Evaluate Person's Pulse Data with Machine Learning

Transcript

1. Evaluate Person's Pulse Data with Machine Learning Gabriela de Queiroz Senior Developer Advocate, IBM gdq@ibm.com
2. 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/
3. 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
4. 4 https://developer.ibm.com/code/patterns/develop-web-based-mobile-health-app-uses-machine-learning/
5. 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
6. General Workflow 6
8. Steps 8
9. Clone/Download the github repo: https://github.com/IBM/pulse-iot-wml-mobile-health 9
10. 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
11. Step 1: Create a Node.js (Node.js Cloudant DB Web Starter) 11
12. 1.1: Create a Node.js and Cloudant DB 12
14. This will will create the app and the CloudantNoSQLDB App CloudantNoSQLDB
15. Step 2: Set up the Watson Machine Learning service in IBM Cloud. 15
16. 2.1: Create a Machine Learning service 16
17. • Create a project • Add new Watson ML model
18. 2.2: Save the credentials 18
19. Step 3: Set up Watson IoT Platform in IBM Cloud. 19
20. • Create a project • Add new Watson ML model
21. • 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
22. Step 4: Set up the Machine Learning Model in IBM Watson Studio. 22
23. 4.1: Create a new project 23
24. 4.2: Create a ML model 24 Under Assets, click on “New Watson Machine Learning Model”
25. Define the model details: 1) Give it a name 2) Associate a Spark Service 3) Associate a Machine Learning Service
26. 4.3: Associate a Spark instance 26
27. 4.4: Associate a Machine Learning service instance 27
28. All good!
29. 4.5: Create a notebook 29 Under Assets > Notebooks > New notebook
30. 4.6: Import Notebook and Define the language and Spark version 30 You can import the ipython notebook "Pulse Rates.ipynb" (in the repo)
31. 4.7: Run the ipython notebook "Pulse Rates.ipynb" 31 Don’t forget to fill your Watson ML Credentials!!
32. Step 5: Deploy the App 32
33. 5.1: After the model is deployed get the credentials and save to .env 33
34. 5.2: Run each line by line the content of the deploy.sh 34
35. The app is ready! 35
36. DOC ID / Month XX, 2018 / © 2018 IBM Corporation How could I leverage the pulse rate app during emergencies and natural disasters? 36
37. DOC ID / Month XX, 2018 / © 2018 IBM Corporation 37 Source: https://news.stjohnvic.com.au/first-aid-hypothermia-treatment/
38. Cave Rescue 38
39. Landslides 39
41. !41