Smart aging-ibm-talk

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Patrice (Polly) Tremoulet from Drexel University's presentation at the Cognitive Systems Institute Group Speaker Series on March 26, 2015.

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Smart aging-ibm-talk

  1. 1. DIGITAL COLLEAGUES FOR SMART AGING | 1 Patrice D. Tremoulet, PhD Director, Applied Informatics Group (AIG)
  2. 2. • What is a Digital Colleague (DC)? – Enablers – Human Augmentation history (abridged) • 1st Use Case for DCs: Aging Workforce – Motivation – Scope of initial effort (& parallel projects) – Approach & Team • Questions / Feedback | 2 Overview
  3. 3. Digital Colleague (DC) Vision | 33/25/2015 A Digital Colleague augments human performance, by serving as: 1. an expert cog 2. a personal work assistant 3. a personal health coach Like coworkers who can “read” each other, DCs discretely monitor employees and provide customized assistance, tailored to the current context, including the work environment, physical and cognitive states, and ongoing task demands. DCs alert employees to relevant new research, news, or products, suggest tools and strategies that can enable better performance and provide guidance to maintain and improve health.
  4. 4. DC Enablers | 43/25/2015 1. A growing community is tackling the challenges associated with building cognitive assistants • Identify and share domain knowledge, customized based upon understanding a particular employee’s interests/needs 2. Inexpensive wearable and environmental sensors make it possible to reliably assess human physical and cognitive states • Human Performance Augmentation • Cognitive supports for people with disabilities • Personalized health support & health education
  5. 5. Human Augmentation History 1945 1960’s 2013 + beyond 2002-2006 Vannevar Bush As We May Think describes “enlarged intimate supplement to one’s memory” (memex) J.C.R. Licklider Man-Computer Symbiosis predicts “human brains and computing machines will be coupled together very tightly” Doug Englebart “Augmenting Human Intellect” calls for “improving the intellectual effectiveness of the individual human being” with computers SRI ARC established DARPA Augmented Cognition Human Performance Augmentation in a wide variety of domains 1990s BRAIN initiative “decade of the brain”
  6. 6. • Electroencephalograph (EEG) • Electrocardiograph (EKG) • Galvanic skin response (GSR) • Pupilometry / Eyetracking Augmented Cognition overview • Goal: Maximize operator cognitive performance in dynamic, complex operational environments • Approach: Physiological-data based assessment of operator cognitive state – Detects, predicts, avoids overload to reduce operator error and maximize effectiveness • Benefit: Mitigate negative effects of cognitive overload – Increase task speed and accuracy – Improve critical situation understanding Sensors C2 System User SMART
  7. 7. Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible Orexin A: alertness Neuropeptide Y: depression Cortisol: stress Dopamine & Norepinephrin: performance •Flexible, conformal, unobtrusive form factor •Real-time biomarker measurements •Correlations to physical and cognitive states that affect performance
  8. 8. 1st use case: Aging Workers Benefits: • Deep fund of work-relevant knowledge. • Can help mentor younger employees. • May be willing to work part time, saving employer costs. • Health costs reduced when people stay cognitively active. • High levels of engagement. Challenges: • Slight cognitive declines begin in the 50s • Physical limitations may require accommodations • User acceptance, usability of both wearable electronics and assistive/augmentative technologies | 83/25/2015
  9. 9. | 9 Aging Workers: Need for DCs
  10. 10. Aging Workforce in US | 103/25/2015 • The Government Accountability Office projected in 2006 that 20% of the workforce would be aged 55 or older by 2015 [1]. • Population research indicates that over 75% of baby boomers are planning to work past retirement age [2].
  11. 11. Aging Workers: Need for DCs | 113/25/2015 • Employers want to retain valuable older employees, but both lack knowledge about tools and strategies that can help identify and accommodate for cognitive and physical changes. • US universities in particular need to be prepared to support an aging workforce. 0 5 10 15 20 25 30 35 40 45 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Percentage aged 60 or over World Average United States of America Repblic of Korea Many other countries are also expecting aging workforces, e.g. South Korea http://data.un.org/Data.aspx?q=aged +over+60&d=PopDiv&f=variableID% 3a33
  12. 12. DC for Smart Aging Opportunities | 123/25/2015 • Seoul, Korea government officials are interested in supporting Smart Aging applied research • NSF Partnerships for Innovation: Building Innovation Capacity program. • Smart Aging Service System (SASS) provides older workers with personalized, private recommendations designed to help reduce health risks and maintain or improve job performance. • Enables older employees with valued skills and experience to continue working. • Reduces health costs by helping employees take ownership of their healthcare. • Provides a framework for developing personalized health recommender systems.
  13. 13. Drexel’s multi-pronged approach | 133/25/2015 1. Faculty develop prototype that detects and mitigates against most common changes in cognitive function (under NSF PFI:BIC) 2. CS PhD student builds Material Science Cognitive Assistant 3. Undergrad student(s) chart out the space of assistive technologies that can support individuals with intellectual disabilities 4. Faculty propose to develop new sensors, smart aging metrics, data repository and analysis tools with IBM and Korea
  14. 14. Digital Colleagues for Smart Aging: Initial effort Goal: Keep aging University workforce healthy and engaged even when faced with changes in any of three cognitive functions: memory, attention, processing speed. Approach: Wearable and environmental sensors feed an intelligent “Digital Colleague” that a) recommends accommodations and strategies to enable continued contributions, and b) provides health alerts and reminders. | 143/25/2015
  15. 15. Initial Prototype Sensor Suite | 153/25/2015 Commercially available products to be used in initial proof-of-concept prototype
  16. 16. Proof-of-concept prototype | 163/25/2015 Digital Colleagues have four major software components: 1. The robust data collection system integrates outputs of sensors into a data store that supports rich queries. 2. The knowledge base backend holds facts and inferences. 3. The Digital Colleague Algorithms component translates information about an employee into recommendations for assessments and/or interventions. 4. The dialog interaction module communicates with the employee.
  17. 17. | 17 Sample recommendations
  18. 18. • Drexel faculty/staff: – Yvonne Michael, Assoc. Prof, School of Public Health – Ibiyonu Lawrence, Clinical Prof., Drexel Univ. College of Medicine – Marcello Balduccini, Asst Prof, College of Computing & Informatics – Gaurav Naik, Sr Research Scientist, Applied Informatics Group • Industry Partners: – Cognitive Compass (CEO, Madelaine Sayko) – General Electric (Research scientists, Luis Tari & Alfredo Gabaldon) – Independence Blue Cross (Sr. VP & CIO, Somesh Nigam) – Evoke Neuroscience (CEO, David Hagedorn) – Abilities Inc. subsid of Viscardi Center (CEO, Jessica Swirsky) | 18 DC for Smart Aging Team
  19. 19. There will nevertheless be a fairly long interim during which the main intellectual advances will be made by men and computers working together in intimate association… those years should be intellectually the most creative and exciting in the history of mankind. Licklider, J.C.R. (1960), “Man-Computer Symbiosis” IRE Transactions on Human Factors in Electronics, volume HFE-1, pages 4-11.
  20. 20. | 20 Additional References: 1. Tishman, F.M., Van Looy, S., and Bruyère, M. 2012 “Employer Strategies for Responding to an Aging Workforce”, National Technical Assistance and Research Center 2012 Report. http://www.dol.gov/odep/pdf/NTAR_Employer_Strategies_Report.pdf 2. Saad, L. (2013). “Three out of Four US Workers Plan to Work Past Retirement Age,” Gallup / Economy Report, May 23rd, 2013. http://www.gallup.com/poll/162758/three-four-workers-plan-work-past-retirement-age.aspx 3. TIAA Cref - Aging Workforce Series, Health Fitness and the Bottom Line, July 2012 https://www.tiaa- cref.org/public/pdf/AgingWorkforceHealthandFitness.pdf 4. Levin, Sharon G. and Paula E. Stephan, “Age and research productivity of academic scientists,” Research in Higher Education, 1989: 30(5): 531- 549. 5. Sörensen, L.E., Pekkonen, M.M., Männikkö, K.H., Louhevaara, V.A., Smolander, J., Alén, M.J. “Associations between work ability, health-related quality of life, physical activity and fitness among middle-aged men,” Applied Ergonomics, Nov, 2008: 39(6):786-91. http://www.ncbi.nlm.nih.gov/pubmed/18166167.
  21. 21. DC HCI Challenges – Sensor Technologies • Unobtrusive, comfortable, but ruggedized form-factors – Robust when dusty, wet, tapped/hit, etc. • Largely autonomous operation. Should be able to forget you are wearing sensors…but: • Could it be useful to cue wearers into a potential health problem? Under what circumstances? Configurable? • Replacement notifications and failure indicators – how delivered and to whom? • Wearer “Reset” option? • Data download notification – to whom? Privacy issues?
  22. 22. Additional DC HCI Challenges to explore: • Do participants know about aging-related cognitive decline and how it may impact work? – How would participants want to interact with their own health data? – What sorts of privacy safeguards would they expect? • How and when would they like to provide information about themselves that sensors can’t currently capture (e.g. job satisfaction ratings)? • What factors should influence how information is requested and guidance is presented (e.g. individual preferences, current capabilities, the types and degrees of limitations, types of recommendations), and how?
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  25. 25. Logistics • Determine pre-mission readiness • Maintenance of safety during mission Commander monitoring health of squad Accelerated Learning / HMI design • Customize training based on cognitive states of trainees • Develop interfaces that support low cognitive workload Dynamic modifications to training exercises to speed learning Medical • Early Medical Problem Detection • Triage • Accident Medical SA • Recording of injury event & treatment for use at all echelons of care Pilot showing symptoms of Hypoxia Monitoring applications Developing technologies to collect and take action on health and readiness data of individuals In-Field Injury Screening Commander & Medic SA Sys. Learning & HMI design -Continuously -With Near-Zero Footprint

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