We use machine learning to extract actionable information from embedded sensors and mobile devices, specializing in applying these techniques to subtly measure and affect human health and activity.
- Nov 8, 2013: CS seminar for students interested in independent study courses. Attend if interested in joining the lab!
- Oct 24, 2013: Yohannes Azeze’s Poster Presentation at Loyola Medical Research Day: “In-Lab versus at-Home Activity Recognition in Individuals with incomplete Spinal Cord Injury”
- Oct 7, 2013: Steve Antos’s J Neuroscience Methods paper accepted: “Hand, belt, pocket or bag: practical activity tracking with mobile phones”
- Sep 27, 2013: Location-aware activity tracking (SVM+HMM model), presented at the Biomedical Engineering Society Conference [pdf]
- Sep 15, 2013: Paper published! Balance board use improves balance and gait in Parkinson’s disease – Archives in PM&R.
- Sep 6, 2013: Computer science department seminar – brief call for students. Come if interested. (12:30pm, Damen center multipurpose room)
- Aug 23, 2013: Yohannes Azeze’s intern presentation on at-home activity recognition of subjects with spinal cord injury – poster at the Rehabilitation Institute of Chicago
- Aug 12, 2013: Prof Albert’s first day at Loyola’s CS department
- June 10, 2013: Monitoring Prosthetic Use in Amputees with Different Functional Capabilities Using Mobile Phones – published! [link], [pdf]
- April 8, 2013: Smartphone outcome tracking in PD: invited talk in the 2-day RIC course Interdisciplinary Care for Parkinson’s Disease.
- March 18, 2013: Location-aware activity tracking paper, submitted to J. Neuroscience Methods: Motion Tracking special issue
- Oct 19, 2012: Using mobile phones for activity recognition in Parkinson’s patients
- (Accepted, Frontiers in Neurology)
- Aug 29, 2012: Our outcomes dashboard won the Henry B. Betts award! Patient movements were tracked and presented to RIC therapists and physicians on the 9th floor as part of their standard of care for two months
- Aug 3, 2012: Our summer intern poster presentations presented to SMPP at RIC [pdf-1] and [pdf-1]
- May 7, 2012: Fall classification by machine learning using mobile phones, published in PLoS ONE [pdf]