在自動化技術日漸普及的現代社會中，「肌少症」愈發容易出現於年齡增長的人們，甚至營養不均衡與少活動的年輕人也是值得注意的潛在高風險族群。然而「樓梯」這個場景仍然是臺灣日常生活最常見的路況之一，因此這類看似健康的人群在爬樓梯的過程中可能會增加心血管負擔進而引發相關健康問題；如此除了無法持續保持正常的爬梯步態，亦會大幅增加在樓梯間跌落的風險。在爬梯的過程中膝關節為最費力的身體部位，為此本研究針對上樓梯的應用開發能夠於適當時機給予右膝關節輔助力的外骨骼輔具。我們首先推導人體下肢爬梯的動態模型與量測人體下肢姿態以及腳底力量分佈，藉此分析爬梯步態行為以及估測膝關節於此過程中所需扭矩變化。然而上述估測扭矩的方法需在腳底裝設力量感測器，在實際應用中具有輔具穿戴不易、與行走不便等諸多缺點。由於爬梯是一個具連貫性動作，因此非常適合利用循環神經網路模型(RNN)找出下肢姿態與膝關節扭矩之間的關係。藉由訓練後的類神經網路模型，外骨骼輔具將可直接透過下肢姿態精準地給予適當的爬梯輔助力；此法具有不需依據步態階段切換控制法則的優點，並省去腳底力量感測器裝設的費用與不便，將可提高外骨骼輔具爬梯舒適性與安全性。本研究將穿著外骨骼實際爬梯實驗分為三種情形進行探討，分別為(1)無人機互動與輔助力；(2)有人機互動且輔助力參數 為0；(3)有人機互動且輔助力參數 為4。以上實驗主要探討在有無存在人機互動下對RNN輸出的影響、以及實際量測外骨骼給予輔助力時下肢肌肉活動量的變化。結果顯示不論是否存在人機互動，皆不會影響RNN的輸出並可準確判斷施力時機點，亦即所訓練的RNN具有不錯的強健性。外骨骼給予輔助力初步實驗結果顯示下肢肌肉活動量並無明顯下降的趨勢，仍然有待後續進一步外骨骼系統改良修正並進行較為長期的臨床穿戴驗證。

Stair-climbing is a laborious task for people with Sarcopenia, in which these healthy-seemly people may have a relatively higher chance of falling from the stairs than healthy people. Referring to the fact that knee joints contribute most during stair-climbing, this thesis proposes an exoskeleton assistive device to provide knee joint assistance. In this study, we derive the dynamic model of lower limbs during stair-climbing and measure the posture of the lower limbs as well as the force distribution of the feet. Using this information, we analyze the gait behavior and particularly estimate the torque of knee joint during stair-climbing. Because installation of force sensor on feet causes users great inconvenience in the wearing & walking process and most importantly, the fact that stair-climbing is a sequential locomotive behavior makes the Recurrent Neural Network Model (RNN) an excellent tool to derive the relationship between the posture of the lower limbs and the torque of the knee joint. Thanks to the use of RNN, there is no need for exoskeleton system to switch between several discrete high level control algorithms developed in each state of stair-climbing and the system is meant to offer appropriate assistance by just using the posture of the lower limbs, i.e., no force sensors are required in our developed system anymore. In this study, three kinds of stair-climbing experiments are conducted to investigate the effects of the RNN output with or without HRI and evaluate the muscle activities from human lower limbs especially when the exoskeleton provides assistance to human, including (1) no human robot interaction (HRI) and zero assistance; (2) with HRI and assistive factor γ = 0; (3) with HRI and assistive factor γ = 4. The results show that the existence of HRI minimally affects the output of the RNN which assures the proper timing of assistance generation, meaning that the trained RNN model has pretty good robustness. However, the measurement results from EMG sensors indicate that there is little variation in the muscle activities of subject’s lower limbs. Further modifications of the exoskeleton mechatronic system and clinical performance evaluation methods should be carefully investigated with a well-designed long term use protocol in the future study.

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