Diagnostico

About the project

This is an intelligent patient monitoring solution that uses BLE to collect data from patients and a unique ML core that predicts body parameters like cholesterol using non-invasive techniques.

Project info

Difficulty: Easy

Platforms: ArduinoCypressEverything ESPMicrochipArm

Estimated time: 3 hours

License: MIT license (MIT)

Items used in this project

Hardware components

STM32 Blue Pill STM32 Blue Pill x 1
36p 2.54 Mm Pin Male 36p 2.54 Mm Pin Male x 1
CC2541 CC2541 x 1
CR2302 CR2302 x 1
CR2302 holder CR2302 holder x 1
Sparkfun Single Lead Heart Rate Monitor - Ad8232 Sparkfun Single Lead Heart Rate Monitor - Ad8232 x 1
Sparkfun Pulse Oximeter And Heart Rate Sensor - Max30101 & Max32664 (qwiic) Sparkfun Pulse Oximeter And Heart Rate Sensor - Max30101 & Max32664 (qwiic) x 1
Passives Passives x 1
Cy8ckit-062-ble Psoc 6 Pioneer Kit Cy8ckit-062-ble Psoc 6 Pioneer Kit x 1

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Software apps and online services

Intel Dev Cloud Intel Dev Cloud
Jupyter Notebook Jupyter Notebook
Anaconda Anaconda
Arduino IDE Arduino IDE
Processing IDE Processing IDE

Hand tools and fabrication machines

Screwdriver Screwdriver x 1
Wrench Wrench x 1
Crimper Crimper x 1
Hot Glue Hot Glue x 1
Utility knife Utility knife x 1
Soldering Station Soldering Station x 1
Flux Flux x 1
Pen Flux Pen Flux x 1
Tweezers Tweezers x 1

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Story

Introduction

All around the world due to the COVID-19 pandemic many people are not getting the required medical attention. Doctors are not able to manage the medical resources and due to this, our current medical framework is collapsing.

In my place, Hyderabad, India many of the patients are not able to get admission into hospitals and they are in-home quarantine. But, if there was a way for a doctor to monitor these people remotely and with less effort, they would be able to help these people better.

Proposed idea

Using a simple wearable device and a software platform we plan to integrate the patient data into a platform and make processed data available for a doctor.

The product we designed collects patient data via a wearable, transmits them to a data aggregator via BLE, and then the aggregator pushes it to a server where a machine learning model generates inference. This along with the patient data is displayed on the doctor's computer.

Advantages

1) Simple and easy to use

2) Helps manage patients more efficiently

3) No extra efforts needed

Schematics, diagrams and documents

Low power BLE wearable with Spo2 , heart rate and ECG monitoring

CAD, enclosures and custom parts

Low power BLE wearable with Spo2 , heart rate and ECG monitoring

Go to download

Code

Max30102 STM32 Blue Pill interfacing code

Processing IDE interface

Machine Learning Core

This notebook is hosted on intel dev cloud where we used the Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz as the core compute instance. The model is basically a Decision Tree Regressor that was trained on data partially collected from a kaggle dataset and partially from real-life sensor data.

ECG Sensor interfacing

Credits

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