---
license: apache-2.0
---
# Synheart Binary Emotion Classification Models (ONNX)

ONNX models for on-device binary emotion classification from HRV features.

This repository contains pre-trained ONNX models for inferring momentary affective states (Baseline vs Stress) from Heart Rate Variability (HRV) features. These models are part of the [Synheart Emotion](https://github.com/synheart-ai/synheart-emotion) SDK ecosystem, enabling privacy-first, real-time emotion inference directly on device.

**Made by [Synheart AI](https://synheart.ai)** - Technology with a heartbeat.

## Model Performance

**ExtraTrees 120_60 Model (trained on WESAD dataset):**

- Accuracy: **78.44%**
- F1-Score: **72.60%**

These models are research-based and trained on the WESAD dataset with Leave-One-Subject-Out (LOSO) cross-validation. The models output probabilistic class scores representing state tendencies, not ground-truth emotional labels.

### Confusion Matrices

Performance evaluation results (LOSO cross-validation) for each model configuration:

#### w120s60_binary (Window: 120s, Step: 60s) - Default Model

![Confusion Matrix - ExtraTrees (w120s60)](w120s60_binary/figures/confusion_matrix_ExtraTrees_loso_binary.png)

#### w120s5_binary (Window: 120s, Step: 5s)

![Confusion Matrix - ExtraTrees (w120s5)](w120s5_binary/figures/confusion_matrix_ExtraTrees_loso_binary.png)

#### w60s5_binary (Window: 60s, Step: 5s)

![Confusion Matrix - ExtraTrees (w60s5)](w60s5_binary/figures/confusion_matrix_ExtraTrees_loso_binary.png)

## Installation

```bash
pip install onnxruntime numpy
```

## Quick Start

```python
import numpy as np
import onnxruntime as ort
import json
from pathlib import Path

class WesadInferenceEngine:
    def __init__(self, model_path, metadata_path=None):
        # Initialize ONNX Runtime session
        self.session = ort.InferenceSession(str(model_path))
        self.input_name = self.session.get_inputs()[0].name

        # Default labels
        self.label_order = ["Baseline", "Stress"]

        # Load metadata if provided
        if metadata_path and Path(metadata_path).exists():
            with open(metadata_path, 'r') as f:
                self.metadata = json.load(f)
                print(f"Model Loaded: {self.metadata.get('model_id', 'Unknown')}")

    def predict(self, hrv_features):
        """
        Predict emotion class from HRV features.
        
        Args:
            hrv_features: list or np.array of 14 features in order:
                ['RMSSD', 'Mean_RR', 'HRV_SDNN', 'pNN50', 'HRV_HF', 'HRV_LF',
                 'HRV_HF_nu', 'HRV_LF_nu', 'HRV_LFHF', 'HRV_TP', 'HRV_SD1SD2',
                 'HRV_Sampen', 'HRV_DFA_alpha1', 'HR']
        
        Returns:
            tuple: (predicted_label, confidence_score)
        """
        input_data = np.array(hrv_features, dtype=np.float32).reshape(1, -1)
        outputs = self.session.run(None, {self.input_name: input_data})
        
        predicted_idx = int(outputs[0][0])
        probabilities = outputs[1][0]
        
        label_name = self.label_order[predicted_idx]
        confidence = probabilities[predicted_idx]
        
        return label_name, confidence

# Usage Example
if __name__ == "__main__":
    MODEL_FILE = "w120s60_binary/models/ExtraTrees.onnx"
    METADATA_FILE = "w120s60_binary/models/ExtraTrees_metadata.json"
    
    # Example HRV features (14 features in correct order)
    example_input = [35.5, 950.2, 55.1, 15.2, 430.0, 620.0, 
                     0.42, 0.78, 1.45, 1450.0, 0.72, 1.3, 1.1, 91.0]
    
    engine = WesadInferenceEngine(MODEL_FILE, METADATA_FILE)
    label, confidence = engine.predict(example_input)
    
    print(f"Predicted Label: {label}")
    print(f"Confidence: {confidence:.2%}")
```

## Repository Structure

```text
├── w120s60_binary/          # Window: 120s, Step: 60s
│   ├── models/
│   │   ├── ExtraTrees.onnx
│   │   └── ExtraTrees_metadata.json
│   └── figures/
│       └── confusion_matrix_ExtraTrees_loso_binary.png
├── w120s5_binary/           # Window: 120s, Step: 5s
│   ├── models/
│   │   ├── ExtraTrees.onnx
│   │   ├── LogReg.onnx
│   │   ├── RF.onnx
│   │   └── [metadata files]
│   └── figures/
│       ├── confusion_matrix_ExtraTrees_loso_binary.png
│       ├── confusion_matrix_LinearSVM_loso_binary.png
│       ├── confusion_matrix_LogReg_loso_binary.png
│       ├── confusion_matrix_RF_loso_binary.png
│       └── confusion_matrix_XGB_loso_binary.png
└── w60s5_binary/            # Window: 60s, Step: 5s
    ├── models/
    │   ├── ExtraTrees.onnx
    │   └── ExtraTrees_metadata.json
    └── figures/
        └── confusion_matrix_ExtraTrees_loso_binary.png
```

## Input Features

The models expect 14 HRV features in this exact order:

1. RMSSD
2. Mean_RR
3. HRV_SDNN
4. pNN50
5. HRV_HF
6. HRV_LF
7. HRV_HF_nu
8. HRV_LF_nu
9. HRV_LFHF
10. HRV_TP
11. HRV_SD1SD2
12. HRV_Sampen
13. HRV_DFA_alpha1
14. HR

## Output

- **Labels**: `"Baseline"` or `"Stress"`
- **Confidence**: Probability score for the predicted class

## About Synheart

**Synheart** is an AI company specializing in on-device human state intelligence from physiological signals. Our emotion inference models are part of a comprehensive SDK ecosystem that includes:

- **Multi-Platform SDKs**: Dart/Flutter, Python, Kotlin, Swift
- **On-Device Processing**: All inference happens locally for privacy
- **Real-Time Performance**: Low-latency emotion detection from HR/RR signals
- **Research-Based**: Models trained on validated datasets (WESAD)

### Using with Synheart Emotion SDKs

For production applications, we recommend using the official **Synheart Emotion SDKs** which provide:

- ✅ Complete feature extraction pipeline (HR/RR → 14 HRV features)
- ✅ Thread-safe data ingestion
- ✅ Sliding window management
- ✅ Unified API across all platforms
- ✅ Integration with Synheart Core (HSI)

**SDK Repositories:**

- [Python SDK](https://github.com/synheart-ai/synheart-emotion-python) - `pip install synheart-emotion`
- [Dart/Flutter SDK](https://github.com/synheart-ai/synheart-emotion-dart)
- [Kotlin SDK](https://github.com/synheart-ai/synheart-emotion-kotlin)
- [Swift SDK](https://github.com/synheart-ai/synheart-emotion-swift)

### Direct ONNX Usage

This repository is ideal if you:

- Want to use the models directly with ONNX Runtime
- Have your own feature extraction pipeline
- Need to integrate into custom inference systems
- Are doing research or experimentation

## Links

- **Synheart AI**: [synheart.ai](https://synheart.ai)
- **Synheart Emotion**: [Main Repository](https://github.com/synheart-ai/synheart-emotion)
- **Documentation**: [Full SDK Documentation](https://github.com/synheart-ai/synheart-emotion)

## License

These models are part of the Synheart Emotion ecosystem. Please refer to the main [Synheart Emotion repository](https://github.com/synheart-ai/synheart-emotion) for licensing information.

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**Made with ❤️ by [Synheart AI](https://synheart.ai)**