'El amor es la experiencia de que los otros no son otros.
belleza es la experiencia de que los objetos no son objetos'.
Rupert Spira

interspecifics

entrainment

Entrainment es un entorno performativo transmedial que revela estados de coherencia cardíaca en respuesta a la escucha activa y en tiempo real de los latidos del corazón humano. Compuesta en vivo y de manera colaborativa, Entrainment presenta una sutil melodía que refleja sobre procesos de coherencia afectiva llendo del caos al unísono y viceversa.

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Entrainment is a transmedial performative environment that reveals states of cardiac coherence in response to active and real-time listening of human heartbeats. Composed live and collaboratively, Entrainment features a subtle melody that reflects on affective coherence shifting from chaos to unison and back.

file structure

ECG_device code, firmware, and specifications that needs to be uploaded to set the ECG device behavior which is basically 1) gather ECG sensor signal, 2) apply real time processing to the signal and 3) broadcast data to the audiovisual performance control mechanisms. Must be uploaded to the ESP32-S2-DevKit-C devices with custom ECG sensor adapters.

signal_processing python code to process the ECG signal for exploratory data analysis, peak detection, Heart Rate Variability and entrainment analysis.

sound_interface components for a sound interface in real time performative heart sensing.

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ECG Entrainment System

Version History and Comparison

Version	Key Features	Signal Processing	ML Integration	Time Sync	Visualization
v1.0	Basic QRS detection	Pan-Tompkins	None	Local time only	Basic plots
v2.0	Signal quality metrics	Enhanced filtering	Basic prediction	NTP sync	Quality indicators
v3.0	Multi-device support	Adaptive thresholds	Entrainment analysis	NTP + drift correction	Real-time status
v4.0	Enhanced error handling	Optimized for ESP32	Advanced ML models	Robust sync	Comprehensive monitoring

Latest Version (v4.0)

Core Features

• Real-time ECG signal processing and QRS detection
• Multi-device synchronization and entrainment analysis
• Machine learning-based signal prediction and analysis
• Robust error handling and recovery mechanisms
• Comprehensive device status monitoring
• Advanced visualization system

Signal Processing

• Optimized Pan-Tompkins algorithm implementation
• Adaptive thresholding for beat detection
• Signal quality assessment
• Real-time filtering and noise reduction
• Efficient memory management for ESP32

Device Communication

• OSC-based message protocol
• NTP time synchronization
• Automatic device discovery and registration
• Connection status monitoring
• Message gap detection and analysis
• Error tracking and reporting

Machine Learning Integration

• LSTM-based signal prediction
• Entrainment analysis between devices
• Phase synchronization detection
• Amplitude coupling analysis
• Temporal alignment assessment

Visualization System

• Real-time ECG signal display
• Heart rate monitoring
• ML prediction visualization
• Device status dashboard
• Connection quality indicators
• Entrainment circular plot
• Performance metrics

Device Status Monitoring

• Connection status (Connected/Disconnected)
• Signal quality indicators
• Message statistics
• Error tracking
• Network performance metrics
• Time synchronization status

Files Included

Firmware

• main-QRS-timer.py: Main ESP32 firmware with QRS detection
• device_config.py: Device configuration and NVS management

Server

• ecg_server.py: Main server application
• ml_engine.py: Machine learning engine
• ecg_server.log: Server log file

Pan-Tompkins Algorithm Implementation

Signal Preprocessing

1. Bandpass filtering (5-15 Hz)
2. Derivative computation
3. Squaring for non-linear amplification
4. Moving window integration

Processing Steps

1. Adaptive thresholding
2. Search back for missed beats
3. Refractory period enforcement
4. T-wave discrimination

Performance Optimizations

• Fixed-point arithmetic
• Efficient buffer management
• Optimized memory usage
• Real-time processing capabilities

Usage

Device Setup

1. Configure device ID and network settings
2. Upload firmware to ESP32
3. Connect ECG sensor
4. Power on device

Server Setup

1. Install required Python packages
2. Configure server settings
3. Start server application
4. Monitor device connections

Monitoring

1. Launch visualization interface
2. Monitor device status
3. Track signal quality
4. Analyze entrainment patterns

Requirements

Hardware

• ESP32 development board
• ECG sensor
• Network connectivity

Software

• Python 3.8+
• Required packages:
  • numpy
  • scipy
  • matplotlib
  • torch
  • pythonosc
  • pandas

Future Improvements

• Enhanced error recovery mechanisms
• Additional ML model architectures
• Extended visualization capabilities
• Advanced signal processing algorithms
• Improved power management
• Extended device support

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

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