TWI687215B - Lower limb exoskeleton robot and aiding method thereof - Google Patents

Abstract

A lower limb exoskeleton robot is for at least one lower limb of a user to wear, and includes three motion sensing units, three exoskeleton motors, three motor encoders and a calculation unit and a lower limb aid. The motion sensing units are disposed adjacent to a hip joint, a knee joint and an ankle joint of the lower limb, respectively. Each of the motion sensing units is for providing a plurality of sensing signals of three dimension of the adjacent joint. Each of the motor encodes is for providing at least one encoding signal from the adjacent exoskeleton motor. The calculation unit is for calculating a plurality of output values according to the sensing signals, the encoding signals and 3-link Denavit-Hartenberg parameters. The output values are converted to three exoskeleton motor control signals, respectively. Thus, the lower limb exoskeleton robot according to the invention is advantageous in aiding the user and increasing the popularity thereof.

Description

Lower limb exoskeleton robot and its auxiliary method

The invention relates to an exoskeleton robot and its auxiliary method, and in particular to an exoskeleton robot applied to the lower limbs of the human body and its auxiliary method.

Global aging issues have continued to develop in recent years, and the demand for rehabilitation in the care of the elderly is increasing day by day. If there is a smart rehabilitation equipment or even a robot can provide assistance to help people with various rehabilitation Healthy exercise control can alleviate the burden of doctors and nurses in rehabilitation medical needs and give certain medical quality.

However, in the rehabilitation medical services, at this stage, the rehabilitation of lower limb joints still needs to invest a lot of human resources in it, and these first-line physical therapists will lead to lack of manpower and lead to overtime work with the aging society. Therefore, there is an urgent need to develop a lower extremity exoskeleton robot and its auxiliary methods that are helpful for the rehabilitation of manpower.

The invention provides a lower extremity exoskeleton robot and an auxiliary method thereof, which are used to assist at least one of the left lower limb and the right lower limb of the user, through the sensing signal provided by the motion sensing unit and the encoding signal provided by the motor encoder, In addition, it can be used to effectively and streamline the movement model of the lower extremity gait and backward gait, so as to enhance the benefit of the lower extremity exoskeleton robot and its auxiliary methods to the user and increase the popularity.

According to the present invention, a lower-limb exoskeleton robot is provided for at least one lower-limb wearer of a user. The lower-limb exoskeleton robot includes a three-motion sensing unit, a three-exoskeleton motor, a three-motor encoder, a calculation unit, and a lower-limb assist device. The motion sensing units are respectively adjacent to the hip joint, knee joint and ankle joint of the lower limb, and each motion sensing unit is used to provide a complex sensing signal of the three-dimensional space of the adjacent joint. The exoskeleton motors are adjacent to the hip joint, knee joint and ankle joint. The three motor encoders are respectively adjacent to the three exoskeleton motors, and each motor encoder provides at least one encoding signal according to the adjacent exoskeleton motor. The calculation unit is communicatively connected to the motion sensing unit and the motor encoder. The calculation unit is used to obtain a complex output value based on the sensing signal, the encoding signal and the three-linked DH parameter operation, and the output values are converted into three exoskeleton motor control signals, respectively. The three exoskeleton motors respectively generate three auxiliary torques according to the control signals of the three exoskeleton motors, and the lower limb assistive devices are driven by the auxiliary torques to respectively assist the hip joint, the knee joint and the ankle joint. In this way, a motion model that can effectively and simply calculate the forward and backward gait of the lower limbs is used to enhance the user's benefit and increase the popularity of the lower extremity exoskeleton robot of the present invention.

According to the aforementioned extremity exoskeleton robot, each motion sensing unit may be a three-axis accelerometer and a three-axis gyroscope.

According to the aforementioned extremity exoskeleton robot, it may further include a myoelectricity measuring end, which is adjacent to the thigh of the lower extremity, and the myoelectricity measuring end is used to measure the myoelectric signal of the thigh, and the myoelectric signal is used to control whether the output values are converted Control signal for exoskeleton motor.

According to the aforementioned lower extremity exoskeleton robot, in the three-linked DH parameter, the distance from the Z _i-1 axis to the Z _i axis observed at the X _i axis is a _i , and the Z _i axis is observed The distance from the X _i-1 axis to the X _i axis is d _i , the value of i is 1 to 3, and the values of a _i and d _i can both be zero.

According to the aforesaid lower extremity exoskeleton robot, it can be worn by two lower extremities of the user, and the number of motion sensing units, exoskeleton motors and motor encoders are six and are adjacent to the two hip joints and two knee joints of the lower extremities And the two ankle joints, the calculation unit communicates with the motion sensing unit and the motor encoder, the calculation unit is used to obtain the output value based on the sensing signal, the encoding signal and the three-linked DH parameter calculation, and the output value is converted into the exoskeleton motor control signal The exoskeleton motor generates auxiliary torque according to the control signal of the exoskeleton motor respectively, and the lower limb assistive device is driven by the auxiliary torque to assist the hip joint, knee joint and ankle joint respectively. The calculation unit includes a neural network-like module. The neural network-like module is used to calculate output values and includes an input layer, a first layer, a second layer, a third layer, and an output layer. The input layer uses the sensing signals and encoded signals of the two lower limbs as variables to input fuzzy rules, and the fuzzy rules are established based on the three-linked DH parameters. In the first layer, the fuzzy rules are subjected to suitability calculation to obtain the strength of complex rules. The second layer normalizes the rule strength to obtain complex normalized values. The third layer multiplies the normalized value and Sugeno fuzzy mode. The output layer sums the calculation results of the third layer to obtain the output value.

The aforementioned lower extremity exoskeleton robot helps the lower extremity exoskeleton robot to effectively grasp and predict the gait of the user.

According to the present invention, an auxiliary method for a lower extremity exoskeleton robot is provided, which is used to assist at least a lower limb of a user. The auxiliary method for a lower extremity exoskeleton robot includes a motion sensing step, a motor coding step, a calculation step, and a lower extremity assistance step. The motion sensing step is to provide complex sensing signals in the three-dimensional space of the hip, knee, and ankle joints of the lower limbs. The motor coding step provides at least one coding signal for each of the exoskeleton motors adjacent to the hip joint, knee joint, and ankle joint. The calculation step is to obtain a complex output value based on the sensing signal, the encoding signal and the three-linked DH parameter calculation, and convert the output values into three exoskeleton motor control signals, respectively. In the lower limb assisting step, the three exoskeleton motors respectively generate three auxiliary torques according to the three exoskeleton motor control signals, and the lower limb assistive devices are driven by the auxiliary torques to assist the hip joint, knee joint and ankle joint, respectively. In this way, to enhance the user's benefit and increase the popularity of the lower limb exoskeleton robot assisting method of the present invention, and further effectively assist the rehabilitation of manpower.

According to the aforementioned auxiliary method of the lower extremity exoskeleton robot, in the motion sensing step, the sensing signals of the joints in the hip joint, knee joint and ankle joint can be provided by a three-axis accelerometer and a three-axis gyroscope.

According to the aforementioned auxiliary method of the lower limb exoskeleton robot, it may further include a myoelectricity measuring step to measure the myoelectric signal of the thigh of the lower limb. In the calculation step, the EMG signal is used to control whether the output value is converted into an exoskeleton motor control signal, respectively.

According to the aforementioned auxiliary method of the lower extremity exoskeleton robot, in the DH parameter of the three linkages, the distance observed from the Z _i-1 axis to the Z _i axis observed at the X _i axis is a _i , and at the Z _i The distance from the X _i-1 axis to the X _i axis observed by the axis is d _i , the value of i is 1 to 3, and the values of a _i and d _i can both be zero.

According to the aforementioned method of assisting the lower limb exoskeleton robot, it can be used to assist the second lower limb of the user. In the motion sensing step, the three-dimensional sensing signals of the two hip joints, the two knee joints, and the two ankle joints of the lower limbs are provided. In the motor encoding step, encoding signals for each of the six exoskeleton motors adjacent to the hip joint, knee joint, and ankle joint are provided. In the calculation step, the neural network module is used to calculate the output value according to the sensing signal, the encoding signal and the three-linked DH parameter, and the output value is converted into the six exoskeleton motor control signals, respectively. In the lower limb assisting step, the six exoskeleton motors respectively generate the six assist torques according to the six exoskeleton motor control signals, and the lower limb assistive devices are driven by the assist torques to assist the hip joint, knee joint, and ankle joint, respectively. The neural network-like module includes an input layer, a first layer, a second layer, a third layer, and an output layer. The input layer uses the sensing signals and encoded signals of the two lower limbs as variables to input fuzzy rules, and the fuzzy rules are established based on the three-linked DH parameters. In the first layer, the fuzzy rules are subjected to suitability calculation to obtain the strength of complex rules. The second layer normalizes the rule strength to obtain complex normalized values. The third layer multiplies the normalized value and Sugeno fuzzy mode. The output layer sums the calculation results of the third layer to obtain the output value.

The aforementioned auxiliary method of the lower limb exoskeleton robot helps the lower limb exoskeleton robot auxiliary method to learn and assist different users in real time.

100‧‧‧Lower limb exoskeleton robot

131, 132, 133, 134, 135, 136‧‧‧ motion sensing unit

141, 142, 143, 144, 145, 146

151, 152, 153, 154, 155, 156

167, 168‧‧‧ muscle inductance measuring terminal

170‧‧‧Calculation unit

190‧‧‧Lower limb aids

80‧‧‧ user

97, 98‧‧‧ thigh

91、92‧‧‧ hip joint

93, 94‧‧‧ Knee

95, 96‧‧‧ ankle

177‧‧‧ neural network module

180‧‧‧ input layer

181‧‧‧First floor

182‧‧‧Second floor

183‧‧‧ third floor

184‧‧‧ output layer

A0‧‧‧sensing signal

B0‧‧‧ coded signal

P _0,11 , P _0,12 , P _0,21 , P _0,22 , P _1,1 , P _1,2 , P _2,1 , P _2,2 , P _3,1 , P _3,2 ‧ ‧‧Calculated value

P _output ‧‧‧ output value

200‧‧‧Auxiliary method of lower limb exoskeleton robot

230‧‧‧Motion sensing steps

250‧‧‧Motor coding steps

260‧‧‧Measuring procedure

270‧‧‧Calculation steps

290‧‧‧Lower limb auxiliary steps

Figure 1 shows a block diagram of a lower extremity exoskeleton robot of the first embodiment of the present invention; Figure 2 shows a schematic diagram of the lower extremity exoskeleton robot of the first embodiment; Figure 3 shows a nerve-like model of the first embodiment A schematic diagram of a network module; and FIG. 4 is a flowchart of a method for assisting a lower extremity exoskeleton robot according to a second embodiment of the present invention.

Hereinafter, a plurality of embodiments of the present invention will be described with reference to the drawings. For clarity, many practical details will be explained in the following description. However, it should be understood that these practical details should not be used to limit the present invention. That is to say, in some embodiments of the present invention, these practical details are unnecessary. In addition, for the sake of simplifying the drawings, some conventionally used structures and elements will be shown in a simple schematic manner in the drawings; and repeated elements may be indicated by the same number.

Please refer to FIGS. 1 and 2. FIG. 1 is a block diagram of the lower extremity exoskeleton robot 100 according to the first embodiment of the present invention, and FIG. 2 is a schematic diagram of the lower extremity exoskeleton robot 100 according to the first embodiment. As can be seen from FIG. 1 and FIG. 2, the lower extremity exoskeleton robot 100 is intended to be worn by at least one lower limb (not otherwise labeled) of the left lower limb and the right lower limb of the user 80. As far as the lower extremity exoskeleton robot 100 is provided for the right lower extremity of the user 80 to wear, the lower extremity exoskeleton machine The person 100 includes motion sensing units 131, 133, 135, exoskeleton motors 141, 143, 145, motor encoders 151, 153, 155, a calculation unit 170, and a lower limb assistive device 190. According to other embodiments of the present invention (not shown), the lower extremity exoskeleton robot can be designed to be worn only on the left lower limb, only on the right lower limb, or both lower limbs (as the The lower extremity exoskeleton robot 100 of the first embodiment.

The motion sensing units 131, 133, and 135 are adjacent to the hip joint 91, the knee joint 93, and the ankle joint 95 of the lower limb, respectively, and each of the motion sensing units 131, 133, and 135 is used to provide a three-dimensional space of the adjacent joints. Complex sensing signal A0. The exoskeleton motors 141, 143, and 145 are adjacent to the hip joint 91, knee joint 93, and ankle joint 95, respectively. The motor encoders 151, 153, and 155 are adjacent to the exoskeleton motors 141, 143, and 145, respectively. Each of the motor encoders 151, 153, and 155 provides at least one encoded signal B0 according to the adjacent exoskeleton motors 141, 143, and 145. . The calculation unit 170 is communicatively connected to the motion sensing units 131, 133, 135 and the motor encoders 151, 153, 155. The calculation unit 170 is used to determine the sensing signal A0, the encoding signal B0, and the three-link DH parameters (3-link Denavit- Hartenberg Parameters) operation obtains a complex output value P _output , and the output value P _{output is} converted into an exoskeleton motor control signal, respectively. The exoskeleton motors 141, 143, and 145 generate three auxiliary torques according to the exoskeleton motor control signals, respectively, and the lower limb assistive device 190 is driven by the auxiliary torque to assist the hip joint 91, the knee joint 93, and the ankle joint 95, respectively. In this way, the lower extremity exoskeleton robot 100 uses a three-linked DH parameter motion model that can efficiently and streamline the forward and backward gait of the lower limb to assist or drive the user 80 to walk and other actions, and can increase the lower extremity exoskeleton The popularity of the robot 100 effectively assists the rehabilitation of manpower.

；以及⁰ M ₃=⁰ M ₁ ¹ M ₂ ² M ₃ 式(2)。 Further, the three-linked DH parameters in the calculation unit 170 are the parameters in the three DH conversion matrices (Denavit-Hartenberg Conversion Matrix) ⁰ M ₁ , ¹ M ₂ , and ² M ₃ , where the DH conversion matrix ^i-1 M _{i is} represented by the following formula (1). The three-link DH parameters of the lower extremity exoskeleton robot 100 of the first embodiment of the present invention are shown in Table 1 below, where the upper body of the user 80 is used as a reference base (axis 0), and the value of i is 1 to 3 , I is 1 for hip joint 91 (first linkage, first axis), i is 2 for knee joint 93 (second linkage, second axis), i is 3 for ankle joint 95 (three linkage, third axis), The Z _i axis describes the main rotations of the joints in the lower limbs of the user 80, such as forward rotation or backward rotation, and the X _i axis describes the movement of the lower limbs of the user 80 upward, downward, left, or right. the first X _i axis viewed from the angle of the shaft to the second Z _i axis is a first _{Z-1 i} is α _i, at the X _i axis is observed distance from Z _i-1 axis to the second Z _i axis is a _i , the distance of the Z _i axis observed from the X _i-1 axis to the second X _i axis is d _i, the angle of the Z _i axis observed from the X _i-1 axis to the second X _i axis is θ _i . Furthermore, the relative movement of the ankle joint 95 relative to the upper body of the user 80 can be expressed by the following formula (2). Please refer to the following formula (1) and formula (2):

; And ⁰ M ₃ = ⁰ M ₁ ¹ M ₂ ² M ₃ formula (2).

Specifically, the lower extremity exoskeleton robot 100 of the first embodiment can be worn by the lower limbs of the user 80. The lower extremity exoskeleton robot 100 includes motion sensing units 131, 132, 133, 134, 135, and 136 (respectively providing sensing Signal A0), exoskeleton motors 141, 142, 143, 144, 145, 146 and motor encoders 151, 152, 153, 154, 155, 156 (providing the encoded signal B0, respectively), namely motion sensing unit, exoskeleton motor The number of motor encoders is six. Among them, motion sensing units 131, 132, exoskeleton motors 141, 142, motor encoders 151, 152 are adjacent to hip joints 91, 92, and motion sensing units 133, 134, Exoskeleton motors 143, 144, motor encoders 153, 154 are adjacent to the knee joints 93, 94, motion sensing units 135, 136, exoskeleton motors 145, 146, motor encoders 155, 156 are adjacent to the ankle joint 95, 96. The arithmetic unit 170 is communicatively connected to the motion sensing units 131, 132, 133, 134, 135, 136 and the motor encoders 151, 152, 153, 154, 155, 156. The arithmetic unit 170 is used to detect the sensing signal A0 and the encoding signal B0 And the three-linked DH parameter operation to obtain the output value P _output , the output value P _{output is} converted into an exoskeleton motor control signal, and the exoskeleton motors 141, 142, 143, 144, 145, and 146 generate auxiliary torques according to the exoskeleton motor control signals, respectively The lower limb assistive device 190 is driven by an assisting torque to assist the hip joint 91, 92, the knee joint 93, 94, and the ankle joint 95, 96, respectively. In this way, the lower extremity exoskeleton robot 100 can provide users 80 with assistance needs for both lower limbs to wear.

In the first embodiment, the motion sensing units 131, 132, 133, 134, 135, 136, exoskeleton motors 141, 142, 143, 144, 145, 146 and motor encoders 151, 152, 153, 154, 155, The installation positions of 156 are not limited to the schematic positions in FIG. 2. Furthermore, the calculation unit 170 is used to obtain three output values P _output according to the sensing signal A0, the encoding signal B0, and the three-linked DH parameter operation, wherein the sensing signal A0 in the three-dimensional space can be further converted into a quaternion (Quaternion) , And then converted to Euler Angles (Eulerian Angles) for calculation. The three output values P _output are converted into three exoskeleton motor control signals, namely, exoskeleton motors 141, 142 (corresponding to hip joints 91, 92) according to one of the exoskeleton motor control signals, exoskeleton motors 143, 144 (corresponding to the knee The joints 93 and 94) are based on the control signals of the two exoskeleton motors, and the exoskeleton motors 145 and 146 (corresponding to the ankle joints 95 and 96) are based on the control signals of the three exoskeleton motors.

Each of the motion sensing units 131, 132, 133, 134, 135, and 136 may be a three-axis accelerometer and a three-axis gyroscope. This helps the lower exoskeleton robot 100 to effectively grasp and predict the gait of the user 80.

The lower extremity exoskeleton robot 100 may further include myoelectric measuring terminals 167 and 168, which are adjacent to the thighs 97 and 98 respectively, and the myoelectric measuring terminals 167 and 168 are used to measure the electromyographic signals of the thighs 97 and 98, respectively. Signal, EMG signal), the EMG signal is used to control whether the output value P _{output is} converted into an exoskeleton motor control signal, respectively. In this way, it helps the lower extremity exoskeleton robot 100 to activate or suspend the assist mode immediately according to the user 80's movement requirements. In addition, the myoelectricity measuring terminals 167 and 168 can be non-invasive electrode patches and can be connected to the amplifier. The myoelectricity measuring terminals 167 and 168 can be set at the places where the thigh 97 and 98 have the most muscle fibers, such as the thigh Head muscles to improve the strength and accuracy of EMG signals.

Please refer to the aforementioned formula (1) and Table 1. In the DH parameters of the three linkages, the distance from the Z _i-1 axis to the Z _i axis observed on the X _i axis is a _i , and on the Z _i The distance from the X _i-1 axis to the X _i axis observed by the axis is d _i , the value of i is 1 to 3, and the values of a _i and d _i can both be zero. In this way, it helps to simplify the amount of calculation data of the calculation unit 170, and can effectively avoid the excessively complicated amount of calculation data, which causes the lower extremity exoskeleton robot 100 to delay the assist timing or generate a malfunction. Further, in the lower extremity exoskeleton robot 100 of the first embodiment, the forward and backward kinematics theory, the zero-moment point (ZMP) theory of the forward and backward gait of the lower limb are used to calculate the stable walking step State model or gait cycle (Gait Cycle) to further simplify the DH parameters of the three linkages.

Please refer to FIG. 3, which illustrates a schematic diagram of the neural network module 177 in the first embodiment. As can be seen from FIGS. 1 and 3, the calculation unit 170 includes a neural network-like module 177. The neural network-like module 177 is used to calculate three output values P _output and can be an adaptive network fuzzy inference system (Adaptive -Network-based Fuzzy Inference System (ANFIS), the neural network-like module 177 includes an input layer 180, a first layer 181, a second layer 182, a third layer 183, and an output layer 184. The input layer 180 inputs fuzzy rules using the sensing signal A0 and the encoded signal B0 of the two lower limbs as variables, and the fuzzy rules are established based on the three-linked DH parameters. The first layer 181 performs the suitability calculation on the fuzzy rules to obtain the strength of the complex rules. The second layer 182 system normalizes the rule strength to obtain complex normalization values. The third layer 183 is to multiply the normalized value and Sugeno fuzzy mode. The output layer 184 sums the calculation results of the third layer 183 to obtain the output value P _output . In this way, it helps the lower limb exoskeleton robot 100 to learn and assist different users in real time.

In the first embodiment, the neural network-like module 177 described in the previous paragraph is based on the following equations (3) to (7), where the value of j is 1 to 2, and the value of k is 1 indicating that the sensing signal A0 is a variable Input, the value of k is 2 indicates that the encoded signal B0 is used as a variable input, and the three output values P _output (respectively assisting the hip joint 91, 92, knee joint 93, 94, ankle joint 95, 96) are calculated. The parameter values in module 177 (including the three-linked DH parameters) may be different.

Referring to the following equation (3), the input layer 180 inputs fuzzy rules using the sensing signal A0 of the lower limbs and the encoded signal B0 as variables to obtain the calculated value P _0,kj , where x represents the sensing signal A0 or the encoded signal B0, and Its membership function is Gaussian function u _Aj , and the learning parameters are a _j , b _j , c _j :

Referring to the following formula (4), the first layer 181 performs the suitability calculation of the fuzzy rules (that is, w _j ) to obtain the strength of the complex number rule, which is the calculated value P _1,j :

Referring to the following formula (5), the second layer 182 normalizes the rule strength to obtain the complex normalized value, which is the calculated value P _2,j , where the calculated value P _{2,j is} between 0 and 1:

Referring to the following formula (6), the third layer 183 multiplies the normalized value with the Sugeno fuzzy mode (ie, f _j ) to obtain the calculated value P _3,j , where p _j , q _j, and r _j are Sugeno fuzzy modes Of parameters:

Referring to the following equation (7), the output layer 184 performs the sum operation of the calculated values P _3,j of the third layer 183 to obtain the output value P _{output of the} neural network-like module 177:

In addition, the lower extremity exoskeleton robot 100 of the present invention can provide a choice of rehabilitation medical mode or autonomous walking human signal control mode in the human-machine interface, so that the lower extremity exoskeleton robot 100 is intelligent and multifunctional. In addition, the lower extremity exoskeleton robot 100 can also realize intelligent control and monitoring, apply the concept of the Internet of Things, use cloud technology to retain the value of 80 for each user, and use a simple Internet device to make rehabilitation actions. Moreover, the data of the user 80 can be stored and applied using big data analysis.

Please refer to FIG. 4, which illustrates a flowchart of a method 200 for assisting a lower extremity exoskeleton robot according to a second embodiment of the present invention. Please refer to FIG. 1 to FIG. 4 together, the lower extremity exoskeleton robot 100 of the first embodiment is used to help explain the lower extremity exoskeleton robot assistance method 200 of the second embodiment, and the lower extremity exoskeleton robot assistance method 200 is used to assist the use At least one of the left lower limb and the right lower limb of the person 80, the lower limb exoskeleton robot assisting method 200 includes a motion sensing step 230, a motor encoding step 250, a calculation step 270, and a lower limb assisting step 290. For the lower limb exoskeleton robot assisting method 200 to assist the right lower limb of the user 80, the motion sensing step 230 provides a complex sensing signal A0 in the three-dimensional space of each of the hip joint 91, knee joint 93 and ankle joint 95 of the lower limb . The motor encoding step 250 provides at least one encoded signal B0 of the exoskeleton motors 141, 143, and 145 adjacent to the hip joint 91, knee joint 93, and ankle joint 95, respectively. The calculation step 270 is based on the sensing signal A0, the encoding signal B0, and the three-linked DH parameter operation to obtain a complex output value P _output , and convert the output values into three exoskeleton motor control signals, respectively. In the lower limb assisting step 290, the three exoskeleton motors 141, 143, and 145 respectively generate three auxiliary torques according to the three exoskeleton motor control signals, and the lower extremity assistive device 190 is driven by the auxiliary torque to assist the hip joint 91 and the knee joint 93, respectively And ankle joint 95. In this way, a three-linked DH parameter motion model that can efficiently and simply calculate the forward and backward gait of the lower limbs is used to enhance the lower limb exoskeleton robot assisted method 200 to assist the rehabilitation of manpower.

Further, the lower limb exoskeleton robot assisting method 200 can be used to assist the lower limb of the user 80. In the motion sensing step 230, three-dimensional sensing signals A0 of the hip joints 91, 92, knee joints 93, 94, and ankle joints 95, 96 of the lower limbs are provided. In the motor coding step 250, the six exoskeleton motors 141, 142, 143, 144, 145, 146 adjacent to the hip joints 91, 92, knee joints 93, 94, and ankle joints 95, 96 are provided, respectively. Encoded signal B0. In the calculation step 270, the neural network module 177 calculates the output value P _output according to the sensing signal A0, the encoding signal B0 and the three-linked DH parameters, and converts the output value P _output to the six exoskeleton motors, respectively control signal. In the lower limb assisting step 290, the six exoskeleton motors 141, 142, 143, 144, 145, and 146 generate the six auxiliary torques according to the six exoskeleton motor control signals, respectively, and the lower extremity assistive device 190 is driven by the auxiliary torque to respectively Auxiliary hip joint 91, 92, knee joint 93, 94 and ankle joint 95, 96.

In the motion sensing step 230 of the lower extremity exoskeleton robot assisting method 200, the sensing signals A0 of the hip joints 91, 92, the knee joints 93, 94, and the ankle joints 95, 96 can be controlled by a three-axis accelerometer and a three-axis gyroscope provide. This helps the lower limb exoskeleton robot assisting method 200 to effectively grasp and predict the gait of the user 80.

The lower limb exoskeleton robot assisting method 200 may further include a myoelectric measurement step 260, which measures the myoelectric signals of the thighs 97 and 98 of the lower limb. In the calculation step 270, the EMG signal is used to control whether the output value P _{output is} converted into an exoskeleton motor control signal, respectively. In this way, the assisting method 200 for the lower limb exoskeleton robot assists the user 80 to activate or suspend the assisting mode in real time.

Please refer to the aforementioned formula (1) and Table 1. In the three-link DH parameter of the lower extremity exoskeleton robot assist method 200, the distance observed from the Z _i-1 axis to the Z _i axis on the X _i axis is a _i, Z _i axis at the distance from the observed X _i axis to the axis of the first _{X-1 i} is a value of d _{i, i} is 1 to 3, and the value a _i and d _i can be both zero. In this way, it helps to simplify the calculation data amount of the calculation step 270, and can effectively avoid the excessively complicated calculation data amount, which causes the lower limb exoskeleton robot assisting method 200 to delay the assisting timing or generate a malfunction.

The neural network module 177 in the calculation step 270 includes an input layer 180, a first layer 181, a second layer 182, a third layer 183, and an output layer 184. The input layer 180 inputs fuzzy rules using the sensing signal A0 and the encoded signal B0 of the two lower limbs as variables, and the fuzzy rules are established based on the three-linked DH parameters. The first layer 181 performs the suitability calculation on the fuzzy rules to obtain the strength of the complex rules. The second layer 182 system normalizes the rule strength to obtain complex normalization values. The third layer 183 is to multiply the normalized value and Sugeno fuzzy mode. The output layer 184 sums the calculation results of the third layer 183 to obtain the output value P _output . In this way, it helps the lower limb exoskeleton robot assisting method 200 to learn and assist different users in real time. For details of the neural network-like module 177, please refer to the related contents of the foregoing equations (3) to (7), and no further description is provided here.

Although the present invention has been disclosed as above in the embodiments, it is not intended to limit the present invention. Any person who is familiar with this art can make various changes and modifications within the spirit and scope of the present invention, so the protection of the present invention The scope shall be determined by the scope of the attached patent application.

100‧‧‧Lower limb exoskeleton robot

131、136‧‧‧Motion sensing unit

141、146‧‧‧Exoskeleton motor

151, 156‧‧‧ Motor encoder

167, 168‧‧‧ muscle inductance measuring terminal

170‧‧‧Calculation unit

177‧‧‧ neural network module

190‧‧‧Lower limb aids

Claims (10)

A lower limb exoskeleton robot for at least one lower limb of a user to wear, the lower limb exoskeleton robot includes: three motion sensing units, which are respectively adjacent to a hip joint, a knee joint and an ankle joint of the lower limb, each The motion sensing unit is used to provide a complex sensing signal of the three-dimensional space adjacent to the joint; a three-exoskeleton motor, which is adjacent to the hip joint, the knee joint, and the ankle joint; and a three-motor encoder, which Adjacent to the three exoskeleton motors respectively, each of the motor encoders provides at least one encoding signal according to the adjacent exoskeleton motors; an arithmetic unit that communicates with the three motion sensing units and the three motor encoders, the The arithmetic unit is used to obtain complex output values based on the sensing signals, the encoding signals, and the three-linked DH parameters, and the output values are converted into three exoskeleton motor control signals; and the lower limb assistive device, wherein the three The exoskeleton motors respectively generate three auxiliary torques according to the three exoskeleton motor control signals, and the lower limb assistive devices are driven by the three auxiliary torques to respectively assist the hip joint, the knee joint and the ankle joint. The lower extremity exoskeleton robot as described in item 1 of the patent application scope, wherein each of the motion sensing units is a three-axis accelerometer and a three-axis gyroscope. The lower extremity exoskeleton robot as described in item 1 of the patent application scope further includes: a muscle inductance measuring end, which is adjacent to the thigh of the lower limb, and the muscle inductance measuring end is used to measure an EMG signal of one of the thighs The EMG signal is used to control whether the output values are converted into the three exoskeleton motor control signals. The lower extremity exoskeleton robot as described in item 1 of the scope of the patent application, wherein in the three-linkage DH parameter, the distance from the Z _i-1 axis to the Z _i axis observed on the X _i axis is a _i , in the Z _i axis is observed from the first distance from the axis to the X _i axis of the first _{X-1 i} is a value of d _{i, i} is 1 to 3, and a _i and a value of d _i are both zero. The lower extremity exoskeleton robot as described in item 1 of the patent application scope, wherein the lower extremity exoskeleton robot is provided for the two lower extremities of the user to wear, the number of the motion sensing units, the exoskeleton motors and the motor encoders They are all six hip joints, two knee joints and two ankle joints which are adjacent to the two lower extremities. The calculation unit communicates with the six motion sensing units and the six motor encoders. The calculation unit is used to The output values are calculated according to the sensing signals, the encoding signals, and the three-linked DH parameter calculation, and the output values are converted into the six exoskeleton motor control signals, and the six exoskeleton motors are based on the six exoskeletons, respectively. The skeletal motor control signal generates the six Auxiliary torque, the lower limb assistive device is driven by the six auxiliary torques to respectively assist the two hip joints, the two knee joints and the two ankle joints; wherein, the calculation unit includes a type of neural network module, the type of neural network module The set is used to calculate the output values and includes: an input layer, using the sensing signals and the encoded signals of the two lower limbs as variables to input a fuzzy rule, which is established based on the three-linked DH parameters; In the first layer, the fuzzy rules are subjected to suitability calculations to obtain the complex rule strength; in the second layer, the rule strengths are normalized to obtain complex normalization values; in the third layer, the normalized values are obtained Multiply operation with Sugeno fuzzy mode; and an output layer, the operation results of the third layer are summed to obtain the output values. An auxiliary method for lower limb exoskeleton robot, which is used to assist at least one lower limb of a user. The auxiliary method for lower limb exoskeleton robot includes: a motion sensing step, providing a hip joint, a knee joint and an ankle joint of the lower limb Complex sensing signals in the three-dimensional space of each joint in A motor coding step, providing at least one coded signal adjacent to each of the three exoskeleton motors respectively disposed on the hip joint, the knee joint, and the ankle joint; a calculation step, based on the sensing signals and the coding signals And three-linked DH parameter operation to obtain complex output values, and convert these output values into three exoskeleton motor control signals; and a lower limb assist step, the three exoskeleton motors generate the output according to the three exoskeleton motor control signals, respectively Three auxiliary moments, the lower limb assistive device is driven by the three auxiliary moments to respectively assist the hip joint, the knee joint and the ankle joint. The auxiliary method of the lower extremity exoskeleton robot as described in item 6 of the patent application scope, wherein in the motion sensing step, the sensing signals of the joints in the hip joint, the knee joint and the ankle joint are controlled by a three-axis accelerometer And three-axis gyroscope. The auxiliary method of the lower extremity exoskeleton robot as described in item 6 of the patent application scope further includes: a myoelectricity measuring step, measuring one of the thighs' myoelectric signal of the lower limb; wherein, in the calculation step, the myoelectric signal It is used to control whether to convert the output values into the three exoskeleton motor control signals. The auxiliary method of the lower extremity exoskeleton robot as described in item 6 of the patent application range, wherein the distance from the Z _i-1 axis to the Z _i axis observed on the X _i axis of the three-linked DH parameter is a _{i, i} in the Z-axis of the observed distance from the axis to the X _i axis of the first _{X-1 i} is the value of D _i, i is 1 to 3, and a value of _i and D _i are both zero. The auxiliary method of the lower extremity exoskeleton robot as described in item 6 of the patent scope, which is used to assist the two lower limbs of the user; wherein, in the motion sensing step, the two hip joints and the two knees of the two lower limbs are provided The sensing signals in the three-dimensional space of the joints and the joints of the two ankle joints; wherein, in the motor coding step, the six outer sides of the two hip joints, the two knee joints and the two ankle joints are provided adjacent to each other The encoded signals of each of the skeletal motors; wherein, in the calculation step, a type of neural network module is used to obtain the output values based on the sensing signals, the encoded signals, and the three-linked DH parameters, and Converting the output values into the six exoskeleton motor control signals; wherein, in the lower extremity assisting step, the six exoskeleton motors respectively generate the six auxiliary torques according to the six exoskeleton motor control signals, and the lower extremity assistive device receives the Six auxiliary torque drives to respectively assist the two hip joints, the two knee joints and the two ankle joints; wherein, the neural network module includes: An input layer, using the sensing signals and the encoded signals of the two lower limbs as variables to input a fuzzy rule, the fuzzy rule is established according to the three-linked DH parameters; a first layer, the fuzzy rule is adapted Operation to get the strength of complex rules; a second layer, which normalizes these rule strengths to obtain complex normalized values; a third layer, which multiplies these normalized values with Sugeno fuzzy mode; and an output Layer, sum the operation results of the third layer to obtain the output values.

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