隨著科技的進步，可穿戴式機器人技術逐漸成熟，許多輔助人體行為的電子產品如雨後春筍般推出，市面上有許多下肢外骨骼的相關產品，以消費者的角度來看，除了產品的性能外，系統運行的時間也是一個至關重要的考量因素。由於外骨骼輔具系統在驅動上需要仰賴電池的供給，電量的消耗就成為影響輔具系統的運行的關鍵，能夠準確地估測電量是一個重要的因素。然而，估測電量是非常困難的，因為電池受到負載、溫度和老化的影響，導致每次充、放電幅度不一致的情況。一個良好的電量估測方法，可以延長系統壽命並且減少電池過度放電的問題。因此，本論文使用深度學習的方式估測電量，原因是它可以更容易地適應電池充、放電狀態的非線性特性。此外，本文設計了外骨骼系統的驅動板和無線藍芽鞋墊驅動板，以驅動感測器從人體收集生理信號，用於步態算法的預測。通過軟硬體整合，開發一套下肢外骨骼系統。

With the advancement of technology, wearable robot technology has gradually matured, and many electronic robot products that assist humans have sprung up like mushrooms after the rain on the market. There are many lower limb exoskeleton products on the market. From the consumer's perspective, in addition to the performance of the product, the duration time of system is also an important consideration. Since exoskeleton assistive systems rely on battery power for operation, the system's power consumption becomes an important factor affecting the assistive system. Accurately estimating the battery state of charge is important. However, it is difficult to estimate the battery state of charge because the battery is affected by load, temperature, and aging, leading to inconsistent charging and discharging every time. A great method of state of charge estimation can increase system life and decrease battery over-discharge. Consequently, this thesis uses deep learning methods to estimate the battery state of charge because it can more easily adapt to the nonlinear characteristics of battery state of charge. Besides, this thesis design the exoskeleton system's driver board and the wireless Bluetooth insole driver board to operate sensors to collect physiological signals from human body for gait algorithm predictions. Through the integration of software and hardware, a lower limb exoskeleton system is developed.

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