TWI687215B - Lower limb exoskeleton robot and aiding method thereof - Google Patents
Lower limb exoskeleton robot and aiding method thereof Download PDFInfo
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本發明是有關於一種外骨骼機器人及其輔助方法,且特別是有關於一種應用於人體下肢的外骨骼機器人及其輔助方法。 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.
全球的老年化議題近幾年持續發燒,所衍生出來老人照護中的復健需求則是逐日攀升,若此時有一個智慧化的復健器材甚至是機器人能給予輔助,幫助人的各項復健運動控制,就能減緩復健醫療需求裡醫生、護理師的負擔,給予一定的醫療品質。 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.
然而,在復健的醫療服務中,現階段下肢關節的復健還是需要投入很多人力資源在其中,且這些第一線的物理治療師都會隨著高齡化社會導致人力缺乏及導致超時工作。因此,市場上亟欲發展一種有助於普及的下肢外骨骼機器人及其輔助方法,以有效協助復健人力。 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.
本發明提供一種下肢外骨骼機器人及其輔助方法,其用以輔助使用者之左側下肢及右側下肢中至少一下肢,藉由運動感測單元提供之感測信號及馬達編碼器提供之編碼信號,並搭配可有效且精簡演算下肢的前進步態(Gait)及後退步態的運動模型,以提升下肢外骨骼機器人及其輔助方法對使用者的助益及增加普及性。 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.
依據本發明提供一種下肢外骨骼機器人,供使用者之至少一下肢穿戴,下肢外骨骼機器人包含三運動感測單元、三外骨骼馬達、三馬達編碼器、演算單元及下肢輔具。運動感測單元分別鄰設於下肢的髖關節、膝關節及踝關節,各運動感測單元用以提供鄰設的關節的三維空間的複數感測信號。外骨骼馬達分別鄰設於髖關節、膝關節及踝關節。所述三馬達編碼器分別鄰設於所述三外骨骼馬達,各馬達編碼器依據鄰設的外骨骼馬達提供至少一編碼信號。演算單元通信連接運動感測單元及馬達編碼器,演算單元用以依據感測信號、編碼信號及三連動的DH參數運算得到複數輸出值,輸出值分別轉換為三外骨骼馬達控制信號。所述三外骨骼馬達分別依據所述三外骨骼馬達控制信號產生三輔助力矩,下肢輔具受輔助力矩驅動以分別輔助髖關節、膝關節及踝關節。藉此,搭配可有效且精簡演算下肢的前進步態及後退步態的運動模型,以提升本發明之下肢外骨骼機器人對使用者的助益及增加普及性。 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.
根據前述的下肢外骨骼機器人,其中各運動感測單元可為三軸加速度計及三軸陀螺儀。 According to the aforementioned extremity exoskeleton robot, each motion sensing unit may be a three-axis accelerometer and a three-axis gyroscope.
根據前述的下肢外骨骼機器人,可更包含肌電感測端,其鄰設下肢的大腿,且肌電感測端用以量測大腿之肌電信號,肌電信號用以控制是否將輸出值分別轉換為外骨骼馬達控制信號。 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.
根據前述的下肢外骨骼機器人,其中所述三連動的DH參數中,在第Xi軸觀察到的從第Zi-1軸到第Zi軸的距離為ai,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的距離為di,i的數值為1至3,且ai及di的數值可皆為零。 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.
根據前述的下肢外骨骼機器人,其可供使用者之二下肢穿戴,運動感測單元、外骨骼馬達及馬達編碼器的數量皆為六個並鄰設於二下肢的二髖關節、二膝關節及二踝關節,演算單元通信連接運動感測單元及馬達編碼器,演算單元用以依據感測信號、編碼信號及三連動的DH參數運算得到輸出值,輸出值分別轉換為外骨骼馬達控制信號,外骨骼馬達分別依據外骨骼馬達控制信號產生輔助力矩,下肢輔具受輔助力矩驅動以分別輔助髖關節、膝關節及踝關節。演算單元包含類神經網路模組,類神經網路模組用以運算輸出值且包含輸入層、第一層、第二層、第三層及輸出層。輸入層係以所述二下肢的感測信號及編碼信號作為變數輸入模糊規則,模糊規則依據三連動的DH參數建立。第一層係將模糊規則進行適合度運算,得到複數規則強度。第二層係將規則強度進行正規化運算,得到複數正規化值。第三層係將正規化值與Sugeno模糊模式相乘運算。輸出層係將第三層的運算結果進行總和運算得到輸出值。 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.
藉由前述的下肢外骨骼機器人,有助於下肢外骨骼機器人有效地掌握及預測使用者的步態。 The aforementioned lower extremity exoskeleton robot helps the lower extremity exoskeleton robot to effectively grasp and predict the gait of the user.
依據本發明提供一種下肢外骨骼機器人輔助方法,其用以輔助使用者之至少一下肢,下肢外骨骼機器人輔助方法包含運動感測步驟、馬達編碼步驟、演算步驟及下肢輔助步驟。運動感測步驟係提供下肢的髖關節、膝關節及踝關節中各關節的三維空間的複數感測信號。馬達編碼步驟係提供分別鄰設於髖關節、膝關節及踝關節的外骨骼馬達中各者的至少一編碼信號。演算步驟係依據感測信號、編碼信號及三連動的DH參數運算得到複數輸出值,並將輸出值分別轉換為三外骨骼馬達控制信號。下肢輔助步驟中,所述三外骨骼馬達分別依據所述三外骨骼馬達控制信號產生三輔助力矩,下肢輔具受輔助力矩驅動以分別輔助髖關節、膝關節及踝關節。藉此,以提升本發明之下肢外骨骼機器人輔助方法對使用者的助益及增加普及性,進一步有效協助復健人力。 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.
根據前述的下肢外骨骼機器人輔助方法,其中運動感測步驟中,髖關節、膝關節及踝關節中各關節的感測信號可由三軸加速度計及三軸陀螺儀提供。 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.
根據前述的下肢外骨骼機器人輔助方法,可更包含肌電感測步驟,量測下肢的大腿之肌電信號。演算步驟中,肌電信號用以控制是否將輸出值分別轉換為外骨骼馬達控制信號。 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.
根據前述的下肢外骨骼機器人輔助方法,其中所述三連動的DH參數中,在第Xi軸觀察到的從第Zi-1軸到第Zi軸的距離為ai,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的距離為di,i的數值為1至3,且ai及di的數值可皆為零。 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.
根據前述的下肢外骨骼機器人輔助方法,其可用以輔助使用者之二下肢。運動感測步驟中,提供二下肢的二髖關節、二膝關節及二踝關節中各關節的三維空間的感測信號。馬達編碼步驟中,提供分別鄰設於髖關節、膝關節及踝關節的所述六外骨骼馬達中各者的編碼信號。演算步驟中,以類神經網路模組依據感測信號、編碼信號及三連動的DH參數運算得到輸出值,並將輸出值分別轉換為所述六外骨骼馬達控制信號。下肢輔助步驟中,所述六外骨骼馬達分別依據所述六外骨骼馬達控制信號產生所述六輔助力矩,下肢輔具受輔助力矩驅動以分別輔助髖關節、膝關節及踝關節。類神經網路模組包含輸入層、第一層、第二層、第三層及輸出層。輸入層係以所述二下肢的感測信號及編碼信號作為變數輸入模糊規則,模糊規則依據三連動的DH參數建立。第一層係將模糊規則進行適合度運算,得到複數規則強度。第二層係將規則強度進行正規化運算,得到複數正規化值。第三層係將正規化值與Sugeno模糊模式相乘運算。輸出層係將第三層的運算結果進行總和運算得到輸出值。 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.
藉由前述的下肢外骨骼機器人輔助方法,有助於下肢外骨骼機器人輔助方法學習並即時輔助不同的使用者。 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‧‧‧下肢外骨骼機器人 100‧‧‧Lower limb exoskeleton robot
131、132、133、134、135、136‧‧‧運動感測單元 131, 132, 133, 134, 135, 136‧‧‧ motion sensing unit
141、142、143、144、145、146‧‧‧外骨骼馬達 141, 142, 143, 144, 145, 146
151、152、153、154、155、156‧‧‧馬達編碼器 151, 152, 153, 154, 155, 156
167、168‧‧‧肌電感測端 167, 168‧‧‧ muscle inductance measuring terminal
170‧‧‧演算單元 170‧‧‧Calculation unit
190‧‧‧下肢輔具 190‧‧‧Lower limb aids
80‧‧‧使用者 80‧‧‧ user
97、98‧‧‧大腿 97, 98‧‧‧ thigh
91、92‧‧‧髖關節 91、92‧‧‧ hip joint
93、94‧‧‧膝關節 93, 94‧‧‧ Knee
95、96‧‧‧踝關節 95, 96‧‧‧ ankle
177‧‧‧類神經網路模組 177‧‧‧ neural network module
180‧‧‧輸入層 180‧‧‧ input layer
181‧‧‧第一層 181‧‧‧First floor
182‧‧‧第二層 182‧‧‧Second floor
183‧‧‧第三層 183‧‧‧ third floor
184‧‧‧輸出層 184‧‧‧ output layer
A0‧‧‧感測信號 A0‧‧‧sensing signal
B0‧‧‧編碼信號 B0‧‧‧ coded signal
P0,11、P0,12、P0,21、P0,22、P1,1、P1,2、P2,1、P2,2、P3,1、P3,2‧‧‧運算值 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
Poutput‧‧‧輸出值 P output ‧‧‧ output value
200‧‧‧下肢外骨骼機器人輔助方法 200‧‧‧Auxiliary method of lower limb exoskeleton robot
230‧‧‧運動感測步驟 230‧‧‧Motion sensing steps
250‧‧‧馬達編碼步驟 250‧‧‧Motor coding steps
260‧‧‧肌電感測步驟 260‧‧‧Measuring procedure
270‧‧‧演算步驟 270‧‧‧Calculation steps
290‧‧‧下肢輔助步驟 290‧‧‧Lower limb auxiliary steps
第1圖繪示本發明第一實施例的下肢外骨骼機器人的方塊圖;第2圖繪示第一實施例的下肢外骨骼機器人的使用示意圖;第3圖繪示第一實施例中類神經網路模組的示意圖;以及第4圖繪示本發明第二實施例的下肢外骨骼機器人輔助方法的流程圖。 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.
以下將參照圖式說明本發明之複數個實施例。為明確說明起見,許多實務上的細節將在以下敘述中一併說明。然而,應瞭解到,這些實務上的細節不應用以限制本發明。也就是說,在本發明部分實施例中,這些實務上的細節是非必要的。此外,為簡化圖式起見,一些習知慣用的結構與元件在圖式中將以簡單示意的方式繪示之;並且重複之元件將可能使用相同的編號表示之。 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.
請參照第1圖及第2圖,第1圖繪示本發明第一實施例的下肢外骨骼機器人100的方塊圖,第2圖繪示第一實施例的下肢外骨骼機器人100的使用示意圖。由第1圖及第2圖可知,下肢外骨骼機器人100供使用者80之左側下肢及右側下肢中至少一下肢(未另標號)穿戴。就下肢外骨骼機器人100供使用者80之右側下肢穿戴而言,下肢外骨骼機器
人100包含運動感測單元131、133、135、外骨骼馬達141、143、145、馬達編碼器151、153、155、演算單元170及下肢輔具190。依據本發明的其他實施例中(圖未揭示),下肢外骨骼機器人可依使用者所需設計為僅供左側下肢穿戴、僅供右側下肢穿戴、或是同時供二下肢穿戴(如本發明之第一實施例的下肢外骨骼機器人100)。
Please refer to FIGS. 1 and 2. FIG. 1 is a block diagram of the lower
運動感測單元131、133、135分別鄰設於下肢的髖關節91、膝關節93及踝關節95,運動感測單元131、133、135中各者用以提供鄰設的關節的三維空間的複數感測信號A0。外骨骼馬達141、143、145分別鄰設於髖關節91、膝關節93及踝關節95。馬達編碼器151、153、155分別鄰設於外骨骼馬達141、143、145,馬達編碼器151、153、155中各者依據鄰設的外骨骼馬達141、143、145提供至少一編碼信號B0。演算單元170通信連接運動感測單元131、133、135及馬達編碼器151、153、155,演算單元170用以依據感測信號A0、編碼信號B0及三連動的DH參數(3-link Denavit-Hartenberg Parameters)運算得到複數輸出值Poutput,輸出值Poutput分別轉換為外骨骼馬達控制信號。外骨骼馬達141、143、145分別依據外骨骼馬達控制信號產生三輔助力矩,下肢輔具190受輔助力矩驅動以分別輔助髖關節91、膝關節93及踝關節95。藉此,下肢外骨骼機器人100使用可有效且精簡演算下肢的前進步態及後退步態的三連動的DH參數之運動模型,以輔助或帶動使用者
80行走等動作,並能增加下肢外骨骼機器人100的普及性,進而有效協助復健人力。
The
進一步而言,演算單元170中的三連動的DH參數係為三個DH轉換矩陣(Denavit-Hartenberg Conversion Matrix)0M1、1M2、2M3中的參數,其中DH轉換矩陣i-1Mi如以下式(1)所示。本發明第一實施例的下肢外骨骼機器人100的三連動的DH參數如以下表1所示,其中係以使用者80的上半身為參考基座(第0軸),i的數值為1至3,i為1代表髖關節91(一連動,第1軸),i為2代表膝關節93(二連動,第2軸),i為3代表踝關節95(三連動,第3軸),第Zi軸描述使用者80的下肢中各關節朝前轉動或朝後轉動等主要轉動,第Xi軸描述使用者80的下肢朝上運動、朝下運動、朝左運動或朝右運動,在第Xi軸觀察到的從第Zi-1軸到第Zi軸的角度為αi,在第Xi軸觀察到的從第Zi-1軸到第Zi軸的距離為ai,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的距離為di,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的角度為θi。再者,踝關節95相對於使用者80的上半身的相對運動可以下列式(2)表示。敬請參照以下式(1)及式(2):;以及0 M 3=0 M 1 1 M 2 2 M 3 式(2)。
Further, the three-linked DH parameters in the
具體而言,第一實施例的下肢外骨骼機器人100可供使用者80之二下肢穿戴,下肢外骨骼機器人100包含運動感測單元131、132、133、134、135、136(分別提供感測信號A0)、外骨骼馬達141、142、143、144、145、146及馬達編碼器151、152、153、154、155、156(分別提供編碼信號B0),即運動感測單元、外骨骼馬達及馬達編碼器的數量皆為六個,其中運動感測單元131、132、外骨骼馬達141、142、馬達編碼器151、152鄰設於髖關節91、92,運動感測單元133、134、外骨骼馬達143、144、馬達編碼器153、154鄰設於膝關節93、94,運動感測單元135、136、外骨骼馬達145、146、馬達編碼器155、156鄰設於踝關節95、96。演算單元170通信連接運動感測單元131、132、133、134、135、136及馬達編碼器151、152、153、154、155、156,演算單元170用以依據感測信號A0、編碼信號B0及三連動的DH參數運算得到輸出值Poutput,輸出值Poutput分別轉換為外骨骼馬達控制信號,外骨骼馬達141、142、143、144、145、146分別依據外骨骼馬達控制信號產生輔助力矩,下肢輔具190受輔助力矩驅動以分別輔助髖關節91、92、膝關節93、94及踝關節95、96。藉此,
下肢外骨骼機器人100可提供二下肢皆有輔助需求的使用者80穿戴。
Specifically, the lower
第一實施例中,運動感測單元131、132、133、134、135、136、外骨骼馬達141、142、143、144、145、146及馬達編碼器151、152、153、154、155、156的設置位置皆不僅限於第2圖中的示意位置。再者,演算單元170用以依據感測信號A0、編碼信號B0及三連動的DH參數運算得到三個輸出值Poutput,其中三維空間的感測信號A0可進一步轉換為四元數(Quaternion),再轉換為歐拉角(Eulerian Angles)以供運算。三個輸出值Poutput分別轉換為三個外骨骼馬達控制信號,即外骨骼馬達141、142(對應髖關節91、92)依據其一外骨骼馬達控制信號,外骨骼馬達143、144(對應膝關節93、94)依據其二外骨骼馬達控制信號,外骨骼馬達145、146(對應踝關節95、96)依據其三外骨骼馬達控制信號。
In the first embodiment, the
運動感測單元131、132、133、134、135、136中各者可為三軸加速度計及三軸陀螺儀。藉此,有助於下肢外骨骼機器人100有效地掌握及預測使用者80的步態。
Each of the
下肢外骨骼機器人100可更包含肌電感測端167、168,其分別鄰設下肢的大腿97、98,且肌電感測端167、168用以分別量測大腿97、98之肌電信號(Electromyographic Signal,EMG signal),肌電信號用以控制是否將輸出值Poutput分別轉換為外骨骼馬達控制信號。藉此,有助於下肢外骨骼機器人100即時依據使用者80
的動作需求啟動或暫停輔助模式。再者,肌電感測端167、168可為非侵入式的電極貼片並可連接放大器,肌電感測端167、168可分別設置於大腿97、98的肌肉纖維最多之處,如大腿股四頭肌,以提高肌電信號的強度及準確度。
The lower
請參照前述式(1)及表1,所述三連動的DH參數中,在第Xi軸觀察到的從第Zi-1軸到第Zi軸的距離為ai,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的距離為di,i的數值為1至3,且ai及di的數值可皆為零。藉此,有助於精簡演算單元170的運算數據量,並可有效避免過於龐雜的運算數據量而導致下肢外骨骼機器人100延誤輔助時機或產生誤動作。進一步而言,第一實施例的下肢外骨骼機器人100中,係應用正逆向運動學理論、下肢前進步態及後退步態的零力矩點(Zero Moment Point,ZMP)理論推算出穩定行走的步態模型或步態週期(Gait Cycle),進而精簡所述三連動的DH參數。
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
請參照第3圖,其繪示第一實施例中類神經網路模組177的示意圖。由第1圖及第3圖可知,演算單元170包含類神經網路模組177,類神經網路模組177用以運算三個輸出值Poutput且可為適應性網路模糊推論系統(Adaptive-Network-based Fuzzy Inference System,ANFIS),類神經網路模組177包含輸入層180、第一層181、第二層182、第三層183及輸出層184。輸入層180係以所述二下肢的感測信號A0及編碼信號B0作為變數輸入模糊規則,模糊規則依據三連動的DH參數建立。第一層181
係將模糊規則進行適合度運算,得到複數規則強度。第二層182係將規則強度進行正規化運算,得到複數正規化值。第三層183係將正規化值與Sugeno模糊模式相乘運算。輸出層184係將第三層183的運算結果進行總和運算得到輸出值Poutput。藉此,有助於下肢外骨骼機器人100學習並即時輔助不同的使用者。
Please refer to FIG. 3, which illustrates a schematic diagram of the
第一實施例中,前段所述的類神經網路模組177依據以下式(3)至式(7),其中j的數值為1至2,k的數值為1表示感測信號A0作為變數輸入,k的數值為2表示編碼信號B0作為變數輸入,且運算出三個輸出值Poutput(分別輔助髖關節91、92、膝關節93、94、踝關節95、96)的類神經網路模組177中的參數數值(包含三連動的DH參數)可能不同。
In the first embodiment, the neural network-
參照以下式(3),輸入層180係以二下肢的感測信號A0及編碼信號B0作為變數輸入模糊規則,得到運算值P0,kj,其中x表示感測信號A0或編碼信號B0,且其隸屬函數為高斯函數uAj,學習參數為aj、bj、cj:
參照以下式(4),第一層181係將模糊規則進行適合度運算(即wj),得到複數規則強度,即是運算值P1,j:
參照以下式(5),第二層182係將規則強度進行正規化運算,得到複數正規化值,即是運算值P2,j,其中運算值P2,j介於0與1之間:
參照以下式(6),第三層183係將正規化值與Sugeno模糊模式(即fj)相乘運算,得到運算值P3,j,其中pj、qj及rj為Sugeno模糊模式的參數:
參照以下式(7),輸出層184係將第三層183的運算值P3,j進行總和運算得到類神經網路模組177的輸出值Poutput:
此外,本發明之下肢外骨骼機器人100能在人機介面中提供選擇復健醫療模式或是自主行走人體信號控制模式,使下肢外骨骼機器人100智慧多功能化。再者,下肢外骨骼機器人100還可實現智慧控制及監控,應用物聯網之概念,運用雲端技術將每個使用者80的數值保留,利用簡單的上網裝置就能做出復健行為的動作,且還能將使用者80的數據備存後利用大數據分析加以應用。
In addition, the lower
請參照第4圖,其繪示本發明第二實施例的下肢外骨骼機器人輔助方法200的流程圖。請一併參考第1圖至第4圖,係以第一實施例的下肢外骨骼機器人100輔助說明第二實施例的下肢外骨骼機器人輔助方法200,下肢外骨骼機器人輔助方法200用以輔助使用者80之左側下肢及右側下肢中至少一下肢,下肢外骨骼機器人輔助方法200包含運動感測步驟230、馬達編碼步驟250、演算步驟270及下肢輔助步驟290。就下肢外骨骼機器人輔助方法200輔助使用者80之右側下肢而言,運動感測步驟230係提供下肢的髖關節91、膝關節93及踝關節95中各關節的三維空間的複數感測信號A0。馬達編碼步驟250係提供分別鄰設於髖關節91、膝關節93及踝關節95的外骨骼馬達141、143、145中各者的至少一編碼信號B0。演算步驟270係依據感測信號A0、編碼信號B0及三連動的DH參數運算得到複數輸出值Poutput,並將輸出值分別轉換為三外骨骼馬達控制信號。下肢輔助步驟290中,所述三外骨骼馬達141、143、145分別依據所述三外骨骼馬達控制信號產生三輔助力矩,下肢輔具190受輔助力矩驅動以分別輔助髖關節91、膝關節93及踝關節95。藉此,搭配可有效且精簡演算下肢的前進步態及後退步態的三連動的DH參數之運動模型,以增進下肢外骨骼機器人輔助方法200協助復健人力。
Please refer to FIG. 4, which illustrates a flowchart of a
進一步而言,下肢外骨骼機器人輔助方法200可用以輔助使用者80之二下肢。運動感測步驟230中,提供二下肢的髖關節91、92、膝關節93、94及踝關節95、96
中各關節的三維空間的感測信號A0。馬達編碼步驟250中,提供分別鄰設於髖關節91、92、膝關節93、94及踝關節95、96的所述六外骨骼馬達141、142、143、144、145、146中各者的編碼信號B0。演算步驟270中,以類神經網路模組177依據感測信號A0、編碼信號B0及三連動的DH參數運算得到輸出值Poutput,並將輸出值Poutput分別轉換為所述六外骨骼馬達控制信號。下肢輔助步驟290中,所述六外骨骼馬達141、142、143、144、145、146分別依據所述六外骨骼馬達控制信號產生所述六輔助力矩,下肢輔具190受輔助力矩驅動以分別輔助髖關節91、92、膝關節93、94及踝關節95、96。
Further, the lower limb exoskeleton
下肢外骨骼機器人輔助方法200的運動感測步驟230中,髖關節91、92、膝關節93、94及踝關節95、96中各關節的感測信號A0可由三軸加速度計及三軸陀螺儀提供。藉此,有助於下肢外骨骼機器人輔助方法200有效地掌握及預測使用者80的步態。
In the
下肢外骨骼機器人輔助方法200可更包含肌電感測步驟260,係量測下肢的大腿97、98之肌電信號。演算步驟270中,肌電信號用以控制是否將輸出值Poutput分別轉換為外骨骼馬達控制信號。藉此,有助於下肢外骨骼機器人輔助方法200即時依據使用者80的動作需求啟動或暫停輔助模式。
The lower limb exoskeleton
請參照前述式(1)及表1,下肢外骨骼機器人輔助方法200的三連動的DH參數中,在第Xi軸觀察到的從第
Zi-1軸到第Zi軸的距離為ai,在第Zi軸觀察到的從第Xi-1軸到第Xi軸的距離為di,i的數值為1至3,且ai及di的數值可皆為零。藉此,有助於精簡演算步驟270的運算數據量,並可有效避免過於龐雜的運算數據量而導致下肢外骨骼機器人輔助方法200延誤輔助時機或產生誤動作。
Please refer to the aforementioned formula (1) and Table 1. In the three-link DH parameter of the lower extremity exoskeleton robot assist
演算步驟270中的類神經網路模組177包含輸入層180、第一層181、第二層182、第三層183及輸出層184。輸入層180係以所述二下肢的感測信號A0及編碼信號B0作為變數輸入模糊規則,模糊規則依據三連動的DH參數建立。第一層181係將模糊規則進行適合度運算,得到複數規則強度。第二層182係將規則強度進行正規化運算,得到複數正規化值。第三層183係將正規化值與Sugeno模糊模式相乘運算。輸出層184係將第三層183的運算結果進行總和運算得到輸出值Poutput。藉此,有助於下肢外骨骼機器人輔助方法200學習並即時輔助不同的使用者。有關類神經網路模組177的細節請參照前述式(3)至式(7)的相關內容,在此不另贅述。
The
雖然本發明已以實施方式揭露如上,然其並非用以限定本發明,任何熟習此技藝者,在不脫離本發明的精神和範圍內,當可作各種的更動與潤飾,因此本發明的保護範圍當視後附的申請專利範圍所界定者為準。 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‧‧‧下肢外骨骼機器人 100‧‧‧Lower limb exoskeleton robot
131、136‧‧‧運動感測單元 131、136‧‧‧Motion sensing unit
141、146‧‧‧外骨骼馬達 141、146‧‧‧Exoskeleton motor
151、156‧‧‧馬達編碼器 151, 156‧‧‧ Motor encoder
167、168‧‧‧肌電感測端 167, 168‧‧‧ muscle inductance measuring terminal
170‧‧‧演算單元 170‧‧‧Calculation unit
177‧‧‧類神經網路模組 177‧‧‧ neural network module
190‧‧‧下肢輔具 190‧‧‧Lower limb aids
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