Real-time monitoring from ESP32 dual-device system · Auto-refreshes every 1 second · Includes live dataset builder, Tiny ML lab, preview, and CSV/JSON export
Dataset Builder
Choose source, columns, features, row count, and export format. Build custom datasets from the live server data.
Auto-refresh 1s
CSV কখন ভালো
Excel, Google Sheets, Power BI, statistical analysis, and clean tabular workflows-এর জন্য CSV best. When you want rows and columns only, choose CSV.
JSON কখন ভালো
API, web app, JavaScript, data exchange, or raw structured export-এর জন্য JSON best. When you want field names preserved exactly, choose JSON.
Columns / Features
Previewing latest live dataset…
Dataset Preview
Live preview based on your selected source, format, row count, and columns.
Preparing dataset preview...
Tiny ML / Custom Model Lab
Apply lightweight browser-side machine learning. Train a linear regression model to estimate how long the relay should stay ON so body temperature can return toward normal, and also test your own custom coefficient-based model.
Linear Regression + Custom
এই মডিউল কী করে
Recent live readings আর fever event duration ব্যবহার করে relay-on time estimate করে. Water temperature, body temperature, ambient temperature, relay state, fever flag, আর servo angle থেকে lightweight prediction তৈরি করা যায়.
Custom model কীভাবে
নিজের coefficient JSON paste করে trained model-এর জায়গায় custom formula apply করতে পারবেন. এতে research, demo, tiny ML prototyping, আর rule-based experimentation সহজ হবে.
Prediction target in °C
Training Features
Live data will be used to fit a lightweight regression model.
Model StatusNot trained
Waiting for live data
Samples0
Training rows used
R² Score—
Higher is better
Predicted Relay Time—
No prediction yet
Prediction InputsFill manually or load from latest live reading
Train or apply a model first, then click Predict Relay Time.
Model Formula / CoefficientsCustom JSON keys can include intercept, body_temp, water_temp, ambient_temp, relay_on, fever, servo_angle
y = intercept + Σ(coefficient × feature)
Use custom coefficients for experiments, custom Tiny ML logic, or research baseline comparison.
🚨
⚠ Fever Detected!Patient body temperature exceeds normal range. Buzzer activated. DC pump engaged for water treatment.
All Devices
Device 1 — ECG
Device 2 — Full Monitor
Patient Status
NORMAL
Patient
Monitoring...
— °C
Last reading: —
🌡️
Body Temperature
—
°C
—
—
💧
Water Temperature
—
°C
—
—
🌡
Ambient Temperature
—
°C
—
Normal
📈
ECG
—
raw signal
—
—
Components & Actuators
💧
DC Pump (Relay)
—
Water treatment system
🔔
Buzzer Alert
—
Fever detection alarm · 1 kHz
⚙️
Servo Motor
—
Angle: —°
🖥
OLED Display
Active
SH1106 128×64 via TCA9548A MUX
📡
WiFi / Supabase
—
Connecting...
Device Status
esp32-health-01 — ECG Monitor
---
Fetching...
esp32-health-02 — Full Monitor
---
Fetching...
ECG Waveform — Live
Raw ADC Signal — Last 80 samples
Device 1
Device 2
Temperature Trends — Recent 100 Readings
Body
Water
Ambient
DC Pump & Fever Events
Recent 100 Temperature Readings
Temperature Fetch Control
এখান থেকে তুমি যতটা চাইবে ততটা latest temperature reading fetch করতে পারবে. Quick choice select করো, বা custom count লিখে Fetch Now চাপো.