Monitoring physical exercises is vital for health promotion, with automated systems becoming standard in personal health surveillance. However, sensor placement variability and unconstrained movements limit their effectiveness. This study proposes the team "3KA"'s one-sensor workout activity recognition method using feature extraction and data augmentation in 2ndWEAR Dataset Challenge. From raw acceleration, angle and signal magnitude vector features were derived, followed by extraction of statistical, fractal/spectral, and higher-order differential features. A fused dataset combining left/right limb data was created, and augmented via sensor rotation and axis inversion. We utilized a soft voting model combining Hist Gradient Boosting with balanced weights and Extreme Gradient Boosting without. Under group 5-fold evaluation, the model achieved 58.83\% macro F1 overall (61.72% arm, 55.95% leg). ANOVA F-score showed fractal/spectral features were most important for arm-based recognition but least for leg-based. The code to reproduce the experiments is publicly available via: https://github.com/Khanghcmut/WEAR\_3K

翻译：监测体育锻炼对健康促进至关重要，自动化系统已成为个人健康监测的标准工具。然而，传感器放置的变异性和无约束运动限制了其有效性。本研究提出了团队“3KA”在第二届WEAR数据集挑战赛中采用特征提取与数据增强的单传感器锻炼活动识别方法。从原始加速度数据中，我们导出了角度和信号幅度向量特征，随后提取了统计特征、分形/频谱特征以及高阶微分特征。通过融合左右肢体数据创建了组合数据集，并利用传感器旋转和轴反转进行数据增强。我们采用了一个软投票模型，该模型结合了带平衡权重的直方图梯度提升与不带权重的极限梯度提升。在组别5折交叉验证下，模型整体宏F1分数达到58.83%（手臂61.72%，腿部55.95%）。ANOVA F分数分析表明，分形/频谱特征对于基于手臂的识别最为重要，但对基于腿部的识别贡献最小。实验复现代码已通过以下链接公开提供：https://github.com/Khanghcmut/WEAR_3K