Understanding Missing Data Bias in Longitudinal Mental Stress Detection

Mental stress has become a growing concern in contemporary society; fortunately, recent developments in wear-able technology now offer a promising solution. However, a common issue in longitudinal tracking with wearable sensors is missing data, which can introduce biases during model training, affecting predictions and leading to unfair outcomes for users. In this work, we explore the impact of missing data on stress detection performance across two longitudinal datasets. Our analysis reveals that biases stemming from missing data can result in the unfair treatment of individuals with higher levels of missing data, detrimentally affecting model performance. Additionally, we assess various imputation methods to mitigate these issues. Our findings indicate that while imputation generally improves model overall performances, performance decreases significantly when missing data exceeds half of the total data. This research provides initial insights into the challenges of missing data in longitudinal studies.