Uncertainty Quantification to Enhance Probabilistic Fusion Based User Identification Using Smartphones

Abstract

User identification through smartphones and wearable sensors holds promise but faces challenges from variability in user activities and sampling windows. This paper presents a method that takes into account uncertainties to enhance the performance of user identification. The conventional sliding window technique is commonly employed for segmenting data, but using small windows can decrease classification performance by creating similar instances with different labels. Conversely, larger windows introduce concept drift resulting from mixed activity patterns. To tackle these challenges, the proposed method assesses the prediction uncertainty of a CNN classifier trained on small window sizes using the Monte Carlo Dropout technique and combines the predictions within a decision window. Uncertainty scores assist in discounting uncertain predictions during the final decision-making. Through experiments on five real-world datasets, the study demonstrates improved performance in identifying users across a range of activities compared to existing methods. It was also directly compared to state-of-the-art methods using two well-known datasets, improving accuracy by 1.29% in one case and 7.98% in the other. These findings validate the effectiveness of the new approach for continuous user identification, even when faced with unpredictable user behavior.