Technology for the Aging
A Collaborative Effort
Edward Riseman
Allen Hanson
Roderic Grupen
Erik Learned
-
Miller
Phebe Sessions
Julie Abramson
Mary Olson
Candy Sidner
UMassAmherst
Supported by the National Science Foundation
MERL
Collaborative Effort
Collaborative Research Effort
Social scientists and computer scientists
Elderly representatives and their stakeholders
caregivers
family
local and state agencies, etc.
Two Part Talk: Social Science and Technology
Enhancing Control and
Empowerment for the Elderly
through Assistive Technology
Social Science Components
November 4, 2005
Washington, D.C.
Dr. Phebe Sessions
Smith College School for Social Work
Outline
Issues in collaboration
Theoretical framework: ecosystemic and
social constructionist
Gerontological literature and traditions
Research plan
Issues in Collaboration
UMass Computer Sciences/Smith SSW
Why social work?
Pragmatism
Commitment to underserved populations
Community
-
building skills
Challenges of collaboration
Novelty and not knowing
Different languages
Theoretical framework
Ecosystemic and Social Constructionist
Strengths perspective
Collaboration, dialogue
Qualitative methods in research
Gerontological literature and traditions
Aging and the deficit perspective: “declinism”
Positive aging and core significance of control
Learning issues and aging
Social networks and aging
Ethical considerations
Research plan
Qualitative methods
Year One: Focus groups with elderly,
families, and service providers
Year Two: Implementation of technology
in common room
Year Three: Revision of technology based
on Year Two findings
Technology Components
Umass Vision and Robotics groups
Professor Emeritus Edward Riseman (PI)
Professor Allen Hanson (co
-
PI)
Professor Roderic Grupen (co
-
PI)
Mitsubishi Electronic Research Laboratory
Dr. Candace Sidner
Technology Overview
ISSUES:
privacy
access
client control
Graphics
Scripts
Vision
Speech
Speech
Synthesis
Tracking
Speech
Understanding
Lifestyle/Event
Coordination
Activity
Modeling
Conversational
Interaction
Assisted
-
Living Clientele
Family
Medical Structure
Interface
Family/Community
Interface
Human Services
Interface
MERL
UMASS
SMITH
Interaction:
Appointment Management
IA
:
Hi,
Maisie
.
Senior
:
Hi
.
IA
:
later
this
morning
at
11
am
you
have
a
dentist
appt
.
Senior
:
Ok
IA
:
You
will
have
to
take
the
bus
so
you
need
to
leave
here
an
hour
before
and
walk
to
the
bus
stop
.
That
means
leaving
about
10
am
.
Senior
:
I
want
to
leave
later
.
IA
:
Well,
the
bus
takes
20
minutes
.
How
long
do
you
need
to
get
to
the
bus
stop?
Senior
:
15
minutes
IA
:
The
bus
comes
every
15
minutes,
so
when
do
you
think
you
should
leave?
Senior
:
10
:
15
IA
:
Okay
.
How
about
I
remind
you
at
10
am
to
get
ready?
Senior
:
Good
.
IA
:
Good
bye
for
now
.
Technical Goals
An effective API to the client’s environment:
Independent Living
Social/family
Medical
Financial
Social services
Legal
…..
Plug and play for rapid development and deployment
Interface driven user interactions
System interfaces
Nimble, reconfigurable, robust, minimally intrusive,
controllable
The Client
-
Care Sandbox
Nimble & Reconfigurable
Component Technologies
Fault tolerant script based interfaces: Collagen plus GUI
Minimal intrusion
Robust
Speech and speech understanding
Command and control
Voice Analysis
-
e.g. stress
Visual interfaces
Virtual Reality
Tracking
Change Detection
Environment
Object locations
Motion/expected tracks, deviation from normal activity
People
Gait and postural models
Speech patterns
Collagen: Collaborative Agents
Principals: Candace L. Sidner and Charles Rich
Java middleware for collaborative interface agents
User and agent collaborate to complete predefined tasks
Speech or text
-
based discourse with agent
mouse, touch screen, others
Provides friendlier, more familiar interface for
inexperienced users
Many existing applications
http://www.merl.com/projects/collagen
Mitsubishi Electric Research Laboratory
Collaborative Interface Agent
* SharedPlans per Grosz, Sidner, Lochbaum, Kraus, et al.
communicate
interact
interact
observe
observe
plan tree
focus stack
*
Collagen
Collagen Example:
Calendar Application Dialog Fragment
1.
User: “Let’s create a new appointment.”
2.
Agent: activates Create Appointment window.
3.
User: enters the appointment’s date and description.
4.
User: “What’s Next?”
5.
Agent: “What time is the appointment?”
6.
User: “11 AM.”
7.
Agent: sets the time of the appointment to 11 AM.
8.
Agent: “I’m going to save this appointment.”
9.
User: “OK.”
10.
Agent: “We have created a new appointment!”
Speech Recognition
Available engines
IBM Viavoice
Microsoft Speech SDK
CMU Sphinx Engine 2 and 4
Mode of application
Continuous speech recognition mode
Speaker dependent
Low accuracy
Voice commands mode
Speaker independent
Higher Accuracy
Still susceptible to noise
Managing Speech Recognition
Technology
Reduce noise
Pressure
-
gradient/Noise
-
canceling Microphone
Sound card/USB pod
Continuous recognition
Training is essential
Do not expect high accuracy
Approach
Design dialog script carefully
Build in a fault tolerant mechanism
Always have a “take me to an operator” option
Constrained uses:
voice command mode
voice stress analysis
Local Experimental Environment
“Smart” Room
Distributed sensor network
Human and robotic residents
Complex environment
: camera locations
Tracking
Distributed sensor network
Sensor management
Selection, reasoning across sensors
Real
-
Time fault
-
tolerant approach
Fault containment units
Hardware, Software
Hierarchical
Wrappers around a resource policy
Augmented by fault and context
reporting mechanisms
Dynamic restructuring
Supports
Activity modeling
Identification
Unusual event detection
Tracking Supports Virtual Worlds
Immersive Virtual Worlds
Provide several levels of interaction styles
Client control over interaction style and level of
access of remote visitors (privacy)
Avatar plus virtual world
Avatar plus real world
Person plus virtual world
Person plus real world
(Registered)
Transparent
Objects
Rapid Viewpoint Changes
Camera context switches e.g. surveillance
Disconcerting, difficult for user
“Snap” Transitions
“VR Smoothed” Transitions
Mixing Virtual and Real Worlds
Events in real world mapped to virtual world
Temporal events synchronized to virtual world
Transparency
-
look through objects
Form of access control
Conclusions
Starting up
Conjectures on the future
Empowering the elderly: they decide
Social etiquette enforced through virtual worlds
Client controls level of access and degree of privacy
Built on
Modern social science theory
Existing technology base
Client
-
care Sandbox model for rapid prototyping
Particulars determined through focus groups
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