Blog 5 - Project Research "A Bigger Heart"

 IoT Project


Introduction

Heart Rate Variability (HRV) in recent years has emerged as a powerful method for assessing overall health and stress levels. As part of our IoT class project, we're exploring how to create an accessible HRV monitoring system using the Micro:bit platform. This project combines hardware sensing, signal processing algorithms, and data analytics to create a complete IoT solution.

What is Heart Rate Variability?

Unlike heart rate, which measures beats per minute, Heart Rate Variability (HRV) measures the variation in time between successive heartbeats. This subtle variation contains valuable information about our autonomic nervous system:

Higher HRV generally indicates better cardiovascular fitness, stronger stress resilience, and better recovery

Lower HRV often correlates with increased stress, poor sleep, or potential health issues


Project Overview

Our lecturer has challenged us to create a system that can:

  1. Detect and capture heart signals using a Micro:bit
  2. Process the raw signals to extract meaningful HRV data
  3. Apply Fast Fourier Transform (FFT) to analyze frequency domains
  4. Transmit heartbeat patterns to another device with haptic feedback
  5. Send collected data to a spreadsheet for further analysis

A unique aspect of this project is creating an emotional connection by allowing one person to physically feel another person's heartbeat through vibration, creating a tangible connection between people regardless of distance.


Hardware Components

The core of our design revolves around these components:

BBC Micro:bit: Our main computing platform with built-in accelerometer, Bluetooth capability, and programmable I/O pins

Pulse Sensor: An analog optical heart rate sensor that can be connected to the Micro:bit

Vibration Motor: For haptic feedback to physically represent another person's heartbeat

Additional Components Ideas: Small OLED screen possibly


The Analog in Component

The analog in portion of our project refers to capturing the analog heart signal. The pulse sensor works by shining light into tissue and measuring how much light is reflected back, which varies with blood flow. This creates an analog waveform that:

  1. Peaks with each heartbeat
  2. Contains subtle variations between peaks (the basis for HRV)
  3. Requires proper filtering to remove noise

The Micro:bit can read this analog signal through its pins and convert it to digital values for processing.


Fast Fourier Transform (FFT) Analysis

One of the most challenging and interesting aspects of this project is implementing FFT on the Micro:bit. Here's why it matters:

FFT converts time-domain signals (the raw heartbeat data) into frequency-domain information. This reveals different frequency bands that correspond to different physiological systems:

High frequency (HF): 0.15-0.4 Hz, reflects parasympathetic activity (rest and digest)

Low frequency (LF): 0.04-0.15 Hz, reflects both sympathetic and parasympathetic activity

Very low frequency (VLF): 0.0033-0.04 Hz, reflects longer-term regulatory mechanisms

The ratio between these frequencies provides insights into autonomic balance and stress levels.


Programming the Micro:bit

We could program the Micro:bit using Python (specifically MicroPython) to:

  1. Sample the analog heart signal at an appropriate frequency (likely 100+ samples per second)
  2. Detect R-peaks (the highest points in the heart signal)
  3. Calculate inter-beat intervals
  4. Apply a simplified FFT algorithm optimised for the Micro:bit's limited processing power
  5. Extract key HRV metrics:
    • SDNN (Standard Deviation of NN intervals)
    • RMSSD (Root Mean Square of Successive Differences)
    • Frequency domain metrics (LF/HF ratio

Data Collection to Spreadsheet

The final step involves getting our processed data into a spreadsheet for long-term tracking and analysis:

  1. The Micro:bit will periodically send data packets containing HRV metrics
  2. Data can be transmitted via:
    • Bluetooth to a phone app (then forwarded to a cloud spreadsheet)
    • USB connection to a computer running a simple Python script
    • Direct integration with Google Sheets API via a companion web application

Challenges and Considerations

Some challenges we anticipate:

  1. Signal Quality: Getting a clean heart signal may require careful sensor placement and additional filtering
  2. Processing Power: The Micro:bit has limited computational resources for complex FFT operations
  3. Battery Life: Continuous monitoring will drain batteries quickly
  4. Data Accuracy: Validating our HRV measurements against commercial devices

The Heartbeat Connection Feature

Perhaps the most innovative aspect of our project is the ability to physically transmit the sensation of a heartbeat between people. Here's how we'll implement this:

  1. Capturing the Heartbeat: The sender's Micro:bit will detect heartbeat peaks from the pulse sensor
  2. Real-time Transmission: Using Bluetooth or radio communication between Micro:bits
  3. Haptic Feedback: The receiver's Micro:bit will activate a vibration motor in sync with the sender's actual heartbeat
  4. Intensity Mapping: We'll map HRV characteristics to vibration intensity, allowing subtle feelings of stress or relaxation to be communicated

This creates an intimate connection between two people - imagine feeling your loved one's actual heartbeat while they're away, or a doctor remotely monitoring a patient's heart rhythm not just through data, but through tactile sensation.


Conclusion

Looking into this project, I'm particularly excited about bridging the gap between cold technical data and warm human connection. Working with the analog heart signals will be very interesting- translating something as intimate as a heartbeat into digital data and then back into physical sensation through vibration.

I expect the FFT implementation to be the most technically challenging aspect of the project, especially on the Micro:bit's limited hardware, but that challenge is another factor of what makes it interesting. Finding creative ways to optimise algorithms for constrained environments is a valuable skill I'm eager to develop.

What I find most compelling about this project is how it combines multiple areas: hardware design, signal processing, wireless communication, and even aspects of biomedical engineering. The emotional dimension of feeling someone else's heartbeat remotely adds a layer of human-centered design that goes beyond typical IoT projects.

As someone interested in the intersection of technology and human experience, I'm looking forward to the moment when we successfully transmit a heartbeat between devices and actually feel that connection. That moment when the technical implementation creates a genuinely emotional response will be worth all the debugging we will have to do.

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