Cerebral Palsy Musculoskeletal Simulator & Gait Sandbox

Neurological Spasticity

Skeletal Rotations

Orthopaedic Surgical Toggle

Simulation Physics

⚡ DEMO MODE ACTIVE - Interacting exits demo
3D Biomechanical Digital Twin Projection (Sagittal)
GAIT STATE: Stance
GROUND REACTION FORCE: 1.05 BW
HIP ANGLE: 12°
KNEE FLEXION: 35°
ANKLE ANGLE: -8°
Surface Electromyography (sEMG) Real-Time Noise Stream

Select curated clinical profiles extracted from public datasets to configure the simulator context automatically.

ID-991: Control Dynamic State

Typically developing child (Age 8). Symmetrical kinematics, normalized sEMG trace with sharp phasic activation cycles.

ID-402: Spastic Diplegia (Crouch)

Bilateral hamstring spasticity. Hip/knee flexors excessively active, preventing complete leg extension during stance phase.

ID-115: Equinus Calf Contracture

Spastic Gastrocnemius pulling ankle joint into fixed plantarflexion. Toe-contact gait profile with zero heel strike.

ID-724: Scissoring Bilateral Gait

Severe inward femoral torsion coupled with adductor tightness, forcing cross-over leg alignment.

Profile states map variables corresponding directly with parameters evaluated inside standard musculoskeletal environments.

"How do we decode the chaotic, misfired signals of a developing brain when they manifest as physical, structural challenges in a child's musculoskeletal system? Cerebral palsy isn't a disease of the bones or muscles themselves, yet the orthopedic surgeon's theater is exactly where the secondary battles against muscle contractures, bony deformities, and joint dislocations are fought. If the brain is sending continuous, hyper-excitable commands to a muscle, how can we expect a growing skeleton to form normally? ... Managing cerebral palsy in children isn't just about straightening bones or lengthening muscles; it is about harmonizing the complex feedback loop between the nervous system and physical movement, turning biological chaos into a path toward freedom."

Overview

This clinical sandbox models the interactive relationships between neurological control parameters (spasticity) and musculoskeletal geometry (torsion) found in children with Cerebral Palsy. Built to echo clinical methodologies, it illustrates the physical deformations occurring on a growing skeleton when subjected to chronic hyper-excitable muscle tension, and allows clinicians to virtually test mechanical modifications.

How to Use

1. Select profiles from the Clinical Patient Registry to observe specific pathological gaits (e.g. Crouch Gait or Equinus Toe-Walking).
2. Manually tune the Pathological Inputs sliders to explore how muscle tone changes raw sEMG dynamic output.
3. Apply Orthopaedic Surgical Toggles to lengthen muscles or realign bones, and review dynamic visual corrections immediately.
4. Engage Sound Mode to map neurological muscle spasticity frequencies directly into sonic space.

Technical Details

The digital twin is computed dynamically using real-time kinematic calculations. Rather than looping static pre-recorded movements, joint angles for hip, knee, and ankle structures are driven by a phase-dependent parametric equation system. The sEMG signal simulates muscle firing spikes using a combination of sine wave modulators and procedural random noise generators, indicating continuous main-thread updates below 200ms INP.

Future Directions

Ongoing efforts target the inclusion of multi-axis joint angles, real-time import of raw C3D clinical biomechanics files, and predictive AI modeling. By analyzing VICON and Zenodo marker datasets directly inside a browser using WebGL frameworks, BioniChaos aims to translate complex clinical laboratory analyses into highly accessible, zero-registration biomechanical tools for practitioners worldwide.

Pediatric Biomechanics & Clinical Data Directory

Access directly available raw datasets, open-source musculoskeletal models, and clinical diagnostic research resources without login restrictions.

LIVE-GaitNeuroKids Dataset (Zenodo)

Features raw 3D kinematic variables, video, and 4-channel sEMG targeting lower limbs for 22 pediatric cerebral palsy and 12 toe-walking patients.

Luca Bergamini's Gait Analysis (GitHub)

Directly access 1,139 walking trials from 178 pediatric patients presenting various stages of spastic diplegia for deep learning classification.

OpenSim / SimTK Musculoskeletal Models

Stanford University's premier biological physics hub featuring crouch gait simulations, tendon transfer modeling, and dynamic joint force studies.

PredictCPGait Predictive Simulation (GitHub)

Repository providing raw code, custom pipelines, and simulation optimization systems for predictive modeling of clinical cerebral palsy gait.