Resources & Tooling

This page summarizes the primary software, frameworks, libraries, and resources used across my research — including simulation, neural modeling, prosthetic design, data analysis, and documentation. It serves as a reference for collaboration, reproducibility, and technical clarity.

SOFTWARE & DEVELOPMENT ENVIRONMENT

  • Python — main environment for simulation logic, neural modeling, data processing, analysis, and visualization.

  • C++ — used for performance-critical modules, real-time prosthetic control code, and compute-heavy simulations.

  • VS Code — code editor and workspace manager for organizing modules, debugging, and project structure.

  • GitHub — version control for code, simulation modules, anatomical models, documentation; ensures reproducibility, collaboration, and history tracking.

ROBOTICS & SIMULATION FRAMEWORKS

  • ROS (Robot Operating System) — middleware for message passing, sensor/actuator interface, modular control pipelines; central for integrating simulated prosthetic modules, neural control, feedback, and physics-based simulation environments.

  • Gazebo — physics-based full-body (or limb-level) simulation for musculoskeletal dynamics, contacts, environment interaction — used for realistic testing of prosthetic control, embodiment, and biomechanics.

  • PyBullet — Python-accessible physics engine, useful for rapid prototyping, mesh-based physics tests, and preliminary control or dynamics experiments before committing to heavier simulation frameworks.

BIOMECHANICS & ANATOMICAL MODELING

  • Blender — for anatomical mesh manipulation, joint rigging, model alignment, and exporting skeleton/mesh for robotics simulation (e.g. URDF-compatible).

  • MakeHuman / MB-Lab — open-source tools to generate base anatomical body meshes (skeleton + musculature), useful as starting point for anatomically informed neuromusculoskeletal models.

  • URDF (Unified Robot Description Format) — defines skeleton linkage, joint constraints, tendon routing, inertial properties to integrate anatomy with robotics simulation frameworks.

  • Anatomical reference datasets / public-domain anatomical resources — for bone geometry, muscle placement, mass & inertia properties, realistic scaling and parameterization of models for accurate simulation.

NUMERICAL, NEURAL & DATA-PROCESSING LIBRARIES

  • NumPy / SciPy — numerical modeling of biomechanics (muscle force–length/velocity curves, tendon dynamics, joint torques), neural signal processing, data manipulation, simulation mathematics.

  • Plotting / visualization libraries — for plotting kinematics, dynamics, neural input/output signals, adaptation curves, simulation logs, and control performance over time.

  • Custom spike-train & innervation models — in-house modules to simulate peripheral neural inputs, EMG/ENG-like signals, neural feedback loops, and control-signal generation for prosthetic control experiments.

DOCUMENTATION, PUBLICATION & LITERATURE RESOURCES

  • Google Docs — for collaborative project documentation, design notes, ethical-framework drafts, planning documents, meeting notes, and general record-keeping.

  • LaTeX (e.g. via Overleaf or local editor) — for formal write-ups, technical papers, simulation methodology documentation, and publication-ready manuscripts.

  • Academic and research literature databases and archives — to stay updated on peer-reviewed research in neuroscience, biomechanics, prosthetics, neural modeling, ethics, and to support background research, validation data, and citation.

PURPOSE OF THIS TOOLBOX

This collection of tools supports the full breadth of research workflows:

  • Building multi-layer neuromusculoskeletal simulations — from anatomical modeling to dynamics, neural inputs, control, and environment interaction.

  • Designing and experimenting with neural-muscular interfaces and prosthetic control — enabling testing of control strategies, feedback loops, and prosthetic behavior under realistic conditions.

  • Conducting neural adaptation, embodiment, and cortical remapping research — via simulation-based experiments, variation in anatomy/feedback, and data analysis of adaptation trajectories.

  • Prototyping biohybrid prosthetic concepts — virtual prototyping, testing design feasibility, signal acquisition/processing, control pipelines, and modular architecture before hardware implementation.

  • Supporting documentation, reproducibility, publication, and collaboration — enabling clear records, versioned code and data, and accessible sharing of methods, results, and design decisions.