Real-Time Activities of Daily Living Recognition Under Long-Tailed Class Distribution

Abstract

Real-time/online Activities of Daily Living (ADL) recognition is an enabling technology for various ubiquitous computing applications. Most of the existing ADL recognition systems consider class balanced data, while the real-life datasets are significantly imbalanced because they follow the long-tailed class distribution. Such imbalanced training data can result in a biased system optimized to favor the majority classes while failing to recognize the minority classes. This paper proposes an online system that recognizes the ADL while considering the long-tailed class distribution. The system first generates hand-crafted and high-level features by using conventional learning and deep learning, which cover the advantage of both technologies to recognize the ADL. Next, the system uses an ensemble technique to concatenate the generated features. Finally, the system minimizes a loss function, which is a linear combination of focal loss for addressing the long-tailed class distribution problem and center loss for enhancing the discriminative power of the deeply learned features. We conduct several experiments on real-life long-tailed datasets to verify the accuracy of the system.