Human Activity Recognition (HAR) has been extensively studied, with recent emphasis on the implementation of advanced Machine Learning (ML) and Deep Learning (DL) algorithms for accurate classification. This study investigates the efficacy of two ML algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in the realm of HAR using data collected from smartphone sensors. The experiments are conducted on a dataset obtained from the UCI repository, comprising accelerometer and gyroscope signals captured from 30 volunteers performing various activities while wearing a smartphone. The dataset undergoes preprocessing, including noise filtering and feature extraction, before being utilized for training and testing the classifiers. Monte Carlo cross-validation is employed to evaluate the models' robustness. The findings reveal that both XGBoost and MiniRocket attain accuracy, F1 score, and AUC values as high as 0.99 in activity classification. XGBoost exhibits a slightly superior performance compared to MiniRocket. Notably, both algorithms surpass the performance of other ML and DL algorithms reported in the literature for HAR tasks. Additionally, the study compares the computational efficiency of the two algorithms, revealing XGBoost's advantage in terms of training time. Furthermore, the performance of MiniRocket, which achieves accuracy and F1 values of 0.94, and an AUC value of 0.96 using raw data and utilizing only one channel from the sensors, highlights the potential of directly leveraging unprocessed signals. It also suggests potential advantages that could be gained by utilizing sensor fusion or channel fusion techniques. Overall, this research sheds light on the effectiveness and computational characteristics of XGBoost and MiniRocket in HAR tasks, providing insights for future studies in activity recognition using smartphone sensor data.

翻译：人体活动识别（HAR）已被广泛研究，近期重点在于运用先进的机器学习（ML）和深度学习（DL）算法实现精准分类。本研究探究了两种机器学习算法——极限梯度提升（XGBoost）与MiniRocket——在基于智能手机传感器数据的人体活动识别中的效能。实验采用来自UCI数据库的数据集，该数据集包含30名志愿者佩戴智能手机进行多种活动时捕获的加速度计和陀螺仪信号。数据经过预处理（包括噪声滤波和特征提取）后，用于训练和测试分类器。采用蒙特卡洛交叉验证评估模型鲁棒性。研究结果显示，XGBoost与MiniRocket在活动分类中的准确率、F1分数和AUC值均高达0.99，其中XGBoost表现略优于MiniRocket。值得注意的是，这两种算法在HAR任务中的性能均超越文献中其他ML及DL算法。此外，研究对比了两者的计算效率，发现XGBoost在训练时间上更具优势。同时，MiniRocket在仅使用传感器原始数据及单一通道时，仍能达到0.94的准确率和F1值、0.96的AUC值，凸显了直接利用未处理信号的潜力，并暗示了通过传感器融合或通道融合技术可能获得的进一步优势。总体而言，本研究揭示了XGBoost与MiniRocket在HAR任务中的效能与计算特性，为未来基于智能手机传感器数据的活动识别研究提供了参考依据。