Dr. N. K. Suryadevara · Ambient Assisted Living (AAL) setup to determine the wellness of a person...
Transcript of Dr. N. K. Suryadevara · Ambient Assisted Living (AAL) setup to determine the wellness of a person...
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Ambient Assisted Living (AAL) setup to determine the wellness of a person living alone in their own home
Dr. N. K. Suryadevara
Senior Member IEEE Professor and Head-Computer Science & Engineering
Geethanjali College of Engineering & Technology Hyderabad-India
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It’s not just hype
New Science could lead to
very long lives
Good News
This Baby Will Live to Be 120. National Geographic May 2013
This Baby Could Live to Be 142 (TIME) March 2015
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Importance of Research
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1950 1960 1970 1980 1990 2000 2010 2020 2030
Year
Old
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pu
lati
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Independent Life
?
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Need Assistance…….
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How does someone die alone in their home without anyone realising?
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Reducing the burden from Caregiver
Their Needs
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Help with Activities of Daily Living (ADL)
Health Monitoring
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Smart Home Monitoring System
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Ambient Assisted Living Setup
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My Research
Health Informatics
System
Activities of Daily Living Monitoring
Physiological Monitoring
Environmental Monitoring
Wellness Determination
Non - Invasive Wearable and Non-Wearable
-
Unobtrusive
Provide Assistance/Advise
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Health Informatics System collects more data
Plenty of Data Collection methods/tools
More resources are required for Analyzing the data
Proper Information can be gained from the analysis (translation) of data
Important indications for proper decision-making
Notify policy development
Our Solution: Web-based reporting tool to analyze and infer right decisions
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Smart Home Monitoring System
AAL Services (Energy Consumption)
(Human Physiological)
Recognition of
Human ADL’s
Human Wellness
Determination
β1, β2
Domestic Objects
usage Trend through
Time Series Data
Mining
Sensors Data
Acquisition
Remote
Interoperability
Internet
Health Care
Provider/
Relatives/
Tele-Care Services
Data
Base
WSN
My Research
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Better Solution-Technologies
Instrumentation Sensing Objects
Wireless Communication
Information Processing
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Sensors Integrated with Everyday Objects
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Sensing Units-HMS
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Monitoring Basic ADL
Microwave, Water Kettle, Toaster or (any other item used regularly in Kitchen)
Room Heater
Television/Radio
Bed, Couch, Chair, Toilet
Any other appliance used as habitual
Preparation of Food (Breakfast,Lunch,Dinner)
Sleeping
Toileting, Self Grooming
Dinning
Relax
Watching TV(while sitting on Couch)
ADL α Household Appliance Usage
N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, “Forecasting the behavior of an elderly using wireless sensors data in a smart home”, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
Passive Infra Red(PIR) Sensors: Mobility(Movements monitoring)
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Smart Sensor Deployment Layout
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Human Emotion and ADL recognitions
Quazi, M.T.; Mukhopadhyay, S.C.; Suryadevara N.K.; Huang, Y.M. “Towards the smart sensors based human emotion recognition”, Proceedings of IEEE International Conference Instrumentation and Measurement Technology (I2MTC)-Austria-2012,Page(s):2365 – 2370, . ISBN: 9781457717710
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Human Emotional State Recognition Unit
An r a ad Ne tra
Quazi, M.T.; Mukhopadhyay, S.C.; Suryadevara N.K.; Huang, Y.M. “Towards the smart sensors based human emotion recognition”, Proceedings of IEEE International Conference Instrumentation and Measurement Technology (I2MTC)-Austria-2012,Page(s):2365 – 2370, . ISBN: 9781457717710
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Sensor activity status
Subject #1 Subject #2
Subject #3 Subject #4 Suryadevara N.K, Mukhopadhyay S.C, “Wireless Sensor Network based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal-2012, Vol: 12 Issue: 6, Page(s): 1965 – 1972
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• Domestic objects are used at regular time intervals in the day to day life
• Usage durations and the frequency of use are varied
• “Human behaviours in constant contexts recur, because the processing that initiates and controls their performance becomes automatic”
• “Frequency of past behaviour reflects the habit strength and has a direct effect on future performance”
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Wellness Determination
β(1,old) = (1- t / T)
β(2,old) = 1+(1- ta / Tn)
Sensor Event
Level-0
C Context Recognition
Level-1
ADL Recognition
Level-2
β1,new = e−t
T
β2,new = eTn−TaTn
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Improved Wellness Function (β1, new) β1,new = e
−tT
Suryadevara N.K, Mukhopadhyay .C, “Determinin We ness thro h an Ambient Assisted Livin Environment,” IEEE Intelligent Systems-May 2014,Page(s):30-37, ISSN:1541-1672
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Improved Wellness Function (β2, new):
β2,new = eTn−TaTn
Suryadevara N.K, Mukhopadhyay .C, “Determinin We ness thro h an Ambient Assisted Livin Environment,” IEEE Intelligent Systems-May 2014,Page(s):30-37, ISSN:1541-1672
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Behaviour Detection
Regular or Irregular
Sensor Activity Pattern
Forecasting Wellness Indices
To Minimize “Fa se A arms”
N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
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Tracking System
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Wellness Determination
N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
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600.
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Fig 9(a) Toilet usage Trend for 70 days Fig 9(b) Toilet usage (Ninth week forecast pattern)
(b)
Fig 9 (c) Toilet usage (Tenth week forecast pattern) Fig 9(d) Chair Usage Trend for 70 days
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1200.
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Forecast
Observed
Forecast
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Observed
Observed
Trend
Observed
Days Days
Days Days
N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
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Sensor Activity Pattern
Discovering Interesting Patterns in Data
Perform Classification of New Data based on Training Data
Sequential Patterns/Rules (Finding inherent regularities in data)
With Time Constraints
Cluster of Similar Instances
Associations
Top-K Sequential Rule Mining (Redundant/Non-Redundant)
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WSN Assisted Intelligent Integrated Healthcare Platform for Wellbeing and Independent Living
The healthcare platform consists of
1. Appliances Monitoring Unit 2. Physiological parameters monitoring unit 3. Human Posture and Position Detection Unit 4. Human Emotion Recognition Unit 5. Automatic Medicine Dispenser Unit 6. Power Management Unit 7. Robust Supervisory Control Unit 8. Safety Box Unit
Conclusion
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• A WSN Assisted and Embedded Processing based smart home to care elderly people.
• The integrated system is able to support people who wish to live independently.
• The developed system is robust and is possible to develop at a low cost due to indigenous development.
•The technology assisted home will alert the caregiver in advance about the trend of the health status, so that necessary precaution can be taken. 30