whatis$contextaware$compu@ng?$ › ~cpoellab › teaching › cse40816_fall11 › lecture5.pdf•...

9/9/11

1

Computer Science and Engineering -‐ University of Notre Dame

Pervasive/Ubiquitous Compu@ng

Context Awareness

What is Context-‐Aware Compu@ng?

•  “SoHware that examines and reacts to an individual’s changing context.” [Schilit, Adams, Want 1994]

•  “…aware of its user’s state and surroundings, and help it adapt its behavior” [Satyanarayanan 2002]

•  Systems that are able to adapt their opera@ons to the current context without explicit user interven@on

•  Aim at increasing usability and effec@veness by taking environmental context into account


9/9/11

2

What is Context?

•  “… any informa@on that can be used to characterize the situa@on of an en@ty.” [Dey et al. 2000]

•  Places, People, Things

–  Loca@on (where?) –  Iden@ty (who?) –  Time (when?) –  Ac@vity (what?)


why?

Tradi@onal Computer View


Computer System input output

Context independent: acts exactly the same

Human in the loop

9/9/11

3

From Abstrac@on to Context Sensi@vity

•  Tradi@onal black box view comes from the desire for abstrac@on

•  This is based on several assump@ons: –  Explicit input/output: slow, intrusive, requiring user a`en@on

–  Sequen@al input-‐output loop •  Move away from the black box model and into context-‐sensi@vity –  human out-‐of-‐the-‐loop (as much as possible) –  reduce explicit interac@on (as much as possible)


Context as Implicit Input/Output


explicit input

explicit output

Context: •  state of the user •  state of the physical environment •  state of the computing system •  history of user-computer interaction • ...

9/9/11

4

Context

•  Iden@ty (user, others, objects) •  Loca@on •  Date/Time •  Environment •  Emo@onal state •  Focus of a`en@on •  Orienta@on •  User preferences •  Calendar (events) •  Browsing history •  Behavioral pa`erns •  Rela@onships (phonebook, call history) •  … the elements of the user’s environment that the computer knows

about…


Classifica@on

•  External (physical) –  Context that can be measured by hardware sensors –  Examples: loca@on, light, sound, movement, touch, temperature, air pressure, etc.

•  Internal (logical) – Mostly specified by the user or captured monitoring the user’s interac@on

–  Examples: the user’s goal, tasks, work context, business processes, the user’s emo@onal state, etc.


9/9/11

5

Why Context-‐Aware Compu@ng?


Context Types Exis@ng Examples Human Concern

Room Ac@vity Smoke Alarm Safety

Room Ac@vity Auto Lights On / Off Convenience

Object Iden@ty Barcode Scanners Efficiency

Personal Iden@ty & Time File Systems Finding Info

Time Calendar Reminders Memory

Why Context-‐Aware Compu@ng?


Exis@ng Examples Context Types Poten@al Examples Human Concern

Ac@vity Convenience

Ac@vity Finding Info

Iden@ty Memory

Iden@ty & Time Safety

Time Efficiency

Iden@ty

Time

Loca@on

Proximity

Ac@vity

History

…

Smoke Alarm

Auto Lights On / Off

Barcode Scanners

File Systems

Calendar Reminders

Health Alert

Auto Cell Phone Off In Mee@ngs

Service Fleet Dispatching

Tag Photos

Proximal Reminders

9/9/11

6

Categories of CA Applica@ons

Manual Automa@c

Gemng Informa@on Proximate Selec@on & Contextual Informa@on

Automa@c Contextual Reconfigura@on

Execu@ng Command Contextual Commands Context-‐Triggered Ac@ons


Proximate Selec@on/Contextual Informa@on


9/9/11

7





9/9/11

8


•  Add, remove, or alter components based on context •  SenSay project: context-‐aware mobile phone




9/9/11

9

Contextual Commands

•  Users can parameterize commands with context-‐filtered values; execu@on changes based on context

•  Example: universal remote control


Context-‐Triggered Ac@ons

•  Simple if-‐then condi@on-‐ac@on rules, automa@cally invoked

•  Reminder: if I step into the car on weekday morning and don’t have suitcase with me, remind me to get it

•  CybreMinder:


9/9/11

10

Context-‐Triggered Ac@ons

•  Challenges: –  Expressiveness of language for rules –  Accuracy of context informa@on

•  Siren:


Context-‐Awareness

•  Context-‐awareness helps technology to “get it right” •  But context is hard to sense (quan@ty, subtleness) •  Computers are not self-‐aware like humans

•  Problems: – When the system does the wrong thing

•  auto-‐locking car doors •  screen saver during presenta@on •  microphone amplifying a whisper


9/9/11

11

Context-‐Awareness

•  Context data must be coupled with the ability to interpret it, but computers are bad at “common sense”.

•  More rules ≠ intelligence •  More rules = more complexity, harder to understand

•  “Human in the Loop”: –  computers can detect, aggregate, portray informa@on –  allow human users to interpret and act on it –  Is this a good strategy for all context-‐aware systems?


Encourage Healthy Dietary Behaviors


•  Interac@ve game to assist teachers to improve dietary behaviors of kindergarten children (“smart lunch tray”)

9/9/11

12

AudioIndex

•  Allowing visually impaired to browse and search audio books. Main device around neck, earpiece with audio feedback, poin@ng device on index finger.


Emergency Medical Response


9/9/11

13

Protech Parolee Monitor

Parent Pager

•  A “child safety product” •  Ac@vates an alarm when a child wanders more than 15 feet from the adult “base” unit

•  Also alarms if submerged in water

9/9/11

14

5 Design Considera@ons

1.  Improving relevance –  Deciding when a communica@on is relevant to the person’s

current (or near future) situa@on. –  For example, gemng no@fica@on about an email from your

travel agent regarding i@nerary changes while packing to leave for the airport.

2.  Minimizing disrup@on 3.  Improving awareness 4.  Reducing overload 5.  Selec@ng channels


1.  Improving relevance 2.  Minimizing disrup@on

–  Deciding when and how to no@fy people that they have a communica@on.

–  For example, your phone should vibrate and not ring, when you are at the symphony (unless it is truly urgent).

3.  Improving awareness 4.  Reducing overload 5.  Selec@ng channels

9/9/11

15


1.  Improving relevance 2.  Minimizing disrup@on 3.  Improving awareness

–  Deciding what informa@on and mechanisms can help people make intelligent communica@on decisions.

–  For example, the caller should be told you are at the movies before the call goes through.

4.  Reducing overload 5.  Selec@ng channels


1.  Improving relevance 2.  Minimizing disrup@on 3.  Improving awareness 4.  Reducing overload

–  Deciding how to reduce the number of communica@ons that don’t apply given your context.

–  For example, filtering out emails about going to lunch when you are away from the office (or already at lunch).

5.  Selec@ng channels

9/9/11

16


1.  Improving relevance 2.  Minimizing disrup@on 3.  Improving awareness 4.  Reducing overload 5.  Selec@ng channels

–  Deciding which communica@on device should be used to get in touch with somebody.

–  For example, rou@ng calls to your home phone instead of your cell phone when you are at home and cellular recep@on is poor.

The “research context” of “context-‐awareness compu@ng in health care”

•  Context-‐aware medical applica@ons – Vocera communica@on system (St.Vincent hospital, USA)

•  Communicator badge system for mobile user –  Push-‐to-‐call bu`on, small text screen, voice dialing, hand-‐free conversa@on –  Biometrically secured with speaker verifica@on

•  Hospital-‐based prototypes – Hospital of the future (Centre for Pervasive Healthcare, Denmark)

•  Hospital bed with a built-‐in display –  Bed “knows” who is using it, and what and who is near it

•  A context-‐aware pill container that is “aware of the pa@ent” •  A context-‐aware Electronic Pa@ent Record

–  Medica@on scheme or pa@ent record

–  Intelligent hospital soHware (University of Cambridge, UK) •  Scenarios of use

–  Remote consulta@on, tracking of pa@ents and equipment, no@fica@on of awareness and pa@ent data •  Implemen@ng a prototype

–  QoS DREAM : middleware plauorm

9/9/11

17

Research in health care domain

The “research context” of “context-‐awareness compu@ng in health care”

•  Hospital-‐based prototypes (cont’d) –  Context-‐aware mobile communica@on (CICESE, Mexico)

•  Mobile devices to recognize the context in which hospital workers perform their tasks. –  Instance messaging with context such as circumstances

»  Loca@on, delivery @ming, role reliance, an so on –  MobileWARD (Mobile Electronic Pa@ent Record, Denmark)

•  Suppor@ng morning procedure tasks in a hospital ward –  Informa@on and func@onali@es according to the loca@on of the nurse and the @me

•  Other prototypes –  medica@on consump@on, distant monitoring and new assistants

•  Scenario –  Representa@on of context for a hospital informa@on system that uses PDAs to deliver pa@ent

informa@on to doctors (Ins@tute of Design, Chicago) •  How context could be described by combining contextual informa@on from the environment,

person and ac@vity

9/9/11

18

Hospital-‐based Projects

•  M : model, P: prototype, A: applica@on, S: scenario •  Pi: presenta@on of informa@on, ES: Execu@on of services, SI: Storage of contextual info.

Diabetes Self-‐Care

•  Eat the right foods •  Get and log daily physical activity •  Take medications as prescribed •  Test blood glucose regularly

•  Diabetes self-care with mobile phones: –  People forget or don’t keep a detailed log –  Recalling similar previous situations becomes difficult –  Create a context-driven recommender application

•  Benefits of the application –  Time and location monitoring –  User input on food consumption and insulin dosage –  Find correlations between time/location and activities –  Augment blood glucose level logs with contextual data –  Use context to find similar situations in the past


9/9/11

19




•  Declarative event-condition-action rules that govern application behavior •  If (battery.load < 30 %) Then disableComponent(‘BluetoothDiscovery’) •  If (battery.load < 15 %) Then invoke(‘UserInterface.warning()’)

•  Application specific behavior: •  Normal blood glucose: 80 – 150 mg/dL •  < 50 mg/dL: hypoglycemic diabetic coma •  > 500 mg/dL: hyperglycemic diabetic coma


whatis$contextaware$compu@ng?$ › ~cpoellab › teaching › cse40816_fall11 › lecture5.pdf•...

Documents