# BodySpec Insights

**Body composition analytics for BodySpec DEXA scan PDFs**

A Python tool to extract and analyze body composition data from BodySpec DEXA scan reports. Automatically parses measurements, computes 30+ derived metrics, and tracks your progress over time.

> **Note:** This tool is specifically designed for BodySpec PDF reports and may not work with other DEXA providers (DexaFit, Hologic, etc.).

## Features

- 📊 **Comprehensive Data Extraction**: Body fat %, lean mass, bone density, regional composition, and more
- 🧮 **Derived Metrics**: Automatically calculates FFMI, FMI, LSTI, SMI, and other body composition indices
- 📁 **Multiple Output Formats**: CSV (for spreadsheet analysis), JSON (for programmatic use), and Markdown (for readable summaries)
- 📈 **Time-Series Ready**: Append mode allows tracking progress across multiple scans
- 🎯 **Regional Analysis**: Breaks down composition by Arms, Legs, Trunk, Android, and Gynoid regions
- ⚖️ **Muscle Balance**: Tracks left/right limb symmetry

## Installation

### Prerequisites

- Python 3.7 or higher
- pip (Python package manager)

### Setup

1. **Clone or download this repository**

2. **Create a virtual environment** (recommended):
   ```bash
   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

   The script requires:
   - `pdfplumber` - PDF text extraction
   - `pandas` - Data manipulation and CSV handling

## Usage

### Basic Command

```bash
python dexa_extract.py <PDF_PATH> --height-in <HEIGHT> [--weight-lb <WEIGHT>] [--outdir <OUTPUT_DIR>]
```

### Required Arguments

- `PDF_PATH` - Path to your DEXA scan PDF report
- `--height-in` - Your height in inches

### Optional Arguments

- `--weight-lb` - Body weight in pounds (used as fallback if PDF doesn't contain total mass)
- `--outdir` - Output directory for results (default: `dexa_out`)

### Examples

**Single scan:**
```bash
python dexa_extract.py data/pdfs/2025-10-06-scan.pdf --height-in 74 --weight-lb 212 --outdir data/results
```

**Batch process multiple scans:**
```bash
# Process all PDFs in a directory (automatically skips already-processed dates)
python dexa_extract.py --batch data/pdfs --height-in 74 --outdir data/results

# Force reprocessing all files
python dexa_extract.py --batch data/pdfs --height-in 74 --outdir data/results --force
```

**Individual scans** (appends to existing files):
```bash
python dexa_extract.py data/pdfs/scan-2025-01.pdf --height-in 74 --outdir data/results
python dexa_extract.py data/pdfs/scan-2025-04.pdf --height-in 74 --outdir data/results
python dexa_extract.py data/pdfs/scan-2025-10.pdf --height-in 74 --outdir data/results
```

**Height conversion** (for reference):
- 5'8" = 68 inches
- 5'10" = 70 inches
- 6'0" = 72 inches
- 6'2" = 74 inches
- 6'4" = 76 inches

## Directory Structure

```
bodyspec-insights/
├── dexa_extract.py          # Main extraction script
├── requirements.txt         # Python dependencies
├── README.md               # This file
├── .gitignore             # Git ignore patterns
├── data/                  # Data directory (gitignored)
│   ├── pdfs/             # Place your BodySpec PDF reports here
│   └── results/          # Results will be saved here
└── venv/                 # Virtual environment (gitignored)
```

## Output Files

The script generates 5 files in the specified output directory:

### 1. `overall.csv`
Time-series data with one row per scan. Includes all primary metrics and derived indices.

**Columns:**
- `MeasuredDate` - Scan date (YYYY-MM-DD)
- `Height_in`, `Height_ft_in` - Height measurements
- `Weight_lb_Input`, `DEXA_TotalMass_lb`, `Adjusted_Body_Weight_lb` - Weight data
- `BodyFat_percent`, `LeanMass_percent` - Body composition percentages
- `FatMass_lb`, `LeanSoftTissue_lb`, `BoneMineralContent_lb`, `FatFreeMass_lb` - Mass measurements
- `BMI`, `FFMI`, `FMI`, `LST_Index`, `SMI`, `BMDI` - Normalized indices
- `ALM_lb` - Appendicular lean mass (arms + legs)
- `VAT_Mass_lb`, `VAT_Volume_in3`, `VAT_Index` - Visceral adipose tissue
- `Android_percent`, `Gynoid_percent`, `AG_Ratio` - Fat distribution
- `Trunk_to_Limb_Fat_Ratio` - Central adiposity indicator
- `Arms_Lean_pct`, `Legs_Lean_pct`, `Trunk_Lean_pct` - Regional lean mass distribution
- `Arm_Symmetry_Index`, `Leg_Symmetry_Index` - Left/right balance (50% = perfect)
- `RMR_cal_per_day` - Resting metabolic rate

### 2. `regional.csv`
Regional body composition breakdown (Arms, Legs, Trunk, Android, Gynoid, Total). Each scan adds 6 rows (one per region).

**Columns:**
- `MeasuredDate` - Date of scan (YYYY-MM-DD)
- `Region` - Body region name
- `FatPercent` - Fat percentage in this region
- `LeanPercent` - Lean tissue percentage in this region
- `TotalMass_lb` - Total mass of the region
- `FatTissue_lb` - Fat mass in the region
- `LeanTissue_lb` - Lean mass in the region
- `BMC_lb` - Bone mineral content in the region

### 3. `muscle_balance.csv`
Left/right limb comparison for tracking muscle symmetry. Each scan adds 6 rows (one per region).

**Columns:**
- `MeasuredDate` - Date of scan (YYYY-MM-DD)
- `Region` - Arms Total, Right Arm, Left Arm, Legs Total, Right Leg, Left Leg
- `FatPercent`, `TotalMass_lb`, `FatMass_lb`, `LeanMass_lb`, `BMC_lb`

### 4. `overall.json`
Structured JSON format containing all extracted data in a hierarchical format.

**Structure:**
```json
{
  "measured_date": "2025-10-06",
  "anthropometrics": { ... },
  "composition": { ... },
  "regional": [ ... ],
  "muscle_balance": [ ... ],
  "supplemental": { ... },
  "bone_density": { ... }
}
```

### 5. `summary.md`
Human-readable Markdown summary of the scan results.

## Extracted Metrics

### Primary Measurements
- **Body Fat %** - Percentage of body weight that is fat
- **Lean Mass %** - Percentage of body weight that is lean tissue (complement of body fat %)
- **Fat Mass** - Total weight of fat tissue
- **Lean Soft Tissue** - Muscle, organs, and other non-bone lean tissue
- **Bone Mineral Content (BMC)** - Total bone mineral weight
- **Fat-Free Mass** - Total body weight minus fat mass

### Derived Indices (Height-Normalized)
- **BMI** - Body Mass Index (standard weight-to-height ratio)
- **FFMI** - Fat-Free Mass Index (normalized muscle mass)
- **FMI** - Fat Mass Index (normalized fat mass)
- **LSTI** - Lean Soft Tissue Index (height-adjusted lean tissue)
- **SMI** - Skeletal Muscle Index (height-adjusted appendicular lean mass)
- **BMDI** - Bone Mineral Density Index (height-adjusted bone content)
- **VAT Index** - Visceral fat normalized by height

### Regional Analysis
- **Android** - Abdominal/trunk fat (higher risk area)
- **Gynoid** - Hip/thigh fat (lower risk area)
- **A/G Ratio** - Android-to-Gynoid ratio (cardiovascular risk indicator)
- **Trunk-to-Limb Fat Ratio** - Ratio of trunk fat to limb fat (central adiposity indicator)
- **Lean Mass Distribution** - Percentage of total lean mass in arms, legs, and trunk

### Symmetry & Balance
- **Arm Symmetry Index** - Right-to-left arm lean mass balance (50% = perfect symmetry)
- **Leg Symmetry Index** - Right-to-left leg lean mass balance (50% = perfect symmetry)

### Supplemental
- **VAT (Visceral Adipose Tissue)** - Deep abdominal fat around organs
- **RMR (Resting Metabolic Rate)** - Estimated daily calorie burn at rest
- **Adjusted Body Weight** - Clinical weight used for medication dosing and nutrition calculations
- **Bone Density** - BMD (g/cm²), T-score, Z-score

## Understanding Your Results

### Body Fat % Ranges (by age and sex)

**Men:**
- Athletes: 6-13%
- Fitness: 14-17%
- Average: 18-24%
- Above Average: 25%+

**Women:**
- Athletes: 14-20%
- Fitness: 21-24%
- Average: 25-31%
- Above Average: 32%+

### FFMI (Fat-Free Mass Index)

Normalized measure of muscle mass:
- **16-17**: Below average
- **18-20**: Average/athletic
- **21-23**: Above average/very muscular
- **24-25**: Elite natural bodybuilder range
- **26+**: Typically requires enhanced training

### A/G Ratio (Android/Gynoid Ratio)

Fat distribution indicator:
- **< 1.0**: Lower risk (more fat in hips/thighs)
- **1.0-1.5**: Moderate
- **> 1.5**: Higher risk (more abdominal fat)

### Trunk-to-Limb Fat Ratio

Central adiposity indicator:
- **< 1.0**: More peripheral fat distribution (healthier)
- **1.0-1.5**: Moderate central fat
- **> 1.5**: High central fat (increased health risk)

### Symmetry Indices

Muscle balance between left and right sides:
- **50%**: Perfect symmetry
- **48-52%**: Normal range (slight asymmetry is common)
- **< 48% or > 52%**: Notable imbalance (may indicate injury, overuse, or compensation patterns)

### VAT Index

Visceral fat normalized by height:
- **< 0.30**: Low visceral fat
- **0.30-0.50**: Moderate
- **> 0.50**: High (increased metabolic risk)

### Lean Mass Distribution

Typical ranges for lean tissue distribution:
- **Arms**: 13-16% of total lean mass
- **Legs**: 32-38% of total lean mass
- **Trunk**: 46-54% of total lean mass

Higher trunk percentage may indicate good core development, while higher leg percentage suggests strong lower body development.

## Tracking Progress

The script appends data to existing CSV files, making it easy to track changes over time:

### Option 1: Batch Processing (Recommended)
```bash
# Place all your PDFs in one directory
data/pdfs/
├── scan-2025-01-15.pdf
├── scan-2025-04-20.pdf
└── scan-2025-10-06.pdf

# Process all at once (automatically skips already-processed dates)
python dexa_extract.py --batch data/pdfs --height-in 74 --outdir data/results

# Add new scans later - only new ones will be processed
cp ~/Downloads/scan-2025-12-15.pdf data/pdfs/
python dexa_extract.py --batch data/pdfs --height-in 74 --outdir data/results
```

### Option 2: Individual Processing
```bash
# Process scans as you get them
python dexa_extract.py data/pdfs/scan-2025-01.pdf --height-in 74 --outdir data/results
python dexa_extract.py data/pdfs/scan-2025-04.pdf --height-in 74 --outdir data/results
python dexa_extract.py data/pdfs/scan-2025-10.pdf --height-in 74 --outdir data/results
```

### Analyzing Results
1. Open `overall.csv` in Excel/Google Sheets to visualize trends
2. Compare `muscle_balance.csv` to track left/right symmetry improvements
3. Review `summary.md` for readable reports of each scan
4. Use `overall.json` for programmatic analysis

## Privacy & Security

⚠️ **Important:** DEXA reports contain personal health information (PHI).

- All PDF files and results are excluded from git via `.gitignore`
- Keep your `data/` directory private
- Don't commit PDFs or output files to version control
- Consider encrypting your data directory if sharing the repository

## Troubleshooting

### "Total mass is missing" error
- Ensure your PDF contains a SUMMARY RESULTS table
- Provide `--weight-lb` as a fallback

### No data extracted or null values
- **Verify your PDF is from BodySpec** - This tool only works with BodySpec reports
- Ensure the PDF is text-based, not a scanned image
- Check that your BodySpec report includes the "SUMMARY RESULTS" table
- Open an issue with a sample (redacted) PDF for support

### Import errors
- Ensure virtual environment is activated: `source venv/bin/activate`
- Reinstall dependencies: `pip install -r requirements.txt`

## Contributing

Contributions welcome! Areas for improvement:

- [ ] Automatic height detection from PDF
- [ ] Data visualization/plotting features
- [ ] GUI interface for non-technical users
- [ ] Export to additional formats (Excel, SQLite, etc.)
- [ ] Support for older BodySpec PDF formats
- [ ] Progress bar for batch processing

## License

MIT License - feel free to use and modify for personal or commercial use.

## Acknowledgments

Built for personal body composition tracking with BodySpec scans. Thanks to BodySpec for providing detailed, consistent DEXA scan reports that make automated analysis possible.

**Disclaimer:** This is an unofficial, independent tool and is not affiliated with or endorsed by BodySpec.

---

**Questions or issues?** Open an issue on GitHub or contact the maintainer.