wendy nilsen - aging in place
TRANSCRIPT
Wendy Nilsen, PhDProgram Director, Smart and
Connected HealthCISE/NSF
AGING IN PLACE NRI PI WORKSHOP
Aging in the United States
From the US Census
Aging in Place is described as technologies to assist older adults and people with chronic diseases to live independently. The US is aging (25% over age 55) and the census predicts a 71% spike in the number of adults over age 60 by 2020. Assess the state of aging in place technologies and identify challenges in the development and application of technologies for in-home care.
2014 Workshop Sponsored by:• National Institutes of Health (host)• Computer Research Association (sponsor)• National Science Foundation (collaborator)
BACKGROUND
Aging in Place concept originally conceived based on the idea that technology could be enhance health outside of hospitals and nursing homes. • Improve and sustain health and increase
the quality of life •Allow people to live at home longer •Reduce healthcare costs:
Hospitalizations/rehospitalizations•Reduce strain on the healthcare
workforce •Reduce caregiver burden
DRIVERS
Personalization and Adaptation •Recognizing that multiple approaches
are needed to address needs of people who are most ill, managing chronic diseases and sustaining health and wellness.
•Creating more personalized technology to serve diverse populations, while creating evidence-based, generalizable solutions from which to adapt.
•Creating solutions with the principles of ‘future’ proofing.
•Designing technologies to empower patient, caregivers and providers with timely and actionable information.
•Ensuring technology does not create a ‘digital’ divide or disadvantages among groups.
PERSONALIZATION & ADAPTATION
Evidence• Validating the effectiveness and reliability of technologies by developing methods of rapidly generate evidence. • Developing ‘testbeds’ to efficiently, economically and systematically explore the use of technologies and involve the community in the research. • Thinking about technologies more broadly.•Creating new robust methods of analysis and sensing-driven decision analysis to create predictive, personalized models of health.
EVIDENCE
Changing Cultures•Organizing opportunities for the various disciplines to transform aging in place from translation of home health care to smart homes that support health.•Changing the current mind-set so that technology in the home is an alternative to care and not just an add-on to care.• Change the disciplinary lens that that describes technology researchers as technicians and researchers as clinicians. •Ensuring we do not develop a “health care at home” system.
CHANGING CULTURE
NSF Solicitation: NSF-13-543NIH Notice Number: NOT-OD-13-041
SMART & CONNECTED HEALTH INTER-AGENCY PROGRAMNATIONAL SCIENCE FOUNDATION/NATIONAL INSTITUTES OF HEALTH
Clinic-based Data
Patient-basedData
Exchange
Health care
System
Medical Team
DecisionSupportNeeds
Patient data• Concerns• Patient Reported Outcomes
• Risk modeling• Diagnostic support • Treatment selection • Guideline adherence• Error detection/correction
• Situational awareness• Population health• Continuity of care• Identify side effects• Inform discovery
Clinic/sensor data• Clinical measures• Laboratory findings • Sensor dataAssessment• Diagnosis• Categorical reporting• Prognosis/Trajectory Plan• Treatment planning• Self-care planning• Post treatment• Community• Surveillance
Patient & Family
Smart and Connected Health: People, Technology, Process
Medical ResearcherCommunity
Smart & Connected HealthJoint National Science Foundation/National Institutes of Health Initiative
•Integration of EHR, clinical and patient data•Access to information, data harmonization•Semantic representation, fusion, visualization
Digital Health Infrastructure
Informatics and Infrastructure
•Data-mining and machine learning•Inference, cognitive decision support system•Bring raw image data to clinical practice
Data to Knowledge to Decision
Reasoning under uncertainty
•Systems for empowering patient•Models of readiness to change•State assessment from images video
Empowered IndividualsEnergized, enabled,
educated
•Assistive technologies embodying computational intelligence•Medical devices, co-robots, cognitive orthotics, rehab coaches
Sensors, Devices, and Robotics
Sensor-based actuation
SCH EXP: Collaborative Research: A Formalism for Customizing the Control of Assistive Machines
Technical Approach:•A formalism that customizes how users share control with intelligent autonomous assistive devices, based on user ability and preference.•Customization to the user and task, and based on the confidence that the user's goal has been predicted correctly.•Customization by the autonomy and by the user.
Brenna Argall, Northwestern UniversitySiddhartha Srinivasa, Carnegie Mellon University
NSF Grant # 1R01EB019335-01
Motivation:For those with severe upper limb motor impairments, caregivers are still relied on for manipulation tasks like meal preparation or personal hygiene.Robotic arms hold much promise, however traditional devices for teleoperation like joysticks become tedious or untenable to control these higher degrees of freedom systems.
Transformative:•Customizable and intuitive control, currently unavailable out-of-the-box on any commercial assistive arms.•Broad and rapid dissemination via simple interfacing with control devices already used to drive powered wheelchairs.
Broader Impacts:•Increasing the independence of those with motor impairments and/or paralysis.•National Robotics Week exhibit at the Museum of Science & Industry in Chicago.•Industrial collaboration plan with Kinova Robotics.
Contacts: •PI Brenna Argall, Northwestern University
and the Rehabilitation Institute of Chicago•PI Siddartha Srinivasa, Carnegie Mellon University
Progress:•Year 1 will develop the technical components.•In subsequent years user studies with high Spinal Cord Injury subjects will evaluate the importance of customizing the control sharing function, the autonomy behaviors and confidence measures.
Customiziation of control sharing functions to the user (U) and task (T)
Crafting a Human-Centric Environment to Support Human Health Needs
Technical Approach:• We perform real-time activity recognition
smart home sensor data “out of the box”• Machine learning techniques map activity
parameters to assessment values • Activity forecasting drives
activity prompting intervention
Diane J. Cook and Sajal K. DasWashington State University
NSF Grant #I1064628
Motivation:Design smart environment technologies to perform
automated health assessment and intervention
Transformative:• Our team combines expertise from machine learning,
pervasive computing, and clinical neuropsychology• We are designing and clinically validating methods to
perform automated functional assessment and intervention
Broader Impacts:• Data collected in the smart homes is cleaned,
anonymized, visualized, and disseminated• Half of the students and faculty involved in this
project are women or from underrepresented groups• Research was integrated into a multi-disciplinary
Gerontechnology class
Contacts: • Diane J. Cook
Washington State University, [email protected]• Sajal K. Das
Missouri S&T, [email protected]• http://ailab.wsu.edu/casas
Progress:• We collected sensor data in 40 homes with older adults• We observe a statistically significant correlation (r=0.79)
using supervised machine learning and (r=0.57) using unsupervised learning between smart home sensor and clinical scores for n=179 older adult participants.
Socially Assistive Human-Machine Interaction for Improved Compliance and Health Outcomes
Technical Approach:• Affective feedback, praise, encouragement, and
relationship building in SAR exercise coach and buddy systems
• Personalization of motivational character backstory• Use of deviation (cheating) detection for user engagement
PI: Maja J Matarić, University of Southern California,
NSF Grant #1117279
Motivation:Our approach is focused on socially assistive robotics (SAR) and is motivated the following domains:
Poststroke rehabilitation Physical and cognitive exercise for older adults General exercise encouragement
Transformative:•Design principles for SARbased therapeutic interventions •Statistically significant large-scale study demonstrating preferences of physical robots over screen-based coaches•Insights regarding the impact of agent embodiment on user engagement in human-robot interaction contexts •Novel methods for autonomous exercise coaching and encouragement
Broader Impacts:• Promoting wellness and longevity in the aging
population • Implementing and testing real-world socially
assistive robots (SAR)• K-12 outreach activities: annual open house and
robotics workshops for students and educators, impacting over 2000 K-12 students each year
Contacts: •Principal Investigator: Maja J Matarić•Partners: Rancho Los Amigos National Rehabilitation Center, be.group•Project URL: http://robotics.usc.edu/interaction/?l=Research:Projects:wellness:index
Progress:•Evaluated SAR exercise system for older adults and developed spatial language framework for natural language interaction with older adults •Evaluated effect of socialcomparative feedback given by a SAR in the post-stroke domain•Evaluating SAR exercise buddy system for overweight and obese youth
Collaborative Aging (in Place) Research Using Technology (CART) (U2C)
NIH RFA-16-021THE PURPOSE OF THIS, INTER-AGENCY FUNDING OPPORTUNITY ANNOUNCEMENT IS TO DEVELOP AND VALIDATE THE INFRASTRUCTURE FOR RAPID AND EFFECTIVE CONDUCT OF FUTURE RESEARCH UTILIZING TECHNOLOGY TO FACILITATE AGING IN PLACE, WITH A SPECIAL EMPHASIS ON PEOPLE FROM UNDERREPRESENTED GROUPS.
