In this work, we propose a system to recognize human activities using the accelerometer and gyroscope of a smartphone, which has the potential to be used as a tool to monitor health conditions in real time. We use the UCI HAR dataset to assess the performance of different classifiers, such as Decision Tree, Naive Bayes, SVM, Random Forest, ANN, and XGBoost, as well as the effect of window sizes, as well as the generalization ability of the proposed system using the Leave-One-Subject-Out method. The ensemble classifiers, such as the XGBoost model, have the best performance among the classifiers used, with a maximum F1 score of 98.1%, as shown by the feature importance plot, which indicates the effectiveness of the combined time- and frequency-domain features used in the proposed
Nathan H Choi, Lawrence Cuenco, Anas Durrani et al.· International Conference on...· 0 citations
Wearable sensor-based alcohol detection requires methods that are aligned with the available label structure, sensing modality, and temporal scale. This paper presents a comparative study across two alcohol sensing settings. The first setting uses the Bar Crawl dataset, where smartphone accelerometer data must be interpreted with delayed transdermal alcohol concentration (TAC) measurements. To address this weak supervision, we apply a backward-search framework anchored on TAC rise and prior motion activity. Candidate motion episodes were identified 35-37 minutes before TAC rise in observable cases, while sedentary behavior and limited observability explained non-detected cases. The second setting uses a wrist-worn IMU drinking gesture dataset with explicit gesture annotations under leave-one-subjectout (LOSO) evaluation. Among six models (LR, RF, SVM, XGB, LGBM, and 1D-CNN), LGBM achieved the best semi-controlled DX-I performance (F1=0.699), while RF achieved the best freeliving DX-II baseline performance $(\mathbf{F} \mathbf{1} \boldsymbol{=} \mathbf{0. 3 8 0})$. The results show that label structure, class imbalance, and evaluation protocol strongly influence valid methodology, reported performance, and generalizability in wearable alcohol sensing.