Extended Embodiment & Neural Adaptation Framework
Toooolbox (core tools used / referenced): Python; C++; GitHub; VS Code; ROS / Gazebo / PyBullet (or equivalent simulation/physics frameworks); anatomical-modeling tools (e.g. Blender, MakeHuman / MB-Lab, URDF) — for altered anatomy or prosthetic-geometry simulation; numerical & data-analysis libraries (NumPy / SciPy, plotting/visualization libraries); custom neural-input / feedback-simulation modules (for neural control/feedback modeling); documentation / write-up tools (docs, manuscript prep, notes).
Overview / Abstract
This project investigates how the nervous system — including motor control, proprioception, and neural representation — adapts when a user employs a prosthetic limb that deviates from native anatomy or uses altered control/feedback modalities. It models and simulates how the brain might remap motor intent, sensory feedback, and body-representation over time under altered biomechanics or sensory conditions. Insights gained are meant to guide prosthetic design and control strategies that promote healthy adaptation, stable embodiment, and long-term comfort — minimizing maladaptive plasticity or rejection.
Research Goals / Specific Aims
Characterize neural and sensorimotor adaptation to altered limb anatomy or feedback/control conditions — simulate how motor-intent representation, proprioceptive feedback, and control signals adjust over time when using non-native or modified prosthetic limbs.
Identify design and control parameters that facilitate healthy embodiment and minimize maladaptive neural adaptation — study effects of limb geometry, feedback modality (sensory, proprioceptive, neural), control latency, feedback fidelity, and usage patterns on adaptation outcomes.
Define metrics and evaluation protocols for embodiment, adaptation, and proprioceptive recalibration — establish quantifiable indicators (simulation- or future user-study-based) for how well a user adapts, perceives the limb, and integrates it into their body schema.
Propose evidence-based guidelines for prosthetic design and training protocols based on adaptation-study insights — design recommendations (geometry, feedback, control algorithm, training regimen) that support stable, functional long-term prosthetic use.
Contribute to broader understanding of sensorimotor plasticity and embodiment in prosthetic contexts — advance theoretical and practical knowledge about how altered body schema and feedback influence neural representation, motor control, and long-term adaptation.
Significance / Why It Matters
Prosthetics must do more than move — for long-term success, the user’s nervous system must adapt. Without understanding adaptation and embodiment, prosthetics risk rejection, discomfort, poor control, or maladaptive outcomes.
Simulation-based adaptation studies can inform prosthetic design and rehabilitation protocols — aligning devices and training with how the brain adapts, improving intuitiveness, control fidelity, and user comfort over time.
This research intersects neuroscience, biomechanics, prosthetics, and human-machine interface design — potentially influencing future neurorehabilitation, prosthetic development, and human-augmentation paradigms.
Modeling adaptation dynamics supports human-centered, ethical prosthetic design — moving beyond one-size-fits-all devices toward personalized prosthetic strategies optimized for long-term embodiment and neural compatibility.
Methods / Approach
Use the neuromusculoskeletal + control simulation framework (from Modular Neuromusculoskeletal Model for Prosthetic Control ) as base; adapt it to simulate different conditions: native anatomy, prosthetic/altered geometry, different feedback or control modalities.
Implement neural-input and feedback models (e.g. spike-train, neural-control modules) to simulate motor intent, proprioceptive feedback, and control loops under varying conditions.
Run longitudinal simulations: simulate repeated “use” over time — monitor how control signals, movement patterns, feedback integration, and stability evolve across usage sessions under different conditions.
Define and record key metrics: control error; movement smoothness; stability under perturbations; proprioceptive error or feedback mismatch; adaptation rate; effects of feedback latency.
Perform parameter sweeps / sensitivity analyses across anatomical, physiological, feedback, and control parameters — to identify which factors most significantly influence adaptation outcomes.
Document all simulation conditions, parameters, and outcomes; analyze results across conditions; derive patterns or guidelines linking design/feedback/control parameters to adaptation success or failure.
(Optional future extension) Use simulation findings to guide possible hardware-in-the-loop testing, prototype design, or detailed prosthetic development and testing.
Expected Deliverables & Outcomes
A collection of simulation-based adaptation studies comparing different anatomy / feedback / control conditions over simulated time.
Quantitative data: adaptation curves; performance metrics (error, stability, control quality); sensitivity-analysis results mapping how variation in feedback/geometry/parameters affects adaptation outcomes.
A set of design and training recommendations / guidelines for prosthetics optimized for healthy adaptation and embodiment.
Documentation: simulation parameters, modeling assumptions, evaluation protocols, limitations — enabling reproducibility and further development.
A conceptual/theoretical report or white-paper summarizing findings, implications for prosthetic design, neuro-rehabilitation, and human-machine interfacing — potentially a draft for future publication or collaboration.
Current Status & Next Steps
Base simulation framework (fromModular Neuromusculoskeletal Model for Prosthetic Control) is available or under development. Present plan: define adaptation-study protocols and set up simulation scenarios with variable geometry / feedback / control conditions.
Next tasks: implement neural-input / feedback simulation modules (if not already present); configure longitudinal simulation experiments (control → dynamics → feedback → adaptation loops); specify metrics and data-logging; run first adaptation simulations.
After initial experiments: analyze data, identify preliminary trends; refine simulation conditions and parameters; define candidate design/feedback/control setups; iterate.
Future plans: compile findings; draft design/training guidelines; consider possible hardware-in-the-loop tests or user-study proposals based on simulation insights.
References / Links / Additional Resources
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