A human body motion posture measurement system

Abstract

The present invention relates to a human motion posture measurement system, which belongs to the field of intelligent measurement in the electronics industry. An inertial navigation wearable device is fixed on a human body part to be measured in a wearable manner, and uses the human body part to be measured as a carrier to measure the three-axis acceleration and the three-axis angular velocity of the human body part in an inertial coordinate system, and send them to a data comprehensive analysis module; the data comprehensive analysis module performs navigation solution based on the three-axis acceleration and the three-axis angular velocity of the human body part in an inertial coordinate system, and obtains the posture, speed and position information of the human body part in the navigation coordinate system; zero-speed detection is performed on the human body part to be measured, and when the human body part to be measured is within the zero-speed range, zero-speed error correction is performed on the posture, speed and position information of the human body part to be measured in the navigation coordinate system, thereby improving the measurement accuracy of human motion posture.

Description

Technical Field

[0001] The invention relates to a human body motion posture measurement system, and belongs to the field of intelligent measurement in the electronics industry. Background Art

[0002] With the continuous development of my country's sports industry and the continuous advancement of science and technology, innovation in athlete training models has increasingly become the main means to improve athletes' competitive level. How to use the power of science and technology to innovate training models has become the main means to solve many problems in athlete training.

[0003] Monitoring athlete motion parameters is essential for improving exercise techniques and performance. Traditional methods of monitoring motion parameters rely on imaging and other technologies. These methods can only roughly observe and interpret the movement process, relying heavily on the experience of professional coaches and failing to achieve precise quantitative analysis. Most current sports involve large movements, high speeds, and demanding full-body coordination. Changes in joint angles are strongly correlated with the final performance. To measure athletes' motion parameters and provide quantitative technical support for technical improvement and performance enhancement, wearable products are needed to accurately measure motion parameters and implement intelligent data processing and analysis, making them easy for athletes and coaches to use. To facilitate use by athletes and coaches, athletes only need to wear an inertial navigation system, while coaches can monitor the athlete's motion parameters using a mobile phone or PC. This is supported by a comprehensive data analysis system. This greatly facilitates use and provides key technical support for technology-driven sports.

[0004] Current wearable products extract motion information based on attitude, velocity, and displacement. Therefore, inertial navigation algorithms (INS) are crucial for calculating these parameters. However, traditional INS algorithms suffer from error accumulation, and MEMS devices have relatively low precision. Long-term use can lead to error divergence, making accurate attitude, velocity, and position information inaccurate. Commonly used zero-velocity correction methods only work for the feet and are ineffective for other body parts, rendering measurement data unusable. Summary of the Invention

[0005] The technical problem solved by the present invention is to overcome the shortcomings of the existing technology and propose a human body motion posture measurement system for measuring the posture information of different parts of the human body. Under long-term use, the measurement error converges without diverging, and accurate measurement of motion parameters of different parts of the human body during exercise is achieved.

[0006] The technical solution of the present invention is: a human motion posture measurement system, which includes an inertial navigation wearable device and a data comprehensive analysis module;

[0007] The inertial navigation wearable device is fixed on the measured human body part in a wearable manner. With the measured human body part as the carrier, it measures the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system and sends them to the data comprehensive analysis module;

[0008] The data comprehensive analysis module performs navigation calculations based on the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system to obtain the posture, velocity and position information of the measured human body part in the navigation coordinate system; it performs zero-speed detection on the movement of the measured human body part, and when the measured human body part is within the zero-speed range, it performs zero-speed error correction on the posture, velocity and position information of the measured human body part in the navigation coordinate system.

[0009] Compared with the prior art, the present invention has the following beneficial effects:

[0010] (1) Based on the accurate detection of the zero-speed interval of each part, the present invention realizes regular estimation and correction of the navigation error of different measured parts of the human body through the zero-speed error correction algorithm and the posture error correction algorithm based on the Kalman filter, solves the error divergence problem of MEMS sensors under long-term use, and improves the measurement accuracy of the system;

[0011] (2) The present invention is based on the fact that in addition to the feet, different parts of the human body, such as the thigh and calf, also have different zero-speed intervals during walking. The zero-speed detection and correction algorithm are performed on different parts of the human body to be tested, and the navigation error is further estimated and corrected, thereby solving the problem that the navigation error of other parts except the feet cannot be corrected regularly.

[0012] (3) The present invention adopts different zero-speed detection algorithms based on the motion data characteristics of different parts of the human body, and sets different energy thresholds in a targeted manner to achieve accurate detection of the zero-speed intervals of all measured parts including the feet, thighs, and calves, providing conditions for regular correction of navigation errors in various parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of an athlete's hip-mounted inertial navigation system according to an embodiment of the present invention;

[0014] FIG2( a ) shows the output value of the foot MEMS accelerometer according to an embodiment of the present invention;

[0015] FIG2( b ) shows the output value of the foot MEMS gyroscope according to an embodiment of the present invention;

[0016] FIG2( c ) is a foot zero-speed detection result according to an embodiment of the present invention;

[0017] FIG3( a ) shows the output value of the calf MEMS accelerometer according to an embodiment of the present invention;

[0018] FIG3( b ) is a result of calf zero-speed detection according to an embodiment of the present invention;

[0019] FIG4( a ) shows the output value of the thigh MEMS accelerometer according to an embodiment of the present invention;

[0020] FIG4( b ) is a result of thigh zero-speed detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be described in further detail below with reference to the accompanying drawings and specific examples:

[0022] like Figure 1 As shown, the present invention provides a human motion posture measurement system, which includes an inertial navigation wearable device and a data comprehensive analysis module;

[0023] The inertial navigation wearable device is fixed on the measured human body part in a wearable manner. With the measured human body part as the carrier, it measures the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system and sends them to the data comprehensive analysis module;

[0024] The data comprehensive analysis module performs navigation calculations based on the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system to obtain the posture, velocity and position information of the measured human body part in the navigation coordinate system; it performs zero-speed detection on the movement of the measured human body part, and when the measured human body part is within the zero-speed range, it performs error correction on the posture, velocity and position information of the measured human body part in the navigation coordinate system.

[0025] Furthermore, the inertial navigation wearable device includes a MEMS sensor, a signal processing module, a communication module and a lithium battery;

[0026] The MEMS sensor integrates a MEMS gyroscope and a MEMS accelerometer. The MEMS gyroscope outputs the three-axis angular velocity in the inertial coordinate system, and the MEMS accelerometer outputs the three-axis acceleration of the measured human body part, and outputs the measurement results to the signal processing module;

[0027] The signal processing module frames and packages the measurement results output by the MEMS sensor and sends them to the communication module;

[0028] The communication module uses wireless communication to send the packaged measurement data frames;

[0029] Lithium batteries are used to power MEMS sensors, signal processing modules, and communication modules.

[0030] Furthermore, the specific implementation of the data comprehensive analysis module is as follows:

[0031] S1. Select the "East-North-Sky" geographic coordinate system as the navigation coordinate system, obtain the three-axis acceleration of the measured human body part and the three-axis angular velocity in the inertial coordinate system, perform navigation calculation, and obtain the attitude, velocity, and position information of the measured human body part in the navigation coordinate system;

[0032] S2. The attitude angle error, velocity error, position error, gyro bias and accelerometer bias of the measured human body part in the navigation coordinate system are used as state quantities, and the velocity error and attitude error of the measured human body part in the zero-speed interval are used as quantity measurements to establish a Kalman filter;

[0033] S3. At each sampling moment of the MEMS sensor, perform a one-step prediction of the Kalman filter state quantity and calculate the one-step prediction mean square error matrix of the state, and proceed to step S4;

[0034] S4, determine whether the measured human body part is in the zero speed interval, if it is in the zero speed interval, proceed to step S5, otherwise, proceed to step S6;

[0035] S5. Update the measurement and measurement matrix of the Kalman filter, calculate the filter gain and update the state estimation mean square error matrix based on the measurement, the state one-step prediction mean square error matrix, the state estimation mean square error matrix, and the measurement noise covariance matrix, perform state estimation based on the filter gain and the measurement matrix, and obtain the velocity error, position error, and attitude angle error of the measured human body part in the navigation coordinate system. Then, based on these estimated errors, correct the attitude, velocity, and position information of the measured human body part in the navigation coordinate system;

[0036] S6. Output the posture, speed and position information of the measured human body part in the navigation coordinate system.

[0037] In one specific embodiment of the present invention, an inertial navigation wearable device consists of a posture measurement sensor, a processing circuit centered around an ESP8266, and a lithium battery. The posture measurement sensor is a highly integrated MTI-3 micro-inertial sensor unit, integrating information from a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer, and boasts compact size and light weight. The processing circuit centered around the ESP8266 implements the functions of the signal processing and communication modules. The inertial navigation wearable device can be worn by an athlete using a strap or adhesive tape to measure the triaxial acceleration and triaxial angular velocity of the measured body part in an inertial coordinate system throughout the athlete's training process.

[0038] If used for swimming, the inertial navigation wearable device should have a waterproof function. The upper and lower shell structures of the inertial navigation device can be sealed with silicone rubber to achieve an IP68 waterproof level.

[0039] The following is a detailed analysis of the specific implementation of the data comprehensive analysis module:

[0040] 1) Inertial navigation solution

[0041] This method uses the "East-North-Sky" geographic coordinate system as the navigation coordinate system and employs a recursive update algorithm to perform navigation calculations, obtaining the attitude, velocity, and position information of the measured human body part in this navigation coordinate system. The inertial navigation update algorithm is divided into three parts: attitude, velocity, and position updates, with the attitude update algorithm being the core.

[0042] The posture of the measured human body part in the navigation coordinate system is calculated through the following steps:

[0043] S1.1. Obtain the three-axis angular velocity of the measured human body part in the inertial coordinate system

[0044] S1.2, according to the three-axis angular velocity of the human body part under test in the inertial coordinate system Calculate the three-axis angular velocity of the measured human body part in the navigation coordinate system

[0045] From the angular velocity equation we get:

[0046]

[0047] in: is the projection of the angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, is the projection of the angular velocity of the carrier coordinate system relative to the inertial coordinate system in the carrier coordinate system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the carrier coordinate system, It is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the carrier coordinate system. The carrier coordinate system is the coordinate system of the measured human body part.

[0048] Since the MEMS sensor has low precision and cannot be sensitive to the angular velocity of the earth's rotation, it can be ignored. In general sports or walking scenes, the speed of people is less than 10m / s, and the radius of the earth is R = 6371393m. so is 10 -7 ~10 -6 Therefore, for MEMS sensors, the above formula can be equivalent to:

[0049]

[0050] S1.3. Calculate the posture quaternion Q of the measured human body part at the current sampling moment k ,

[0051] Qk =[q1 q2 q3 q4]:

[0052]

[0053] Where Δt is the three-axis angular velocity in the inertial coordinate system The sampling interval, is the coordinate transformation matrix from the carrier coordinate system to the navigation coordinate system, Q k-1 It is the posture quaternion of the measured human body part at the last sampling moment.

[0054] and Q k The initial value of is calculated by the initial posture angles θ0, γ0, ψ0 of the measured human body part in the navigation coordinate system obtained by the initial alignment, and then calculated by the continuously updated quaternion.

[0055] S1.4, according to the posture quaternion Q of the measured human body part at the current sampling moment k , calculate the coordinate transformation matrix from the carrier coordinate system to the navigation coordinate system

[0056]

[0057] S1.5. Coordinate transformation matrix from carrier coordinate system to navigation coordinate system The posture of the measured human body part in the navigation coordinate system is calculated, where the posture of the measured human body part in the navigation coordinate system includes a pitch angle θ, a roll angle γ, and a yaw angle ψ of the measured human body part.

[0058] The specific calculation method is: get

[0059]

[0060] The velocity of the measured human body part in the navigation coordinate system is calculated by the following steps:

[0061] S1.6. Coordinate conversion matrix from the carrier coordinate system to the navigation coordinate system Substitute into the specific force equation to obtain the projection of the acceleration of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system

[0062] The specific force equation is as follows:

[0063]

[0064] in, From formula (4), we can get f b is the three-axis acceleration of the carrier in the inertial coordinate system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, g n is the projection of gravitational acceleration in the navigation coordinate system.

[0065] Since the human movement speed in general scenarios is less than 10m / s, the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system on the navigation coordinate system is Projection of the velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system The projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system Can be ignored, g n is the projection of gravity acceleration in the navigation coordinate system, so it can be calculated That is, the projection of the acceleration of the human body relative to the earth in the navigation coordinate system.

[0066] S1.7, according to the formula Update the projection of the velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system That is the speed of the measured human body part in the navigation coordinate system, is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the last sampling moment in the navigation coordinate system,

[0067] It is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the current sampling moment in the navigation coordinate system.

[0068] The position of the measured human body part in the navigation coordinate system is updated by the following equation:

[0069]

[0070] Where Δt is the sampling interval of the MEMS sensor, P k-1 is the position at the last sampling moment, P k is the position at the current sampling moment, It is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the last sampling moment in the navigation coordinate system.

[0071] In summary, the posture, speed and position information of the human body during movement or walking can be obtained.

[0072] 2) Zero speed detection

[0073] The low precision of MEMS inertial sensors is the primary factor affecting system navigation accuracy. Over extended periods of use, navigation errors accumulate over time, severely impacting the accuracy of final measurements. By using different zero-speed detection algorithms to detect periods of human rest during motion and then performing parameter corrections within these intervals, velocity errors can be effectively eliminated while constraining position and heading errors.

[0074] As a person walks, IMU sensors worn on different parts of the body can detect the periodic changes in the foot as it lifts, steps, lands, and comes to rest. Analysis has shown that, in addition to the foot, the thigh and calf also experience periodic zero-speed intervals during walking. These periodic zero-speed intervals can be detected using different detection algorithms and targeted energy thresholds.

[0075] Zero-speed detection algorithms mainly include the following four:

[0076] (a) Generalized Likelihood Ratio Detection (GLRT)

[0077]

[0078] (b) Accelerometer measurement variance detection algorithm (MV)

[0079]

[0080] (c) Accelerometer measurement amplitude detection algorithm (MAG)

[0081]

[0082] (d) Angular Velocity Measurement Energy Detection (ARE)

[0083]

[0084] in, is the acceleration value output by the accelerometer at the kth sampling moment, is the angular velocity value output by the gyroscope at the kth sampling moment. Let the number of samples be W, and the observed value from the nth sampling moment to the n+W-1th sampling moment is and is a statistic of the energy of motion, and are the variances of the accelerometer and gyroscope measurement noise, respectively, g is the acceleration of gravity, ||a|| 2 =a T a, is the sample mean, that is:

[0085]

[0086] The method of the present invention for determining whether the speed of the measured human body part is within the zero speed range is as follows:

[0087] The raw data output by the MEMS gyroscope and MEMS accelerometer are sent to the zero-speed detector, which calculates the statistics of the motion energy of the measured human body part and sets the corresponding threshold of the zero-speed detector. When the statistics of the zero-speed detector are lower than the preset threshold of the zero-speed detector, the measured human body part is considered to be within the zero-speed interval; otherwise, the measured human body part is considered to be outside the zero-speed interval.

[0088] Depending on the part of the human body being measured, the zero-speed detector uses different algorithms to calculate the energy statistics of the movement of the measured part of the human body. Specifically: if the part of the human body being measured is the foot, the zero-speed detector uses the GLRT or ARE algorithm to calculate the energy statistics of the movement of the measured part of the human body; if the part of the human body being measured is the thigh or calf, the zero-speed detector uses the MAG or MV algorithm to calculate the energy statistics of the movement of the measured part of the human body.

[0089] In a specific embodiment of the present invention, based on the motion data characteristics of different parts of the human body during exercise, the foot zero-speed detection algorithm can use GLRT, and the energy detection threshold can be set to 25,000; the calf zero-speed detection algorithm can use the MAG algorithm, and the energy detection threshold can be set to 1,000; and the thigh zero-speed detection algorithm can use the MAG algorithm, and the energy detection threshold can be set to 750. By using different zero-speed detection algorithms and appropriately setting the energy detection threshold, the zero-speed interval of the corresponding body part can be effectively detected, that is, the interval where the energy statistics of the measured body part's motion are less than the detection threshold.

[0090] Figures 2(a), 2(b), and 2(c) show the MEMS accelerometer and gyroscope outputs, respectively, and the zero-speed detection results for the foot during walking. Using the GLRT zero-speed detection algorithm and an energy detection threshold of 25,000, the periodic zero-speed intervals of the foot during walking can be effectively detected.

[0091] Figures 3(a) and 3(b) show the output of the MEMS accelerometer and the zero-speed detection results for the calf during walking, respectively. It can be seen that the MAG zero-speed detection algorithm, with an energy detection threshold of 1000, can effectively detect the periodic zero-speed intervals of the calf during walking.

[0092] Figures 4(a) and 4(b) show the output of a MEMS accelerometer worn on the thigh and the zero-speed detection results for that area, respectively, during walking. As can be seen, the MAG zero-speed detection algorithm, with an energy detection threshold of 750, can also effectively detect periodic zero-speed intervals in the thigh during walking.

[0093] On the basis of accurate detection of the zero-speed interval of each part, the navigation error of the corresponding measured part can be regularly estimated and corrected through the zero-speed error correction algorithm and attitude error correction algorithm based on the Kalman filter.

[0094] 3) Kalman filter and zero-speed error correction algorithm

[0095] The principle of Kalman filtering is to use the velocity error and attitude angle error in the zero-speed range as measurement, establish a Kalman filter, and estimate the velocity error, position error and attitude angle error of the measured human body part, and then compensate the estimated errors to the corresponding variables to obtain an estimate close to the true value of the state variable.

[0096] The state variables of the Kalman filter include velocity error, position error, and attitude error. Therefore, it is necessary to establish a suitable state equation based on the error equation of inertial navigation, MEMS sensor characteristics, and human motion characteristics.

[0097] 3.1 Error equation

[0098] (a) Attitude error equation

[0099] The MEMS attitude error equation is:

[0100]

[0101] Where: φ is the attitude angle error, ε b is the gyro zero bias.

[0102] (b) Velocity error equation

[0103] The MEMS velocity error equation is as follows:

[0104]

[0105] Where: δV is the velocity error, f n is the projection of acceleration in the navigation coordinate system, ▽ b is the accelerometer bias.

[0106] (c) Position error equation

[0107] The MEMS position error equation is as follows:

[0108]

[0109] Where: δP is the position error, δV is the velocity error.

[0110] 3.2 Correction algorithm and measurement equation

[0111] (a) Zero speed error correction

[0112] When a motion is detected as stationary, its true velocity should theoretically be zero. However, due to large measurement errors in MEMS sensors, the velocity calculated by the MEMS inertial navigation system is not actually zero. The zero-velocity error correction method treats the velocity calculated by the MEMS inertial navigation system during the stationary phase as a velocity error. This velocity error is then used as a measurement for Kalman filter estimation, thereby suppressing navigation parameter errors.

[0113] Therefore, the speed error based on the zero-speed error correction algorithm is ΔV, and

[0114]

[0115] Among them, V x 、V y 、V z They are the three-axis components of the velocity value of the measured human body part obtained by navigation solution.

[0116] (b) Attitude error correction

[0117] During the stationary phase, the attitude angle theoretically remains unchanged between the two moments. However, due to the large measurement error of the MEMS sensor, the attitude angle difference between the two moments can be non-zero. Therefore, the attitude angle difference between the two moments within the zero-speed range can be used as a measurement to suppress attitude angle errors.

[0118] Therefore, the quantity measured based on the attitude error correction algorithm is and

[0119]

[0120] Where: ie is the angular velocity of the earth's rotation, and L is the latitude of the earth where the carrier is located.

[0121] 3.3 Kalman Filter

[0122] (a) Equation of state

[0123] Combining the attitude error equation, velocity error equation, and position error equation, the state equation expression can be obtained as follows:

[0124] X k =Φ k / k-1 X k-1 +Γ k-1 W k-1

[0125] X is the state variable, Φ is the one-step transfer matrix, Γ is the process noise distribution matrix, W is the process noise matrix, k-1 and k represent the k-1th sampling time and the kth sampling time respectively, and k / k-1 represents the one-step prediction from the k-1th sampling time to the kth sampling time.

[0126] in:

[0127]

[0128] in:

[0129] is the attitude angle error of the measured human body part in the navigation coordinate system, δv x δv y δv z is the velocity error of the measured human body part in the navigation coordinate system, δxδyδz is the position error of the measured human body part in the navigation coordinate system, ε bx ε by ε bz is the gyroscope bias, bx ▽ by ▽ bz is the accelerometer bias;

[0130] The one-step transfer matrix is

[0131]

[0132] The process noise matrix is

[0133] W=[w gx w gy w gz w ax w ay w az ] T

[0134] Where W is the process noise, w gx 、w gy 、w gz are the noise of the three-axis gyroscope, w ax 、w ay 、w az is the noise of the triaxial accelerometer, is The antisymmetric matrix formed; is the three-axis acceleration of the carrier in the navigation coordinate system.

[0135] The process noise distribution matrix is

[0136]

[0137] (b) Measurement equation

[0138] Combining the zero-speed error correction and attitude error correction, the measurement equation can be expressed as:

[0139] Z k =H k X k +U k (20)

[0140] Among them, the quantity is

[0141]

[0142] V x 、V y 、V z are the three-axis components of the velocity of the measured human body part in the navigation coordinate system;

[0143] They are the posture angle data of the measured human body parts at the previous sampling moment and the current sampling moment respectively;

[0144] The measurement matrix is

[0145]

[0146] H 21 =[0 0-ω ie tanγcosψcosLΔt]

[0147] H 24 =[0secγsinθΔt secγcosθΔt]

[0148] Among them, ω ie is the angular velocity of the Earth's rotation, L is the latitude of the Earth where the carrier is located; θ, γ and ψ are the pitch angle, roll angle and yaw angle of the measured human body part respectively; Δt is the sampling interval of the MEMS sensor.

[0149] The measurement noise matrix U is

[0150]

[0151] in, are the three-axis velocity error noise, is the attitude angle error noise.

[0152] (c) Filtering algorithm

[0153] According to the Kalman filtering algorithm, the continuous equation is discretized and substituted into the following formula:

[0154] One-step state prediction

[0155]

[0156] in, is the optimal estimate of the state at the previous sampling moment, is the state estimation from the previous sampling time to the current sampling time, Φ k / k-1 is the one-step transfer matrix from the previous sampling moment to the current sampling moment.

[0157] State one-step prediction mean square error matrix

[0158]

[0159] Among them, P k / k-1 is the mean square error matrix from the previous sampling moment to the current moment, P k-1 is the mean square error matrix of the previous sampling moment, Γ k-1 is the process noise distribution matrix at the previous sampling moment, Q k-1 is the process noise covariance matrix at the previous sampling moment.

[0160] Filter gain

[0161]

[0162] Among them, K k is the filter gain at the current sampling moment, P k / k-1 is the mean square error matrix at the current sampling moment, H k is the measurement matrix at the current sampling moment, R k is the noise covariance matrix measured at the current sampling moment.

[0163] State Estimation

[0164]

[0165] in, is the optimal estimate of the state at the current sampling moment, is the state estimation from the previous sampling moment to the current sampling moment, K k is the filter gain at the current sampling moment, Z k is the quantity measured at the current sampling moment, H k is the measurement matrix at the current sampling moment.

[0166] State estimation mean square error matrix

[0167] P k =(IK k H k )P k / k-1 (25)

[0168] Among them, P k is the mean square error matrix at the current sampling moment, P k / k-1is the mean square error matrix from the previous sampling moment to the current sampling moment, I is the unit matrix, K k is the filter gain at the current sampling moment, H k is the measurement matrix at the current sampling moment.

[0169] Since zero-speed measurement is only available in the zero-speed interval, the Kalman filter only performs time updates but no measurement updates in the zero-speed interval. When a zero-speed interval is detected, the filter performs both time updates and measurement updates.

[0170] The above description is only the best specific implementation method of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

[0171] The contents not described in detail in the specification of the present invention belong to the common knowledge of professionals in this field.

Claims

1. A human body motion posture measurement system, characterized in that Inertial navigation wearable device and data comprehensive analysis module; The inertial navigation wearable device is fixed on the measured human body part in a wearable manner. With the measured human body part as the carrier, it measures the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system and sends them to the data comprehensive analysis module; The data comprehensive analysis module performs navigation calculations based on the three-axis acceleration and three-axis angular velocity of the measured human body part in the inertial coordinate system to obtain the attitude, velocity and position information of the measured human body part in the navigation coordinate system; it performs zero-speed detection on the movement of the measured human body part, and when the measured human body part is within the zero-speed range, it performs zero-speed error correction on the attitude, velocity and position information of the measured human body part in the navigation coordinate system; The specific implementation of the data comprehensive analysis module is as follows: S1. Select the "East-North-Sky" geographic coordinate system as the navigation coordinate system, obtain the three-axis acceleration of the measured human body part and the three-axis angular velocity in the inertial coordinate system, perform navigation calculation, and obtain the attitude, velocity, and position information of the measured human body part in the navigation coordinate system; S2. The attitude angle error, velocity error, position error, gyro bias and accelerometer bias of the measured human body part in the navigation coordinate system are used as state quantities, and the velocity error and attitude error of the measured human body part in the zero-speed interval are used as quantity measurements to establish a Kalman filter; S3. At each sampling moment of the MEMS sensor, perform a one-step prediction of the Kalman filter state quantity and calculate the one-step prediction mean square error matrix of the state, and proceed to step S4; S4, determine whether the measured human body part is in the zero speed interval, if it is in the zero speed interval, proceed to step S5, otherwise, proceed to step S6; S5. Update the measurement and measurement matrix of the Kalman filter, calculate the filter gain and update the state estimation mean square error matrix based on the measurement, the state one-step prediction mean square error matrix, the state estimation mean square error matrix, and the measurement noise covariance matrix, perform state estimation based on the filter gain and the measurement matrix, and obtain the velocity error, position error, and attitude angle error of the measured human body part in the navigation coordinate system. Then, based on these estimated errors, correct the attitude, velocity, and position information of the measured human body part in the navigation coordinate system; S6, outputting the posture, velocity and position information of the measured human body part in the navigation coordinate system; The posture of the measured human body part in the navigation coordinate system is calculated through the following steps: S1.

1. Obtain the three-axis angular velocity of the measured human body part in the inertial coordinate system S1.2, according to the three-axis angular velocity of the human body part under test in the inertial coordinate system Calculate the three-axis angular velocity of the measured human body part in the navigation coordinate system S1.

3. Calculate the posture quaternion Q of the measured human body part at the current sampling moment k : Where Δt is the sampling interval of the MEMS sensor, Q k-1 is the posture quaternion of the measured human body part at the last sampling moment; S1.4, according to the posture quaternion Q of the measured human body part at the current sampling moment k , calculate the coordinate transformation matrix from the carrier coordinate system to the navigation coordinate system S1.

5. Coordinate transformation matrix from carrier coordinate system to navigation coordinate system Calculating the posture of the measured human body part in the navigation coordinate system, wherein the posture of the measured human body part in the navigation coordinate system includes the pitch angle θ, the roll angle γ and the yaw angle ψ of the measured human body part; The specific calculation method is: get: θ=arcsin(T 32 ) In step S1, the speed of the measured human body part in the navigation coordinate system is calculated by the following steps: S1.

6. Coordinate conversion matrix from the carrier coordinate system to the navigation coordinate system Substitute into the specific force equation to obtain the projection of the acceleration of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system The specific force equation is as follows: Among them, f b is the three-axis acceleration of the carrier in the inertial coordinate system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, g n is the projection of gravitational acceleration in the navigation coordinate system; S1.7, according to the formula Update the projection of the velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, which is the velocity of the measured human body part in the navigation coordinate system. is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the last sampling moment in the navigation coordinate system, It is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the current sampling moment in the navigation coordinate system.

2. A human body motion posture measurement system according to claim 1, characterized in that The inertial navigation wearable device includes a MEMS sensor, a signal processing module, a communication module and a lithium battery; The MEMS sensor integrates a MEMS gyroscope and a MEMS accelerometer. The MEMS gyroscope outputs the three-axis angular velocity in the inertial coordinate system, and the MEMS accelerometer outputs the three-axis acceleration of the measured human body part, and outputs the measurement results to the signal processing module; The signal processing module frames and packages the measurement results output by the MEMS sensor and sends them to the communication module; The communication module uses wireless communication to send the packaged measurement data frames; Lithium batteries are used to power MEMS sensors, signal processing modules, and communication modules.

3. A human body motion posture measurement system according to claim 1, characterized in that In step S1, the position of the measured human body part in the navigation coordinate system is updated by the following equation: Where Δt is the sampling interval of the MEMS sensor, P k-1 is the position at the last sampling moment, P k is the position at the current sampling moment, It is the projection of the velocity of the navigation coordinate system relative to the earth coordinate system at the last sampling moment in the navigation coordinate system.

4. A human body motion posture measurement system according to claim 1, characterized in that The method for determining whether the speed of the measured human body part is within the zero speed range is: The raw data output by the MEMS gyroscope and MEMS accelerometer are sent to the zero-speed detector, which calculates the statistics of the motion energy of the measured human body part and sets the corresponding threshold of the zero-speed detector. When the statistics of the zero-speed detector are lower than the preset threshold of the zero-speed detector, the measured human body part is considered to be within the zero-speed interval; otherwise, the measured human body part is considered to be outside the zero-speed interval.

5. A human body motion posture measurement system according to claim 1, characterized in that Depending on the part of the human body being measured, the zero-speed detector uses different algorithms to calculate the energy statistics of the movement of the measured part of the human body. Specifically: if the part of the human body being measured is the human foot, the zero-speed detector uses the GLRT or ARE algorithm to calculate the energy statistics; if the part of the human body being measured is the human thigh or calf, the zero-speed detector uses the MAG or MV algorithm to calculate the energy statistics.

6. A human body motion posture measurement system according to claim 1, characterized in that The state quantity X in the Kalman filtering method in step S2 is: in: is the attitude angle error of the measured human body part in the navigation coordinate system, δv x ,δv y ,δv z is the velocity error of the measured human body part in the navigation coordinate system, δx, δy, δz are the position errors of the measured human body part in the navigation coordinate system, ε bx , ε by , ε bz is the gyroscope bias, is the accelerometer bias; The state equation is: X k =Φ k / k-1 X k-1 +C k-1 W k-1 Where X is the state quantity, Φ is the one-step transfer matrix, Γ is the process noise distribution matrix, W is the process noise matrix, k-1 and k represent the k-1th sampling time and the kth sampling time respectively, and k / k-1 represents the one-step prediction from the k-1th sampling time to the kth sampling time; In=[in gx In gy In gz In ax In ay In az ] T Where W is the process noise matrix, w gx 、w gy 、w gz are the noise of the three-axis gyroscope, w ax 、w ay 、w az is the noise of the triaxial accelerometer, is The antisymmetric matrix formed; is the three-axis acceleration of the carrier in the navigation coordinate system; the process noise distribution matrix Γ is: The quantity measured is: V x 、V y 、V z are the three-axis components of the velocity of the measured human body part in the navigation coordinate system; ψ Zk , ψ Zk-1 They are the posture angle data of the measured human body parts at the previous sampling moment and the current sampling moment respectively; The measurement equation is: Z k =H k X k +U k H 21 =[0 0-ω ie tanγcosψcosLΔt] H 24 =[0secγsinθΔt secγcosθΔt] Among them, ω ie is the Earth's rotation angular velocity, L is the Earth's latitude where the carrier is located, and U is the measurement noise matrix; are the three-axis velocity error noise, is the attitude angle error noise; θ, γ and ψ are the pitch angle, roll angle and yaw angle of the measured human body part respectively; Δt is the sampling interval of the MEMS sensor.

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