DIY Sleep Tracker Using Arduino: A Step‑by‑Step Guide

Sleep is one of the most critical pillars of health, yet many of us still lack reliable, continuous insight into how we actually rest at night. Commercial sleep trackers can be pricey, and their proprietary ecosystems often lock you into a specific brand. For hobbyists, makers, and anyone who enjoys a hands‑on approach, building a sleep tracker with an Arduino offers a low‑cost, fully customizable alternative that can grow with your curiosity. In this guide we’ll walk through every stage of the project—from selecting the right sensors to polishing the final wearable—so you can create a functional, data‑rich sleep monitor that you truly understand.

Why Choose Arduino for Sleep Tracking?

• Open hardware – Arduino boards are widely documented, inexpensive, and supported by a massive community. This means you can find schematics, libraries, and troubleshooting advice with a quick search.
• Modular design – Adding or swapping sensors is straightforward. Want to experiment with a pulse‑oximeter or a tiny temperature probe? You can do it without redesigning the whole system.
• Real‑time processing – The microcontroller can filter and preprocess data on the fly, reducing the amount of raw information you need to store and making post‑processing easier.
• Scalability – Start with a single accelerometer to detect movement, then expand to heart‑rate, skin temperature, or ambient light as you become more comfortable with the platform.

Core Components and Materials List

*Tip:* If you prefer a ready‑made wearable form factor, you can mount the components on a soft silicone wristband or a small fabric pouch sewn into a pillowcase. The key is to keep the sensor suite close to the body without causing discomfort.

Understanding the Sensors – What to Measure and Why

1. Accelerometer (MPU‑6050)

*Detects micro‑movements.* During REM sleep the body is largely still, while light sleep shows frequent repositioning. By analyzing the magnitude and frequency of motion, you can infer sleep continuity and detect awakenings.

2. Pulse Sensor

*Provides heart‑rate variability (HRV).* HRV tends to increase during deep sleep and decrease during REM. The sensor outputs a voltage proportional to blood volume changes; after filtering, you can extract inter‑beat intervals (IBI) for HRV calculations.

3. Skin Temperature (MAX30205)

*Tracks peripheral temperature.* A slight drop in skin temperature is a hallmark of the transition into deep sleep. Because the sensor is digital (I²C), it offers precise readings with minimal noise.

4. Ambient Light (TSL2591) – *Optional*

*Measures room darkness.* Light exposure during the night can disrupt circadian rhythms. Recording ambient lux helps you correlate sleep interruptions with environmental factors.

Each sensor operates on either I²C (MPU‑6050, MAX30205, TSL2591) or analog (Pulse Sensor) interfaces, making wiring simple and leaving room for future expansion.

Building the Hardware – Wiring and Assembly

1. Power Distribution

> Note: The Nano 33 IoT runs at 3.3 V logic. If you use a 5 V sensor, add a level‑shifter or a simple voltage divider on the SDA/SCL lines.

2. I²C Bus

• Connect SDA (A4 on Nano) to SDA pins of MPU‑6050, MAX30205, and TSL2591.
• Connect SCL (A5 on Nano) to SCL pins of the same sensors.
• Add 4.7 kΩ pull‑up resistors on both SDA and SCL lines (to 3.3 V) if the module does not already include them.

3. Pulse Sensor

• Connect the signal wire to A0 (analog input).
• Place a 10 kΩ series resistor between the signal line and the Arduino to limit current spikes.
• Add a 100 nF decoupling capacitor from signal to ground to smooth high‑frequency noise.

4. SD Card Module (SPI)

5. Power Management

• Connect the Li‑Po battery to the TP4056 module (BAT+ to battery, BAT‑ to GND).
• The OUT+ of TP4056 goes to the Arduino’s Vin (or directly to the 3.3 V regulator if you use a separate regulator).
• Include a switch between the battery and the regulator for easy on/off control.

6. Enclosure Assembly

1. Mount the Arduino on a small standoff inside the case.
2. Secure the sensors on the inner surface facing outward (accelerometer and pulse sensor) or against the skin (temperature sensor).
3. Route the wires through a small opening, sealing with silicone to keep moisture out.
4. Attach the SD card slot on the exterior for easy removal of data cards.

A quick prototype can be assembled on a breadboard; once verified, transition to a perfboard or custom PCB for a more robust final product.

Programming the Arduino – Code Walkthrough

Below is a concise sketch that demonstrates sensor initialization, data acquisition, basic filtering, and logging to an SD card. The code is deliberately modular so you can add or remove sensors without breaking the flow.

/* DIY Sleep Tracker – Arduino Nano 33 IoT
   -------------------------------------------------
   Libraries required:
   - Wire.h (built‑in)
   - MPU6050_light.h (https://github.com/rfetick/MPU6050_light)
   - SparkFun_MAX30205.h (https://github.com/sparkfun/SparkFun_MAX30205_Arduino_Library)
   - Adafruit_TSL2591.h (https://github.com/adafruit/Adafruit_TSL2591)
   - SD.h (built‑in)
*/

#include <Wire.h>
#include <MPU6050_light.h>
#include <SparkFun_MAX30205.h>
#include <Adafruit_TSL2591.h>
#include <SD.h>

#define ACCEL_INT_PIN 2          // optional interrupt pin
#define PULSE_PIN     A0
#define CS_PIN        10         // SD card chip select

MPU6050 mpu(Wire);
MAX30205 tempSensor;
Adafruit_TSL2591 lightSensor = Adafruit_TSL2591(2591);

File dataFile;

// ---------- Helper Functions ----------
float readPulse() {
  static const uint16_t threshold = 512; // adjust after calibration
  static uint32_t lastBeat = 0;
  static uint16_t beatCount = 0;

  uint16_t val = analogRead(PULSE_PIN);
  if (val > threshold && (millis() - lastBeat) > 300) { // debounce ~300 ms
    lastBeat = millis();
    beatCount++;
  }
  return (float)beatCount; // cumulative beats since start
}

void logData(String line) {
  dataFile = SD.open("sleep.csv", FILE_WRITE);
  if (dataFile) {
    dataFile.println(line);
    dataFile.close();
  }
}

// ---------- Setup ----------
void setup() {
  Serial.begin(115200);
  while (!Serial) ; // wait for Serial Monitor

  // Initialize I2C
  Wire.begin();

  // MPU‑6050
  mpu.begin();
  mpu.calcOffsets(); // calibrate at rest

  // Temperature sensor
  if (!tempSensor.begin()) {
    Serial.println(F("MAX30205 not detected!"));
  }

  // Light sensor (optional)
  if (!lightSensor.begin()) {
    Serial.println(F("TSL2591 not detected!"));
  } else {
    lightSensor.setGain(TSL2591_GAIN_LOW);
    lightSensor.setTiming(TSL2591_INTEGRATIONTIME_100MS);
  }

  // SD card
  if (!SD.begin(CS_PIN)) {
    Serial.println(F("SD init failed!"));
    while (true);
  }

  // Write CSV header
  logData("timestamp,accelX,accelY,accelZ,heartBeats,tempC,lux");
}

// ---------- Main Loop ----------
void loop() {
  // Timestamp (ms since start)
  unsigned long t = millis();

  // ---- Accelerometer ----
  mpu.update();
  float ax = mpu.getAccX();
  float ay = mpu.getAccY();
  float az = mpu.getAccZ();

  // ---- Pulse ----
  float beats = readPulse(); // cumulative beats

  // ---- Temperature ----
  float tempC = tempSensor.readTempC();

  // ---- Light (optional) ----
  uint16_t lux = 0;
  if (lightSensor.enabled) {
    sensors_event_t ev;
    lightSensor.getEvent(&ev);
    lux = ev.light;
  }

  // Build CSV line
  String line = String(t) + "," +
                String(ax, 3) + "," +
                String(ay, 3) + "," +
                String(az, 3) + "," +
                String(beats, 0) + "," +
                String(tempC, 2) + "," +
                String(lux);

  // Log to SD
  logData(line);

  // Optional: stream to Serial for real‑time monitoring
  Serial.println(line);

  // Sleep to reduce power consumption (adjust as needed)
  delay(200); // 5 Hz sampling – sufficient for sleep analysis
}

Key points in the sketch:

• Sensor calibration – `mpu.calcOffsets()` runs a quick static calibration; repeat after each power‑cycle for best accuracy.
• Heart‑beat detection – A simple threshold works for many users, but you can replace it with a band‑pass filter and peak detection for higher fidelity.
• Data logging – Each record is a CSV line, making it easy to import into Excel, Python (pandas), or R for post‑processing.
• Power saving – The `delay(200)` yields a 5 Hz sample rate, which is more than enough for movement and HRV analysis while conserving battery life.

Feel free to modularize the code further, e.g., moving each sensor into its own class or adding Bluetooth transmission (`BLEPeripheral`) if you want live streaming to a phone or PC.

Data Storage and Retrieval – Using SD Card and Serial Output

1. Why an SD Card?

• Offline reliability – No need for a constant Wi‑Fi or Bluetooth connection while you sleep.
• Large capacity – A 2 GB card can store weeks of 5 Hz data (≈ 30 MB).
• Portability – Simply remove the card and plug it into any computer.

2. File Format

• CSV (Comma‑Separated Values) – Universally readable.
• Header row – `timestamp,accelX,accelY,accelZ,heartBeats,tempC,lux` makes column identification trivial.
• Timestamp – Milliseconds since power‑on; you can later convert to wall‑clock time by noting the start time in a separate log.

3. Retrieving Data

1. Eject the SD card after the recording session.
2. Insert it into a computer or a USB‑SD adapter.
3. Open the `sleep.csv` file in your preferred analysis tool.

If you prefer a wireless approach, you can add a BLE UART service to stream the same CSV lines to a mobile app, but keep the SD card as a fallback backup.

Calibration and Baseline Testing

Before trusting the tracker for nightly use, perform a short calibration routine:

1. Static Accelerometer Test – Place the device on a flat surface. The Z‑axis should read close to 1 g, while X and Y read near 0 g. Adjust offsets in the sketch if deviations exceed ±0.05 g.
2. Pulse Sensor Baseline – Record a 2‑minute session while at rest. Verify that the beat count matches a known heart‑rate monitor (e.g., a chest strap). Tweak the threshold or add a moving‑average filter if false peaks appear.
3. Temperature Sensor Check – Compare the reading against a calibrated digital thermometer. The MAX30205 is accurate to ±0.1 °C, but ensure the sensor is in good thermal contact with the skin (use a thin silicone pad).
4. Light Sensor Validation – In a dark room, lux should be < 1; under a desk lamp, it should be around 200‑300 lux.

Document the offset values you apply; they become part of the device’s “profile” and can be stored in EEPROM for automatic loading on power‑up.

Analyzing the Collected Data – From Raw Numbers to Sleep Stages

While the Arduino provides raw sensor streams, the real insight comes from post‑processing. Below is a high‑level workflow you can implement in Python (pandas + SciPy) or R.

1. Pre‑processing

• Resample to a uniform 1 Hz grid (interpolate missing points).
• Filter accelerometer data with a low‑pass Butterworth filter (cut‑off ≈ 0.5 Hz) to isolate gross body movements.
• Derive inter‑beat intervals (IBI) from the cumulative beat count, then compute HRV (RMSSD) over 5‑minute windows.

2. Feature Extraction

3. Simple Rule‑Based Sleep Staging

These thresholds are starting points; refine them by comparing against a reference device (e.g., a commercial tracker) for a few nights.

4. Visualization

• Hypnogram – Plot sleep stage over time.
• HRV curve – Overlay with movement index to spot correlations.
• Temperature profile – Observe the cooling curve at sleep onset.

Export the visualizations as PNG or PDF for personal records or to share with a sleep specialist.

Enhancing Accuracy – Multi‑Sensor Fusion and Filtering Techniques

If you find the basic rule‑based approach too noisy, consider these upgrades:

1. Kalman Filter – Fuse accelerometer and HRV data into a single state estimate of “sleep depth.”
2. Machine Learning – Train a lightweight decision tree or random forest on labeled data (e.g., nights where you also wore a clinical polysomnography device). Export the model to the Arduino using TensorFlow Lite for Microcontrollers for on‑device inference.
3. Additional Sensors –
• Electrodermal Activity (EDA) sensor for stress detection.
• Microphone (with a low‑pass filter) to capture snoring patterns, which can be correlated with REM.

These enhancements increase complexity but also bring the DIY tracker closer to research‑grade quality.

Power Management and Wearability Considerations

Battery Life Estimation

A 500 mAh Li‑Po will last roughly 8‑10 hours at this sampling rate. To extend runtime:

• Reduce sampling to 1 Hz after the first hour of sleep (most transitions happen early).
• Put the MPU‑6050 into low‑power mode (`mpu.sleep()` after a period of inactivity).
• Turn off the SD card when not writing (use `SD.end()`).

Form Factor Tips

• Weight – Keep the total mass under 30 g to avoid discomfort.
• Strap material – Soft silicone or breathable fabric reduces skin irritation.
• Heat dissipation – The MAX30205 can be affected by the board’s heat; place it on a small thermal pad away from the MCU.

Common Pitfalls and Troubleshooting Tips

A systematic “swap one component at a time” approach usually isolates the faulty part quickly.

Scaling Up – From Prototype to a More Refined Device

1. Custom PCB – Design a two‑layer board that integrates the Arduino Nano 33 IoT footprint, sensor footprints, and a power‑management section. This reduces wiring errors and improves reliability.
2. Enclosure 3‑D Printing – Use a flexible TPU filament for the outer shell; embed slots for the SD card and a small USB‑C port for charging.
3. Wireless Sync – Add a BLE service that pushes the CSV data to a companion desktop app, enabling automatic backups after each night.
4. Open‑Source Release – Publish the schematics, code, and analysis scripts on GitHub under an MIT license. Community contributions can add new features (e.g., automatic sleep‑stage classification).

By iterating on the hardware and software, you can evolve the tracker from a hobbyist experiment into a robust personal health tool.

Safety, Comfort, and Ethical Considerations

• Electrical safety – Keep all exposed conductors insulated; use a polyfuse (e.g., 500 mA) on the power line to protect against short circuits.
• Skin compatibility – Use medical‑grade silicone or hypoallergenic fabric for any part that contacts the skin for extended periods.
• Data privacy – Sleep data is personal. Store the CSV files on encrypted drives or use password‑protected cloud backups if you sync them.
• Interpretation limits – A DIY tracker can highlight trends but is not a diagnostic device. Encourage users to consult a sleep specialist for persistent issues.

Resources and Next Steps

• Component datasheets – MPU‑6050 (InvenSense), MAX30205 (Maxim Integrated), TSL2591 (AMS).
• Arduino libraries – `MPU6050_light`, `SparkFun_MAX30205`, `Adafruit_TSL2591`, `SD`.
• Signal‑processing tutorials – “Heart‑Rate Variability Analysis in Python” (available on SciPy.org).
• Community forums – Arduino Forum, r/DIY on Reddit, and the Open‑Source Hardware Association (OSHWA) mailing list.
• Future projects – Add a vibration motor for gentle wake‑up alerts, or integrate a real‑time clock (RTC) module to timestamp recordings without relying on the Arduino’s uptime counter.

With the foundations laid out in this guide, you now have everything you need to design, build, and refine a fully functional Arduino‑based sleep tracker. The journey from soldering the first wire to visualizing nightly sleep patterns is both educational and rewarding—plus, you’ll gain a deeper appreciation for the subtle rhythms that keep you refreshed each morning. Happy building, and may your nights be restful!