Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911639541907456 |
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| author | Delaney, Blaise Patel, Salil Xing, Yuji Dootson, Dominic Sevegnani, Karin Antoniades, Chrystalina |
| author_facet | Delaney, Blaise Patel, Salil Xing, Yuji Dootson, Dominic Sevegnani, Karin Antoniades, Chrystalina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18058 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity Delaney, Blaise Patel, Salil Xing, Yuji Dootson, Dominic Sevegnani, Karin Antoniades, Chrystalina Machine Learning 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. |
| title | Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.18058 |