ソナタ：臨床データ不足下での慣性キネマティクスのためのハイブリッド世界モデル
Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
臨床データが少ない状況で、6軸トランクIMU表現学習のためのコンパクトな潜在世界モデル「Sonata」を導入し、将来状態予測により臨床判別と転倒リスク予測を向上させる。
著者: Blaise Delaney, Salil Patel, Yuji Xing, Dominic Dootson, Karin Sevegnani, Chrystalina Antoniades
分類: cs.LG
原文アブストラクト
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.