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Technology Enabled High-Touch Care 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 is 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
© 2004, Medical Automation Research Center Goals of the Eldercare Technology 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
© 2004, Medical Automation Research Center Model for Technology Enabled Care Older Adult Data Service Provider Adult Child Physician Services Personal Health Maintenance Preventive Interventions Improved Communications Inference, Archiving, and Analysis
© 2004, Medical Automation Research Center Challenges 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
© 2004, Medical Automation Research Center Monitoring ADLs: Meal Preparation 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
© 2004, Medical Automation Research Center A mattress pad that measures: Subject position Body temperature Breathing Pulse Movement Room light level Processed vibration signal gives pulse rate and respiration Passive Sleep Monitor Subject position Validated Pulse and Respiration against standard clinical measures
© 2004, Medical Automation Research Center 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 Monitor Original Signal Falling Person Detected
Assisted Living Pilot in partnership with Volunteers of America National Services
© 2004, Medical Automation Research Center Monitoring System Overview Data Manage r Collects data from multiple sensor units Data Analysis Server Caregiver / Care Provider Prepares relevant reports Sends data log for analysis, update software Alert generated Falls Sleep/Bed Exit ADLs Stove
© 2004, Medical Automation Research Center -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 Pilot site
© 2004, Medical Automation Research Center Activities 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
© 2004, Medical Automation Research Center Caregiver Report Screen
© 2004, Medical Automation Research Center Restless night Restful night Sleep Quality Report
© 2004, Medical Automation Research Center 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
© 2004, Medical Automation Research Center Future 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 Alzheimers patients) Enhance the alerting sub-system with additional sensors (e.g. fall event detector) and further refine the rules
© 2004, Medical Automation Research Center Other Eldercare Technologies Projects
© 2004, Medical Automation Research Center Thank You Medical Automation Research Center University of Virginia Majd Alwan, Director, Robotics and Eldercare Technologies Robin Felder, Center Director,
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