Search bioRxiv⌕ Search

Biology subjects

Hulleck, A. A.

Publications and source records attributed to Hulleck, A. A..

2 recordsLinked to original sources

Kinematics to Kinetics: Evaluating OpenGRF for Scalable Ground Reaction Force Estimation in Healthy and Pathological Gait

Ground reaction forces (GRFs) are important markers of gait impairment and rehabilitation, but direct measurement with force plates is expensive and limited to laboratory settings. OpenGRF is an open-source framework that estimates GRFs from motion-capture data using musculoskeletal modeling, yet its validity in pathological gait remains uncertain. This study evaluated the accuracy of OpenGRF for estimating three-dimensional GRFs during gait in healthy adults and in people with Parkinsons disease (PD), stroke, hip osteoarthritis (HOA), and total hip arthroplasty, and assessed its ability to reproduce clinically relevant GRF peak magnitudes and timings. OpenGRF estimates were compared with force-plate measurements in healthy participants and in cohorts with PD (OFF/ON medication), stroke, and HOA before surgery (M0) and 6 months after surgery (M6). Accuracy was quantified using root mean square error (RMSE), normalized RMSE (NRMSE), and Pearson correlation coefficients (PCC). Peak analyses examined biases in anterior-posterior (AP) braking and propulsive peaks, first and second vertical peaks (V1, V2), and the main mediolateral peak, as well as timing shifts across the gait cycle. One-dimensional statistical parametric mapping tested waveform differences (p = 0.01). OpenGRF reproduced overall GRF profiles across groups. Vertical GRF showed the best agreement (PCC 0.84-0.94; NRMSE 0.14-0.23), whereas mediolateral GRF showed low absolute error (RMSE 1.45-1.95 %BW) and moderate-to-good agreement (PCC 0.70-0.82). AP GRF was least accurate (PCC 0.61-0.73), especially during propulsion in PD (NRMSE up to 0.39). OpenGRF can reasonably estimate GRFs in healthy and pathological gait and may support kinetic gait assessment when force plates are unavailable.

bioengineering↗

MMH: A multimodal dataset of whole-body kinematics, bilateral ground reaction forces, and lower-limb surface electromyography signals during load lifting and lowering

This study presents the MMH dataset, a laboratory-collected in vivo dataset comprising whole-body kinematics, three-dimensional ground reaction forces and two-dimensional centres of pressure under both feet, as well as surface electromyography (sEMG) signals of twelve lower-limb muscles (six muscles per leg) during load lifting and lowering tasks. Ten healthy, normal-weight, young male adults each performed 72 trials combining one- and two-handed load (2 kg) lifting and lowering. These trials include multiple initial and final load locations while using three different lifting techniques (stoop, semi-squat, and full-squat). The kinematic and force-plate measurements provide rich input for ergonomic risk assessment tools and optimisation-based musculoskeletal models aimed at quantifying and managing musculoskeletal risk of injury. Also, the sEMG recordings enable the development of EMG-assisted musculoskeletal models and support validation of predictions from optimisation-based models. These makes the multimodal MMH dataset a valuable resource for biomechanics, ergonomics, and human movement research.

bioengineering↗