aceLoad-Decomposition Learning Enhances Ground Reaction Force Estimation Using IMUs
Accurate estimation of ground reaction forces (GRFs) is fundamental to gait analysis and supports functional gait assessment, surgical planning, and rehabilitation. We propose a physics-learning hybrid framework that combines linked-segment dynamics with a bidirectional long short-term memory network to estimate vertical GRFs (vGRFs) from seven inertial measurement units (IMUs). The learning module produces an unconstrained allocation variable that decomposes the physics-derived net force into limb-specific vGRF…
Combining linked-segment physics with a BiLSTM network estimated vertical ground reaction forces from seven wearable IMUs with errors below a high-accuracy reference in intra- and inter-subject tests.
The authors describe transfer across populations, sessions, and tasks only as potential and frame the work as support for further development, so clinical use is not yet demonstrated.
Evidence
- Peer-reviewedIEEE Transactions on Neural Systems and Rehabilitation Engineering2026-10-01
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Truvace Impact Record TRV-2026-1452, v1: “Load-Decomposition Learning Enhances Ground Reaction Force Estimation Using IMUs.” Truvace, 2026-10-15. /record/TRV-2026-1452 (accessed at citation time). sha256 ac6edafcf81ba986…
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