Evaluate Person's Pulse Data with Machine Learning

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Evaluate Person's Pulse Data with Machine Learning Gabriela de Queiroz Senior Developer Advocate, IBM [email protected]

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CALL FOR CODE 2 • Worldwide, multi-year initiative that inspires developers to solve pressing global problems with sustainable software solutions. • Create applications with IBM Cloud technology that improve disaster preparedness and relief, build resilient communities, and safeguard the health and well-being of individuals and institutions. https://developer.ibm.com/callforcode/

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Agenda 3 In this 30-minute online meetup you will learn: - How to build a mobile web app to check a person's pulse rate - How to evaluate a person's pulse data with Machine Learning - How to leverage pulse data during emergencies and natural disasters

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4 https://developer.ibm.com/code/patterns/develop-web-based-mobile-health-app-uses-machine-learning/

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DISCLAIMER: This application is used for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review. It is not intended to serve as a medical application. There is no representation as to the accuracy of the output of this application and it is presented without warranty. 5

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General Workflow 6

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Steps 8

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Clone/Download the github repo: https://github.com/IBM/pulse-iot-wml-mobile-health 9

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Steps: 1.Create the Node.js app. 2.Set up the Watson Machine Learning service in IBM Cloud. 3.Set up Watson IoT Platform in IBM Cloud. 4.Set up the machine learning model in IBM Watson Studio. 5.Try it out on your smartphone. 10

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Step 1: Create a Node.js (Node.js Cloudant DB Web Starter) 11

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1.1: Create a Node.js and Cloudant DB 12

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This will will create the app and the CloudantNoSQLDB App CloudantNoSQLDB

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Step 2: Set up the Watson Machine Learning service in IBM Cloud. 15

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2.1: Create a Machine Learning service 16

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• Create a project • Add new Watson ML model

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2.2: Save the credentials 18

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Step 3: Set up Watson IoT Platform in IBM Cloud. 19

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• Create a project • Add new Watson ML model

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• Add it by binding from within your node.js app (click on "+ Add A Connection" to add this service to your app) Make sure the service is binded

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Step 4: Set up the Machine Learning Model in IBM Watson Studio. 22

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4.1: Create a new project 23

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4.2: Create a ML model 24 Under Assets, click on “New Watson Machine Learning Model”

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Define the model details: 1) Give it a name 2) Associate a Spark Service 3) Associate a Machine Learning Service

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4.3: Associate a Spark instance 26

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4.4: Associate a Machine Learning service instance 27

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All good!

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4.5: Create a notebook 29 Under Assets > Notebooks > New notebook

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4.6: Import Notebook and Define the language and Spark version 30 You can import the ipython notebook "Pulse Rates.ipynb" (in the repo)

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4.7: Run the ipython notebook "Pulse Rates.ipynb" 31 Don’t forget to fill your Watson ML Credentials!!