Knowledge Transfer for Body Sensor Networks: Characterization and Resistance of Negative Transfer in Over-Constrained Environments

Transfer Learning (TL) has shown potential for body sensor network (BSN) applications. However, TL performance is influenced by various factors such as the BSN environment, user-specific physiological signs or sensor modalities, leading to negative transfer (NT) effects. Limited research has defined NT from perspectives such as data quality, domain transferability, and transfer components, but the cognition of NT in multimodal BSNs remains insufficient. In this paper, we explore the correlation between transfer modes and NT, proposing a knowledge transfer framework composed of common domain adaptation constraints to reveal the negative transfer effects in over-constrained environments. This effect can explain why strongly constrained transfer algorithms are effective but not necessarily optimal in terms of performance. In the three typical BSN scenarios of emotion recognition, fall detection, and daily activity recognition, we demonstrate the NT in over-constrained environments and its potential influencing factors using different constraint combinations and based on different multi-modal body sensor fusion architectures. Extensive experiments focus on discussing the impact of category-condition constraints, domain similarity, and fusion architectures on the NT effect in BSNs. This work can provide a new perspective for the design of multimodal BSN knowledge transfer scheme.