Read a recent Breaking Ground Post written by Emily Scheerer ’14, computer science, is a CWIT Scholar and Technology Editor for The Retriever Weekly. Abigail Williams ’14, chemical engineering, is the president of the UMBC chapter of the Society of Women Engineers.

UMBC Information Systems

Sensor-based assessment of the quality
of human motion during therapeutic exercise

Dr. Portia Taylor
Social Security Administration

12-1pm Wednesday, 10 April 2013, ITE 459

Advances in technology and research have been employed in recent years to develop efficient mechanisms to deliver home-based exercise therapy to patients suffering from knee osteoarthritis, a degenerative disease associated with aging. Essential to the success of a therapeutic home-exercise program is the quality of the motion performed by the patient. The unsupervised nature of home-based exercise may lead to incorrect exercise performance by patients; however, current home-based exercise programs do not provide mechanisms for monitoring the quality of motion performed or for providing feedback to the patient. This lack of support has been found to be a factor in patient non-compliance to home exercise programs.

Our goal is to provide a motion sensor-based system that can evaluate the quality of exercise to support home rehabilitation. We introduce the Quality Assessment Framework (QAF) that uses low-cost motion sensors with data processing and machine learning techniques to assess the quality of human motion performed during therapeutic exercises. Data from fifteen persons with knee osteoarthritis was collected in a laboratory environment, and a classifier was trained using multi-label learning methods to detect descriptive characteristics of the patient's motion. These characteristics represent errors in the exercise performance as well as variables regularly monitored by the patient's therapist, such as speed.

Results from multi-label learning are presented and recommendations made on requirements for an in-home therapeutic exercise system. The QAF can be adapted to the home therapy needs of conditions other than knee OA. We present a preliminary design of the InForm Exercise System that utilizes the QAF and has the potential to present feedback to patients completing home exercise programs.

Portia Taylor received her BS degree in Computer Science from Grambling State University in 2007 and a Ph.D. degree in Biomedical Engineering from Carnegie Mellon University in 2012. At CMU, she was part of the Quality of LifeTechnology Center, a NSF Engineering Research Center dedicated to the development of technologies for the elderly and disabled. Currently, Dr. Taylor works at the Social Security Administration as an IT Fellow. Her research interests include machine learning applications in biomedical engineering, intelligent systems for rehabilitation and physical therapy, and heath information technology.

UMBC CSEE Colloquium

Machine learning techniques for predicting chronic diseases

Vladimir Korolev

1:00pm Friday, 5 April 2013, ITE 227, UMBC

In recent years we saw an explosion of cheap genetic tests, which lead to the emergence of personalized medicine. Personalized medicine is defined as practice of medicine that is tailored to specifics of individual patient. My work addresses the problem predicting an individual’s predisposition towards certain chronic diseases based on the their genetic makeup. The benefits of such work allow for more selective administration of invasive tests such as biopsies, which are known to cause health problems themselves.

Recently NIH has done a number of Gene Wide Association Studies q that resulted in massive datasets containing subjects’ generic makeup and labeled with clinical data including occurrence of chronic diseases. Unfortunately, given the relatively small number of patients in such studies and the vast number of genes possessed by human beings, these datasets cannot be analyzed with traditional statistical predictive models, which require a large number of samples (patients) with a very few features per sample.

My work attempts to solve this problem by employing state of the art machine learning techniques. In the past year I have built a software system that is capable of crunching of multi-terabyte scale datasets to refactor the NIH data into the form that is palatable by modern big data systems. I have run initial stages of feature selection. I will present the current state of the work and future plans. Another goal of this work is to ensure the repeatability of the experiments and flexibility to run with any similar dataset from current and future studies

Vlad Korolev is a PhD student in the UMBC Computer Science program. His research interests are in the are of personalized medicine, machine learning and large scale data processing. Vlad has considerable experience in the industry specializing in IT security, large scale data processing and the organization of software development processes.

Each semester UMBC sets aside several days for partnering organizations to be showcased on campus. Through our Corporate Visibility Days program, organizations are given the opportunity to highlight themselves to the UMBC student body.  Traditional schedules include information sessions, career-related workshops,networking opportunities and lunch.

Held from 11:00 am - 2:00 pm, you do not need to attend the entire event, please stop by when you are able and stay for as long as you can!

** Kennedy Krieger Institute

April 4 ~ 11:00 am - 2:00 pm
Commons 331

** Mitre Corporation
April 10 ~ 11:00 am - 2:00 pm
University Center 312

On-Campus Interviews offer students a chance to conveniently interview for professional positions on campus.  Employers travel to our campus for the specific purpose of interviewing preselected students for currently open positions.  This is a great opportunity to interview with employers specifically interested in UMBC students and offers the advantage of a smaller applicant pool.  Interviews take place during the fall and spring. The logistics (viewing positions, applying, signing up for interview time slots, etc.) are all handled via UMBCworks.

Don't miss out on employers coming to UMBC to recruit YOU this fall! The following is a list of employers coming to conduct on-campus interviews with application deadlines within the next month.

** CGI Federal Consultant
Position:  Entry Level IT Consultant (9255641)
Resume Submission End:  April 7

** Enterprise Holdings
Position:  Management Trainee (9255896)
Resume Submission End:  April 5

** Target
Position:  Executive Team Leader (9255878)
Resume Submission End:  April 5

**  Insight Global
Position:  Recriter/Account Manager (9257146)
Resume Submission End:  April 10

A full list of On-Campus Interviews scheduled is available on our website calendar and on UMBCworks, please check regularly for any updates or changes that may occur.

The following full-time positions have been posted within the past week to UMBCworks. Login to your account (via the link in the Jobs & Internships topic in myUMBC) and find details and application instructions as well as hundreds of other job postings!

9257398 - Account Manager - NFP ProBen
9257926 - Bilingual Retirement Specialist - Lincoln Financial Group
9257374 - Case Manager-Admissions - Humanim
9257335 - Telephone Interpreter - Dream Management, Inc.
9257304 - EDI Representative - Stanley Black & Decker

The following part-time positions have been posted within the past week to UMBCworks. Login to your account (via the link in the Jobs & Internships topic in myUMBC) and find details and application instructions as well as hundreds of other job postings!

9257379 - Call Center Representative (Job 159182) - Baltimore County Employees Federal Credit Union
9257325 - Economic Justice Fellow - Maryland Consumer Rights Coalition
9255073 - Job Coach/Community Support Person - New Directions
9257291 - Mission Website Developer - UMBC Goddard Earth Sciences and Technology Center (GEST)