Elias, L. A. | Search bioRxiv

bioRxiv2026

A Biomechanical Hand Model to Quantify Finger Joint Kinematics Using a 3D Motion Capture System

The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks involving metacarpophalangeal (MCP) joint flexion-extension (F-E) and carpometacarpal (CMC) thumb opposition-reposition (O-R) at two movement frequencies (0.50 and 0.75 Hz). Kinematic analysis quantified the range of movement (RoM), mean speed, and normalized total harmonic distortion (TDHN). Statistical analysis identified task type as the primary factor modulating all three metrics across digits, with large effect sizes [Formula]. Movement frequency significantly influenced mean speed [Formula] and moderately affected TDHN [Formula], while thumb RoM remained statistically unchanged across frequencies (p = 0.063). Participants consistently reproduced the intended sinusoidal trajectories, as indicated by low TDHN values (below 19%). The findings support the analysis of coordinated hand movements across various tasks under controlled time conditions. They also demonstrate that the simplified biomechanical model accurately captured both individual and co-ordinated finger movements. This provides a valuable reference for studies on motor control and for applications in rehabilitation and assistive technology.

bioengineering↗

bioRxiv2026

MyoGen: Unified Biophysical Modeling of Human Neuromotor Activity and Resulting Signals

Understanding human motor control requires integrating cortical and spinal cord activity, muscle mechanics, and electrophysiology, levels that are often studied separately. We present MyoGen, an open-source framework that unifies spinal circuitry, proprioceptive feedback, musculotendon dynamics, cortical activity, and multimodal electromyography (EMG) generation in a single, interoperable platform. Spinal motor neurons and the resulting motor unit (MU) action potentials represent the only neural cells that can be accessed at scale in humans. We used human MU ensembles to validate our model across a wide range of experimental conditions. Using data-driven optimization, we found that MyoGen produces MU population activity that closely matches experimental discharge-rate distributions and discharge variability across human muscles. Moreover, it generates decomposable surface and intramuscular EMG, reproduces beta-band modulation of descending drive and its nonlinear transformation into force, and implements complete sensorimotor loops. Dimensionality reduction of simulated agonist-antagonist EMG reveals low-dimensional control manifolds consistent with experimental recordings from both healthy and spinal cord injured individuals. MyoGen provides physiologically grounded, ground-truth data and integrates seamlessly with analysis pipelines, enabling systematic investigation of motor control principles, validation of signal-processing algorithms, and exploration of sensorimotor interactions that are experimentally inaccessible.

neuroscience↗