Researchers at the University of Massachusetts Medical School www.umassmed.edu are studying how to remotely and noninvasively monitor patients with serious cardiac disease to keep them healthier and prevent emergency room visits and hospitalizations.
David McManus MD, Associate Professor of Medicine in the Division of Cardiovascular Medicine along with colleagues from the Massachusetts Institute of Technology, Northeastern University, and the University of Connecticut, School of Engineering, are developing and testing wearable devices capable of monitoring heart patients for disease-related complications.
The research team is developing a vest able to detect subclinical cardiac dysfunction and a smart watch that will assess rhythm abnormalities. The observational study will collect data from patients who wear the devices, develop computer programs to analyze the data, and then eventually assist in identifying at-risk patients.
Dr. McManus also an adjunct professor at Worcester Polytechnic Institute has collaborated on research projects funded by NIH, DOD, and Philips Home Healthcare to develop and test wearable health monitoring devices.
This includes researching sensors and computer-assisted algorithms to assess cardiac dysfunction and blood loss along with developing wearable garments to detect heart failure decompensation plus smartphone/watch apps for monitoring heart rhythm abnormalities.
The present research is funded by a grant from the National Science Foundation’s www.nsf.gov Smart and Connected Health Program (SCH). The goal for SCH program is to support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, and person-centered to focus on well-being rather than disease.
The program’s purpose is to develop next generation healthcare solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral cognitive processes as well as system and process modeling.