Technology for the Aging

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17 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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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