| name | emoPAD-universe |
| description | emoPAD Universe - Emotion Universe Skill
Helps users locate emotions in the PAD (Pleasure-Arousal-Dominance) coordinate system,
and provides emoNebula feature: continuous real-time emotion PAD monitoring, with a popup window
displaying the emotion nebula chart every 5 minutes.
## Cross-Platform Support
Supports Linux and Windows operating systems:
- **Linux**: Uses eog (Eye of GNOME) to display image windows
- **Windows**: Uses the system default image viewer to display
## Auto-Start
After installing this skill, the emoPAD service and emoNebula will start automatically, no manual operation needed.
## Supported Hardware
- EEG: KSEEG102 (Bluetooth BLE)
- PPG: Cheez PPG Sensor (Serial)
- GSR: Sichiray GSR V2 (Serial)
Theoretically, similar devices should also work. Future versions will gradually add support for mainstream brands, including:
- Muse series EEG devices
- Emotiv EEG devices
- Oura Ring smart ring
- Whoop smart wristband
- Other mainstream EEG devices and wearable devices
## Dependency Installation
Dependencies will be checked and installed automatically when installing the skill, no manual operation needed.
## Usage
- `openclaw emopad status` - Get current PAD status
- `openclaw emopad snapshot` - Manually generate emotion nebula chart
- `openclaw emopad stop` - Stop service
- `openclaw emopad start` - Restart service
## Important Notes
**About Emotion PAD Calculation**: Currently based on heuristic methods, mapping relationships summarized from extensive literature.
This method temporarily cannot reflect individual differences. Future versions will introduce personalized calibration training modules to truly achieve personalized emotion recognition. |
emoPAD Universe
Cross-Platform Support
emoPAD Universe supports the following operating systems:
| OS | Image Viewer | Notes |
|---|
| Linux | eog (Eye of GNOME) | Window mode, closable |
| Windows | System default image viewer | Window mode, closable |
Auto-Start
After installing this skill, the following operations will be performed automatically:
- Check and install required Python dependencies
- Start emoPAD service (listening on http://127.0.0.1:8766)
- Start emoNebula auto-report (popup window displaying emotion nebula chart every 5 minutes)
No manual start needed, ready to use after installation.
Tools
emopad_status
Get current emotion PAD status and sensor connection status
Description: Returns values for three dimensions: Pleasure, Arousal, Dominance, and connection status of EEG, PPG, GSR sensors
Parameters: None
Returns: Formatted emotion status text, including sensor connection status
emopad_snapshot
Generate current emotion nebula chart
Description: Generate 3D PAD cube visualization screenshot
Parameters: None
Returns:
- Status message
- PNG image data
emopad_start_nebula
Start emoNebula auto-report
Description: Automatically generate and display emotion nebula chart in popup window every 5 minutes. Requires at least 2 sensors connected to display image, otherwise shows data missing reminder.
Parameters: None
Returns: Status message
emopad_stop_nebula
Stop emoNebula auto-report
Description: Stop automatically displaying emotion nebula chart
Parameters: None
Returns: Status message
Configuration
serial_port: /dev/ttyACM0
baudrate: 115200
eeg_window_sec: 2
ppg_gsr_window_sec: 60
hop_sec: 2
history_length: 120
nebula_interval: 300
service_host: 127.0.0.1
service_port: 8766
Dependencies
- mne
- heartpy
- neurokit2
- bleak
- pyvista
- pyserial
- scipy
- numpy
- PyWavelets
- fastapi
- uvicorn
- pillow
- requests
- pyyaml
Hardware Support
Currently Supported Devices
| Type | Model | Connection |
|---|
| EEG | KSEEG102 | Bluetooth BLE |
| PPG | Cheez PPG Sensor | Serial |
| GSR | Sichiray GSR V2 | Serial |
Future Planned Support
- Muse series EEG devices
- Emotiv EEG devices
- Oura Ring smart ring
- Whoop smart wristband
- Other mainstream EEG devices and wearable devices
About Emotion PAD Calculation
Important Note: Currently, emotion PAD calculation is based on heuristic methods, mapping relationships summarized from extensive literature.
Characteristics of this method:
- ✅ Based on statistical patterns from scientific literature
- ✅ Suitable for emotion recognition in general population
- ⚠️ Temporarily cannot reflect individual differences
Future Improvements: Will introduce personalized calibration training modules in new versions, through user-specific data training, to achieve true personalized emotion recognition.