Class imbalance in multi-resident activity recognition: an evaluative study on explainability of deep learning approaches

Recognizing multiple residents' activities is a pivotal domain within active and assisted living technologies, where the diversity of actions in a multi-occupant home poses a challenge due to their uneven distribution. Frequent activities contrast with those occurring sporadically, necessitating adept handling of class imbalance to ensure the integrity of activity recognition systems based on raw sensor data. While deep learning has proven its merit in identifying activities for solitary residents within balanced datasets, its application to multi-resident scenarios requires careful consideration. This study provides a comprehensive survey on the issue of class imbalance and explores the efficacy of Long Short-Term Memory and Bidirectional Long Short-Term Memory networks in discerning activities of multiple residents, considering both individual and aggregate labeling of actions. Through rigorous experimentation with data-level and algorithmic strategies to address class imbalances, this research scrutinizes the explicability of deep learning models, enhancing their transparency and reliability. Performance metrics are drawn from a series of evaluations on three distinct, highly imbalanced smart home datasets, offering insights into the models' behavior and contributing to the advancement of trustworthy multi-resident activity recognition systems.