PulseMind: GSR Monitoring System Documentation

Table of Contents

1. System Overview
2. Scientific Background
3. Hardware Implementation
4. Software Architecture
5. Data Processing
6. Visualization System
7. System Flow
8. Mathematical Formulations
9. Mermaid Diagrams
10. References

System Overview

PulseMind is an integrated Galvanic Skin Response (GSR) monitoring system that combines:

• ESP32-based hardware sensor
• GitHub-based data storage
• Web-based visualization dashboard

The system measures electrodermal activity (EDA) as an indicator of sympathetic nervous system arousal, which correlates with psychological stress levels.

Scientific Background

Electrodermal Activity Fundamentals

GSR measures changes in skin conductance (SC) with two components:

1. Skin Conductance Level (SCL): Tonic baseline level (0.05-20 μS)
2. Skin Conductance Response (SCR): Phasic responses to stimuli (>0.05 μS)

Key physiological relationships:

• Sweat gland activity ∝ Skin conductance
• Sympathetic arousal ∝ Sweat production
• Stress level ∝ SCL magnitude

Based on Boucsein (2012) and Dawson et al. (2011), we classify stress levels:

Conductance (μS)	Stress Level	Physiological State
<2.0	Relaxed	Parasympathetic dominance
2.0-5.0	Normal	Balanced autonomic tone
5.0-10.0	Stressed	Moderate sympathetic arousal
>10.0	High Stress	Strong sympathetic activation

Hardware Implementation

Circuit Design

graph LR
    A[Finger Electrodes] --> B[Voltage Divider]
    B --> C[ESP32 ADC Pin 34]
    C --> D[3.3V Regulator]
    D --> E[WiFi Module]

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Key parameters:

• Electrode voltage: 3.3V DC
• Series resistance: 1MΩ
• ADC resolution: 12-bit (0-4095)
• Sampling rate: 10Hz (100ms interval)

Signal Processing Chain

1. Raw ADC reading (0-4095)

2. Voltage conversion:

   V = (ADC × 3.3) / 4095
   

3. Conductance calculation:

   G = (V / R) × 10^6 [μS]
   

   Where R = 1MΩ (constant)

4. Moving average filter (window size=5):

   G_filtered = Σ(G_i...G_i+4) / 5
   

Software Architecture

Arduino Code Structure

classDiagram
    class GSRMonitor {
        +float edaBuffer[5]
        +String fileSHA
        +void connectWiFi()
        +void calibrateGSR()
        +float readGSR()
        +String getCurrentSHA()
        +void uploadData(float conductance)
        +String getSCLStatus(float conductance)
    }

    class WiFiManager {
        +const char* ssid
        +const char* password
        +void reconnect()
    }

    class GitHubClient {
        +const char* token
        +const char* repo
        +String getSHA()
        +void updateFile()
    }

    GSRMonitor --> WiFiManager
    GSRMonitor --> GitHubClient

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Key components:

1. WiFi Connectivity: Manages network connection with automatic reconnection
2. GSR Sensor: Handles calibration and data acquisition
3. GitHub API Client: Manages data storage in repository

Data Flow

sequenceDiagram
    participant Sensor
    participant ESP32
    participant GitHub
    participant Dashboard
    
    loop Every 30 seconds
        Sensor->>ESP32: Analog Reading
        ESP32->>ESP32: Convert to μS
        ESP32->>ESP32: Apply Filter
        ESP32->>GitHub: GET SHA
        GitHub-->>ESP32: Current SHA
        ESP32->>GitHub: PUT New Data
        GitHub->>Dashboard: Data Update
        Dashboard->>Dashboard: Visualize
    end

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Data Processing

Calibration Protocol

1. 30-second baseline measurement (300 samples)
2. Calculate mean baseline conductance:

   G_baseline = (Σ(V_i)/n) × (10^6/R)
   

3. Adaptive thresholds:

   G_relaxed = 0.5 × G_baseline
   G_stressed = 2.5 × G_baseline
   

Noise Reduction Techniques

1. Moving average filter:

   window = [G1, G2, G3, G4, G5]
   G_filtered = sum(window) / len(window)

2. Outlier rejection (3σ principle):

   if |G_i - μ| > 3σ:
       discard sample
   

Visualization System

Dashboard Architecture

flowchart TB
    subgraph Web Dashboard
        A[Data Fetcher] --> B[Chart Renderer]
        A --> C[Status Indicator]
        A --> D[Trend Analyzer]
        B --> E[GSR Time Series]
        C --> F[Stress Meter]
        D --> G[Forecast Model]
    end

    subgraph Data Source
        H[GitHub JSON] --> A
    end

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Key components:

1. Real-time GSR chart (Chart.js)
2. Stress level classification
3. Historical trend analysis
4. Predictive forecasting

Stress Level Calculation

stress_level = 
  if G < 2.0: (G/2.0)×25
  elif G < 5.0: 25 + ((G-2.0)/3.0)×25
  elif G < 10.0: 50 + ((G-5.0)/5.0)×25
  else: 75 + ((G-10.0)/10.0)×25

System Flow

Complete System Workflow

journey
    title PulseMind System Workflow
    section Hardware
      ESP32 Boot: 5: ESP32
      WiFi Connect: 3: ESP32
      Sensor Calibrate: 8: ESP32
      Data Acquisition: 15: ESP32
    section Software
      Data Processing: 10: ESP32
      GitHub Sync: 7: Cloud
    section Visualization
      Data Fetch: 5: Dashboard
      Real-time Update: 20: Dashboard
      User Feedback: 15: User

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Mathematical Formulations

Key Equations

1. Conductance Conversion:

   G(t) = (V(t) × 10^6) / R
   

   Where:

   • V(t) = Measured voltage at time t
   • R = Fixed 1MΩ resistance

2. Moving Average:

   Ḡ(t) = 1/N Σ G(t-i) for i=0 to N-1
   

   (N=5 in implementation)

3. Stress Score:

   S(t) = 100 × (G(t) - G_min)/(G_max - G_min)
   

   Where:

   • G_min = 1.0 μS
   • G_max = 20.0 μS

4. Recovery Rate:

   R = (T_recovery / T_total) × 100%
   

   Where:

   • T_recovery = Time spent below baseline
   • T_total = Total observation time

Mermaid Diagrams

System Architecture

graph TD
    A[GSR Sensor] --> B[ESP32]
    B --> C[WiFi]
    C --> D[GitHub API]
    D --> E[Web Dashboard]
    E --> F[User]
    F -->|Feedback| A

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Data Processing Pipeline

flowchart LR
    A[Raw ADC] --> B[Voltage Conversion]
    B --> C[Conductance Calc]
    C --> D[Filtering]
    D --> E[Classification]
    E --> F[Storage]
    F --> G[Visualization]

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References

1. Boucsein, W. (2012). Electrodermal Activity. Springer Science.
2. Dawson, M. E., et al. (2011). The electrodermal system. Handbook of Psychophysiology.
3. Lykken, D. T., & Venables, P. H. (1971). Direct measurement of skin conductance. Psychophysiology.

This documentation provides a comprehensive technical overview of the PulseMind system, from physiological principles to implementation details. The system demonstrates how physiological signals can be captured, processed, and visualized to provide meaningful stress monitoring.