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Hacking health: IoT, analytics and other trends

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Slides from "Hacking Health" workshop:
HIP613: IBM Bluemix For Healthcare (Part 1)
Feb 23rd, 2017

Published in: Health & Medicine
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Hacking health: IoT, analytics and other trends

  1. 1. Internet of Things And other interesting technology trends Jim Boland Software Architect Cognos Analytics IBM Canada Ltd @neoslimjimhttps://www.slideshare.net/JimBoland3/hacking-health-iot-analytics-and-other-trends
  2. 2. Trends in computing • Wearable technology • iBeacons and micro-location context • Contextual and anticipatory computing • IoT, big data and analytics • Wrap up: how all this is about plugging us into the IoT! Mainframe Desktop PC Laptop Mobile (smartPhone/tablet) Trends: Moore's law, computing distribution, personal/specialized, mobility ...
  3. 3. Wearables
  4. 4. vandrico.com/wearables • Head: 63. 84 • Neck: 12 12 • Chest: 10 21 • Torso: 19 27 • Arm: 8 11 • Wrist: 119 213 • Hand: 5 8 • Fingers: 10 11 • Legs: 9 13 • Feet: 9 12 2014 2017
  5. 5. Wearables • Wearables complement (rather than replace) smartPhones • Wearable technology is about the more seamless integration of tech: • Notifications - SmartWatches, SmartGlasses • Sensors - Fitness/health devices • Remotes and Identification • Virtual Assistants
  6. 6. SmartWatch & Glasses • While much of the hype is with these, not the early success...
  7. 7. Fitness/Health • Fitness/health devices the early leader in wearables • Sensors to monitor heart rate, activity, sleep, etc. • Nike abandoned the Fuelband
  8. 8. Remotes • Want an interface like Minority Report and Tony Stark? • Map physical gestures to intent, from: • Fingers - Fin ring • Arm - Myo ( Thalmic Labs) • Wrist - Glance (kiwi wearables)
  9. 9. Identification Wearables represent your identity for: • Payment systems - Apple Watch and ApplePay • Application/device - vivalnk tatoos • Physical security - Kevo locks • Contextual/anticipatory computing As well, wearable biometric sensors create ways for establishing identity: • Touch ID - Fingerprint scanners • Nymi - heartbeat signature
  10. 10. Smart Shoes? • Vibration in respective shoe to indicate right/left turn
  11. 11. Summary on wearables • Overall themes: • Collect sensor data to gain context of the user • Unobtrusive means to feed information back to the user • Represent the user remotely (intent and identity) • There are DYI prototyping kits out there (e.g. MbientLab’s Metawear) • There are technical challenges still: security and power
  12. 12. iBeacon • Conceptually similar to a GPS satellite, but at a micro-location (and often indoors) scale • In a store with a beacon in each department, the phone app knows where it is, by which beacon(s) it sees • iBeacons don't track/collect data from phones. Without an installed app, there is no interaction with the phone/beacon iBeacons "I'm beacon #53"
  13. 13. Micro-location • Emergency ward patient tracking • Contextual retail assistance • Indoor mapping/directions • Home automation • Tealeaf/Google Analytics style analytics of in-store foot traffic • Micro-location based geo-fencing/context
  14. 14. Contextual Computing Better understand the context of a user request, in terms of: • Context from device sensors • Context from user interaction history • Context from user profile/social graph Providing context of who, where, when and the previous conversation
  15. 15. Siri example Next request in context of first
  16. 16. Affective Computing • Special case: using context of a user, to understand how people feel and emotions (and adapting responses as a result) • With the pervasive sensors of smartphones and wearables, there is a new source of information
  17. 17. Anticipatory Computing • Contextual computing: context is used to interpret a user request • Anticipatory computing: monitors context and anticipates user's need without an explicit request
  18. 18. "Invisible buttons" • Amber Case [ESRI/Geoloqi] coined the term "invisible buttons" • Concept: instead of touching a button on a smartphone as the trigger for an action, the user becomes the trigger by entering a geofenced area • Ultimate example of the User Interface getting out of the way • Example: my house! (Kevo lock, 21 iBeacons, Hue lights, airPlay speakers)
  19. 19. IoT and Big Data • IoT = devices sending and receiving data • IoT = LOTS of devices, sending LOTS of data, ALL the time • IoT = Big Data! 1001010 1001010 1001010 1001010
  20. 20. Making sense of big data • How do you get value from all that data? • Analytics can help!
  21. 21. Data in motion • Streaming analytics • Scalable solutions to tame the firehose • Latency sensitive response • Realtime feedback • Moving averages • Identify out of range values • Smooth out jittery sensor data
  22. 22. Data at Rest • Batch analytics • Historical analysis • Trends • Outliers • Correlations in the data • Predictive analytics • Stream analytics, batch analytics - both have a role in IoT! Database
  23. 23. Example IoT Architecture Local Hub/ IoT Gateway Device Sensor Sensor Device Bluemix - Streaming Analytics - Batch Analytics - Other business logic MQTT BLE WIFI ZigBee
  24. 24. Analytics in bluemix • Trying analytics out! • Freemium account: https://console.ng.bluemix.net
  25. 25. Watson Analytics • Cognitive computing - machines doing the analysis • Freemium account: www.ibm.com/analytics/watson-analytics/
  26. 26. Node-Red • https://nodered.org/ • Run locally (e.g. Rpi) or cloud (e.g Bluemix)

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