Physics-Informed Inverse Neuromechanical Estimation Dataset for Neural Delay Recovery

Dataset Creators and Affiliation: Jakaria Habib Department of Information and Communication Engineering, Pabna University of Science and Technology, Bangladesh. Overview and Purpose: This repo holds the simulation data, trained ML models and associated python analysis scripts to support physics-informed inverse neural activation delay estimation in musculoskeletal dynamics. The data was generated during dynamic gait simulations using OpenSim v4.5 with the 3DGaitModel2354 lower-limb musculoskeletal architecture. Neural activation delay (electromechanical delay, EMD), the time that elapses between the arrival of central motor excitation impulse and mechanical release in pre-contraction period, is a primary control characteristic behind neural coordination and postural stability. This dataset presents a deterministic app state space and machine learning framework that may be applicable to the inverse parameter identification problem: recovering latent neural delay parameters directly from observable biomechanical signals. Dataset Structure and Variable Specifications: The dataset consists of 45,445 observations (lower-limb joint kinematics and Hill-type muscle activation trajectories) across five physiological neural delay conditions varying from 0.015 seconds to 0.060 seconds (15 ms to 60 ms). The input features include seven biomechanical variables including the right knee flexion angle (radians), right knee angular velocity (rad/s), right hip flexion angle (radians), right hip angular velocity (rad/s), rectus femoris muscle activation (0 to 1), vastus intermedius muscle activation (0 to 1) and biceps femoris muscle activation (0 to 1). The target variable being the latent neural delay parameter tau. Perturbation Validation and Trained Artifacts: Besides nominal baseline simulations, a validation dataset of simulated femur shapes generated by applying a structural 7 percent increase in femur mass is also available to test the performance of the continuous mapping approximation and its resilience to anatomical variations. Files are categorized by baseline simulation data, perturbation validation data, and artifacts from trained models such as the Random Forest regressor and associated scaler.数据集创作者与所属机构：贾卡里亚·哈比卜（Jakaria Habib），孟加拉国巴布纳科学技术大学信息与通信工程系。 概述与研究目的：本仓库包含仿真数据、训练完成的机器学习模型及配套Python分析脚本，用于支持肌肉骨骼动力学中基于物理信息的神经激活延迟逆向估计研究。本数据集源自使用OpenSim v4.5与3DGaitModel2354下肢肌肉骨骼架构开展的动态步态仿真实验。 神经激活延迟（electromechanical delay, EMD，肌电机械延迟）指中枢运动兴奋冲动到达与预收缩期机械释放之间的时间间隔，是神经协调与姿势稳定的核心控制特征。本数据集提供了确定性应用状态空间与机器学习框架，可用于解决逆向参数识别问题：直接从可观测的生物力学信号中恢复潜在神经延迟参数。 数据集结构与变量说明：本数据集包含45445条观测样本，涵盖5种生理神经延迟条件下的下肢关节运动学与希尔型（Hill-type）肌肉激活轨迹，神经延迟范围为0.015秒至0.060秒（15毫秒至60毫秒）。 输入特征包含7项生物力学变量，分别为右侧膝关节屈曲角度（弧度）、右侧膝关节角速度（弧度/秒）、右侧髋关节屈曲角度（弧度）、右侧髋关节角速度（弧度/秒）、股直肌激活水平（0至1）、股中间肌激活水平（0至1）与股二头肌激活水平（0至1）；目标变量为潜在神经延迟参数τ（tau）。 扰动验证与训练成果：除标准基线仿真数据外，本数据集还包含通过将股骨质量提升7%生成的仿真股骨形状验证集，用于测试连续映射近似方法的性能及其对解剖学变异的鲁棒性。文件按基线仿真数据、扰动验证数据与训练模型成果（如随机森林回归器及配套标准化器）进行分类存储。