Assisted Living Technologies for Older Adults - Department of
Transcript of Assisted Living Technologies for Older Adults - Department of
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A Tutorial on: Assisted Living Technologies for
Older Adults
Speaker: Parisa Rashidi
University of Florida
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Introduction
Technologies, tools, infrastructure
Algorithms
Use Cases
Design Issues
Future
Outline
Tools, Infrastructure
Algorithms
Design
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Assisted living technologies for older adults, a.k.a
Gerontechnology
Gerontology + Technology
AAL: Ambient Assisted Living
Assisted Living + Ambient Intelligence
This Tutorial is about …
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Introduction
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Scope 8.5 million seniors require some form of assistive care
80% of those over 65 are living with at least one chronic disease Every 69 seconds someone in America develops Alzheimer’s disease
Costs Alzheimer’s Disease: $18,500-$36,000 Nursing home care costs: $70,000-80,000 annually Annual loss to employers: $33 billion due to working family care givers
Caregiver gap Nurses shortage: 120,000 and 159,300 doctors by 2025 Understaffed nursing homes: 91% Family caregivers in US: 31% of households
70% of caregivers care for someone over age 50
Why Important?
Statistics from http://www.hoaloharobotics.com/ 5
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Why Important?
By 2030, 1 in 5 Americans will be age 65 or older
Average life expectancy 81 years
By 2040: Alzheimer related costs will be 2 trillion dollars
0
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1950 1960 1970 1980 1990 2000 2010 2020 2030
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Why Important?
By 2050, 1 in 5 person in the world will be age 60 or older
UN Report, Department of Economic and Social Affairs, Population Division , 2001 http://www.un.org/esa/population/publications/worldageing19502050/
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An increase in age-related disease
Rising healthcare costs
Shortage of professionals
Increase in number of individuals unable to live independently
Facilities cannot handle coming “age wave”
Consequences
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Independent Life
?
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Older Adults Challenges
Normal age related challenges
Physical limitations
Balance, reaching, etc.
Perceptual
Vision, hearing
Cognitive
Memory, parallel tasks
Chronic age related diseases
Alzheimer’s Disease (AD)
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They need help with daily activities
Activities of Daily Living (ADL)
e.g. Personal grooming
Instrumented Activities of Daily Living (IADL)
e.g. Transportation, cooking
Enhanced Activities of Daily Living (EADL)
e.g. Reading, social engagement
Memory Functions
Health monitoring
Removing the burden from caregiver
Older Adults Needs
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Tools and Infrastructure
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Tools & Infrastructure
What makes Ambient Assisted Living (AAL) possible?
Smart homes
Mobile devices
Wearable sensors
Smart fabrics
Assistive robotics ?
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“Smart Homes”
Tools & Infrastructure:
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Sensors & actuators integrated into everyday objects
Knowledge acquisition about inhabitant
Smart Homes
Environment
Smart Home
Perceptions
(sensors)
Actions
(controllers) 15
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PIR (Passive Infrared Sensor)
RFID
Ultrasonic
Pressure sensors (in beds, floor)
Contact switch sensors
…
Smart Home Sensors
Floor Pressure Sensor. Noguchi et al. 2002
PIR RFID Ultrasonic 16
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US
Aging in Place, TigerPlace (U. of Missouri), Aware Home (Georgia Tech), CASAS (Washington State U.), Elite Care (OHSU, OR), House_n (MIT)
Asia
Welfare Techno House (Japan), Ubiquitous Home (Japan)
Europe
iDorm (University of Essex), HIS (France)
Example Smart Homes
Takaoka Welfare Techno House Aware Home, GaTech CASAS, WSU
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“Wearable & Mobile Devices”
Tools & Infrastructure:
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Applications
Health monitoring
Navigation and stray prevention
Mobile persuasive technologies
Wearable & Mobile Sensors
LifeShirt By Vivometrics® AMON, 2003, ETH Zurich Epidermal Electronics, 2011
Smart Cane, UCLA, 2008
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Measurements & Sensors
Movement
• Accelerometer
• Gyroscope
Biochemical
• Stress markers (lactate in sweat)
• Wound healing (pH and infection markers)
Vital Signs
• Respiration sensors
• Thermal sensors
• Galvanic skin response (GSR) sensors
• Cardiac Activity
• Pulse oximeter
• ECG devices
• Doppler radars 20
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Holter type
Patches
Body-worn
Smart garments
Garment level
Fabric level
Fiber level
Wearable Device Types
*A. Dittmar; R. Meffre; F. De Oliveira; C. Gehin; G. Delhomme; , "Wearable Medical Devices Using Textile and Flexible Technologies for Ambulatory Monitoring," Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the , vol., no., pp.7161-7164, 2005
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Most common setup Sensors on body + a handheld or wearable data hub to
communicate data wirelessly + a central node to process data
Short range standards
• Bluetooth (IEEE 802.15.1)
• ZigBee (IEEE 802.15.4)
Short range technologies
• RF
• Inductive links, Intrabody Communication
Data Transfer Architecture
Sensors Handheld
device Central Unit
ZigBee GPRS 22
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Pros.
Anywhere, anytime
Portable
Continuous recordings rather than “snapshot “
Avoid “white coat” syndrome
Cons.
Anywhere, anytime
Should be worn/carried all the time
Wearing a tag can be regarded as stigma
Privacy concern, 24/7 monitoring
Why Wearable and Mobile?
Portable
nuisance
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“Robots”
Tools & Infrastructure:
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Helpful in physical tasks
Communication
People consider them as social entities.
Assistive Robotics
Care-O-bot® by Fraunhofer IPA: grasping items and bringing them to resident PARO by U Penn, 2011 RIBA , Japan: Transferring patients, 2009
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How Robots Help with ADL?
Task # Robots
Support movement 35
Reducing need for movement 34
Feeding 7
Grooming 6
Bathing 4
Toileting 3
Dressing 2
Data from Understanding the potential for robot assistance for older adults in the home environment (HFA-TR-1102). Smarr, C. A., Fausset, C. B., Rogers, W. A. (2011). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory. Link. 26
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Reducing the need for movement
Example ADL Assistive Robots
Topio Dio by Tosy Care-O-bot® by Fraunhofer IPA: grasping
items and bringing them to resident
Dusty II by GA Tech: Retrieving objects from floor
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How Robots Help with IADL?
Task # Robots
Housekeeping 53
Meal preparation 14
Medication Management 13
Laundry 7
Shopping 5
Telephone use 4
Money Management 0
Transportation 0
Data from Understanding the potential for robot assistance for older adults in the home environment (HFA-TR-1102). Smarr, C. A., Fausset, C. B., Rogers, W. A. (2011). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory. Link. 28
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Example IADL Assistive Robots
PERMMA by U Penn, 2011 uBot-5 by UMAss, 2011
Roomba by iRobot, 2011
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How Robots Help with EADL?
Task # Robots
Social Communication 46
Hobbies 29
New Learning 16
Data from Understanding the potential for robot assistance for older adults in the home environment (HFA-TR-1102). Smarr, C. A., Fausset, C. B., Rogers, W. A. (2011). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory. Link. 30
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Example EADL Assistive Robots
PARO, Japan, 1993 Pearl by CMU, 2002 iCat by Philips, 2006
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Algorithms & Methods
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The ones we will discuss
Activity recognition from
Wearable & mobile sensors
Ambient sensors
Camera (Vision)
Context Modeling
Other algorithms
Indoor Location detection
Reminding
Algorithms
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Different mediums generate different types of data
Data Sources
Data Types
Time Series
Physiological Signs (e.g.
ECG)
Accelerometer, Gyroscope
Image, Video
Camera, thermography
Text
PIR, RFID
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“Activity Recognition”
Algorithms & Methods:
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What is Activity Recognition?
The basic building block in many applications
Recognizing user activities from a stream of sensor events
… A B C D A C D F …
An Activity (Sequence of sensor events)
A Sensor Event
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Fine grained (individual movements, especially in vision)
Coarse grained (activity)
Activity Resolution
Movement: e.g. stretching arm
Action: e.g. walking
Activity: e.g. preparing meal
Co
mp
lex
ity
Group Activity: e.g. team sports
Crowd Activity: e.g. crowd surveillance 37
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Activity Recognition: “Wearable & Mobile”
Algorithms & Methods:
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Mostly in form of time series
Accelerometer [& gyroscope]
Most actions in form of distinct, periodic motion patterns
Walking, running, sitting,..
Usual features
Average, standard deviation
Time between peaks, FFT energy, Binned distribution
Correlation between axes
…
Activity Data from Wearable Sensors
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Example activities from mobile phone accelerometer
Example Activities
Kwapisz et al, SIGKDD exploration, 2010
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Stages
Data collection
Preprocessing
Feature extraction
Mean, SD, FFT coefficients
Dimensionality Reduction
Classification
Processing Steps
Data
preprocess
Features
Post- process
Classify
d=(x,y,z)
at 60 HZ
d[1..60]
corresponding to 1
second
E= 100,
f_max=2 HZ
Select some
features
0.7 Walking
0.3 Cycling
*See: A Tutorial Introduction to Automated Activity and Intention Recognition by Sebastian Bader, Thomas Kirste. Link
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Supervised
SVM, DT, …
Semi-supervised
Unsupervised
Clustering
Motif discovery
Classification
Total Energy
Stand Run
Very Low Very High
Low
Main Frequency
Low
Sit
High
Walk
A simple decision tree
*See: A Tutorial Introduction to Automated Activity and Intention Recognition by Sebastian Bader, Thomas Kirste. Link
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Activity Recognition: “Ambient Sensors”
Algorithms & Methods:
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More complex activities need more sophisticated sensors
Sensor networks of PIR sensors, contact switch sensors, pressure sensors, object sensors, etc.
Approaches
Supervised
Probabilistic
Semi/Unsupervised
Activity Recognition
PIR PIR
Floor Pressure Sensors
Object Sensor
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Graphical models
Naïve Bayes (NB)
Hidden Markov Model (HMM)
Dynamic Bayesian Network (DBN)
Conditional Random Field (CRF)
Probabilistic Approaches
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A very simple model, yet effective in practice [Tapia 2004]
Assumes observations are independent of each other
Y = activity (e.g. taking medications)
X = observation (e.g. sensor M1 is ON)
Naïve Bayes
Y
x1 x2 x3 x4
𝑃 𝑦 𝑥 = 𝑃(𝑦) 𝑃(𝑥𝑚|𝑦)
𝑀
𝑚=1
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A model for inferring hidden states from observations
Well known, efficient algorithms
Hidden Markov Model (HMM)
Hidden Node
(Activity)
Observation
(Sensor event)
Transition Probability
Emission
Probability
y1 y2 y3 y4
x1 x2 x3 x4
𝑃(𝑦, 𝑥) = 𝑃 𝑥𝑡 𝑦𝑡 . 𝑃 𝑦𝑡 𝑦𝑡−1
𝑇
𝑡=1
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Coupled Hidden Markov Model (CHMM) [Wang 2010]
O = observations
A, B = activities
Multiple Residents?
A1 A2 A3 A4
B1 B2 B3 B4
O1 O2 O3 O4
O5 O6 O7 O8
Inter-chain
Probability
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Hierarchal Hidden Markov Model (HHMM) [Choo 2008, Nguyen 2006]
Each state itself is an HHMM
Hierarchal Definition of Activities?
B1 B2 B3 B4
C1 C2 C3 C4
O1 O2 O3 O4
Terminal State A1
Internal State
Production State
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Hidden Semi-Markov Model (HSMM) [Duong 2006]
Activity duration modeling
Arbitrary probability distribution of staying in a state
Hidden Semi-Markov Model
y1 y2 y3 y4
x1 x2 x3 x4
Arbitrary Duration Distribution
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Markov Network
First Order Logic
Markov Logic
Network
Markov logic networks [Helaoui 2011]
Easily including background knowledge of activities + non-deterministic approach
First order logic + Markov network
Markov Logic Network
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Dynamic Bayesian Network (DBN)
Conditional Random Fields (CRF)
…
Other Graphical Models
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Data annotation problem!
Emerging patterns
Mining frequent patterns [Gu 2009, Heierman 2003]
Mining periodic sequential patterns [Rashidi 2008]
Stream mining
Tilted time model [Rashidi 2010]
Unsupervised Methods
a b c h d a d c b o p a b g e q y d c a r h c b
■ h d ■ o p ■ ■
■ h d ■ o p ■ ■
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Transfer learning [TLM van Kastere 2010, VW Zheng 2009]
Bootstrap for a new resident
Bootstrap in a new building
…
Semi-supervised learning [D Guan 2007,]
Co-training
Active learning [M Mandaviani 2007, Rashidi 2011]
Other Techniques
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Activity Recognition: “Vision”
Algorithms & Methods:
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Used in many related application domains
Video surveillance, sports analysis, …
Advantages
Rich information
Disadvantages Highly varied activities in natural environment
Privacy concerns
Algorithm complexity
Vision Based Systems
[Cheng and Trivedi,2007]
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Taxonomy of methods [Aggarwal & Ryoo 2011]
Algorithms H
um
an A
ctiv
ity
Re
cog
nit
ion
Single Layered Approach
Space-time Approaches
Space-time Volume
Trajectories
Space-time Features
Sequential Approaches
Exemplar-based
State-based
Hierarchal Approach
Statistical
Syntactic
Description-based 59
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Suitable for recognition of gestures & actions
Two different representations
Space-time distribution
Data oriented, spatio-temporal features
Sequence
Semantic oriented, tracking
Single Layered
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Space-time approach representation
Volume
Trajectories
Local features
Space-time Approaches
2D nonparametric template matching, Bobick & Davis, IEEE Trans. Pattern Anal. Mach. Intel, 2001
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Sequential approach
Exemplar:
Directly build template sequence from training examples
State-based
Build a model such as HMM
Sequential Approaches
y1 y2 y3 y4
x1 x2 x3 x4
…
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Hie
rarc
hal
Statistical: As states
(e.g. HHMM, LHMM, …)
Syntactic: As Symbols
(e.g. CFG, SCFG, ..)
Descriptive: As Logical Relations
(MLN …)
Hierarchal Approach
Robust to Uncertainty
Encoding Complex
Logic
Deep Hierarchy
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“…”
Algorithms & Methods:
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Different types of context data
Information from sensors
Activities and their structure
User profile & preferences
Static data (e.g. rooms)
Context Information
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1. Key-value models
e.g. Context Modeling language (CML)
2. Simple markup schema
e.g. HomeML
3. Ontology
e.g. SOUPA
4. Uncertain context
e.g. Meta-data (e.g. freshness, confidence, resolution)
5. Situation modeling & reasoning
e.g. Situation calculus
Context Modeling Approaches
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Indoor Location Identification
Method Disadvantage
Smart floor Physical reconstruction
Infrared motion sensors Inaccurate, sensing motion
(not presence)
Vision Privacy
Infrared (active badge) Direct sight
Ultrasonic Expensive
RFID Range
WiFi Interference, inaccurate
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Multiple residents
Active Identification
RFID Badges
Anonymous
Motion models (Wilson 2005, Crandall 2009)
Person Identification
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Problems [Pollack 2003 , Horvitz 2002, 2011]
When to remind?
What to remind?
Avoiding activity conflicts
Solutions
Planning & scheduling
Reinforcement learning
Reminders
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Some Case Studies
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Applications
Cognitive Orthotics
• Reminders ● Navigation and stray prevention
• Planners
Health Monitoring
• Continuous Monitoring of Vital Signs ● Sleep Monitoring
• ADL
Therapy & Rehabilitation
• Tele-Health
Emergency Detection
• Fall Detection
• Medical emergency
Emotional Wellbeing
• Social Connectedness
• Facilitating Communication 71
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Simple reminders
NeuroPager (1994), MAPS (2005), MemoJog (2005)
AI-based
PEAT (1997), Autominder (2003)
Reminders
[Davies 2009]
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Developed by Martha E. Pollack et al. (U. Of Michigan)
Reminders about daily activities
Plan manager to store daily plans
Resolving potential conflicts
Updating the plan as execution proceeds
Models plans as Disjunctive Temporal Problems
Constraint satisfaction approach
Payoff function
Autominder
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COACH: Monitoring hand-washing activity and prompting [Mihailidis 2007, U Toronto]
Vision
Detecting current state
Markov Decision process (MDP)
Prompting
COACH
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Opportunity Knocks (OK): public transit assistance [Patterson 2004]
iRoute: Learns walking preference of dementia patients [Hossain 2011]
Commercial
GPS shoes
ComfortZone
Outdoor Stray Prevention
ComfortZone GPS Shoes
Bracelet for tracking patients
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SenseCam
Microsoft Research, Cambridge, UK, 2004-2011
Now commercially available as REVUE
Memory Aid
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MedSignals
MD.2
Medication Management
MedSignals 77
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“CASAS Smart Home”
Case Studies:
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CASAS Project
Center for Advanced Studies in Adaptive Systems
One of the large-scale smart home projects in the nation
A couple of on campus testbeds
Dozens of real home deployment
A smart home data repository
http://ailab.eecs.wsu.edu/casas
Data Repository
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On-campus Testbeds
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Actual Deployments
Patients with mild form of dementia
Noninvasive deployment
Prompting systems
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Prompting Technology
• Context-based
• Prompt only if task not initiated
• Prompt can be re-issued
I’ve done
this task
I won’t do
this task
I will do it
now
I will do it
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Design Issues
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Issues: Physical interference with movement
Difficulty in removing and placing
Weight
Frequency and difficulty of maintenance
Charging
Cleaning
Social and fashion concerns
Suggestions: Use common devices to avoid stigmatization
Lightweight
Easy to maintain
Wearable & Mobile Design Issues
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Simple Interface
Limit possibility of error
Avoid cognitive overload
Limit options
keep dialogs linear
Avoid parallel tasks
Consider all stakeholders
Patient, formal onsite/offsite caregivers, informal onsite/offsite caregivers, technical personnel
User Interface Design Issues
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Ethics
Perfect transparency
Control over the system
Fight laziness
Privacy
Encrypt data
Patient authentication (Owner aware)
Privacy & Ethics
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Challenges & Future
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Healthy older adults use technology more often*
“Not being perceived as useful” *
Better a known devil than an unknown god
Privacy Concerns
Big brother
Stigmatization
Are they ready to adopt?
*Heart and Kalderon, Older adults: Are they ready to adopt health-related ICT?, 2011 88
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Smart homes
Location detection
Privacy/unobtrusiveness vs. accuracy
Difficulty with multiple residents
PIR sensor proximity is important
Reliability
Distinguishing anomalies from normal changes
Become more context aware
Standard protocol
Smart Home Challenges
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Wearable & mobile
Power harvesting
Size
Smart fabrics
Limitations when skin is dry or during intense activity
Still hybrid
Wearable & Mobile Challenges
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Assistive robotics
Marketing and price
Lack of reliable technology
A robot fully capable of helping with all ADLs
Adaptive robots
More user studies
Assistive Robotics Challenges
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Legal, ethical
Telemedicine
Lack of regulations
Which state regulations? Patient’s or Physician?
Who is responsible for malpractice?
Risk of fake physicians
Physician out-of-state competition
Insurance & reimbursement
Patient confidentiality
Legal & Ethical Challenges
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Technology
Device interoperability
Legal issues
Patient centric
Integrate all
Robots + smart home + wearable/mobile sensors + e-textile
Technology transfer, go beyond prototype
Future
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Resources
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2011 technical report on “robot assistance for older adults” Understanding the potential for robot assistance for older adults in the home environment (HFA-TR-
1102). Smarr, C. A., Fausset, C. B., Rogers, W. A. (2011). Atlanta, GA: Georgia Institute of Technology,
School of Psychology, Human Factors and Aging Laboratory.
2009 review article on “Assistive social robots in elderly care” Broekens J., Heerink M., Rosendal H. Assistive social robots in elderly care: a review. Gerontechnology 2009;
8(2):94-103
2011 technical report on “Robot acceptance” Beer, J. M., Prakash, A., Mitzner, T. L., & Rogers, W. A. (2011). Understanding robot acceptance (HFA-TR-
1103). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory.
Assistive Robotics
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Human Activity Analysis Survey J.K. Aggarwal and M.S. Ryoo. 2011. Human activity analysis: A review. ACM Comput. Surv. 43, 3, Article 16.
Survey: Recognition of Human Activities Turaga, P.; Chellappa, R.; Subrahmanian, V.S.; Udrea, O.; , "Machine Recognition of Human Activities: A
Survey," Circuits and Systems for Video Technology, IEEE Transactions on , vol.18, no.11, pp.1473-1488, Nov. 2008.
CVPR 2011 Tutorial on Human Activity Recognition: Frontiers of Human Activity Analysis http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/
Vision
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Wearable monitoring systems book Bonfiglio, Annalisa; De Rossi, Danilo, Wearable Monitoring Systems, Springer, 2011.
On-board data mining book S. Tanner et al., On-board data mining, Scientific Data Mining and Knowledge Discovery, , Volume . ISBN
978-3-642-02789-5. Springer-Verlag Berlin Heidelberg, 2009, p. 345
Excellent tutorial on time series Eamonn Keogh’s VLDB06 Tutorial
Wearable Sensors
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Activity Recognition Book Chen, Liming, Nugent, CD, Biswas, J and Hoey, J. “Activity Recognition in Pervasive Intelligent
Environments”, 2011, Springer.
Context Aware Modeling Survey Claudio Bettini, et al., “A survey of context modeling and reasoning techniques”, Pervasive and Mobile
Computing, Volume 6, Issue 2, April 2010, Pages 161-180.
HMM Tutorial Rabiner, L.R.; , "A tutorial on hidden Markov models and selected applications in speech
recognition," Proceedings of the IEEE , vol.77, no.2, pp.257-286, Feb 1989.
Activity Recognition
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Intelligent Environments Book Monekosso, Dorothy; Kuno, Yoshinori. “Intelligent Environments: Methods, Algorithms and
Applications”, 2011, Springer.
Smart Environments Book Diane J. Cook, Sajal K. Das. Smart environments: technologies, protocols, and applications. John Wiley
and Sons, 2005.
Smart Home Survey Marie Chan, Daniel Estve, Christophe Escriba, and Eric Campo. 2008. A review of smart homes-Present
state and future challenges. Comput. Methods Prog. Biomed. 91, 1 (July 2008)
Smart Homes
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Legal and ethical issues in telemedicine and robotics B.M. Dickens, R.J. Cook, Legal and ethical issues in telemedicine and robotics, Int. J. Gynecol. Obstet. 94
(2006) 73–78.
Telemedicine: Licensing and Other Legal Issues Gil Siegal, Telemedicine: Licensing and Other Legal Issues, Otolaryngologic Clinics of North America,
Volume 44, Issue 6, December 2011, Pages 1375-1384
Older adults: Are they ready to adopt health-related ICT? Tsipi Heart, Efrat Kalderon, Older adults: Are they ready to adopt health-related ICT?, International
Journal of Medical Informatics,, ISSN 1386-5056, 2011.
Legal, Ethical
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Designing for Older Adults Arthur D. Fisk, Wendy A. Rogers, Neil Charness, Joseph Sharit. Designing Displays for Older Adults. CRC
Press, 26.3.2009.
Design meets disability Graham Pullin.“Design meets disability”, 2009, MIT Press.
Design
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Gerontechnology Journal: International journal on the fundamental aspects of technology to serve the ageing society http://www.gerontechnology.info/Journal/
Assistive Technology: Journal of Assistive Technologies http://www.emeraldinsight.com/journals.htm?issn=1754-9450
Ambient Assisted Living Joint Programme of EU http://www.aal-europe.eu/
General Resources
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Washington State University CASAS dataset http://ailab.eecs.wsu.edu/casas/datasets/index.html
My collection of links http://www.cise.ufl.edu/~prashidi/Datasets/ambientIntelligence.html
PAIR datasets http://homepages.inf.ed.ac.uk/cgeib/PlanRec/Resources.html
Datasets
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1. Danny Wyatt, Matthai Philipose, and Tanzeem Choudhury. 2005. Unsupervised activity recognition using automatically mined common sense. In Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (AAAI'05), Anthony Cohn (Ed.), Vol. 1. AAAI Press 21-27.
2. Emmanuel Munguia Tapia, Tanzeem Choudhury and Matthai Philipose, Building Reliable Activity Models Using Hierarchical Shrinkage and Mined Ontology. Lecture Notes in Computer Science, 2006, Volume 3968/2006.
3. Latfi, Fatiha, and Bernard Lefebvre. Ontology-Based Management of the Telehealth Smart Home , Dedicated to Elderly in Loss of Cognitive Autonomy.Management 258: 12-12, 2007.
4. Chen, Luke, Nugent, Chris D., Mulvenna, Maurice, Finlay, Dewar and Hong, Xin (2009) Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments. In: Intelligent Patient Management, Studies in Computational Intelligence. Springer Berlin / Heidelberg, pp. 279-296. ISBN 978-3-642-00178-9.
5. A.K. Dey, et al. “A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of ContextAware Applications”, Human-Computer Interaction Journal, Vol. 16(2-4), pp. 97-166, 2001.
References
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6. Tsu-yu Wu , Chia-chun Lian , Jane Yung-jen. “Joint Recognition of Multiple Concurrent Activities using Factorial Conditional Random Fields”. 2007 AAAI Workshop on Plan, Activity, and Intent Recognition.
7. Derek Hao Hu and Qiang Yang. 2008. CIGAR: concurrent and interleaving goal and activity recognition. In Proceedings of the 23rd national conference on Artificial intelligence - Volume 3(AAAI'08), Anthony Cohn (Ed.), Vol. 3. AAAI Press 1363-1368.
8. Joseph Modayil, Tongxin Bai, and Henry Kautz. 2008. Improving the recognition of interleaved activities. In Proceedings of the 10th international conference on Ubiquitous computing(UbiComp '08). ACM, New York, NY, USA, 40-43.
9. Tao Gu; Zhanqing Wu; Xianping Tao; Hung Keng Pung; Jian Lu; , "epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition," Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on , vol., no., pp.1-9, 9-13 March 2009.
References
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10. Parisa Rashidi, Diane J. Cook, Lawrence B. Holder, and Maureen Schmitter-Edgecombe. 2011. Discovering Activities to Recognize and Track in a Smart Environment. IEEE Trans. on Knowl. and Data Eng. 23, 4 (April 2011), 527-539.
11. Parisa Rashidi and Diane J. Cook. 2010. Mining Sensor Streams for Discovering Human Activity Patterns over Time. In Proceedings of the 2010 IEEE International Conference on Data Mining(ICDM '10). IEEE Computer Society, Washington, DC, USA, 431-440.
12. Liang Wang, Tao Gu, Xianping Tao, and Jian Lu. 2009. Sensor-Based Human Activity Recognition in a Multi-user Scenario. In Proceedings of the European Conference on Ambient Intelligence (AmI '09), Manfred Tscheligi, Boris Ruyter, Panos Markopoulus, Reiner Wichert, Thomas Mirlacher, Alexander Meschterjakov, and Wolfgang Reitberger (Eds.). Springer-Verlag, Berlin, Heidelberg, 78-87.
13. Aaron S. Crandall and Diane J. Cook. 2009. Coping with multiple residents in a smart environment. J. Ambient Intell. Smart Environ. 1, 4 (December 2009), 323-334.
References
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14. Shaogang Gong and Tao Xiang. 2003. Recognition of Group Activities using Dynamic Probabilistic Networks. In Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2 (ICCV '03), Vol. 2. IEEE Computer Society, Washington, DC, USA, 742-.
15. Nam Thanh Nguyen, Svetha Venkatesh, Hung H. Bui. Recognising Behaviours of Multiple People with Hierarchical Probabilistic Model and Statistical Data Association. In Proceedings of BMVC'2006. pp.1239~1248.
16. Nuria Oliver, Ashutosh Garg, Eric Horvitz, Layered representations for learning and inferring office activity from multiple sensory channels, Computer Vision and Image Understanding, Volume 96, Issue 2, November 2004, Pages 163-180.
17. T. Choudhury, S. Basu, in Advances in Neural Information Processing Systems 17, L. K. Saul, Y. Weiss, L. Bottou, Eds. (MIT Press, 2004), pp. 281-288.
References
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18. Tapia EM, Intille SS, Larson K. Activity recognition in the home using simple and ubiquitous sensors. Pervasive Computing. 2004:158–175.
19. Duong, T.V.; Phung, D.Q.; Bui, H.H.; Venkatesh, S.; , "Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model," Pattern Recognition, 2006. ICPR 2006. 18th International Conference on , vol.3, no., pp.202-207
20. Pau-Choo Chung and Chin-De Liu. 2008. A daily behavior enabled hidden Markov model for human behavior understanding. Pattern Recogn. 41, 5 (May 2008), 1589-1597.
21. Rim Helaoui, Mathias Niepert, and Heiner Stuckenschmidt. 2011. Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships. Pervasive Mob. Comput. 7, 6 (December 2011), 660-670.
References
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