Abstract
In this paper, we developed an effective wrapper-based model to optimize the recognition of physical human daily life activities recorded by smartphone built-in sensors. The proposed model employs Sequential Forward Selection (SFS) method in combination with K-Nearest Neighbor (KNN) classifier based on a group of five commonly used distance measures: (Manhattan, Euclidean, Chebyshev, Canberra, and Correlation) each having a specific geometric neighborhood interpretation. The proposed SFS-KNN multi-distance approach enables each distance metric to guide the feature selection process. It examines how the resulting feature subsets, determined by each distance, impact the overall recognition performance. The goal is to identify the best feature subset that achieves the highest accuracy with the lowest dimensionality. . Our proposed distance-based wrapper model was validated on two publicly available WISDM and UCI-HAR datasets under 10-fold cross-validation. The experimental results obtained on both datasets showed that the performance of the proposed model is significantly affected by the distance measure used due to generating different feature subsets for each distance. For the WISDM dataset, our model achieves an overall accuracy of 93.39% based on Euclidean distance, with a reduction ratio of 71.42%. It also offers a substantial reduction of 95.9% in the feature dimensions of the UCI-HAR dataset using Correlation distance, with a recognition rate of 99.33%. These outcomes confirm the superiority of our wrapper model over other feature selection-based approaches proposed for the same datasets.