Center of excellence for mobile sensor data-to-knowledge (MD2K)

The Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) is enabling the collection of high-frequency mobile sensor data for the development and validation of novel multisensory biomarkers and sensor-triggered interventions.

[1]  H. Krumholz,et al.  Telemonitoring in patients with heart failure. , 2010, The New England journal of medicine.

[2]  Paul Mineiro,et al.  Machine learning on Big Data , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[3]  L. Stevenson,et al.  Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial , 2011, The Lancet.

[4]  Nigel Cross,et al.  Design Thinking: Understanding How Designers Think and Work , 2011 .

[5]  Mani B. Srivastava,et al.  ipShield: A Framework For Enforcing Context-Aware Privacy , 2014, NSDI.

[6]  Susan Stewart,et al.  Design Thinking: Understanding How Designers Think and Work , 2011 .

[7]  Yixin Chen,et al.  Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[8]  Ida Sim,et al.  Making Sense of Mobile Health Data: An Open Architecture to Improve Individual- and Population-Level Health , 2012, Journal of medical Internet research.

[9]  Evangelos Kalogerakis,et al.  RisQ: recognizing smoking gestures with inertial sensors on a wristband , 2014, MobiSys.

[10]  Michael Böhm,et al.  Impact of Remote Telemedical Management on Mortality and Hospitalizations in Ambulatory Patients With Chronic Heart Failure: The Telemedical Interventional Monitoring in Heart Failure Study , 2011, Circulation.

[11]  Ara Darzi,et al.  Preparing for precision medicine. , 2012, The New England journal of medicine.

[12]  R. Canby,et al.  Intrathoracic impedance vs daily weight monitoring for predicting worsening heart failure events: results of the Fluid Accumulation Status Trial (FAST). , 2011, Congestive heart failure.

[13]  Andrew Boyle,et al.  Development of a method to risk stratify patients with heart failure for 30-day readmission using implantable device diagnostics. , 2013, The American journal of cardiology.

[14]  Deborah Estrin,et al.  Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K). , 2017, IEEE pervasive computing.

[15]  Ayesha Hasan,et al.  Safety and accuracy of a wireless pulmonary artery pressure monitoring system in patients with heart failure. , 2011, American heart journal.

[16]  Pan Hu,et al.  iShadow: design of a wearable, real-time mobile gaze tracker , 2014, MobiSys.

[17]  Emre Ertin,et al.  Continuous inference of psychological stress from sensory measurements collected in the natural environment , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[18]  Stephen S Intille,et al.  An Ecological Framework for Cancer Communication: Implications for Research , 2005, Journal of medical Internet research.

[19]  Carla H. Lagorio,et al.  Psychology , 1929, Nature.

[20]  D. Estrin,et al.  Open mHealth Architecture: An Engine for Health Care Innovation , 2010, Science.

[21]  Francis S. Collins,et al.  The case for a US prospective cohort study of genes and environment , 2004, Nature.

[22]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[23]  M. Borggrefe,et al.  Intrathoracic Impedance Monitoring, Audible Patient Alerts, and Outcome in Patients With Heart Failure , 2011, Circulation.

[24]  Emre Ertin,et al.  Contactless sensing of physiological signals using wideband RF probes , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[25]  Misha Pavel,et al.  Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research , 2013, Computer.

[26]  Siddharth Shah,et al.  AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field , 2011, SenSys.

[27]  Organización Mundial de la Salud.  World Health Statistics 2008 , 2018 .