  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.
