阅读说明：本技术 基于多传感器融合的下肢运动位姿快速预测系统及方法 (Multi-sensor fusion-based lower limb movement pose rapid prediction system and method ) 是由 陶云飞 黄玉平 郑继贵 陈靓 于 2019-08-28 设计创作，主要内容包括：基于多传感器融合的下肢运动位姿快速预测系统,包括表面肌电传感器、惯性传感器、位姿响应模块、位姿解算模块和外部辅助装置。外部辅助装置穿戴在穿戴者腰部和下肢上；表面肌电传感器实时获取穿戴者下肢各肌肉表面肌电信号；惯性传感器固定在外部辅助装置上；位姿响应模块根据采集的各肌肉表面肌电信号时序确定当前下肢各肌肉表面肌电信号对应的运动状态；位姿解算模块将惯性传感器采集的各关节运动角度与解算估计出的关节运动角度进行短时动态加权数据融合,实时获得穿戴者下肢运动姿态和关节的位置坐标。本发明同时提供了下肢运动位姿快速预测方法。本发明能够对人体下肢运动位姿进行快速预测,同时提高了连续运动量的估计精度。(The system for quickly predicting the motion pose of the lower limb based on multi-sensor fusion comprises a surface electromyography sensor, an inertial sensor, a pose response module, a pose resolving module and an external auxiliary device. The external assisting device is worn on the waist and lower limbs of the wearer; the method comprises the following steps that a surface electromyography sensor acquires electromyography signals of the surfaces of muscles of the lower limb of a wearer in real time; the inertial sensor is fixed on the external auxiliary device; the pose response module determines the motion state corresponding to the electromyographic signals of the surfaces of the muscles of the current lower limb according to the time sequence of the electromyographic signals of the surfaces of the muscles collected; the pose calculation module carries out short-time dynamic weighted data fusion on each joint motion angle acquired by the inertial sensor and the calculated and estimated joint motion angle, and obtains the lower limb motion attitude of the wearer and the position coordinates of the joints in real time. The invention also provides a method for quickly predicting the motion pose of the lower limbs. The invention can quickly predict the motion pose of the lower limbs of the human body and simultaneously improve the estimation precision of the continuous motion amount.)

1. The lower limb movement pose rapid prediction system based on multi-sensor fusion is characterized in that: the system comprises a surface electromyography sensor, an inertial sensor, a pose response module, a pose resolving module and an external auxiliary device;

the external auxiliary device is worn on the waist and the lower limbs of a wearer and can move along with the left and right lower limbs of the human body;

the surface electromyographic sensors are adhered to the surfaces of lower limb rectus femoris, medial femoral muscle, lateral femoral muscle, biceps femoris muscle, semitendinosus muscle, tibialis anterior muscle, gastrocnemius muscle and soleus muscle of a wearer, and are used for acquiring electromyographic signals of the surfaces of the muscles of the lower limb of the wearer in real time and transmitting the signals to the pose response module;

the inertial sensor is fixed on the external auxiliary device and used for acquiring the hip joint movement angle, the knee joint movement angle and the ankle joint movement angle of the lower limb of the wearer in real time;

the pose response module is fixed on an external auxiliary device, carries out filtering processing on the electromyographic signals of the surface of the lower limb of the wearer, then matches the time sequence of the electromyographic signals of the surface of each muscle collected with the time sequence of the muscle activity of the lower limb of the human body stored in a time sequence database of the muscle activity of the lower limb of the human body in advance, and finds the motion state corresponding to the electromyographic signals of the surface of each muscle of the current lower limb;

the pose calculation module is fixed on an external auxiliary device, and is used for calculating signals acquired by the surface electromyography, estimating the hip joint motion angle, the knee joint motion angle and the ankle joint motion angle of the lower limb of a wearer, carrying out short-time dynamic weighted data fusion on the hip joint motion angle, the knee joint motion angle and the ankle joint motion angle acquired by the inertial sensor and the calculated lower limb hip joint motion angle, the knee joint motion angle and the ankle joint motion angle of the wearer, obtaining the lower limb motion posture of the wearer in real time, and calculating the position coordinates of the hip joint, the knee joint and the ankle joint through the size of the external auxiliary device.

2. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 1, characterized in that: the external auxiliary device comprises a waist coaming (1), a thigh outer side plate (3), a thigh fixing bandage (5), a shank outer side plate (6), a shank fixing bandage (8), a pedal plate (10) and a pedal plate bandage (9), wherein the waist coaming (1) is worn on the waist of a wearer, the thigh outer side plate (3) is worn on the outer side of the thigh of the wearer through the thigh fixing bandage (5), the shank outer side plate (6) is worn on the outer side of the shank of the wearer through the shank fixing bandage (8), and the pedal plate (10) is worn under the feet of the wearer through the pedal plate bandage (9); universal joints are adopted to connect the waist coaming (1) and the thigh outer side plate (3), the thigh outer side plate (3) and the shank outer side plate (6), and the shank outer side plate (6) and the pedal (10).

3. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 2, characterized in that: the inertial sensors comprise a waist inertial sensor (2), a thigh inertial sensor (4), a shank inertial sensor (7) and a foot inertial sensor (11);

the waist inertial sensor (2) is fixed on the outer side of the front end of the waist boarding (1), the thigh inertial sensor (4) is fixed on the outer surface of the thigh outer side plate (3), the shank inertial sensor (7) is fixed on the outer surface of the shank outer side plate (6), and the foot inertial sensor (11) is fixed on the outer surface of the pedal (10).

4. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 1, characterized in that: the acquisition mode of the human body lower limb muscle activity time sequence database is as follows:

s1, a large number of testers wear external auxiliary devices to perform corresponding movement according to use requirements, and surface electromyographic signal training samples of a large number of rectus femoris muscles, medial femoral muscles, lateral femoral muscles, biceps femoris muscles, semitendinosus muscles, tibialis anterior muscles, gastrocnemius muscles and soleus muscles are obtained;

s2, performing endpoint detection on the myoelectric signal training samples on the surfaces of the muscles by adopting a cepstrum method, and extracting effective activity section data;

s3, extracting characteristic parameters of effective activity segment data of the muscle surface electromyogram signal training samples by adopting a characteristic extraction method, wherein the characteristic parameters comprise an absolute value mean value, a root mean square, an average power frequency and a median frequency;

and S4, acquiring 8 muscle activity time sequences corresponding to different motions according to the characteristic parameters to form a motion characteristic matching list, wherein the motion characteristic matching list forms a human body lower limb muscle activity time sequence database.

5. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 4, characterized in that: the method of forming the motion feature matching list in step S4 is as follows:

for a certain movement, carrying out binary coding conversion on four characteristic parameters in a muscle surface electromyographic signal training sample according to increasing and decreasing trends, and sequentially arranging according to the absolute value mean value, the root mean square, the average power frequency and the median frequency to form coding strips;

the effective activity segment data of the surface electromyogram signal training samples of 8 muscles form 8 coding strips, the coding strips are combined and stored into a data matrix, different data matrices are stored for different movements, and finally a movement characteristic matching list for different movements is formed.

6. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 5, characterized in that: the method for carrying out the binary code conversion on four characteristic parameters in a muscle surface electromyogram signal training sample according to the increasing and decreasing trend comprises the following steps:

if the value of a certain characteristic parameter is increased, the value is 01, if the value of the characteristic parameter is decreased, the value is 10, and if the value of the characteristic parameter is kept unchanged, the value is 00.

7. The lower limb exercise based on multi-sensor fusion of claim 1The dynamic pose rapid prediction system is characterized in that the flexion and extension angles of the hip joint are α_{Hip joint}The abduction and adduction angle of the hip joint is β_{Hip joint}The angle of internal rotation and external rotation of the hip joint is gamma_{Hip joint}The hip joint motion angle acquired by the inertial sensor is recorded as (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) The pose calculation module calculates the estimated hip joint movement angle of the lower limb of the wearer as (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) The pose solution module will (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

during a time t preceding the current movement instant, there are n hip-joint movement angles (α) acquired by the inertial sensors_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And the n posture resolving modules resolve the estimated hip joint movement angle of the lower limb of the wearer (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Then, within time t, the reference value of the hip joint movement angleSatisfies the following conditions:

wherein i is 1,2, 3.

Average value of measured deviation of hip joint movement angle

the mean square error of the measured deviation of the hip joint movement angle:

wherein j is 1,2, σ_{1 hip j}Measuring the mean square deviation, sigma, of the deviation for hip flexion and extension motion angles_{2 hip j}Measuring the mean square error, sigma, of the deviation for the abduction and adduction angles of the hip joint_{3 hip j}The mean square error of the measured deviation of the external rotation and internal rotation angles of the hip joint is (sigma)_{1 hip 1}，σ_{2 hip 1}，σ_{3 hip 1}) Calculating and estimating the mean square error of the hip joint motion angle measurement deviation for a pose calculation module, (sigma)_{1 hip 2}，σ_{2 hip 2}，σ_{3 hip 2}) Measuring the mean square error of the deviation for the inertial sensor hip joint movement angle;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 hip j}A short-time dynamic weighting fusion coefficient for measuring the flexion and extension movement angles of the hip joint, when k is 2, w_{2 hip j}A short-time dynamic weighting fusion coefficient for measuring abduction and adduction movement angles of the hip joint, when k is 3, w_{3 hip j}Short-time dynamic weighting fusion coefficients for measuring the hip joint external rotation and internal rotation motion angles;

hip joint motion angle after data fusion:

8. the multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 1, wherein the flexion and extension angle of the knee joint is α_KneeThe knee joint motion angle acquired by the inertial sensor is recorded as α_{Knee 2}The pose resolving module resolves the estimated knee joint motion angle of the lower limb of the wearer to α_{Knee 1}The pose resolving module will α_{Knee 2}And α_{Knee 1}The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the knee joint movement angle acquired by the n inertial sensors is α_{Knee 2}The n posture calculation module calculates the estimated knee joint movement angle of the lower limb of the wearer to be α_{Knee 1}Then, within the time t, the reference value of the knee joint movement angle

average value of measured deviation of knee joint movement angle

the mean square error of the measured deviation of the knee joint movement angle is as follows:

wherein j is 1,2, σ_{Knee j}Measuring the mean square error, sigma, of the deviation for knee joint movement angles_{Knee 2}For measuring the mean square deviation, sigma, of the deviation of the knee joint movement angle of the inertial sensor_{Knee 1}Calculating the mean square error of the measurement deviation of the knee joint motion angle for a pose calculating module;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein j is 1,2, w_{Knee j}For knee joint movementShort-time dynamic weighting fusion coefficients of angle measurement;

knee joint motion angle after data fusion:

α_knee＝w_{Knee 1}α_{Knee 1}+w_{Knee 2}α_{Knee 2}。

9. The multi-sensor fusion-based lower limb movement pose rapid prediction system according to claim 1, wherein the plantar flexion and dorsiflexion angle of the ankle joint is α_{Ankle joint}The angle of the ankle joint varus and valgus is β_{Ankle joint}The internal rotation and external rotation angle of the ankle joint is gamma_{Ankle joint}The ankle joint movement angle acquired by the inertial sensor is recorded as (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) The pose calculation module calculates the estimated motion angle of the ankle joint of the lower limb of the wearer to be (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The pose solution module will (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) And (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the ankle movement angle acquired by the n inertial sensors is (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) The n posture resolving module resolves the estimated movement angle of the lower limb ankle joint of the wearer (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Then, within the time t, the reference value of the ankle joint movement angle

average value of measured deviation of ankle joint movement angle

the mean square error of the measured deviation of the ankle joint movement angle is:

wherein j is 1,2, σ_{1 ankle j}Measuring the mean square deviation, sigma, of the deviation for the plantar flexion and dorsiflexion motion angles of the ankle joint_{2 ankle j}Measuring the mean square error, sigma, of the deviation for the ankle inversion and eversion motion angles_{3 ankle j}The mean square error of the deviation is measured for the internal rotation and external rotation motion angles of the ankle joint (sigma)_{1 ankle 1}，σ_{2 ankle 1}，σ_{3 ankle 1}) Calculating the mean square error (sigma) of the measurement deviation of the ankle joint motion angle for a pose calculation module_{1 ankle 2}，σ_{2 ankle 2}，σ_{3 ankle 2}) Measuring the mean square error of deviation for the motion angle of the ankle joint of the inertial sensor;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 ankle j}A short-time dynamic weighting fusion coefficient is measured for the plantar flexion and dorsiflexion motion angles of the ankle joint, when k is 2, w_{2 ankle j}The short-time dynamic weighting fusion coefficient for measuring the ankle joint inversion and eversion motion angle is that when k is 3, w_{3 ankle j}Short-time dynamic weighting fusion coefficients for ankle internal rotation and external rotation movement angle measurement;

ankle joint motion angle after data fusion:

10. the method for quickly predicting the motion pose of the lower limbs based on multi-sensor fusion is characterized by comprising the following steps of:

(1) the wearer pastes surface electromyography sensors on the surfaces of lower limb rectus femoris, medial femoral muscle, lateral femoral muscle, biceps femoris muscle, semitendinosus muscle, tibialis anterior muscle, gastrocnemius muscle and soleus muscle;

(2) the external auxiliary device is worn by a wearer, wherein the waistline plate is worn on the waist of the wearer, the thigh outer side plate is worn on the outer side of the thigh of the wearer through a thigh fixing bandage, the shank outer side plate is worn on the outer side of the shank of the wearer through a shank fixing bandage, and the pedal plate is worn under the foot of the wearer through a pedal bandage; universal joints are adopted to connect the waist coaming and the thigh outer side plate, the thigh outer side plate and the shank outer side plate, and the shank outer side plate and the pedal plate;

(3) fixing an inertial sensor on the outer side of the front end of the waist coaming, the outer thigh side plate, the outer shank side plate and the outer surface of the pedal;

(4) the lower limbs of a wearer do any movement, and the surface myoelectric sensor and the inertial sensor acquire information;

(5) the surface electromyographic sensor transmits the surface electromyographic signals to the pose response module, and the pose response module matches the acquired time sequence of the electromyographic signals of the surfaces of the muscles with a database of the activity time sequence of the muscles of the lower limbs of the human body to find the motion state at the moment;

(6) the surface electromyographic sensor and the inertial sensor transmit the acquired signals to the pose resolving module, and the pose resolving module estimates the movement angle of the hip joint of the lower limb of the wearer through the surface electromyographic signals (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The hip joint motion angle collected by the inertial sensor is fused by short-time dynamic weighting (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) Knee joint movement angle α_{Knee 2}Ankle joint motion angle (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) Angle of motion with hip (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angleDegree (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Data fusion is carried out, and the hip joint motion angle (α) of the human body lower limb motion posture is obtained in real time_{Hip joint}，β_{Hip joint}，γ_{Hip joint}) Knee joint movement angle α_KneeAnkle joint motion angle (α)_{Ankle joint}，β_{Ankle joint}，γ_{Ankle joint}) The position coordinates (x) of hip, knee and ankle joints are respectively calculated by the installation sizes of the waist coaming, the thigh outer side plate, the shank outer side plate and the foot pedal of the external auxiliary device_{Hip joint},y_{Hip joint},z_{Hip joint})、(x_Knee,y_Knee,z_Knee)、(x_{Ankle joint},y_{Ankle joint},z_{Ankle joint})。

Technical Field

The invention relates to a lower limb movement pose rapid prediction system and method based on multi-sensor fusion, and belongs to the field of exoskeleton robots and man-machine cooperative control.

Background

At present, in the aspects of rehabilitation training and power-assisted walking aid, an exoskeleton robot is increasingly favored by patients as an important device capable of replacing manual assistance. However, the existing exoskeleton robot has poor human-computer synergy effect, mainly because the human motion information prediction mode is mostly carried out by adopting a physical sensor mode, and the physical sensor can acquire the human motion information only under the condition that the body of a wearer has a certain motion amplitude, so that large time delay exists, and the requirement of quick prediction cannot be realized; meanwhile, a single sensor has certain accuracy in predicting a certain discrete motion, and the estimation of continuous motion amount has great defects.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the system and the method for quickly predicting the lower limb movement pose based on multi-sensor fusion can quickly predict the lower limb movement pose of a human body and improve the estimation precision of continuous movement amount.

The technical solution of the invention is as follows:

the lower limb movement pose rapid prediction system based on multi-sensor fusion comprises a surface electromyography sensor, an inertial sensor, a pose response module, a pose resolving module and an external auxiliary device;

the external auxiliary device is worn on the waist and the lower limbs of a wearer and can move along with the left and right lower limbs of the human body;

the surface electromyographic sensors are adhered to the surfaces of lower limb rectus femoris, medial femoral muscle, lateral femoral muscle, biceps femoris muscle, semitendinosus muscle, tibialis anterior muscle, gastrocnemius muscle and soleus muscle of a wearer, and are used for acquiring electromyographic signals of the surfaces of the muscles of the lower limb of the wearer in real time and transmitting the signals to the pose response module;

the inertial sensor is fixed on the external auxiliary device and used for acquiring the hip joint movement angle, the knee joint movement angle and the ankle joint movement angle of the lower limb of the wearer in real time;

the pose response module is fixed on an external auxiliary device, carries out filtering processing on the electromyographic signals of the surface of the lower limb of the wearer, then matches the time sequence of the electromyographic signals of the surface of each muscle collected with the time sequence of the muscle activity of the lower limb of the human body stored in a time sequence database of the muscle activity of the lower limb of the human body in advance, and finds the motion state corresponding to the electromyographic signals of the surface of each muscle of the current lower limb;

the pose calculation module is fixed on an external auxiliary device, and is used for calculating signals acquired by the surface electromyography, estimating the hip joint motion angle, the knee joint motion angle and the ankle joint motion angle of the lower limb of a wearer, carrying out short-time dynamic weighted data fusion on the hip joint motion angle, the knee joint motion angle and the ankle joint motion angle acquired by the inertial sensor and the calculated lower limb hip joint motion angle, the knee joint motion angle and the ankle joint motion angle of the wearer, obtaining the lower limb motion posture of the wearer in real time, and calculating the position coordinates of the hip joint, the knee joint and the ankle joint through the size of the external auxiliary device.

The external auxiliary device comprises a waist coaming, a thigh outer side plate, a thigh fixing bandage, a shank outer side plate, a shank fixing bandage, a pedal plate and a pedal plate bandage, wherein the waist coaming is worn on the waist of a wearer, the thigh outer side plate is worn on the outer side of the thigh of the wearer through the thigh fixing bandage, the shank outer side plate is worn on the outer side of the shank of the wearer through the shank fixing bandage, and the pedal plate is worn under the foot of the wearer through the pedal plate bandage; universal joints are adopted to connect the waist coaming and the thigh outer side plate, the thigh outer side plate and the shank outer side plate, and the shank outer side plate and the pedal plate.

The inertial sensors comprise a waist inertial sensor, a thigh inertial sensor, a shank inertial sensor and a foot inertial sensor;

the waist inertial sensor is fixed on the outer side of the front end of the waist coaming, the thigh inertial sensor is fixed on the outer surface of the thigh outer side plate, the shank inertial sensor is fixed on the outer surface of the shank outer side plate, and the foot inertial sensor is fixed on the outer surface of the pedal.

The acquisition mode of the human body lower limb muscle activity time sequence database is as follows:

s1, a large number of testers wear external auxiliary devices to perform corresponding movement according to use requirements, and surface electromyographic signal training samples of a large number of rectus femoris muscles, medial femoral muscles, lateral femoral muscles, biceps femoris muscles, semitendinosus muscles, tibialis anterior muscles, gastrocnemius muscles and soleus muscles are obtained;

s2, performing endpoint detection on the myoelectric signal training samples on the surfaces of the muscles by adopting a cepstrum method, and extracting effective activity section data;

s3, extracting characteristic parameters of effective activity segment data of the muscle surface electromyogram signal training samples by adopting a characteristic extraction method, wherein the characteristic parameters comprise an absolute value mean value, a root mean square, an average power frequency and a median frequency;

and S4, acquiring 8 muscle activity time sequences corresponding to different motions according to the characteristic parameters to form a motion characteristic matching list, wherein the motion characteristic matching list forms a human body lower limb muscle activity time sequence database.

The method of forming the motion feature matching list in step S4 is as follows:

for a certain movement, carrying out binary coding conversion on four characteristic parameters in a muscle surface electromyographic signal training sample according to increasing and decreasing trends, and sequentially arranging according to the absolute value mean value, the root mean square, the average power frequency and the median frequency to form coding strips;

the effective activity segment data of the surface electromyogram signal training samples of 8 muscles form 8 coding strips, the coding strips are combined and stored into a data matrix, different data matrices are stored for different movements, and finally a movement characteristic matching list for different movements is formed.

The method for carrying out the binary code conversion on four characteristic parameters in a muscle surface electromyogram signal training sample according to the increasing and decreasing trend comprises the following steps:

if the value of a certain characteristic parameter is increased, the value is 01, if the value of the characteristic parameter is decreased, the value is 10, and if the value of the characteristic parameter is kept unchanged, the value is 00.

Provided for flexion and extension of hip jointAngle α_{Hip joint}The abduction and adduction angle of the hip joint is β_{Hip joint}The angle of internal rotation and external rotation of the hip joint is gamma_{Hip joint}The hip joint motion angle acquired by the inertial sensor is recorded as (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) The pose calculation module calculates the estimated hip joint movement angle of the lower limb of the wearer as (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) The pose solution module will (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

during a time t preceding the current movement instant, there are n hip-joint movement angles (α) acquired by the inertial sensors_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And the n posture resolving modules resolve the estimated hip joint movement angle of the lower limb of the wearer (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Then, within time t, the reference value of the hip joint movement angle

Satisfies the following conditions:

wherein i is 1,2, 3.

Average value of measured deviation of hip joint movement angle

Satisfies the following conditions:

the mean square error of the measured deviation of the hip joint movement angle:

wherein j is 1,2, σ_{1 hip jointj}Measuring the mean square deviation, sigma, of the deviation for hip flexion and extension motion angles_{2 hip j}Measuring the mean square error, sigma, of the deviation for the abduction and adduction angles of the hip joint_{3 hip j}The mean square error of the measured deviation of the external rotation and internal rotation angles of the hip joint is (sigma)_{1 hip 1}，σ_{2 hip 1}，σ_{3 hip 1}) Calculating and estimating the mean square error of the hip joint motion angle measurement deviation for a pose calculation module, (sigma)_{1 hip 2}，σ_{2 hip 2}，σ_{3 hip 2}) Measuring the mean square error of the deviation for the inertial sensor hip joint movement angle;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 hip j}A short-time dynamic weighting fusion coefficient for measuring the flexion and extension movement angles of the hip joint, when k is 2, w_{2 hip j}A short-time dynamic weighting fusion coefficient for measuring abduction and adduction movement angles of the hip joint, when k is 3, w_{3 hip j}Short-time dynamic weighting fusion coefficients for measuring the hip joint external rotation and internal rotation motion angles;

hip joint motion angle after data fusion:

let the flexion and extension angle of the knee joint be α_KneeThe knee joint motion angle acquired by the inertial sensor is recorded as α_{Knee 2}The pose resolving module resolves the estimated knee joint motion angle of the lower limb of the wearer to α_{Knee 1}The pose resolving module will α_{Knee 2}And α_{Knee 1}The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the knee joint movement angle acquired by the n inertial sensors is α_{Knee 2}The n posture calculation module calculates the estimated knee joint movement angle of the lower limb of the wearer to be α_{Knee 1}Within time tReference value of knee joint movement angle

Satisfies the following conditions:

average value of measured deviation of knee joint movement angle

Satisfies the following conditions:

the mean square error of the measured deviation of the knee joint movement angle is as follows:

wherein j is 1,2, σ_{Knee j}Measuring the mean square error, sigma, of the deviation for knee joint movement angles_{Knee 2}For measuring the mean square deviation, sigma, of the deviation of the knee joint movement angle of the inertial sensor_{Knee 1}Calculating the mean square error of the measurement deviation of the knee joint motion angle for a pose calculating module;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein j is 1,2, w_{Knee j}Short-time dynamic weighting fusion coefficients for knee joint movement angle measurement;

knee joint motion angle after data fusion:

α_knee＝w_{Knee 1}α_{Knee 1}+w_{Knee 2}α_{Knee 2}。

Let the angle of plantar flexion and dorsiflexion of the ankle joint be α_{Ankle joint}The angle of the ankle joint varus and valgus is β_{Ankle joint}Inward rotation and outward rotation of ankle jointThe angle of rotation is gamma_{Ankle joint}The ankle joint movement angle acquired by the inertial sensor is recorded as (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) The pose calculation module calculates the estimated motion angle of the ankle joint of the lower limb of the wearer to be (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The pose solution module will (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) And (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the ankle movement angle acquired by the n inertial sensors is (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) The n posture resolving module resolves the estimated movement angle of the lower limb ankle joint of the wearer (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Then, within the time t, the reference value of the ankle joint movement angle

Satisfies the following conditions:

average value of measured deviation of ankle joint movement angle

Satisfies the following conditions:

the mean square error of the measured deviation of the ankle joint movement angle is:

wherein j is 1,2, σ_{1 ankle j}Measuring the mean square deviation, sigma, of the deviation for the plantar flexion and dorsiflexion motion angles of the ankle joint_{2 ankle j}Measuring the mean square error, sigma, of the deviation for the ankle inversion and eversion motion angles_{3 ankle j}The mean square error of the deviation is measured for the internal rotation and external rotation motion angles of the ankle joint (sigma)_{1 ankle 1}，σ_{2 ankle 1}，σ_{3 ankle 1}) Calculating the mean square error (sigma) of the measurement deviation of the ankle joint motion angle for a pose calculation module_{1 ankle 2}，σ_{2 ankle 2}，σ_{3 ankle 2}) Measuring the mean square error of deviation for the motion angle of the ankle joint of the inertial sensor;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 ankle j}A short-time dynamic weighting fusion coefficient is measured for the plantar flexion and dorsiflexion motion angles of the ankle joint, when k is 2, w_{2 ankle j}The short-time dynamic weighting fusion coefficient for measuring the ankle joint inversion and eversion motion angle is that when k is 3, w_{3 ankle j}Short-time dynamic weighting fusion coefficients for ankle internal rotation and external rotation movement angle measurement;

ankle joint motion angle after data fusion:

the method for quickly predicting the motion pose of the lower limbs based on multi-sensor fusion comprises the following steps:

(1) the wearer pastes surface electromyography sensors on the surfaces of lower limb rectus femoris, medial femoral muscle, lateral femoral muscle, biceps femoris muscle, semitendinosus muscle, tibialis anterior muscle, gastrocnemius muscle and soleus muscle;

(2) the external auxiliary device is worn by a wearer, wherein the waistline plate is worn on the waist of the wearer, the thigh outer side plate is worn on the outer side of the thigh of the wearer through a thigh fixing bandage, the shank outer side plate is worn on the outer side of the shank of the wearer through a shank fixing bandage, and the pedal plate is worn under the foot of the wearer through a pedal bandage; universal joints are adopted to connect the waist coaming and the thigh outer side plate, the thigh outer side plate and the shank outer side plate, and the shank outer side plate and the pedal plate;

(3) fixing an inertial sensor on the outer side of the front end of the waist coaming, the outer thigh side plate, the outer shank side plate and the outer surface of the pedal;

(4) the lower limbs of a wearer do any movement, and the surface myoelectric sensor and the inertial sensor acquire information;

(5) the surface electromyographic sensor transmits the surface electromyographic signals to the pose response module, and the pose response module matches the acquired time sequence of the electromyographic signals of the surfaces of the muscles with a database of the activity time sequence of the muscles of the lower limbs of the human body to find the motion state at the moment;

(6) the surface electromyographic sensor and the inertial sensor transmit the acquired signals to the pose resolving module, and the pose resolving module estimates the movement angle of the hip joint of the lower limb of the wearer through the surface electromyographic signals (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The hip joint motion angle collected by the inertial sensor is fused by short-time dynamic weighting (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) Knee joint movement angle α_{Knee 2}Ankle joint motion angle (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) Angle of motion with hip (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Data fusion is carried out, and the hip joint motion angle (α) of the human body lower limb motion posture is obtained in real time_{Hip joint}，β_{Hip joint}，γ_{Hip joint}) Knee joint movement angle α_KneeAnkle joint motion angle (α)_{Ankle joint}，β_{Ankle joint}，γ_{Ankle joint}) The position coordinates (x) of hip, knee and ankle joints are respectively calculated by the installation sizes of the waist coaming, the thigh outer side plate, the shank outer side plate and the foot pedal of the external auxiliary device_{Hip joint},y_{Hip joint},z_{Hip joint})、(x_Knee,y_Knee,z_Knee)、(x_{Ankle joint},y_{Ankle joint},z_{Ankle joint})。

Compared with the prior art, the invention has the following beneficial effects:

(1) according to the invention, the surface electromyography sensor is adhered to the surface of the lower limb muscle, so that the electromyography signal of the muscle can be collected in real time, and the quick and real-time judgment on the motion state of the lower limb of a wearer is realized by combining a pre-established database of the activity time sequence of the muscle of the lower limb of the human body.

(2) According to the invention, each joint angle obtained by the estimation of the surface electromyography sensor and each joint angle obtained by the inertia sensor are subjected to data fusion by a short-time dynamic weighting fusion method, so that the accurate estimation of the continuous motion amount is realized.

(3) According to the invention, the surface electromyographic sensor is pasted on the surface of the lower limb muscle, the electromyographic signal of the muscle can be collected in real time, the pose calculation module predicts the motion angle and position coordinate of each joint through a short-time dynamic weighting data fusion method, so that the real-time prediction of the motion pose of the lower limb of the human body is realized, and 7 degrees of freedom of the lower limb of the human body and corresponding joint action combinations can be predicted.

Drawings

FIG. 1 is a flow chart of a method for rapidly predicting a motion pose of a lower limb of a human body based on multi-sensor fusion according to the invention;

FIG. 2 is a schematic diagram of a system for rapidly predicting a motion pose of a lower limb of a human body based on multi-sensor fusion according to the present invention;

FIG. 3 is a schematic view of the external attachment of the present invention (only the reference numerals on one side are shown, and the same are provided on the opposite side);

1-waist coaming; 2-a waist inertial sensor; 3-thigh lateral plate; 4-thigh inertial sensors; 5-thigh fixing bandage; 6-outer shank plate; 7-a calf portion inertial sensor; 8-shank fixing band; 9-foot board binding band; 10-a foot pedal; 11-foot inertial sensor.

Detailed Description

The invention provides a lower limb movement pose rapid prediction system based on multi-sensor fusion, which comprises a surface electromyography sensor, an inertial sensor, a pose response module, a pose resolving module and an external auxiliary device.

FIG. 2 is a schematic diagram of a human body lower limb movement pose rapid prediction system based on multi-sensor fusion.

Specifically, the external assisting device is worn on the waist and lower limbs of the wearer and can move along with the left and right lower limbs of the human body. As shown in fig. 3, the external auxiliary device includes a waist panel 1, a thigh outer panel 3, a thigh fixing band 5, a shank outer panel 6, a shank fixing band 8, a foot board 10 and a foot board band 9, the waist panel 1 is worn on the waist of the wearer, the thigh outer panel 3 is worn on the outer side of the thigh of the wearer through the thigh fixing band 5, the shank outer panel 6 is worn on the outer side of the shank of the wearer through the shank fixing band 8, and the foot board 10 is worn under the foot of the wearer through the foot board band 9; universal joints are adopted to connect the waist boarding 1 and the thigh outer side plate 3, the thigh outer side plate 3 and the shank outer side plate 6, and the shank outer side plate 6 and the pedal 10.

The inertial sensor is used for acquiring the angle signal of the lower limb of the wearer in real time. Specifically, the inertial sensors include a waist inertial sensor 2, a thigh inertial sensor 4, a calf inertial sensor 7, and a foot inertial sensor 11. The waist inertial sensor 2 is fixed on the outer side of the front end of the waist boarding 1, the thigh inertial sensor 4 is fixed on the outer surface of the thigh outer side plate 3, the shank inertial sensor 7 is fixed on the outer surface of the shank outer side plate 6, and the foot inertial sensor 11 is fixed on the outer surface of the foot board 10.

The surface electromyographic sensors are adhered to the surfaces of lower limb rectus femoris, medial femoral muscle, lateral femoral muscle, biceps femoris muscle, semitendinosus muscle, tibialis anterior muscle, gastrocnemius muscle and soleus muscle of a wearer, and are used for acquiring electromyographic signals of the surfaces of the muscles of the lower limb of the wearer in real time and transmitting the signals to the pose response module;

the pose response module is fixed on an external auxiliary device, carries out filtering processing on the electromyographic signals of the lower limb surface of the wearer, then matches the time sequence of the collected electromyographic signals of the muscle surface with the time sequence of the activity of the muscles of the lower limb of the human body, which is stored in a database in advance, and finds the motion state corresponding to the electromyographic signals of the lower limb surface at present.

The acquisition mode of the human body lower limb muscle activity time sequence database is as follows:

a large number of testers wear external auxiliary devices to perform corresponding exercises according to use requirements to obtain a large number of training samples, end point detection is performed on the training samples to extract effective activity section data, a characteristic extraction method is adopted to extract series characteristic parameters, 8 muscle activity time sequences corresponding to different exercises are obtained through arrangement and combination of different characteristic parameters and increasing and decreasing trends of the different characteristic parameters, then a special coding mode is adopted to form an exercise characteristic matching list, the exercise characteristic matching list comprises starting point and end point information of the 8 muscles in the exercise process, and the exercise characteristic matching list forms a human body lower limb muscle activity time sequence database.

For example, a tester performs a certain exercise to obtain surface electromyogram signal training samples of rectus femoris, vastus medialis, vastus lateralis, biceps femoris, semitendinosus, tibialis anterior, gastrocnemius and soleus, performs endpoint detection on the surface electromyogram signal training samples of each muscle by using a cepstrum method to extract effective activity segment data, extracts characteristic parameters of the effective activity segment data of the surface electromyogram signal training samples of each muscle by using a time domain characteristic extraction method and a frequency domain characteristic extraction method, wherein the characteristic parameters comprise an absolute value mean, a root mean square, a mean power frequency and a median frequency, performs binary coding conversion on the characteristic parameters according to the increasing and decreasing trend of the characteristic parameters to form coding strips, the characteristic parameter value is 01 if the characteristic parameter value is increased, the characteristic parameter value is 10 if the characteristic parameter value is decreased, the characteristic parameter value is 00 if the characteristic parameter value is maintained, the effective activity segment data of the surface electromyogram signal training samples of each muscle has 4 characteristic parameters, the coding strip arranges the increasing trend of each characteristic parameter in turn according to the sequence of the absolute value mean, the root mean square, the average power frequency and the median frequency, such as 01001000, the coding strip represents a certain movement, the absolute value mean of a certain muscle is increased, the root mean square is unchanged, the average power frequency is reduced, and the median frequency is unchanged.

For a certain movement, all characteristic parameters of the effective activity segment data of the surface electromyogram signal training samples of 8 muscles are obtained simultaneously, the increasing and decreasing trends of the characteristic parameters are subjected to binary coding conversion to form 8 coding strips, the coding strips are combined and stored into a data matrix, different data matrices are stored for different movements, and finally a movement characteristic matching list for different movements is formed.

Pose resolving module fixed on external auxiliaryOn the device, the motion angle of the hip joint of the lower limb of the wearer is calculated and estimated by a signal acquired by a surface electromyogram based on a particle swarm optimization neural network model (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Hip joint motion angle acquired by inertial sensor (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) Knee joint movement angle α_{Knee 2}Ankle joint motion angle (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) Angle of motion with hip (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Dynamic weighted data fusion is carried out, the lower limb movement posture of the wearer is obtained in real time, and the position coordinates (x) of hip, knee and ankle joints are respectively calculated through the mounting sizes of a middle waist coaming, a thigh outer side plate, a shank outer side plate and a pedal of an external auxiliary device_{Hip joint},y_{Hip joint},z_{Hip joint})、(x_Knee,y_Knee,z_Knee)、(x_{Ankle joint},y_{Ankle joint},z_{Ankle joint})。

Wherein, the motion poses of the lower limbs of the human body are the positions and the postures of the hip, the knee and the ankle, and the postures of the hip, the knee and the ankle comprise the bending angles and the extending angles of the hip joint, namely α_{Hip joint}The angle of abduction and adduction of the hip joint is β_{Hip joint}The angle of internal rotation of the hip joint and external rotation of the hip joint is gamma_{Hip joint}The angle of knee flexion and knee extension is α_KneeThe angle of plantar flexion of the ankle joint and dorsiflexion of the ankle joint is α_{Ankle joint}The angle of ankle inversion and ankle eversion is β_{Ankle joint}The angle of the internal rotation of the ankle joint and the external rotation of the ankle joint is gamma_{Ankle joint}。

The pose resolving module will (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

during a time t preceding the current movement instant, there are n hip-joint movement angles (α) acquired by the inertial sensors_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) And the n posture resolving modules resolve the estimated hip joint movement angle of the lower limb of the wearer (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Then, within time t, the reference value of the hip joint movement angle

Satisfies the following conditions:

wherein i is 1,2, 3.

Average value of measured deviation of hip joint movement angle

Satisfies the following conditions:

the mean square error of the measured deviation of the hip joint movement angle:

wherein j is 1,2, σ_{1 hip j}Measuring the mean square deviation, sigma, of the deviation for hip flexion and extension motion angles_{2 hip j}Measuring the mean square error, sigma, of the deviation for the abduction and adduction angles of the hip joint_{3 hip j}The mean square error of the measured deviation of the external rotation and internal rotation angles of the hip joint is (sigma)_{1 hip 1}，σ_{2 hip 1}，σ_{3 hip 1}) Calculating and estimating the mean square error of the hip joint motion angle measurement deviation for a pose calculation module, (sigma)_{1 hip 2}，σ_{2 hip 2}，σ_{3 hip 2}) Measuring the mean square error of the deviation for the inertial sensor hip joint movement angle;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 hip j}A short-time dynamic weighting fusion coefficient for measuring the flexion and extension movement angles of the hip joint, when k is 2, w_{2 hip j}A short-time dynamic weighting fusion coefficient for measuring abduction and adduction movement angles of the hip joint, when k is 3, w_{3 hip j}Short-time dynamic weighting fusion coefficients for measuring the hip joint external rotation and internal rotation motion angles;

hip joint motion angle after data fusion:

the pose resolving module will α_{Knee 2}And α_{Knee 1}The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the knee joint movement angle acquired by the n inertial sensors is α_{Knee 2}The n posture calculation module calculates the estimated knee joint movement angle of the lower limb of the wearer to be α_{Knee 1}Then, within the time t, the reference value of the knee joint movement angle

Satisfies the following conditions:

average value of measured deviation of knee joint movement angle

Satisfies the following conditions:

the mean square error of the measured deviation of the knee joint movement angle is as follows:

wherein j is 1,2, σ_{Knee j}Measuring the mean square error, sigma, of the deviation for knee joint movement angles_{Knee 2}For measuring the mean square deviation, sigma, of the deviation of the knee joint movement angle of the inertial sensor_{Knee 1}Calculating the mean square error of the measurement deviation of the knee joint motion angle for a pose calculating module;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein j is 1,2, w_{Knee j}Short-time dynamic weighting fusion coefficients for knee joint movement angle measurement;

knee joint motion angle after data fusion:

α_knee＝w_{Knee 1}α_{Knee 1}+w_{Knee 2}α_{Knee 2}。

The pose resolving module will (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) And (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The method for short-time dynamic weighting data fusion comprises the following steps:

in the previous time t of the current movement moment, the ankle movement angle acquired by the n inertial sensors is (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) The n posture resolving module resolves the estimated movement angle of the lower limb ankle joint of the wearer (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Then, within the time t, the reference value of the ankle joint movement angleSatisfies the following conditions:

then the motion angle of the ankle jointAverage value of measurement deviation

Satisfies the following conditions:

the mean square error of the measured deviation of the ankle joint movement angle is:

wherein j is 1,2, σ_{1 ankle j}Measuring the mean square deviation, sigma, of the deviation for the plantar flexion and dorsiflexion motion angles of the ankle joint_{2 ankle j}Measuring the mean square error, sigma, of the deviation for the ankle inversion and eversion motion angles_{3 ankle j}The mean square error of the deviation is measured for the internal rotation and external rotation motion angles of the ankle joint (sigma)_{1 ankle 1}，σ_{2 ankle 1}，σ_{3 ankle 1}) Calculating the mean square error (sigma) of the measurement deviation of the ankle joint motion angle for a pose calculation module_{1 ankle 2}，σ_{2 ankle 2}，σ_{3 ankle 2}) Measuring the mean square error of deviation for the motion angle of the ankle joint of the inertial sensor;

and solving the short-time dynamic weighting fusion coefficient by using the following formula:

wherein k is 1,2,3, and w is 1_{1 ankle}j is a short-time dynamic weighting fusion coefficient of the measurement of the motion angles of plantar flexion and dorsiflexion of the ankle joint, and when k is 2, w_{2 ankle}j is a short-time dynamic weighting fusion coefficient for measuring the ankle joint inversion and eversion motion angles, and when k is 3, w_{3 ankle}j is a short-time dynamic weighting fusion coefficient for measuring the internal rotation and external rotation motion angles of the ankle joint;

ankle joint motion angle after data fusion:

the pose calculation module calculates signals acquired by the surface electromyography through a joint angle estimation model to estimate the hip joint motion angle of the lower limb of the wearer (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}). The joint angle estimation model is realized by adopting a neural network model based on particle swarm optimization.

Inputting characteristic parameters [ X ] of effective activity segment data of myoelectric signal training samples on the surface of each muscle based on a neural network model optimized by particle swarm₁,X₂,X₃,...,X₃₂]The output is the angle of the wearer's lower extremity hip joint movement (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Adopting a Sigmoid function as an action function of a hidden layer and adopting a linear function as an action function of an output layer;

the mean square error generated by the neural network on the training set is used as an objective function, and the following fitness function is constructed to calculate the individual fitness value:

in the formula: y is_ln′Computing an output for the neural network; t is_ln′Outputting for the target; n is the number of training samples; l is the number of output neurons;

in a D-dimensional search space, in each iteration process, the speed and the position of the particles are updated through the individual extremum and the global extremum, and the optimal fitness value is found, so that the neural network parameters are optimized, and the updating formula is as follows:

wherein w is the inertial weight; d ═ 1,2, …, D; 1,2, …,m，m>32, a first step of removing the first layer; k is the current iteration number; x is the number of_idIs the position of the particle; v. of_idIs the velocity of the particle; c. C₁And c₂Is an acceleration factor; r is₁And r₂Is distributed in [0, 1 ]]A random number in between;is an individual extremum of the particle;

is the global extreme of the population of particles.

Meanwhile, the invention provides a method for quickly predicting the motion pose of the lower limbs of the human body by using the system for quickly predicting the motion pose of the lower limbs of the human body based on multi-sensor fusion, which comprises the following steps as shown in figure 1:

the method comprises the following steps: the wearer removes hair and cutin from the central position of the lower limb muscle belly, coats alcohol and the like, and sequentially sticks the surface electromyography sensor according to the use requirement;

step two: the external auxiliary device is worn by a wearer, wherein the waistline plate is worn on the waist of the wearer, the thigh outer side plate is worn on the outer side of the thigh of the wearer through a thigh fixing bandage, the shank outer side plate is worn on the outer side of the shank of the wearer through a shank fixing bandage, and the pedal plate is worn under the foot of the wearer through a pedal bandage; universal joints are adopted to connect the waist coaming and the thigh outer side plate, the thigh outer side plate and the shank outer side plate, and the shank outer side plate and the pedal plate; fixing an inertial sensor on the outer side of the front end of the waist coaming, the outer thigh side plate, the outer shank side plate and the outer surface of the pedal;

step three: when the lower limbs of a wearer do any movement, all the surface electromyographic sensors and the inertial sensors are arranged to acquire information, respectively acquire surface electromyographic signals of the rectus femoris muscle, the medial femoral muscle, the lateral femoral muscle, the biceps femoris muscle, the semitendinosus muscle, the tibialis anterior muscle, the gastrocnemius muscle and the soleus muscle of the left and right lower limbs, and acquire angle signals of thighs, calves and feet of the left and right lower limbs;

step four: the surface electromyography sensor transmits 8 measured paths of surface electromyography signals to the pose response module, the pose response module matches the activity time sequence of the 8 paths of surface electromyography signals with a database of activity time sequences of muscles of lower limbs of a human body to find the motion pose at the moment, and the activity time sequences of the group of surface electromyography signals are filled into the database as new samples;

fifthly, the surface electromyographic sensor and the inertial sensor transmit the acquired signals to a pose resolving module, and the pose resolving module estimates the hip joint movement angle of the lower limb of the wearer through the surface electromyographic signals (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) The hip joint motion angle collected by the inertial sensor is fused by short-time dynamic weighting (α)_{Hip 2}，β_{Hip 2}，γ_{Hip 2}) Knee joint movement angle α_{Knee 2}Ankle joint motion angle (α)_{Ankle 2}，β_{Ankle 2}，γ_{Ankle 2}) Angle of motion with hip (α)_{Hip 1}，β_{Hip 1}，γ_{Hip 1}) Knee joint movement angle α_{Knee 1}Ankle joint motion angle (α)_{Ankle 1}，β_{Ankle 1}，γ_{Ankle 1}) Data fusion is carried out, and the hip joint motion angle (α) of the human body lower limb motion posture is obtained in real time_{Hip joint}，β_{Hip joint}，γ_{Hip joint}) Knee joint movement angle α_KneeAnkle joint motion angle (α)_{Ankle joint}，β_{Ankle joint}，γ_{Ankle joint}) The position coordinates (x) of hip, knee and ankle joints are respectively calculated by the installation sizes of the waist coaming, the thigh outer side plate, the shank outer side plate and the foot pedal of the external auxiliary device_{Hip joint},y_{Hip joint},z_{Hip joint})、(x_Knee,y_Knee,z_Knee)、(x_{Ankle joint},y_{Ankle joint},z_{Ankle joint}) And the quick prediction of the motion pose of the lower limbs of the human body is realized.

According to the invention, the surface electromyography sensor is adhered to the surface of the lower limb muscle, so that the electromyography signal of the muscle can be collected in real time, and the quick and real-time judgment on the motion state of the lower limb of a wearer is realized by combining a pre-established database of the activity time sequence of the muscle of the lower limb of the human body. On the basis, the pose calculation module predicts the motion angle and position coordinates of each joint through a short-time dynamic weighting data fusion method, and realizes the real-time prediction of the motion pose of the lower limbs of the human body and the accurate estimation of the continuous motion amount.

Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.