Interpretable Feature Selection for Smartphone-Based Human Activity Recognition Using Sparse PLS-DA and Multinomial Logistic Regression

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Abstract
Smartphone-based human activity recognition (HAR) uses features collected from smartphone sensors to distinguish between different human activities. The large number of correlated features generated by smartphone sensors can make feature selection and model interpretation challenging. This study evaluates a two-stage feature selection and classification framework for HAR, in which sparse partial least squares discriminant analysis (sPLS-DA) is used primarily as a supervised feature selection method, while multinomial logistic regression is employed as the subsequent classifier. The approach is compared with LASSO and Elastic Net regularization under consistent training, testing, and cross-validation procedures. The experiment used 30 repetitions. In each repetition, 21 subjects were used for training and nine subjects were used for testing, with five-fold subject-level cross-validation inside the training set. The original variables selected by each method were also tested with the same unpenalized multinomial logistic regression (MLR) classifier. Native LASSO and Elastic Net gave the highest predictive performance, with mean accuracies of 0.9514 ± 0.0166 and 0.9504 ± 0.0187, respectively, and mean multiclass AUC values of about 0.997. LASSO selected 169.8 ± 25.4 features on average, while Elastic Net selected 255.5 ± 114.4. sPLS-DA selected 353.7 ± 47.5 features on average, while its native max.dist classifier reached a mean accuracy of 0.8831 ± 0.0283 and the selected feature sets had the highest mean pairwise Jaccard overlap (0.6611). Using unpenalized MLR on the selected feature subsets reduced accuracy for all three methods. Overall, LASSO gave the best balance between predictive performance and a smaller feature set, while sPLS-DA showed higher feature-set overlap but kept many more features. These results show that predictive performance, feature-set size, and selection stability should be considered together in smartphone-based HAR.
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