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

翻译：基于步态的帕金森病评估日益依赖异构传感器，但临床系统很少同时采集全部模态。设备升级、方案变更或多中心部署可能引入新传感器，而历史患者数据因隐私和存储限制难以获取。这种模态增量场景面临三大挑战：不可靠的跨模态蒸馏、模态特定的统计偏移以及保留后的可塑性退化。我们提出MOSAIC——一种轻量级持续学习框架。首先，识别"有毒教师"现象并引入模态特定预热机制，在蒸馏前稳定新学模态表征；其次，提出统计解耦的MSBN架构，在保持共享语义骨干的同时隔离传感器统计特性；最后，设计课程引导的排斥性目标函数用于可塑性恢复，在保护旧知识的同时恢复模态特定容量。在三个多模态帕金森步态数据集上的实验表明，MOSAIC提升了最终性能并缓解了灾难性遗忘。项目代码见：https://github.com/minlinzeng/MOSAIC_Modality-Specific-Adaptation-for-Incremental-Continual-Learning-in-PD-Gait-Assessment.git