technology enabled high-touch care majd alwan, ph.d. medical automation research center university...

Technology Enabled High-Touch Technology Enabled High-Touch CareCare

Majd Alwan, Ph.D.Medical Automation Research Center

University of Virginia

Improving healthcare quality and efficiency through the development of advanced technologies

© 2004, Medical Automation Research Center. All Rights Reserved.

© 2004, Medical Automation Research Center

What What isis the MARC? the MARC?

MARC is a research, development and consulting organization providing medical and industrial clients with innovative automation solutions.

Expertise in: Collaborative multidisciplinary research and

development relating to medical care and health issues Automation of healthcare processes Eldercare Technologies Program: Low-cost in-home

monitoring and assistive technologies


Goals of the Eldercare Goals of the Eldercare Technology ProgramTechnology Program

Provide novel technological solutions:– Improve quality of life for elders/disabled– Multiply caregiver ability to interact positively– Reduce risks and potentially reduce care costs

Launch collaborative research initiatives with interested parties


Model for Technology Enabled CareModel for Technology Enabled Care

Older AdultData

Service Provider

Adult Child

Physician

ServicesPersonal Health Maintenance

Preventive Interventions

Improved Communications

Inference, Archiving,

and Analysis


Challenges and ApproachChallenges and Approach

Minimally invasive sensing technology

User Centered design

Completely passive approach– Burden on data analysis

Clinical validation Ability to retrofitting existing

structures Low-bandwidth

– Data reduction at the residence Affordable technologies

Economic impact studies

Privacy and acceptance by older adults and adult children

Acceptance and maximum utility to caregiver

Emphasis on maintaining high touch

Compliance

Early adoption, proliferation, and deployability everywhere, including rural areas

Reimbursement


Monitoring ADLs: Meal Monitoring ADLs: Meal Preparation and ShoweringPreparation and Showering

Identified minimum set of sensors targeting Activities of Daily Living (ADLs) and developed Inference Rules for

The system was validated through comparisons to a customized PDA activity log in a real “living laboratory” setting:

• No lunch, dinner or showering events were missed by the detection algorithm

• Inference rules are reliable: high correlations between the user’s activity log and the detected activities


A mattress pad that measures:

• Subject position• Body

temperature• Breathing• Pulse• Movement• Room light

levelProcessed vibration signal gives pulse rate and respiration

Passive Sleep Passive Sleep MonitorMonitor

Subjectposition

Validated Pulse and Respiration against standard clinical measures


The sensor system shown mounted on the baseboard in walkway path

Detects gait from step-induced floor vibrations

Longitudinal gait analysis and fall detection in natural settings

Passive Gait Passive Gait MonitorMonitor

Original Signal

Falling Person Detected

Assisted Living PilotAssisted Living Pilot

in partnership with Volunteers of America National Services


Monitoring System OverviewMonitoring System Overview

Data Manag

er

Collects data from multiple sensor units

Data Analysi

s Server

Caregiver / Care Provider

Prepares relevant reports

Sends data log for analysis, update

software

Alert generated

Falls

Sleep/Bed Exit

ADLs

Stove


- Movement detection – traveling throughout the home space, getting up, moving from room to room, entering/leaving rooms/home, bathroom, shower area, kitchen, living room etc.

– Temperature detection – monitors temperature over cook stove

– Bed sensor – monitors presence and movement in bed, bed-exit and whether heart rate is outside a predefined normal range

Sensor Components Installed in the Sensor Components Installed in the Pilot sitePilot site


Activities Monitored and AlertsActivities Monitored and Alerts

Optimized the System and the Activities Inference Engine for congregate care settings and augmented with immediate alerting capability

Activities monitored: Bathroom use, Bathing/ Showering, Time in bed, Time out of bed, Movement in bed (restlessness)

Implemented alerts: possible fall, forgotten stove, low pulse and high pulse

The Fall alert was based on bed-exit, inferred from the bed-pad and motion sensing only; no fall event sensor


Caregiver Report ScreenCaregiver Report Screen


Restless night

Restful night

Sleep Quality ReportSleep Quality Report


Pilot Results Pilot Results

• The technology was acceptable to all participants• Common fears among surveyed older adults included fear of falls and not receiving help quickly • Statistically significant (p=0.03) increase in the quality of life of residents after only three months of monitoring, maybe due to increased sense of security• No significant change in caregiver burdens and strains• Somewhat high false alerts rate• Caregivers rely on the reports in care planning

Mean and Standard Deviation

ColumnA B

30

25

20

15

10

5

0


Future WorkFuture Work

Economic impact assessment studies Larger and longer studies in different care settings Intervention efficacy studies (such as sleep studies) Customize solutions for special populations (dementia

and Alzheimer’s patients) Enhance the alerting sub-system with additional

sensors (e.g. fall event detector) and further refine the rules


Other Eldercare Technologies Other Eldercare Technologies ProjectsProjects

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-1.5

-1.0

-0.5

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2.0

2.5

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-1.0

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0.0

0.5

Time (Seconds)

Pulse Oximeter Signal

Empirical Technologies CorporationCharlottesville, Virginia434 296-7000

using the ETC Fiber-Optic Loop-back Sensor

Preliminary Heartbeat Detection using a Bathroom Scale

raw ETC Signal Signal

ETC signal FFT filtered from 0.8 - 1.5 Hz


Thank YouThank You

Medical Automation Research Center

University of Virginiahttp://marc.med.virginia.edu

Majd Alwan, Director, Robotics and Eldercare Technologies [email protected]

Robin Felder, Center Director, [email protected]

technology enabled high-touch care majd alwan, ph.d. medical automation research center university...

Documents

medical care

analysis slide

care planning slide

assistive technologies

pilot site slide

data log

high pulse

sensor system