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Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field.

Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data