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Ambient Intelligence for Connected Home Assisted Living
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Bringing Ambient Intelligence to Connected Home Products I O T 3 3 2 - R Neel Sendas Sr. Technical Account Manager Amazon Web Services, Inc. Krishnan Ganapathy Enterprise Solutions Architect Amazon Internet Services Pvt Ltd.
- 3. Related breakouts IOT208 - What's new with AWS IoT analytics services? GPSTEC413 - Best practices and design patterns for building IoT solutions on AWS ARC339-R1 - [REPEAT 1] Best practices for IoT architecture using AWS smart product solution API305-R - [REPEAT] Building serverless machine-learning workflows
- 4. Agenda What is Ambient Intelligence (AmI)? Connected Homes for Assisted Living AWS Technologies for Assisted Living AWS IoT Analytics Deep Dive Demo Challenges and Best Practices
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ““The future will be one where our environment satisfies our needs, mostly without our having to think about it. The intelligence is ambient - much like the light in this room.” Roel Piper - 1999 Ambient Intelligence
- 7. ambient adjective am·bi·ent| ˈam-bē-ənt existing or present on all sides : encompassing Ambient intelligence brings ‘smarts’ to a connected home, which means a home able to: • assure more security to its dwellers • communicate with the outside world • save energy • be more comfortable What is Ambient Intelligence (AmI)?
- 8. Use Case – Ambient Assisted Living Need for AmI in Assisted living • Aging population • Mobility detection • Vital statistics monitoring • Location detection • Activity monitoring • Eliminating social isolation
- 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 10. Connected Homes for Assisted Living - Building Blocks Sensors Depth Sensors, Motion Sensors, Thermal Sensors Event Orchestration and Visualization Conversational AI, Actuator Intelligent Event Recognition Computer Vision Algorithms, Anomaly Detection
- 11. Assisted Living – Technology Enablers • Advancement in home automation (or Domotics) technologies • Wireless Sensor Networks • Wearable Biomedical Sensors and Activity Trackers • Environment Sensors • Protocol Interoperability Hubs – ZigBee/ZigBee PRO, BLE, LoRaWAN • IoT Gateway Platforms • Networking Integration with Care Centers & family members • Edge Computing and ML Advancements • Predictive modeling and Anomaly detection
- 12. 360° View of Ambient Assisted Living • Detection of fall or sudden injury • Communicate with family members • Track physical regimen • Detect Leading Indicators • Co-relate medical diagnosis Vital Parameter Checks Activity Tracker User Location Detection Extended Assistance
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. AWS Technologies for Assisted Living • Amazon FreeRTOS • AWS IoT Core and AWS IoT Greengrass • Amazon Sagemaker Neo • AWS IoT Analytics • Amazon QuickSight • AWS IoT Events • Alexa for Business
- 15. AWS IoT Analytics for Assisted Living Connected Elderly Home CameraThermostat Medical House Door lock Elderly Person Wearables IoT Core IoT Analytics Channel IoT Analytics Pipeline Medical Institution Medical History Enrichment Weather Information Weather Station Pipeline Enrichment Activities Inhabitant safety alerts IoT Analytics Datastore Monitoring Dashboards Extended Assistance Medical Support Family Members Facilities Management Machine Learning Inference Pre-built Machine Learning Models
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 17. AWS IoT Device Simulator
- 18. Ingest Messages into AWS IoT Analytics Channel
- 19. Enrich Messages using AWS IoT Analytics Pipeline
- 20. public class LambdaFunctionHandler implements RequestHandler<Object, InmateVitalStats> { @Override public InmateVitalStats handleRequest(Object input, Context context) { context.getLogger().log("Input: " + input); // Build an AthenaClient client AthenaClientFactory factory = new AthenaClientFactory(); AthenaClient athenaClient = factory.createClient(); // Iterate through event object and get the input parameters Map inputMap = (Map) ((List)input).get(0); String subjectId = (String) inputMap.get ("subjectId") ; String deviceId = (String)inputMap.get ("deviceId") ; Double systolic = (Double) inputMap.get ("systolic") ; Double diastolic = (Double) inputMap.get ("diastolic") ; Double pulse = (Double) inputMap.get ("pulse") ; InmateVitalStats inmateVitalStats = new InmateVitalStats (deviceId, subjectId, systolic, diastolic, pulse); // Generate athena query and pass in the parameters received by the health sensors String queryExecutionId = submitAthenaQuery(athenaClient, inmateVitalStats); try { waitForQueryToComplete(athenaClient, queryExecutionId); } catch (InterruptedException e) { e.printStackTrace(); } processResultRows(athenaClient, queryExecutionId); return inmateVitalStats; } Lambda Function for Enriching Messages
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 23. Challenges • Privacy • Sensor Accuracy • Data Store and Compute Capacity Best Practices • Remote Management and Monitoring • New Houses vs Old Houses (BLE vs ZigBee) • Privacy preserving configurations • Non-Intrusive
- 24. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Neel Sendas nsendas@amazon.com Krishnan Ganapathy ganapatk@amazon.com
- 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Editor's Notes
- Welcome, my name is Neel Sendas – Sr Technical Account Manager at AWS and my collegue Krishnan G
- Before we get started let’s give you a quick reminder of other related breakout sessions – IoT-208 – What’s new in IoT Analytics GPSTEC413 – Partner focused talk on best practices around IoT solutions from the field ARC339- Another solution that highlights best practice API305 – How you use multiple AI/ML services to build a serverless ML solution
- So here’s the agenda, we are gonna talk about What is AmI intelligence? How it applies to a particular use case – Ambient Intelligence in Assisted Living home Talk about a few AWS solutions that one can use to build an AmI Solution Walk you through a one of the AWS solutions in particular – AWS IoT Analytics that one can use for AmI And then we are gonna see a demo
- So what is Ambient Intelligence? You can read the definitation here but essesially it’s an intelligent environment that fullfils the needs of individuals in that environment It’s an environment that adapts to your needs and takes intelligence decisions on your behalf
- It’s an environment that orchestrates data collected from different sensors and smart devices and creates context awareness for the environment you are in It weaves data collected from all the sensors around and creates a pattern And as a results provides better and security lifestyle for people in though environments It takes the Human Computer Interaction to a different level – It takes the sensor data gathered from all the smart devices and sensors around us and apply the intelligence of bigdata to machine learning to generate insights that would not have to been otherwise possible to generate.
- Once particular area where Ambient Intelligence can make a lot of difference in assisted living for our aging polutation You might talk that Aging population in developed countries is a becoming a big problem. More than 20% of the population in 3 countries Germany, Italy, and Japan are over 65 years. There are over 68,000 people in Japan are centenarians. We need the necessary trained staffs and infrastructure to take care of this problem. That’s when AmI comes in. They can multiply the efforts of a lean staff and maintain quality of care Additionally - to err is human. Decision making is difficult. Sensors can reduce or eliminate errors IoT sensors and devices make it easier to manage this problem Mobility devices – sensors such a accelerometer, gyroscope can monitor monitor movement, fall, detect, abnormal movements, Assisted living homes in eurpo are using smart tiles to monitor pressure and activity Collect vitals – BP moinitors, ECG Location detection – using RFID and GPS sensors can locate whether an individual and stayed outside in the backyard (especially important for alzeimers patients) Elimimate social isolation by using Intelligent conversational tools Aging population – By 2034 Error rate, challenges Mobility detection - Fall, Immobility, slow movement Vital statistics monitoring - Respiratory rate, BP, temperature Location detection - Front door, backyard, living room Eating habits, fluid intake, medication frequency Eleminating social isolation Healthcare is a great concern diag
- Here are the key building blocks of Ambient Intelligence in Connected homes Sensors – depth sensors can help regonize people without compromising privacy Thermal sensors – heat sensors for human activity Intelligent event recognition – using computer vision algorithm, outlier events, compute at the edge Identifying and managing dense activities - Multiple synchronous and asynchronous activites. Real time synchronization tools. Not sure how Reinforcement Learning & Temporal Learning happen in a connected home ?
- Krishnan to improve upon this slide
- We can improve upon this slide
- Non-intrusive Security and privacy – healthcare is a very intimate space. We need to respect privacy Interoperability and lack of standardization means you have to do the heavy lifting of protocol interoperability IoT Analytics, Lambda on the edge using IoT Analytics can help. Imbalanced data set – Oversampling, SMOTE, Sythenic dataset, and transfer learning Sensor accuracy is an issue