傳統的運動評估依賴昂貴的設備和專業人員的數據解讀，限制了其可及性並難以深入分析肌肉層面的表現。本研究提出一種創新的下肢運動能力評估方法，採用結合慣性測量單元（IMU）與表面肌電（sEMG）技術的穿戴式感測器，並應用於反向跳（CMJ）與深蹲跳（SJ）。
為了驗證感測器的準確性與可靠性，本研究進行了組內相關係數（ICC）信度分析與 Bland-Altman 效度分析，並結合機器學習進行運動表現分類。結果顯示，股內側肌（VM）的一致性高於腓腸肌（GA），其中力學指標（飛行時間、跳躍高度、最大峰值力）的 ICC 值均超過 0.96，證實穿戴式感測器在力學測量方面的可靠性。在肌肉激活指標方面，除了離心階段 RMS 之外，其他指標呈現中等一致性，反映出神經肌肉激活的變異性。效度分析顯示，VM 的測量結果與測力板數據高度一致，特別是在飛行時間與跳躍高度指標上，97% 的數據點落在 95% 一致性界限內，而最大峰值力的測量精度仍有提升空間。在機器學習分析中，K-means 聚類成功區分出 CMJ 男性群體與 SJ 女性群體內運動表現高低的受測者，並且其力學指標存在顯著差異（p < 0.05）。在監督式學習模型（KNN、SVM、RF、LR）中，分類準確率均超過 95%，證明了穿戴式感測器數據可應用於運動表現分類。
本研究證實，穿戴式感測器提供了一種低成本、高準確度且具效度的下肢運動能力評估方法，能有效分析力學與神經肌肉層面的表現。透過機器學習技術，本方法可實現運動員分類與運動能力評估，為運動科學研究與實際訓練應用提供新的可能性。

Traditional sports assessments rely on expensive equipment and expert interpretation, limiting accessibility and muscle-level analysis. This study proposes an innovative lower limb performance assessment using a wearable sensor integrating IMU and sEMG technologies, applied to Countermovement Jump (CMJ) and Squat Jump (SJ).
To validate the sensor’s accuracy and reliability, Intraclass Correlation Coefficient (ICC) and Bland-Altman analysis were conducted, alongside machine learning for performance classification. Results showed VM had higher consistency than GA, with mechanical indicators (flight time, jump height, peak force) achieving ICC > 0.96, confirming the wearable sensor’s reliability in mechanical measurements. For muscle activation indicators, all except eccentric-phase RMS showed moderate consistency, indicating variability in neuromuscular activation. Validity analysis demonstrated that VM had a higher level of agreement with force plate data, particularly in flight time and jump height, where 97% of data points fell within the 95% limits of agreement, while peak force measurements showed room for improvement. In machine learning analysis, K-means clustering successfully distinguished athletes with higher and lower performance levels within the male CMJ group and the female SJ group, revealing significant differences in mechanical indicators (p < 0.05). Supervised learning models (KNN, SVM, RF, LR) achieved over 95% classification accuracy, demonstrating the feasibility of using wearable sensor data for athletic performance classification.
This study confirms that wearable sensors provide a cost-effective, accurate, and valid alternative for lower limb performance assessment, effectively analyzing both mechanical and neuromuscular aspects. By integrating machine learning, this method enables athlete classification and performance evaluation, offering new possibilities for sports science research and real-world training applications without relying on expensive laboratory equipment.

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