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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
#### w120s5_binary (Window: 120s, Step: 5s)
#### w60s5_binary (Window: 60s, Step: 5s)
## 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.
---
**Made with ❤️ by [Synheart AI](https://synheart.ai)** |