臺灣博碩士論文加值系統

中樞神經損傷的病患，其大腦傳達給肌肉系統的神經命令受到阻礙，因而失去自主運動的能力。治療上，一般採用保守的神經復健或可藉由義肢或輔具，協助病患達成日常生活動作，但是義肢或輔具有荷重以及美觀的問題，造成患者接受度偏低。因此，近年來學者提出植入式的功能性電刺激系統，可由神經電訊號擷取肢體運動資訊，藉由回授來控制電流，刺激肌肉收縮，以恢復運動功能，並且完全不需體外元件。現存的神經電訊號回饋技術只是利用皮膚感覺訊號擷取觸覺和滑動資訊，用於開關(on-off)控制，並未應用肢體運動資訊作為回授控制之用。本研究旨在擷取踝關節肌肉長度與力量相關的本體感覺訊號，以估測踝關節之角度與扭矩。

本研究以白兔之踝關節功能性電刺激系統為目標。首先在踝關節肌肉未受電刺激下，發展由神經電訊號估測踝關節角度的方法；以具有生理結構的肌梭模型為基礎，配合大範圍的關節運動和神經電訊號，分別建立脛骨和腓骨神經電訊號和踝關節角度的模型，再以角度結合器將兩模型輸出組合成為最終角度。另外，為加速參數學習時間，也發展類神經模糊模型。在估測踝關節主動扭矩時需要關節被動扭矩，此扭矩來自關節結締組織，現有被動扭矩模型無法描述關節扭矩與角度的穩態遲滯行為，因此利用Preisach模型建立被動扭矩與角度的關係。在肌肉收縮方面，則以電流刺激作動和拮抗肌，量測雙向的主動扭矩，再分別以Hill型模型建立扭矩與電流強度和關節角度之關係。肌肉受電刺激收縮時，神經電訊號可能包含來自高基氏肌腱器的訊號和電刺激干擾。因此本研究假設神經電訊號為肌梭與高基氏肌腱器成分之代數和，以建立主動扭矩相關的神經電訊號模型。最終由上述模型與神經電訊號可分離之假設，建立具生理意義的踝關節扭矩、角度與神經電訊號之系統架構，以發展在電刺激下同時估測踝關節角度及扭矩之關節運動估測器。本研究以12隻紐西蘭白兔進行活體實驗來發展上述方法。

由實驗結果發現，肌梭模型比類神經模糊模型具有更佳的角度估測性能，並能用在大範圍的運動軌跡上，間接證實被動牽張擷取的訊號主要來自肌梭響應；模型中預測關節運動方向的角度結合器，能夠消除神經電訊號與角度遲滯造成之誤差。而類神經模糊模型的參數學習效率可滿足線上應用的需求。在被動扭矩上，Preisach模型能改善扭矩與角度遲滯造成的誤差，結合主動扭矩模型後可以精確估測扭矩總和。在肌肉等長收縮下，主動扭矩可估測出其相關的神經電訊號成分。最終在電刺激下，關節運動估測器可同時估測踝關節角度與扭矩。

本研究提出神經電訊號成分分離之假設，並根據生理意義，建立踝關節角度、扭矩與神經電訊號之關係，再結合所有模型成功地實現踝關節角度與扭矩的估測，未來可能用於實現踝關節之完全植入式的功能性電刺激系統。

For subjects with spinal cord injuries, neural commands from the brain to the muscle system are blocked, and therefore there is a loss of voluntary action. Generally, treatment adopts conservative neural rehabilitation. An alternative is to use a prosthesis, which helps patients perform daily activities; however, the load and appearance of the prosthesis have led to low patient acceptability. Therefore, in recent years, researches proposed using implantable functional electrical stimulation system in which electroneurograms (ENGs) has been used to extract information of body motion for use as a feedback signal in controlling current and stimulating muscle contractions to restore motor function. The system could replace external components completely. Currently, ENG feedback methods only used skin sensory signals to extract tactile and slip information in on-off control. Body motion information is not applied for feedback control. The aim of this study is to estimate ankle joint angle and moment by extracting proprioception signals which are related to muscle length and force.

In this study, it is focused on developing functional electrical stimulation system of rabbit ankle joint. A method for estimating ankle joint angles from ENGs without muscular stimulation is first developed. Muscle spindle models with physiological structure and large-range joint movements were used to construct two models that relate joint angle to tibial and peroneal ENGs. Then, the two estimated angles were calculated by a combiner to obtain a final angle estimate. Besides, a neuro-fuzzy system is also developed for accelerating parameter learning process. When active joint moment is estimated, passive joint moment is needed which comes from all tissues surrounding the joint. The existing passive moment models cannot describe the steady-state hysteretic behavior of joint angle and moment. Therefore, the Preisach-based model was adopted to build the relationship between passive joint moment and angle. For muscle contraction, the agonist-antagonist muscles were activated by electrical stimulation to obtain the active joint moments at different joint angles and then two Hill-type models were built to relate stimulus intensity and joint angle. When the muscles are contracted by electrical stimulation, their ENGs may contain Golgi tendon organ signals or interference from the electrical stimulation. Therefore, the ENG was hypothesized to be the algebraic sum of muscle spindle and Golgi tendon organ components. Then the active-moment-related ENG models were then constructed. Finally, a system architecture with physiological basis to relate ankle joint moment, angle and ENGs was constructed, in accordance with the above-mentioned models and the hypothesis of ENG separability. Then, an ankle motion estimator capable of simultaneously estimating ankle joint angle and moment under electrical stimulation was built. In vivo experiments of 12 New Zealand rabbits were performed to validate the above-mentioned system architecture.

The experimental results showed that the muscle spindle method outperforms the neuro-fuzzy system in terms of angle estimation and it can be used on large-range movement. It was indirectly verified that ENG changes recorded during passive stretches mainly come from the muscle spindle response. The combiner of the model which was used to predict joint movement direction could eliminate the hysteretic error between ENGs and angle. Besides, the neuro-fuzzy system had a higher parameter learning speed, and was thus considered satisfactory for online applications. For the passive moment, the Preisach-based model can reduce estimation error caused by the moment-angle hysteresis. After integrating with the active moment model, better total moment can be estimated. For isometric muscle contraction, the active-moment-related ENG can be used to estimate the active moment. Finally, the ankle motion estimator can simultaneously estimate ankle joint angle and moment when subjected to electrical stimulation.

This study proposed the hypothesis of ENG separability and constructed the relationship among ankle joint angle, moment and ENGs based on physiology of muscle spindle and dynamics of muscle subjected to electrical stimulation. By integrating all the models, the estimation of ankle joint angle and moment was achieved successfully. In the future, the method might be used in realizing completely implanted functional electrical stimulation system.

中文摘要....................................................i
英文摘要..................................................iii
誌謝......................................................vi
目錄.....................................................vii
圖目錄......................................................x
表目錄....................................................xiv
符號表.....................................................xv
第一章 緒論.................................................1
1.1 前言..................................................1
1.2 神經肌肉生理............................................3
1.2.1 關節扭矩與肌肉力學關係.................................3
1.2.2 本體感覺上傳訊號......................................8
1.3 文獻回顧..............................................15
1.3.1 主被動扭矩模型.......................................15
1.3.2 神經電訊號模型.......................................18
1.4 研究動機與目的.........................................24
1.5 本文架構..............................................27
第二章 方法與實驗設計.......................................28
2.1 神經電訊號之量測.......................................28
2.1.1 銬型電極與實驗平台....................................28
2.1.2 動物實驗............................................34
2.1.3 神經電訊號處理.......................................36
2.1.4 神經電訊號與關節運動之關係.............................37
2.2 肌梭模型估測關節運動軌跡.................................39
2.2.1 肌梭模型與逆向方法....................................39
2.2.2 模型識別與角度估測之方法...............................44
2.2.3 被動牽張運動之實驗設計................................48
2.2.4 模型性能評估與統計分析................................50
2.3 適應性網路模糊推論系統估測關節運動軌跡.....................52
2.3.1 模型結構與參數學習....................................52
2.3.2 模型之結構識別.......................................55
2.3.3 實驗設計與模型性能評估................................58
2.4 被動關節扭矩建模.......................................61
2.4.1 關節扭矩與Preisach模型之關係..........................61
2.4.2 被動關節扭矩模型.....................................66
2.4.3 被動關節扭矩模型之識別和評估...........................68
2.5 主動關節扭矩建模.......................................75
2.5.1 以Hill型肌肉模型描述主動關節扭矩.......................75
2.5.2 電刺激實驗設計與模型識別...............................77
2.6 電刺激下之神經電訊號估測關節運動..........................81
2.6.1 肌肉電刺激之神經電訊號處理.............................81
2.6.2 主動關節扭矩與神經電訊號之模型.........................82
2.6.3 電刺激時神經電訊號回饋關節運動之方法....................83
第三章 結果................................................86
3.1 以肌梭模型估測關節角度..................................87
3.2 以適應性網路模糊推論系統估測關節角度......................93
3.3 被動關節扭矩估測......................................100
3.4 主動關節扭矩估測......................................105
3.5 受電刺激干擾的神經電訊號估測關節運動.....................114
3.5.1 電刺激下之神經電訊號處理..............................114
3.5.2 主動扭矩與神經電訊號之關係............................118
3.5.3 關節角度與扭矩之估測結果..............................121
第四章 討論...............................................127
4.1 基於肌梭模型之估測器...................................127
4.2 適應性網路模糊推論系統.................................131
4.3 關節扭矩模型..........................................133
4.4 電刺激下之神經電訊號估測方法............................137
4.5 神經電訊號之量測與來源.................................139
4.6 臨床應用.............................................141
第五章 結論與建議..........................................143
5.1 結論................................................143
5.2 建議................................................145
參考文獻..................................................146
附錄A 兔子R11和R12之主動關節扭矩估測與受電刺激干擾的神經電訊號估測關節運動結果................................................153
附錄B 兔子R10、R11和R12之模型參數值.........................163

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