透過函數型資料分析建構手勢辨識模型

Abstract

隨著科技的進步，穿戴式裝置在醫療健康監測和人體活動識別領域的應用越來越廣泛。這些裝置通過高精度的感測器連續收集資料，提供了研究人體動作細微變化的新可能性。本研究利用函數型資料分析(Functional Data Analysis, FDA)方法，對穿戴於手腕上的裝置所收集的加速度計和陀螺儀資料進行深入分析，旨在應用函數型資料分析方法建立手勢識別模型。本研究在函數型資料分析的架構下，將資料資料經過初步處理後，使用B-Spline基底把離散型時間序列資料轉換為函數型資料，以提高其質量並降低背景噪聲；接著，應用函數型主成分分析(Functional Principal Component Analysis, FPCA)來降低資料維度，從而提取出最具代表性的特徵；最後利用提取出的主成份分數(Principal Component Score, PC Score)作為特徵變數，並透過XGBoost建立手勢辨識的分類模型。此模型應用於某公司提供的48名參與者的10種不同手勢的時間序列資料資料上，並透過交叉驗證的方式進行模型訓練與模型預測表現的評估。分析結果顯示，此流程能夠建立一個在測試集上達到接近平均80%敏感度的模型，具有不錯的分類能力和泛化能力，證明了結合FDA和FPCA的方法不僅能有效降低資料維度，還能保留用於手勢識別的關鍵動作特徵。未來可探索其他分類方法或優化FDA前處理流程，以進一步提升分類預測效果，也可以將此方法擴展到更多類型的動作識別應用中，以實現在更廣泛場景下的應用。

With technological advancements, wearable devices are increasingly used in health monitoring and activity recognition. These devices collect time-series data through high-precision sensors, offering new possibilities for studying subtle human movements. This study applied functional data analysis (FDA) to analyze accelerometer and gyroscope data from wrist-worn devices and establish a gesture recognition model. After data processing, B-Spline bases were used to convert discrete time series data into functional data to improve quality and reduce noise. Functional principal component analysis (FPCA) is applied to reduce dimensionality, extracting representative features. The extracted principal component (PC) scores were used as feature variables, and XGBoost was employed to build the classification model. This model is applied to the time series data of ten gestures from 48 participants. The classification model was trained, and prediction performance was evaluated through cross-validation. The results show an average sensitivity of about 80% on the testing set, demonstrating good classification and generalization abilities. This confirms that combining FDA and FPCA effectively reduces data dimensionality while preserving key features for gesture recognition. Future work can explore other classification methods or optimize the FDA preprocessing workflow to enhance prediction performance. This method can also be extended to other action recognition applications.