Holocene-Human

Seeing is not enough, Feeling is not enough, One has to believe

Comprehensive Human Biomechanical Modeling for Sprint Performance Optimization

This repository contains the implementation of a comprehensive framework for developing an advanced human body metrics model specifically tailored for sprint performance optimization, with particular focus on 400m events.

Project Overview

The Holocene Human project aims to create a sophisticated system integrating anthropometric measurements, biomechanical analysis, physiological parameters, and performance metrics to construct a holistic digital representation of the human male body in athletic contexts. The goal is to establish predictive relationships between body metrics and athletic performance, culminating in a specialized language learning model (LLM) capable of providing expert-level insights for performance optimization.

Core Concepts and Theoretical Frameworks

Integrated Biomechanical Modeling

Our approach integrates multiple biomechanical modeling paradigms:

• Musculoskeletal Modeling: Implementation of Hill-type muscle models combined with rigid body dynamics to simulate muscle forces, joint torques, and body segment movements during sprint activities.
• Energy Systems Modeling: Mathematical representation of ATP-PC, glycolytic, and oxidative energy systems with differential equations tracking substrate utilization, fatigue accumulation, and recovery kinetics.
• Neuromuscular Control: Optimization-based control strategies using both feedforward and feedback mechanisms to replicate human motor control patterns in sprint events.

Performance Prediction Framework

The project implements several complementary frameworks for performance prediction:

• Deterministic Models: Physics-based simulations solving equations of motion with measured body parameters and force inputs.
• Statistical Inference: Bayesian networks and hierarchical models capturing dependencies between physiological capabilities and performance outcomes.
• Machine Learning Approaches: Gradient-boosting and deep learning models trained on extensive datasets to identify non-linear relationships between metrics and performance.

Knowledge Distillation Pipeline

A key innovation is our knowledge distillation approach that transfers insights from computationally expensive simulations to lightweight models for real-time analysis:

• Teacher-Student Architecture: Complex physics-based solvers (teachers) distill knowledge into neural network models (students) through specialized training regimes.
• Performance-Interpretability Trade-off Management: Maintains interpretability while achieving near-solver-level accuracy through constrained model architectures.
• Incremental Learning System: Continues to improve as new data becomes available without forgetting previously learned relationships.

Algorithms and Technical Implementations

Data Processing and Feature Engineering

• Signal Processing Algorithms:

  • Butterworth filtering (4th order, zero-phase) for kinematic data
  • Wavelet-based noise reduction for force plate data
  • Dynamic time warping for stride pattern alignment
  • Principal component analysis for dimensionality reduction

• Feature Engineering Techniques:

  • Biomechanical feature extraction (joint angles, angular velocities, power calculations)
  • Time-domain and frequency-domain features from sensor data
  • Body segment parameter estimation using regression equations and geometric models
  • Phase-specific feature generation for different parts of the race (start, acceleration, maintenance, deceleration)

Machine Learning Models

• Regression Algorithms:

  • Gradient Boosted Trees (XGBoost) for performance time prediction
  • Gaussian Process Regression for uncertainty quantification
  • Multi-task learning for joint prediction of multiple performance metrics

• Deep Learning Architectures:

  • 1D CNNs for temporal biomechanical signal processing
  • LSTMs and GRUs for sequence modeling of race phases
  • Graph Neural Networks for modeling inter-joint relationships and full-body coordination
  • Transformer-based models for context-dependent biomechanical analysis

Optimization Techniques

• Training Prescription Optimization:

  • Bayesian optimization for hyperparameter tuning
  • Genetic algorithms for training program design
  • Multi-objective optimization balancing performance gains and injury risk

• Technique Optimization:

  • Direct collocation methods for optimal control problems
  • Reinforcement learning for technique adaptation strategies
  • Sensitivity analysis to identify key technical factors for individual athletes

Knowledge Distillation Methods

• Model Compression:

  • Temperature-scaled softening for knowledge transfer
  • Progressive layer-wise distillation
  • Attention transfer mechanisms
  • Contrastive representation learning

• Numerical Solver Acceleration:

  • Neural network surrogates for partial differential equations
  • Physics-informed neural networks maintaining physical constraints
  • Hybrid analytical-empirical models combining theoretical knowledge with data-driven insights

Project Structure

holocene-human/
├── data/                      # Data storage directory
│   ├── raw/                   # Raw data from various sources
│   ├── processed/             # Cleaned and processed datasets
│   ├── features/              # Engineered features
│   └── ontology/              # Knowledge representation schemas
├── docs/                      # Documentation
│   ├── protocols/             # Data collection protocols
│   ├── models/                # Model documentation
│   └── papers/                # Research papers and citations
├── notebooks/                 # Jupyter notebooks for analysis
├── src/                       # Source code
│   ├── data_collection/       # Scripts for data acquisition
│   ├── preprocessing/         # Data cleaning and standardization
│   ├── feature_engineering/   # Feature generation code
│   ├── modeling/              # Statistical and ML models
│   │   ├── biomechanical/     # Physics-based models
│   │   ├── statistical/       # Statistical analysis
│   │   └── machine_learning/  # ML model implementations
│   ├── llm/                   # LLM development code
│   │   ├── training/          # Training pipeline
│   │   ├── evaluation/        # Evaluation metrics
│   │   └── deployment/        # Model serving
│   ├── visualization/         # Data visualization tools
│   └── cli/                   # Command-line interfaces
│       ├── distillation.py    # Knowledge distillation CLI
│       └── analysis.py        # Data analysis tools
├── tests/                     # Test code
├── requirements.txt           # Python dependencies
├── environment.yml            # Conda environment
└── README.md                  # Project documentation

Key Components and Modules

Data Collection Module

• Motion capture integration with Vicon/Qualisys systems
• Force plate data acquisition (AMTI, Kistler)
• Wireless EMG data collection
• Inertial measurement unit (IMU) integration
• Anthropometric measurement tools and protocols

Preprocessing Module

• Automated data cleaning and outlier detection
• Measurement standardization and normalization
• Missing data imputation using physiologically-informed algorithms
• Data synchronization across measurement systems

Biomechanical Analysis Module

• Inverse dynamics calculations
• Muscle force estimation
• Joint power analysis
• Center of mass calculations
• Energetics and efficiency metrics

Knowledge Distillation Module

• Dataset generation from solver calculations
• Model training with knowledge transfer
• Model evaluation against ground truth
• Deployment of distilled models for real-time analysis

Visualization and Reporting

• Interactive 3D biomechanical visualizations
• Performance metric dashboards
• Automated report generation
• Comparison tools for technique analysis

Getting Started

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (for deep learning components)
• R (for statistical analysis)
• OpenSim (for musculoskeletal modeling)
• Motion capture system compatible software (Vicon/Qualisys)

Installation

1. Clone this repository:

git clone https://github.com/yourusername/holocene-human.git
cd holocene-human

2. Set up the Python environment:

# Using conda
conda env create -f environment.yml

# Activate the environment
conda activate holocene-human

# OR using pip
pip install -r requirements.txt

3. Install additional software:
   • OpenSim (follow instructions at https://simtk.org/projects/opensim)
   • R and required packages (details in docs/installation.md)
   • Motion capture system SDK (system-specific)

Quick Start

# Process raw data
python -m src.cli.distillation process-data --config configs/processing.yaml

# Engineer features
python -m src.cli.distillation engineer-features --config configs/features.yaml

# Train a distilled model
python -m src.cli.distillation train-model --model-type numeric --model-name sprint_predictor_v1 --data-path data/features/sprint_dataset.csv

Data Collection

The project requires several types of data:

1. Anthropometric Measurements

   • Standard anthropometric parameters (height, weight, limb lengths)
   • Advanced body composition (DXA, BIA, 3D scanning)
   • Muscle architecture assessment (ultrasound measurements of fascicle length, pennation angle)
   • Segment inertial properties

2. Biomechanical Data

   • 3D motion capture (full-body marker set at 200+ Hz)
   • Ground reaction forces (1000+ Hz sampling)
   • Musculoskeletal modeling (OpenSim integration)
   • EMG for muscle activation patterns
   • Spatiotemporal parameters (stride length, frequency, contact times)

3. Physiological Assessment

   • Energy system profiling (VO2max, anaerobic capacity tests)
   • Muscle fiber typing (biopsy or indirect assessment)
   • Fatigue profiling and recovery metrics
   • Strength and power assessments (force-velocity profiling)

4. Performance Metrics

   • Race analysis (split times, velocity curves)
   • Training performance metrics (practice times, GPS data)
   • Competition results and conditions
   • Technical execution scores

Detailed protocols for all measurements are provided in the docs/protocols/ directory.

Data Processing Pipeline

The data processing pipeline includes:

1. Data Standardization - Converting measurements to standard units and formats
2. Feature Engineering - Creating composite features that capture meaningful relationships
3. Data Integration - Combining data from different domains into a unified structure
4. Knowledge Representation - Formalizing the relationships in an ontological framework

Models

The project implements several modeling approaches:

1. Statistical Models - For identifying relationships and patterns
2. Biomechanical Models - Physics-based models for movement analysis
3. Machine Learning Models - For predictive modeling and pattern recognition
4. LLM Development - Specialized language model for performance insights

Recent Implementations

Several new features have been added to enhance the biomechanical modeling capabilities:

Biomechanical Model Validation

• Implementation of a validation framework that compares model outputs against published literature
• Statistical validation tools for assessing model accuracy and reliability
• Automated validation reporting for documentation and reproducibility

Sensitivity Analysis

• Parameter sensitivity analysis for biomechanical models
• Identification of critical parameters that significantly impact performance predictions
• Visualization tools for sensitivity results to guide model optimization

Personalized Biomechanical Models

• Framework for creating athlete-specific biomechanical models
• Adaptation algorithms to tailor models based on individual anthropometric and physiological data
• Customization pipeline for model parameters based on performance data

Motion Capture System Integration

• Unified interface for multiple motion capture systems (Vicon, Optitrack)
• Standardized data format for seamless integration of different hardware platforms
• Real-time data processing capabilities for immediate feedback
• Synthetic data generation for testing and development

Contributing

We welcome contributions from researchers in sports science, biomechanics, data science, and related fields. Please see our contributing guidelines for more information.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this framework in your research, please cite:

Sachikonye, K.F. (2023). Holocene Human: Comprehensive Human Biomechanical Modeling for Sprint Performance Optimization: Data Collection, Analysis, and Implementation Framework. Department of Biomechanics and Sports Science, Fullscreen Triangle.

Acknowledgments

• Research participants and athletes
• Collaborating institutions and laboratories
• Funding agencies