We introduce Sonata, a compact latent world model for six-axis trunk IMU representation learning under clinical data scarcity. Clinical cohorts typically comprise tens to hundreds of patients, making web-scale masked-reconstruction objectives poorly matched to the problem. Sonata is a 3.77 M-parameter hybrid model, pre-trained on a harmonised corpus of nine public datasets (739 subjects, 190k windows) with a latent world-model objective that predicts future state rather than reconstructing raw sensor traces. In a controlled comparison against a matched autoregressive forecasting baseline (MAE) on the same backbone, Sonata yields consistently stronger frozen-probe clinical discrimination, prospective fall-risk prediction, and cross-cohort transfer across a 14-arm evaluation suite, while producing higher-rank, more structured latent representations. At 3.77 M parameters the model is compatible with on-device wearable inference, offering a step toward general kinematic world models for neurological assessment.

翻译：我们提出了Sonata——一种紧凑的潜空间世界模型，用于临床数据稀缺条件下六轴躯干惯性测量单元（IMU）的表征学习。临床队列通常仅包含数十至数百名患者，这使得网络规模的掩码重建目标难以与该问题相匹配。Sonata是一个仅含377万参数的混合模型，在由九个公开数据集（739名受试者、19万时间窗口）融合整理而成的语料库上进行预训练，其采用潜空间世界模型目标——预测未来状态而非重构原始传感器轨迹。在与相同骨干网络上匹配的自回归预测基线（掩码自编码器）进行的受控对比中，Sonata在14项评估套件上始终展现出更强的冻结探针临床判别能力、前瞻性跌倒风险预测性能及跨队列迁移能力，同时生成更高秩、更具结构化的潜表征。凭借377万参数，该模型与可穿戴设备端推理能力兼容，为迈向面向神经学评估的通用运动学世界模型迈出了重要一步。