Amy Lingley-Papadopoulos

Professorial Lecturer in Biomedical Engineering
Office TBD | Office Hours: W: 11:30 am - 12:30 pm
[email protected]

Dr. Amy Papadopoulos is an adjunct professor in the BME department, the chief technology officer of Syndesy Technologies Inc, and owner of Loudoun International Fencing Club.  Previously, she was the senior research scientist at AFrame Digital, Inc.  She led their research in areas such as predictive modeling, gait analysis, and fall detection, and was particularly interested in recognizing changes in patterns that may be indicative of an increased fall risk or declining medical condition.  She received a B.S. in electrical and computer engineering from the University of Virginia, her M.S. in computer engineering from the University of California, Santa Barbara, and her D.Sc. in biomedical engineering from The George Washington University.  Over the past 30 years, she has worked as a software developer working on fault-tolerant computing systems, voice-mail systems, and GSM applications.  Her doctoral research was in the area of optical coherence tomography and image analysis.  Dr. Papadopoulos was the recipient of a scholarship from the ARCS foundation, was co-developer on three patents, and has authored several papers published in peer-reviewed journals.  She is also a member of IEEE and SPIE.


  • D.Sc., George Washington University,  2009
  • M.S., University of California, 1994
  • B.S., University of Virginia, 1988
  • Evans J, Papadopoulos A, Silvers CT, Charness N, Boot WR, Schlachta-Fairchild L, Crump C, Martinez M, Ent CB. “Remote Health Monitoring for Older Adults and Those with Heart Failure:  Adherence and System Usability.”  Telemedicine and e-Health, 22(6), June 2016, pp. 480-488.
  • Papadopoulos A, Vivaldi N, Crump C, Silvers CT.  “Differentiating Walking from other Activities of Daily Living in Older Adults Using Wrist-Based Accelerometers.”  Current Aging Science, 8(3), November 2015, pp 266-275.
  • Papadopoulos A, Vivaldi N, Crump C, Silvers CT.  “Wrist-gathered acceleration data and their correlation with physical activity in the elderly”,  International Journal of Biomedical Science and Engineering, 2(5), November  2014, pp. 38-44.
  • Charness N, Fox M, Papadopoulos A, Crump C. “Metrics for Assessing the Reliability of a Telemedicine Remote Monitoring System”, Telemedicine and e-Health, 19(6), June 2013, pp. 487-492.
  • United States Patent 6,449,733. “On-line replacement of process pairs in a clustered processor architecture,” W Barltett, J Granberg, C Lingley, R Parkison, G Smith, N Trickey, Sep 2002.
  • United States Patent 8,618,930, “Falls Recognition System – Continuation in Part to Mobile Wireless Customizable Health and Condition Monitor,” A Papadopoulos, C Crump, B Wilson, Dec 2013
  • United States Patent 9,526,421, “Method and System for Wellness Management – Continuation in Part,” A Papadopoulos, C Crump, B Wilson, Published June 2014.

Principal Investigator:  NIH Phase II Grant — Apr 2012 – Mar 2015

Non-Intrusive Automated Portable Data Collection System for Aging Surveys

The project aimed to prove that the proposed monitoring system provided an effective means of collecting real-time survey data (including physiological, activity, sleep, and self-report data) from elderly individuals for an extended period of time and was capable of recognizing deviations from individualized baseline norms that could be indicative of illness or need for intervention.


Principal Investigator:  Commonwealth of Virginia Small Business Innovation Research Grant — Sep 2012 – Feb 2013

The grant allowed for developing methods of monitoring and alerting on changes in patterns of daily activities such as bathing and toileting.


Principal Investigator:  NIH Phase I Grant — Aug 2011 – Nov 2012

Continuous Fall Risk Monitoring System: Walking vs Activities of Daily Living

The long-term goal of this project was to develop a nonintrusive system for the ongoing assessment of fall risk in the elderly. The goal of the Phase I was to test the feasibility of using tri-axial acceleration data gathered from a wrist monitor to differentiate between periods of walking and other activities of daily living.


Principal Investigator:  Commonwealth of Virginia Small Business Innovation Research Grant — Jan 2012 – Jul 2012

The grant allowed for the study of automated activity recognition.


Device specification and firmware development: Center for Commercialization of Advanced Technology — Sep 2012 – Feb 2013

Mobile Heart Rate Monitor

The goal was to add and adapt a novel TRL6 prototype heart rate and heart rate variability sensor to a TRL9 networked wireless health and environmental monitor in wristwatch form.