科技的進步提高了電動輔助自行車的性能，使得騎乘者可以在更多元的地形騎乘，並適用於各種使用場景。然而，在原本的踩踏、轉向、剎車、變速器控制之外，額外的輔助動力控制引發了人力、輔助馬達、變速器和環境因素之間的搭配問題，因此經驗不足的騎士常見上坡時採用重齒比導致輔助動力效果不彰的問題。電動輔助自行車、騎乘者和環境之間的不匹配可能會導致里程焦慮、騎乘缺乏健康益處、或導致負面騎乘體驗。而考慮個人偏好特徵的自動控制策略除了可以減輕電動輔助自行車手動操作的負擔、並減少人-車之間的不佳搭配，對於操作技術有限的騎乘者而言，這樣的控制策略在多變的地形更能突顯其優勢。本研究考量可能會影響變速和輔助動力控制的騎乘者表現和反應特性，嘗試闡明電動輔助自行車控制決策過程中涉及的人為因素。本研究完成四項實驗，從中分別探討騎乘者如何應對模擬和真實的環境阻力以及對騎乘負荷的偏好。第一項實驗探討騎乘者、電動輔助自行車設定和環境阻力之間的相互作用，在室內固定式電動輔助自行車騎乘過程中測量變速器檔位、輔助動力大小和坡度阻力對生理和主觀反應的影響，並提出用於電動輔助控制的目標應使最低騎乘負荷為 0.75 W/kg，而維持中等強度的最高負荷則為 1.26 W/kg (6.1 MET)。第二項實驗探討了室內電動自行車騎乘時，偏好的騎乘負荷在電動輔助自行車控制決策過程中的作用，在騎乘實驗中提供固定坡度阻力來研究首選負載，同時也在固定時間間隔產生坡度阻力變化來誘發電動輔助自行車控制行為。過程中允許根據實驗參與者依需求調整變速器檔位和輔助動力程度，並依據減少的操作次數與趨於穩定的踩踏功率來辨別實驗參與者已獲得偏好的騎乘負荷，結果顯示偏好的騎乘負荷範圍為 0.92 到 1.17 W/kg，該騎乘負荷導致中等強度的生理負荷（5.3至5.6 MET），而該偏好騎乘負荷的範圍與型態則反映了騎乘者-電動輔助自行車-環境間的互動結果。在第三項實驗中，採用了相同的室內設置，並模擬一條真實路線的坡度阻力，在更複雜的阻力變化條件中探討偏好騎乘負荷在電動輔助自行車操作決策時的角色。在模擬環境中可排除交通狀況的影響，同時允許觀察電動自行車的控制行為以及在受控條件下產生的騎乘負荷和生理反應，實驗結果顯示，在不同坡度阻力條件中，實驗參與者透過操作獲得類似的負荷（0.95至1.14 W/kg）。在最後的實驗中，則對室外實際騎乘的數據集進行了整體處理和解釋。實驗結果顯示，在相同騎乘速度下，實驗參與者在各個坡度條件下也採用相同的踩踏功率輸出，並驗證從實際騎乘數據中獲得個人偏好負荷的可行性。本研究結果可用於開發以人為本的自動控制策略或根據個人喜好客製化電動自行車控制的自適應方法。

Technological advancements have enhanced the performance of electric-assisted bicycles to facilitate cycling in various terrains and accommodate several riding scenarios. The additional assistive power raised cooperation issues among human force, assistive motor, transmission, and environmental factors, particularly for less-experienced riders. Mismatches among e-bikes, humans, and the environment under dynamic cycling conditions may lead to drawbacks such as range anxiety, a lack of health benefits, or negative riding experiences. An automatic control strategy that considers personally preferred characteristics could alleviate the burdens and enhance the interaction between humans, bikes, and the environment. This research addressed the attributes of e-bike controlling by considering the significance of human performances and responses that may influence the timing and magnitude of transmission and assistance adjustments. The present aims were to elucidate the human factors involved in the decision-making process of e-bike control. A series of studies explored how humans respond to simulated and real environmental resistances. The first study examined the interplay among humans, bikes, and the environment. Riders' responses to e-bike control parameters and environmental resistances were assessed. The collective impact of transmission, assistance, and slope resistance on physiological and subjective responses was measured during indoor stationary e-bike cycling sessions. The second study addressed the role of preferred riding load in the decision-making process of e-bike control during an indoor stationary e-bike riding session. Static slope resistance was supplied to investigate the preferred load, while periodic slope resistance changes were provided to induce e-bike controls. Transmission and assistance adjustments were allowed as per the rider's needs. The range and pattern of preferred load gained knowledge about the human-bike-environment interaction. The third study addressed the role of riding load preference in an indoor riding session with simulated route resistances. A data processing method was proposed to extract the preferred riding parameters. Participants adopted a similar riding load across various slope resistances. Finally, datasets from outdoor ridings were processed and interpreted integrally. The effects of speed, age, and gender were significant on the preferred riding load. Findings from these studies can be used in developing a human-centered automatic control strategy or an adaptive method that tailors e-bike controls to individual preferences.

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