因應工業 4.0及大數據時代的需求，藉由感測器獲取時序序列資料已是相當廣泛的方法，而感測器的應用如：智慧型手機及手錶的人體動態識別也日益常見，本論文所設計的石墨烯/PVDF 壓電螺旋形感測器能夠因應多種不同的外力透過石墨烯/PVDF 壓電薄膜產生不同波型的電壓時序資料，藉此判斷不同的外力種類。
本論文所設計之石墨烯/PVDF 壓電螺旋形感測器為新型的感測器，因此藉由五種不同的外力標準化實驗，以量測在不同的外力種類及大小下電壓信號變化，並建立外力大小與電壓信號的函數關係以利後續量測外力時使用，藉這些量測到的信號，透過特殊的信號採樣方式以及信號前處理方式以機器學習中的隨機森林演算法、支持向量機(SVM)、K-近鄰演算法(KNN)進行學習採樣特定時間長短的電壓資料，再透過十折交互驗證得到辨識率及標準差為 92.82%（± 1.07%）的分類模型。
本論文最後藉由熱塑性聚氨酯(TPU)所設計之穿戴式裝置與石墨烯/PVDF 壓電螺旋形感測器組合後，配戴於手臂、手腕及手指，藉由手的不同動作對感測器產生不同外力，以造成與標準化相似的電壓信號，該電壓信號透過分類模型後辨識率為 83.97%（± 0.85%），並且透過量測受力與電壓信號間的函數關係量測外力。

In response to the demands of Industry 4.0 and the era of big data, the acquisition of timeseries data through sensors has become a widely adopted method. The application of sensors in body dynamics recognition, such as in smart phones and smart watches, is increasingly common. The designed graphene/ PVDF piezoelectric helical sensor in this thesis can generate voltage time-series data with different waveforms in response to various external forces through the graphene/PVDF piezoelectric film, enabling the identification of different types of external forces.
The designed graphene/PVDF piezoelectric helical sensor in this thesis is a novel type of sensor. Therefore, through five different standardized experiments involving external forces, measurements were taken to observe voltage signal variations under different types and magnitudes of external forces. The functional relationship between external force magnitude and voltage signal was established to facilitate subsequent force measurements. Using the acquired signals, machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were applied with special signal sampling and preprocessing methods to learn and sample voltage data of specific time durations. Through a ten-fold cross-validation, a classification model with an accuracy rate of 92.82% (±1.07%) was obtained.
Finally, this thesis employed a wearable device designed with thermoplastic polyurethane (TPU) and integrated it with the graphene/PVDF piezoelectric helical sensor. The wearable device was worn on the arm, wrist, and finger, generating different external forces through various hand postures. This resulted in voltage signals resembling the standardized patterns. The recognition rate of the voltage signals through the classification model was 83.97%(±0.85% ), and the force was measured through the functional relationship between force and voltage signals.

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