Biology subjects

Refai, M. I.

Publications and source records attributed to Refai, M. I..

2 recordsLinked to original sources

bioRxiv2025

MyoBack: A Musculoskeletal Model of the Human Back with Integrated Exoskeleton

Given the challenges of real-life experimentation, musculoskeletal simulation models could become essential in biomedical research. This is especially critical for the human back, a key structure involved in daily movements, where modeling and simulation could streamline design and support the development of treatments and robotic rehabilitation techniques, such as exoskeletons. However, musculoskeletal simulation engines are computationally demanding and lack contact dynamics, restricting current models use in studying prolonged behaviors or optimizing system design while maintaining physiological accuracy. To overcome this limitation, this work proposes MyoBack, a human back model part of the MyoSuite framework relying on the physics engine MuJoCo. This model is derived from a physiologically accurate model built in the state-of-the-art musculoskeletal simulation software OpenSim and replicates the latters kinematic properties accurately, with some discrepancies regarding muscle dynamics stemming from engine differences. The MyoBack model was also validated empirically by integrating a passive back exoskeleton in simulation and comparing forces exerted on the back with values from experimental trials. Over different tasks, the model reproduced measured force progressions well, resulting in RMSE = 11% for a stoop and RMSE = 16% for a squat motion pattern relative to peak forces. The MyoBack model can be accessed here: https://github.com/rohwalia/MyoBack

bioengineering↗

bioRxiv2024

Towards Wearable Electromyography for Personalized Musculoskeletal Trunk Models using an Inverse Synergy-based Approach

Electromyography (EMG)-driven musculoskeletal models (EMS) of the trunk are used for estimating lumbosacral joint moments and compressive loads during lifting tasks. These models provide personalized estimates of the parameters using information from many sensors. However, to advance technology from labs to workplaces, there is a need for sensor reduction to improve wearability and applicability. Therefore we introduce an EMG sensor reduction approach based on inverse synergy extrapolation, to reconstruct unmeasured EMG signals for different box-lifting techniques. 12 participants performed an array of tasks (squat, stoop, unilateral twist and bilateral twist) with different weights (0 kg, 7.5 kg and 15 kg). We found that two synergies were sufficient to explain the different lifting tasks (median variance accounted for of 0.91). Building upon this, we used two sensors at optimal subject-specific muscle locations to reconstruct the EMG of four unmeasured channels. Evaluation of the reconstructed and reference EMG showed median coefficients of determination (R2) between 0.70 and 0.86, with median root mean squared errors (RMSE) ranging from 0.02 to 0.04 relative to maximal voluntary contraction. This indicates that our proposed method shows promise for sensor reduction for driving a trunk EMS for ambulatory biomechanical risk assessment in occupational settings and exoskeleton control.

bioengineering↗