Streaming Sensor Data from the Home What does it all mean? How can - - PowerPoint PPT Presentation

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Streaming Sensor Data from the Home What does it all mean? How can - - PowerPoint PPT Presentation

Streaming Sensor Data from the Home What does it all mean? How can it help you? ! Holly%Jimison,%Misha%Pavel,%% Xuan%Sean%Li,%Krissy%Mainello% College%of%Computer%&%InformaAon%Science% Bouve%College%of%Health%Sciences%


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SLIDE 1

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Holly%Jimison,%Misha%Pavel,%% Xuan%“Sean”%Li,%Krissy%Mainello%

College%of%Computer%&%InformaAon%Science% Bouve%College%of%Health%Sciences% ConsorAum%on%Technology%for%ProacAve%Care%

Northeastern%University%

%

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!

!

Streaming Sensor Data from the Home

What does it all mean? How can it help you?

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SLIDE 2
  • Funding'

– Na&onal!Science!Founda&on! – Na&onal!Ins&tute!on!Aging! – Alzheimer’s!Associa&on!/!Intel!Company! – Na&onal!Ins&tute!on!Standards!&!Technology! – TEKES!(Finland!Government)!

  • No'conflicts'of'interest'

'

  • Collabora5ve'work'with'

– Oregon!Health!&!Science!University! – University!of!California!at!Berkeley!

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SLIDE 3

Scalable Approach to Delivering Health Interventions to the Home

! Sensors, algorithms, mobile communications for

lifestyle interventions

! Remote, just-in-time, continuous care ! Incorporate principles of health behavior change ! Optimal use of lower cost personnel ! Integrate family & informal caregivers into the health

care team (untapped resource)

! Platform for testing sustained cognitive interventions

in the home

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SLIDE 4

Modular Software for Multiple Protocols

! Cognitive Exercise (computer game format) ! Novelty exercise ! Physical Exercise ! Sleep Management ! Socialization ! Medication Management ! Mood Management (depression)

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SLIDE 5

Behavioral Markers = Continuous Monitoring & Computational Models

Home health based on unobtrusive, continuous monitoring

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SLIDE 6

Models!to!Infer!Ac&vi&es!of!Daily!Living !

6

Pavel et al., IEEE Special Issue, in press

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SLIDE 7

Hayes, ORCATECH 2007

Bedroom Bathroom Living Rm Front Door Kitchen

Sensor Events Private Home Activity Monitoring in the Home

Hayes et al., www.orcatech.org

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SLIDE 8

Hayes, ORCATECH 2007

Sensor Events Residential Facility

Bedroom Bathroom Living Rm Front Door Kitchen

Activity Monitoring in the Home

Hayes et al., www.orcatech.org

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SLIDE 9

Measuring!Gait!in!the!Home !

  • Unobtrusive'gait'measurement'in<home'with'passive'

infrared'(PIR)'sensors'<'Hagler,%et%al.,%IEEE%Trans%Biomed%Eng,%2010% – Four!restricted!view!PIR!sensors! – Measure!gait!velocity!whenever!a! – ! subjects!passes!through!the!! – ! “sensorVline”! – Deployed!for!the!Intelligent!! – !!!!Systems!for!Assessing!! – !!!!Aging!Changes!(ISAAC)!study! – 200+!subjects!monitored!for!>!4!years!

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Subject!1 !

12/07 08/08 11/09 12/10 30 40 50 60 70 80 90

Time Velocity (cm/s)

0.005 0.01 0.015 0.02 0.025 0.03 0.035 Stroke

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Aus&n!et!al,!Sept!2011!V!EMBC!(Gait)!

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Subject!2 !

07/07 02/09 09/10 50 60 70 80 90

Time Velocity (cm/s)

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 CDR=0.5 and MCI diagnosis

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Aus&n!et!al,!Sept!2011!V!EMBC!(Gait)!

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Health!Coaching!Pla_orm !

Family Interface

  • Safety

monitoring

  • Soft alerts
  • Team-based

care

  • Socialization
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SLIDE 13

Automated!Coaching!for!Physical!Exercise !

  • Collabora5on'with''

– Oregon!Health!and!Science!University! – University!California!Berkeley!

  • Pre<recorded'video'clips'for'

tailored'exercise'and'Kinect' Camera'

  • Real<5me'feedback'based'on'

image'interpreta5on'from'Kinect' skeleton'representa5on'

  • Monitoring'of'balance,'flexibility,'

strength,'endurance'

  • Poten5al'for'remote'interac5on''
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SLIDE 14

Sleep Module

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Assessment

  • Sleep Hygiene
  • Anxiety
  • Circadian Rhythm

Tailored Intervention

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SLIDE 15

Socialization Protocols for Cognitive Health

! Web cams and Skype software given to participants

and their remote family partner

! Frequent spontaneous use among participants

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Cogni&on!V!Monitoring!&!Interven&on !

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Computer!Game!to!Measure!Execu&ve!Func&on!

!

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Model!the!&ming!of!the!mouse!clicks !

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Search for Next Target Move to Next Target

R

t +

( )

,

S

t n d

+

M

t

  • S. Hagler et al., www.ORCATECH.org

Recall Next Target

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SLIDE 19

Es&mates!from!Game!Predict!TMT!Scores !

19

2

0.78 R = 0.0001 p <

  • S. Hagler et al., www.ORCATECH.org
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SLIDE 20

Cognitive Modeling Example: Memory

B B A B A C B A C D B A C D A B C D E C D E B C D E B C D E B F D E B F B D E F G D E F G H E F G H D E F G H D G E F H D E F H D I

Characterize Memory Capacity

  • Intervening number of events
  • Intervening time
  • Memory load

Simple Memory Model: Discrete Buffer

5 10 15 0.5 1

Subject 1020, N = 8687 Probability of Correct Intervening Number of Events

5 10 15 20 25 0.5 1

Probability of Correct Intervening Time [sec]

Characterize Memory Capacity with a Single Parameter

M Pavel, et al., www.ORCATECH.org

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SLIDE 21

Dynamic!User!Model!to!Support!Tailored!Messaging !

Family Interface

  • Safety

monitoring

  • Soft alerts
  • Team-based

care

  • Socialization
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SLIDE 22

Family Caregiver Interface

Link to Demo

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!!!!!!!!!!!!!!!!!!!!!!Monitoring!V>!Interven&on!

  • Ac5vity'Monitoring'in'the'Home'
  • Cogni5ve'Monitoring'

– Adap&ve!Computer!Games!–!Divided!Aaen&on,!Planning,!Memory,! Verbal!Fluency,!+++! – Linguis&c!Complexity!–!Emails,!phone!

  • Motor'Speed'

– Speed!of!Walking,!Computer!Typing,!Mouse!Movements!

  • Sleep'Monitoring'
  • Depression'–'affect'on'phone,'linguis5c'analysis'
  • Medica5on'Management'–'Context'aware'reminding'
  • Socializa5on'–'Skype,'phone,'emails'
  • Physical'Exercise'–'Interac5ve'video'

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