针对帕金森步态评估中传感器逐步增加的难题，提出高效增量学习框架。
MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson's Disease Gait Assessment
- 通过特定模态预热机制稳定新传感器表示，避免知识误传。
- 分离传感器统计特征，共享语义主干，提升多模态适应性。
- 设计引导式反向目标，恢复旧模型遗忘的模态能力，适合医疗系统迭代。
基于步态的帕金森病评估越来越多依赖异构传感器，但临床系统通常无法同时采集所有模态。新传感器可能因设备升级、协议变更或跨中心部署而陆续加入，而历史患者数据常因隐私与存储限制不可用。这种模态增量场景面临三大挑战：不可靠的跨模态知识蒸馏、模态特有的统计分布偏移，以及知识保存后可塑性下降。本文提出MOSAIC——一种轻量级持续学习框架。首先，识别出毒教师现象，引入模态特定预热策略，在蒸馏前稳定新模态表征；其次，提出解耦统计的MSBN架构，隔离传感器统计信息的同时保持共享语义主干；最后，设计课程引导的排斥目标函数实现可塑性恢复，在保留旧知识的同时重建模态特异性能力。在三个多模态帕金森步态数据集上的实验表明，MOSAIC显著提升最终性能并缓解遗忘问题。项目代码已开源。
原文摘要 · Abstract (English)
Gait-based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously. New sensors may arrive through device upgrades, protocol changes, or multi-center deployment, while historical patient data are often unavailable because of privacy and storage constraints. This modality-incremental setting faces three challenges: unreliable cross-modal distillation, modality-specific statistical shifts, and reduced plasticity after preservation. We propose MOSAIC, a compact continual learning framework. First, we identify the Toxic Teacher phenomenon and introduce Modality-Specific Warm-Up to stabilize newly learned modality representations before distillation. Second, we propose a statistics-decoupled MSBN architecture that isolates sensor statistics while maintaining a shared semantic backbone. Third, we design a curriculum-guided repulsive objective for Plasticity Recovery, preserving legacy knowledge while recovering modality-specific capacity. Experiments on three multimodal Parkinson's gait datasets show that MOSAIC improves final performance and mitigates forgetting. Project code is available at: https://github.com/minlinzeng/MOSAIC_Modality-Specific-Adaptation-for-Incremental-Continual-Learning-in-PD-Gait-Assessment.git
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