501: Obtain the proportional gain coefficient, the differential gain coefficient, and the integral gain coefficient.

[0143] The proportional gain coefficient refers to the gain coefficient of the driving proportional term, is used to quickly respond to the current error, the differential gain coefficient refers to the gain coefficient of the driving differential term, is used to suppress the error change rate, and the integral gain coefficient refers to the gain coefficient of the driving integral term, is used to eliminate the long-term deviation. For specific obtaining methods, refer to the steps of Figure 6 .

[0144] Step S502: Analyze the driving proportional term, the driving differential term, the driving integral term, the proportional gain coefficient, the differential gain coefficient, and the integral gain coefficient to determine the device driving signal.

[0145] The device driving signal refers to a signal used to control the opening and closing of the fixing device, and is obtained by the processing terminal weighting and summing the driving proportional term, the driving differential term, the driving integral term, the proportional gain coefficient, the differential gain coefficient, and the integral gain coefficient.

[0146] Step S503: Analyze the device driving signal to determine the basic fixed pressure parameter.

[0147] The basic fixed pressure parameter refers to the pressure that needs to be adjusted, and is obtained by the processing terminal mapping the device driving signal to the pressure. For specific methods, refer to the steps of Figure 7 .

[0148] Step S504: Optimize the basic fixed pressure parameter according to a preset pressure optimization algorithm to generate the device driving parameter.

[0149] The pressure optimization algorithm refers to an algorithm used to optimize the pressure, so that the pressure distribution is more uniform and meets the comfort requirement. For specific details, refer toFigure 8 The steps.

[0150] The device drive parameters in this step are the same as those in step S1032. They are obtained by the processing terminal after optimizing the basic fixed pressure parameters according to the pressure optimization algorithm. For details, please refer to [link / reference needed]. Figure 8 The steps.

[0151] Reference Figure 6 The steps to obtain the proportional gain coefficient, differential gain coefficient, and integral gain coefficient include:

[0152] Step S600: Obtain the body mass index of the personnel.

[0153] Among them, the body mass index (BMI) refers to the subject's body mass index, which is obtained by the operator inputting it into the processing terminal.

[0154] Step S601: Analyze the body mass index of personnel and the preset gain calibration parameters to determine the integral gain coefficient.

[0155] Among them, the gain calibration coefficient refers to the calibration coefficient in the gain mapping function, including the adjustment calibration coefficient and the fixed calibration coefficient, which are obtained through experimental calibration.

[0156] The integral gain coefficient in this step is the same as the integral gain coefficient in step S501. It is obtained by multiplying the user's weight index by the processing terminal and the adjustment calibration coefficient in the gain calibration coefficient, and then summing the product with the fixed calibration coefficient. This satisfies the requirement that users with larger weights need a stronger integral gain to compensate for the cumulative error caused by inertia or drag, while users with smaller weights need a smaller integral gain coefficient to avoid overshoot.

[0157] Step S602: Analyze the sensor parameters, the preset maximum acceleration threshold, and the preset basic differential coefficients to determine the differential gain coefficient.

[0158] The maximum acceleration threshold refers to the maximum acceleration of a person walking; the specific value is determined by the operator based on the actual situation. The fundamental differential coefficient refers to the differential coefficient when the acceleration is zero; the specific value is determined by the operator based on the actual situation.

[0159] The differential gain coefficient in this step is the same as the differential gain coefficient in step S501. The processing terminal calculates the quotient of the acceleration corresponding to the sensor parameters and the maximum acceleration threshold, then calculates the sum of the quotient and 1, and finally calculates the product of the basic differential coefficient and the calculated sum to obtain the differential gain coefficient.

[0160] Step S603: Analyze the sensor parameters to determine the gait support phase or gait swing phase.

[0161] The stance phase refers to the state that the leg is in a supporting state, and the swing phase refers to the state that the leg is in a swinging state, which is determined by the processing terminal after analyzing the sensor parameters. For example, when the vertical acceleration peak value and the angular velocity in the sensor parameters are close to 0, it indicates that the foot is in contact with the ground and is in the support phase, and when the angular velocity in the sensor parameters is in periodic fluctuation, it indicates that the leg is in the swing phase.

[0162] Step S6031: If it is determined that the stance phase is determined, the preset enhancement gain coefficient is defined as the proportional gain coefficient.

[0163] If the processing terminal determines the stance phase, it indicates that the leg is in the support phase, and a larger proportional gain coefficient is needed to enhance the stability, so the enhancement gain coefficient is defined as the proportional gain coefficient.

[0164] The enhancement gain coefficient refers to the proportional gain coefficient when the leg of the person is in the support phase, and in the embodiment of the application, the enhancement gain coefficient is set to 0.8 times the maximum driving signal.

[0165] Step S6032: If it is determined that the swing phase is determined, the preset reduction gain coefficient is defined as the proportional gain coefficient.

[0166] If the processing terminal determines the swing phase, it indicates that the leg is in the swing phase, and the proportional gain coefficient needs to be reduced to reduce energy consumption and interference, so the reduction gain coefficient is defined as the proportional gain coefficient.

[0167] The reduction gain coefficient refers to the proportional gain coefficient when the leg of the person is in the swing phase, and in the embodiment of the application, the reduction gain coefficient is set to 0.5 times the maximum driving signal.

[0168] Referring to Figure 7 The step of analyzing the device driving signal to determine the basic fixed pressure parameter comprises:

[0169] Step S700: Analyzing the device driving signal and the preset driving pressure coefficient to determine the pressure change amount.

[0170] The driving pressure coefficient refers to the conversion coefficient between the driving signal and the pressure, which is determined by the operator according to the driving signal at different pressures in the experiment, and then the quotient of the pressure and the driving signal is calculated.

[0171] The pressure change amount refers to the change pressure corresponding to the device driving signal, which is obtained by the processing terminal calculating the product of the device driving signal and the driving pressure coefficient.

[0172] Step S701: Obtain the initial detection pressure of the fixing device.

[0173] The initial detection pressure refers to the air pressure before the fixing device is adjusted, which is detected by the pressure sensor and sent to the processing terminal.

[0174] Step S702: Analyzing the pressure change and the initial detection pressure to determine the basic fixed pressure parameter.

[0175] The basic fixed pressure parameter in this step is consistent with the basic fixed pressure parameter in step S503, and the sum of the pressure change and the initial detection pressure is calculated by the processing terminal.

[0176] Referring to Figure 8 According to the preset pressure optimization algorithm, the basic fixed pressure parameter is optimized to generate the device driving parameter.

[0177] Step S800: Obtaining the total pressure value and the maximum safe pressure value.

[0178] The total pressure value refers to the total pressure value in the independent cavity of the air bag according to the user comfort, and the specific value is determined by the operator according to the actual situation. The maximum safe pressure value refers to the maximum pressure value that the independent cavity of the air bag can withstand, and the specific value is determined by the operator according to the actual situation.

[0179] Step S801: Analyzing the total pressure value and the maximum safe pressure value to determine the optimization constraint condition.

[0180] The optimization constraint condition refers to the constraint condition in the process of optimizing the pressure, which is determined by the processing terminal according to the total pressure value and the maximum safe pressure value, that is, the comprehensive pressure is less than the total pressure value, and the local pressure is less than the maximum safe pressure value.

[0181] Step S802: Analyzing the basic fixed pressure parameter, the preset physiological adaptation pressure parameter and the preset uniform adaptation coefficient to determine the optimization objective function.

[0182] The physiological adaptation pressure parameter refers to the cavity pressure value in the independent cavity of the air bag according to the user comfort, and the specific value is determined by the operator according to the actual situation. The uniform adaptation coefficient refers to the weight coefficient of balancing the uniformity of pressure and the physiological adaptation, which is taken as 0.5 in the embodiment of the application.

[0183] The optimization objective function refers to an objective function for optimizing the pressure, which is determined by the processing terminal according to the basic fixed pressure parameter, the physiological adaptive pressure parameter and the uniform adaptive coefficient, for example, the pressure gradient of adjacent sub-chambers is determined according to the basic fixed pressure parameter, then the pressure difference is determined according to the difference between the basic fixed pressure parameter and the physiological adaptive pressure parameter, so that the pressure distribution is close to the physiological adaptive value, and finally the objective function of minimizing the pressure gradient and the pressure distribution close to the physiological adaptive value is obtained by multiplying the pressure difference by the uniform adaptive coefficient and adding the pressure gradient.

[0184] Step S803: iteratively optimizing the optimization objective function according to the optimization constraint condition to generate the device driving parameter.

[0185] In this step, the device driving parameter is consistent with the device driving parameter in step S504, and the optimization objective function is decomposed into pressure gradient optimization and physiological adaptation optimization by the processing terminal according to the alternating direction multiplier method, and the optimization constraint condition is used as the boundary to perform alternating iteration until convergence, and the specific process is as follows: initializing variables, including initializing the Lagrange multiplier to 0, setting the penalty parameter to 1, and setting the iteration number to 10 times; then iteratively optimizing, which is divided into three sub-problems: solving the optimization objective function according to gradient descent to update the pressure distribution, update the auxiliary variable, and update the Lagrange multiplier; finally, the pressure distribution is corrected according to the optimization constraint condition to ensure that the pressure distribution meets the optimization constraint condition.

[0186] Based on the same inventive concept, an action gait analysis system is provided, comprising:

[0187] The acquisition module is configured to acquire an opening trigger signal, a device detection parameter, a sensor parameter, a skin deformation parameter, a device driving parameter, a real-time looseness coefficient threshold, a looseness coefficient weight, a single working time length, a proportional gain coefficient, a differential gain coefficient, an integral gain coefficient, a body mass index of a person, an initial detection pressure, a total pressure value and a maximum safe pressure value.

[0188] The memory is configured to store a program of an action gait analysis method.

[0189] The processor can load and execute the program in the memory, and implement an action gait analysis method.

[0190] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0191] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded by a processor and performing an action gait analysis method.

[0192] The computer storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0193] Based on the same inventive concept, the embodiment of the present application provides an intelligent terminal, which comprises a memory and a processor, and the memory stores a computer program capable of being loaded by the processor and performing an action gait analysis method.

[0194] Those skilled in the art can clearly understand that, for the convenience and brevity, only the division of the above functional modules is taken as an example for description, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0195] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features, unless specifically described. That is, each feature is only an example of a series of equivalent or similar features.

Claims

1. A motion gait analysis method, characterized by, The method comprises the following steps: obtaining an opening trigger signal of a preset sensor; obtaining device detection parameters of a preset fixing device, sensor parameters of the sensor and skin deformation parameters based on the opening trigger signal; the skin deformation parameters are measured by a skin surface strain sensor, and a strain signal is converted into an acceleration signal through a transfer function, Laplace transform or frequency domain analysis, and then the skin deformation parameters are obtained through inverse transform; analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine a fixed state recognition result of the sensor; judging whether the fixed state recognition result meets a preset fixed state loosening result requirement; if not, the device detection parameters, the sensor parameters and the skin deformation parameters of the fixing device are continuously obtained for cyclic judgment; if yes, a device driving parameter is obtained; controlling the fixing device to adjust the tightness degree to stabilize the sensor according to the device driving parameter.

2. A motion gait analysis method according to claim 1, wherein, The step of analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine the fixed state recognition result of the sensor comprises: analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine a loosening detection coefficient of the sensor; obtaining a real-time loosening coefficient threshold of the sensor; judging whether the loosening detection coefficient meets the real-time loosening coefficient threshold requirement; if yes, a preset sensor stabilization result is defined as the fixed state recognition result of the sensor; if not, a preset sensor loosening result is defined as the fixed state recognition result of the sensor.

3. A motion gait analysis method according to claim 2, wherein, The step of analyzing the device detection parameters, the sensor parameters and the skin deformation parameters to determine the loosening detection coefficient of the sensor comprises: analyzing the sensor parameters, the skin deformation parameters and a preset maximum acceleration to determine a normalized acceleration coefficient; analyzing the device detection parameters and a preset reference pressure value to determine a normalized pressure coefficient; analyzing the device detection parameters and a preset maximum allowed deformation to determine a normalized deformation coefficient; obtaining a loosening coefficient weight; analyzing the normalized acceleration coefficient, the normalized pressure coefficient, the normalized deformation coefficient and the loosening coefficient weight to determine the loosening detection coefficient.

4. The motion gait analysis method of claim 2, wherein, The step of obtaining the real-time loosening coefficient threshold of the sensor comprises: obtaining a single working duration of the sensor; analyzing the single working duration and a preset threshold decay coefficient to determine a threshold adjustment coefficient; analyzing the threshold adjustment coefficient, a preset adjustable threshold and a preset minimum fixed threshold to determine the real-time loosening coefficient threshold.

5. The motion gait analysis method of claim 2, wherein, The step of obtaining the device driving parameter comprises: analyzing the loosening detection coefficient and the real-time loosening coefficient threshold to determine a driving proportional term, a driving differential term and a driving integral term; obtaining a proportional gain coefficient, a differential gain coefficient and an integral gain coefficient; analyzing the driving proportional term, the driving differential term, the driving integral term, the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient to determine a device driving signal; analyzing the device driving signal to determine a basic fixed pressure parameter; optimizing the basic fixed pressure parameter according to a preset pressure optimization algorithm to generate the device driving parameter.

6. A motion gait analysis method according to claim 5, wherein, The step of obtaining the proportional gain coefficient, the differential gain coefficient and the integral gain coefficient comprises: obtaining a body mass index of the person; analyzing the body mass index of the person and a preset gain calibration parameter to determine the integral gain coefficient; analyzing the sensor parameter, a preset maximum acceleration threshold and a preset basic differential coefficient to determine the differential gain coefficient; analyzing the sensor parameter to determine a stance phase or a swing phase of gait; if the stance phase of gait is determined, defining a preset enhancement gain coefficient as the proportional gain coefficient; if the swing phase of gait is determined, defining a preset reduction gain coefficient as the proportional gain coefficient.

7. A motion gait analysis method according to claim 5, wherein, The step of analyzing the device driving signal to determine the basic fixed pressure parameter comprises: analyzing the device driving signal and a preset driving pressure coefficient to determine a pressure variation; obtaining an initial detection pressure of the fixed device; analyzing the pressure variation and the initial detection pressure to determine the basic fixed pressure parameter.

8. A motion gait analysis method according to claim 5, wherein, The step of optimizing the basic fixed pressure parameter according to a preset pressure optimization algorithm to generate the device driving parameter comprises: obtaining a total pressure value and a maximum safe pressure value; analyzing the total pressure value and the maximum safe pressure value to determine an optimization constraint condition; analyzing the basic fixed pressure parameter, a preset physiological adaptation pressure parameter and a preset uniform adaptation coefficient to determine an optimization objective function; iteratively optimizing the optimization objective function according to the optimization constraint condition to generate the device driving parameter.

9. A motion gait analysis system characterized by, comprise: an obtaining module, configured to obtain an opening trigger signal, a device detection parameter, a sensor parameter, a skin deformation parameter and a device driving parameter; a memory, configured to store a program of the motion gait analysis method according to any one of claims 1 to 8; a processor, the program in the memory being loadable and executable by the processor and realizing the motion gait analysis method according to any one of claims 1 to 8.

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