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 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. This work introduces CRoP, a novel static personalization approach using an off-the-shelf pre-trained model and pruning to optimize personalization and generalization. CRoP shows superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains, highlighting 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.

翻译：深度学习与物联网技术的进步推动了多样化人体感知应用的发展。然而，受多种因素或上下文影响，人体感知数据中存在显著的模式差异，这种自然分布偏移对通用神经网络模型的性能提出了挑战。为解决此问题，个性化方法将模型适配至特定用户。但现有个性化研究大多忽视了传感数据中用户内部跨上下文的异质性，限制了用户内部的泛化能力。这一局限在临床应用中尤为关键，因为有限的数据可用性同时制约了泛化能力与个性化效果。值得注意的是，由于治疗进展等外部因素，用户内部的感知属性预期会发生变化，这进一步加剧了挑战。本文提出CRoP——一种创新的静态个性化方法，该方法利用现成的预训练模型与剪枝技术来优化个性化与泛化性能。在四个包含两个真实世界健康领域数据集的人体感知数据集上，CRoP展现出卓越的个性化效能与用户内部鲁棒性，凸显了其实践价值与社会影响。此外，为验证CRoP的泛化能力及设计合理性，我们通过梯度内积分析、消融实验以及与前沿基线的对比，提供了实证依据。