Team members

Emre Ertin (Sensor Platform Technologist, Ohio State)
Deepak Ganesan (Thrust 1 Lead, UMass Amherst)
Benjamin Marlin (UMass Amherst)

Ju Gao (Ohio State)
Siddarth Baskar (Ohio State)
Addison Mayberry (UMass)


Development and validation of any new mHealth biomarker requires conducting research studies in lab and field settings to collect raw sensor data with appropriate labels (e.g., self-reports). Raw sensor data are of increasing interest as it significantly expands the useful life of the information collected. Biomedical studies often archive biospecimens in biobanks so they can be reprocessed to take advantage of future improvements in assays and support biomedical discoveries not possible at the time of data collection. In similar fashion, archived raw sensor data can be used to obtain new biomarkers that were not available at the time of data collection.

sensor approach

For example, if the activity trackers stored raw sensor data from accelerometers and gyroscopes (100+ HZ instead of few samples of activity counts per day), the same sensor data can also be used to track eating, drinking, brushing, smoking, etc., from hand gesture signatures, in addition to activity counts.

Therefore, MD2K natively supports collection of high-frequency raw sensor data and its real-time wireless streaming to the study smartphone, in order to facilitate triggering of notifications triggered by real-time computation of biomarkers from these sensor data. Such notifications may be used to confirm/refute the biomarker detection for field validation, to collect self-reports to understand the surrounding context, or to deliver a just-in-time intervention. Such high-frequency collection and streaming of mobile sensor data places significant constraints on battery life, and most consumer-grade sensors are not optimized to last the entire day in such a mode of collecting and streaming raw sensor data. To overcome this challenge, MD2K has developed a variety of new sensors that provide this capability and still last the entire day or longer.

Several sensors have been developed and deployed by MD2K in mHealth field studies:

EasySense —It is a contactless microradar sensor that can detect heart and lung motion and assess change in the lung fluid level;

MotionSenseHRV — It is a wrist-worn sensor that can measure hand gestures via accelerometers and gyroscopes and interbeat intervals via optical sensors for computing heart rate variability indices; and

AutoSense — It is a chest-worn sensor suite that can measure cardiorespiratory parameters via ECG and respiration, and movement of the torso via accelerometers.

iShadow — Our team has also developed computational eyeglasses, which are currently being evaluated for its utility in assessing fatigue and visual exposure to cues (e.g., alcohol advertisements).





Copyright © 2018 MD2K. MD2K is supported by the National Institutes of Health Big Data to Knowledge Initiative (Grant #1U54EB020404)
Team: Cornell Tech, GA Tech, Harvard, U. Memphis, Northwestern, Ohio State, Open mHealth, UCLA, UCSD, UCSF, UMass, U. Michigan, Utah, WVU