Coping With Imbalanced Data in the Automated Detection of Reminiscence From Everyday Life Conversations of Older Adults

Abstract

Reminiscence—the act of recalling or telling others about relevant personal past experiences—plays an important role in the well-being of older adults. Therefore, it is relevant to develop intelligent systems aiming at improving the well-being of the elderly by reliably detecting reminiscence in their everyday life conversations. Data imbalance is one of the main challenges in the automatic detection of reminiscence from everyday conversations, as reminiscing is a rare event. In this paper, we address the problem by proposing a methodology for coping with imbalanced data in the detection of reminiscence in conversations of older adults. The methodology combines data augmentation using BERT (Bidirectional Encoder Representations from Transformer) and feature extraction techniques leveraging natural language processing for the German language. We evaluate the proposed methodology on a dataset comprising transcripts of social conversations of older adults held in German. We compare our results with a previous work addressing the problem on the same dataset and we show that our approach strongly outperforms the baseline. The results in this study may support the development of intelligent systems for the real-time detection of reminiscence in everyday life of older adults and the design of digital health interventions to support their well-being.

Keywords

• Natural language processing
• data augmentation
• BERT
• machine learning
• reminiscence
• well-being