Related projects

MD2K's research is part of a continuing research effort on the utility of mobile sensors in mHealth.

Other projects include:

mPerf (2017)

Current workforce evaluation tools, such as interviews, cognitive assessments and questionnaires, can be helpful in assessing job performance, but do not always capture how an individual will perform in the actual workplace. In addition, these traditional tools can be limited by administrative delays and other measurement issues. Such current workforce evaluation tools as interviews, cognitive assessments and questionnaires do not always capture how an individual performs on a day-to-day basis. mPerf will address this challenge by building upon an open-source software platform developed by the NIH-supported Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K), also headquartered at the University of Memphis. This platform allows researchers to gather, analyze and store high-frequency mobile sensor data to discover and validate mHealth biomarkers. mPerf will extend this platform to model and predict work performance based on passively collected sensor-based markers of activity, behavior and context. Find out more about mPerf here.  mPerf is funded through Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC), a multi-year research effort of the Intelligence Advanced Research Projects Activity Program, operated under the Office of the Director of National Intelligence.

mProv (2016)

mProv is developing a data cyberinfrastructure that addresses the unique aspects of mobile sensor data to facilitate their use and analysis by researchers in computing, engineering and other disciplines. This includes developing techniques for integrating metadata and data capture over mobile streaming data, and for propagating such data in order to enable reasoning about uncertainty and variability; runtime infrastructure and APIs for efficient sensor data acquisition and reply (integrated with human data capture); and mechanisms for managing privacy policies. To seed the entire effort with real data, mPRov utilizes data collected in ongoing user studies in the MD2K Center of Excellence and collaborate with Open Humans project to recruit individuals who are willing to donate their sensor data for research use without restriction. Finally, metadata for mobile sensors that collect data in users’ natural environments may itself be privacy sensitive. mPRov will investigate privacy mechanisms to ensure privacy of data contributors, while facilitating research with their data and associated metadata. mProv is funded through the National Science Foundation and American Cancer Institute.

AutoSense (2007)

AutoSense is an unobtrusively wearable wireless sensor system for continuous assessment of personal exposures to addictive substances and psychosocial stress as experienced by human participants in their natural environments. Currently, AutoSense consists of an arm band with four wireless sensors and a chestband with six wireless sensors. All the ten sensors are integrated onto an embedded platform called “mote,” a tiny self-contained, battery-powered computer with a wireless radio that can host multiple sensors, collect and process data from them using customized algorithms, and communicate on secure wireless channels. More details are available in an ACM SenSys'11 paper. AutoSense was funded through a grant from the National Institutes of Health as part of the Genes Environment and Health Initaitive and the National Institute on Drug Abuse.

FieldStream (2009)

FieldStream (NetSE: Large: Collaborative Research: FieldStream: Network Data Services for Exposure Biology Studies in Natural Environments) was a collaborative project funded by the National Science Foundation and involving researchers from Carnegie Mellon University, Georgia Institute of Technology, University of California at Los Angeles, University of Massachusetts at Amherst, and University of Memphis. The project was funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). FieldStream was funded through a grant from the National Science Foundation NetSE progrm. More about FieldStream here