Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics

# Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
Source: [https://arxiv.org/abs/2605.30374](https://arxiv.org/abs/2605.30374)
[View PDF](https://arxiv.org/pdf/2605.30374)

> Abstract:Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time\-consuming and difficult to apply in clinical settings\. This study developed a deep learning framework to predict these hip dynamics parameters directly from lower\-limb gait kinematics and compared three representative sequence models under a unified protocol\. Gait data were collected from 60 healthy adults under three metronome\-guided cadence conditions\. Ten bilateral lower\-limb joint angles were used as inputs, and OpenSim\-derived hip muscle forces and hip joint moments were used as reference outputs\. Three deep learning models of LSTM, Transformer, and Mamba were trained and evaluated using the same subject\-level split, preprocessing pipeline, and metrics\. The best model was then directly tested on an external cohort of 9 patients with osteonecrosis of the femoral head \(ONFH\) without retraining\. In the healthy\-subject benchmark, Transformer achieved the best subject\-level mean performance for both hip muscle force prediction \(RMSE = 1\.33 N/kg, MAE = 0\.57 N/kg, R2 = 0\.819\) and hip joint moment prediction \(RMSE = 0\.11 Nm/kg, MAE = 0\.07 Nm/kg, R2 = 0\.862\), with similar advantages across walking cadences\. In zero\-shot external validation, Transformer retained moderate predictive ability in ONFH for hip muscle force prediction \(RMSE = 1\.51 N/kg, MAE = 0\.70 N/kg, R2 = 0\.537\) and hip joint moment prediction \(RMSE = 0\.17 Nm/kg, MAE = 0\.12 Nm/kg, R2 = 0\.569\)\. These findings support the feasibility of estimating hip dynamics from gait kinematics, identify Transformer as a strong baseline, and highlight the need for broader pathological validation and improved generalization before clinical application\.

## Submission history

From: Jiaqi Zhang \[[view email](https://arxiv.org/show-email/36131e69/2605.30374)\] **\[v1\]**Sun, 24 May 2026 14:00:42 UTC \(1,656 KB\)