CRoP: Context-wise Robust Static Human-Sensing Personalization

TL;DR

CRoP is a static personalization method that enhances intra-user robustness in human sensing by adaptively pruning pre-trained models, effectively handling distribution shifts and limited data in health-related applications.

Contribution

It introduces CRoP, a novel approach that uses adaptive pruning on pre-trained models to improve intra-user generalization and robustness in human sensing tasks.

Findings

CRoP outperforms existing methods on four human-sensing datasets.

CRoP maintains high performance despite limited user-specific data.

Empirical analysis validates CRoP's robustness and design choices.

Abstract

The advancement in deep learning and internet-of-things have led to diverse human sensing applications. However, distinct patterns in human sensing, influenced by various factors or contexts, challenge the generic neural network model's performance due to natural distribution shifts. To address this, personalization tailors models to individual users. Yet most personalization studies overlook intra-user heterogeneity across contexts in sensory data, limiting intra-user generalizability. This limitation is especially critical in clinical applications, where limited data availability hampers both generalizability and personalization. Notably, intra-user sensing attributes are expected to change due to external factors such as treatment progression, further complicating the challenges. To address the intra-user generalization challenge, this work introduces CRoP, a novel static personalization approach. CRoP leverages off-the-shelf pre-trained models as generic starting points and captures user-specific traits through adaptive pruning on a minimal sub-network while allowing generic knowledge to be incorporated in remaining parameters. CRoP demonstrates superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains, underscoring its practical and social impact. Additionally, to support CRoP's generalization ability and design choices, we provide empirical justification through gradient inner product analysis, ablation studies, and comparisons against state-of-the-art baselines.