Smart Home-based Health Platform
for Behavioral Monitoring and
Alteration for Diabetic and Obese Individuals

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   Research Community Resources
   Project Management Page


Researchers in the social and life sciences, as well as medical researchers and practitioners, have long sought the ability to continuously and automatically monitor research subjects or patients for a variety of conditions or disorders. Additionally, the use of monitoring data to influence treatment dosage or regimen within real-time constraints is an important objective of behavior modification practice for psychological, medical, and sports-medicine therapies.

In terms of practical constraints related to health care, the United States faces several important obstacles to delivering acceptable quality-of-service at a reasonable unit cost per patient. Firstly, obesity, diabetes, heart disease, and stroke are but a few manifestations of patient noncompliance with respect to diet, exercise, and medication. Secondly, Alzheimer¡¯s and other senility-related disorders, although of different etiology, threaten to overload our health care delivery and maintenance systems, resulting in potentially severe economic impact.

As one example, over the past two decades the prevalence of overweight and obesity has increased at an alarming rate throughout the U.S. Recent data from the Centers for Disease Control and Preventions (2004) show that 60% of all adults in Florida are overweight or obese. Excess body weight contributes to numerous harmful consequences, including diabetes, heart disease, high blood pressure, stroke, arthritis, and certain cancers. Sedentary lifestyle and high caloric intake are important contributors to the obesity epidemic and to the development of many chronic diseases. It is well known that diet and exercise can significantly reduce the prevalence of obesity. However, lifestyle and economical factors makes it difficult for individuals to self-assess and self-modify their behavior. For example, studies in the United States and abroad have found that improved blood glucose control markedly benefits people with diabetes. Every percentage point decrease in A1C blood test results reduces the risk of eye, kidney and central nervous system complications by 40 percent.

In response to these significant challenges in life sciences research and health care, this multidisciplinary research team is developing technology, science, and validation techniques to create monitoring and analysis platforms consisting of economically deployable connectivity technology and personal wearable devices. This will enable the automatic gathering of rich behavioral information in a manner transparent to the patient, which will be automatically or humanly analyzed and reported to care givers for analysis, and interpreted for behavior modification in individuals or in the context of a social network.

We believe our research outcomes and outputs, including prototypes and community resources will foster cross-disciplinary discoveries and innovations in the life sciences and medicine, and will facilitate behavior modification in support of research in social, behavioral, and medical science.



    • Dr. Sumi Helal
    • Dr. Steve Anton
    • Dr. Michael Perri
    • Dr. Mark Schmalz
    • Dr. Andres Mendez
    • Chao Chen
    • Raja Bose
    • Eunju Kim
    • Jung Wook Park
    • Heyoung Lee
    • Duccki Lee
    • Dr. Diane Cook


  • A. Helal, D. Cook, M. Schmalz, "Smart Home-based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients," Journal of Diabetes Science and Technology, Volume 3, Number 1, January 2009.
  • A. Helal, A. Mendez-Vazquez, S. Hossain, "Specification and Synthesis of Sensory Datasets in Pervasive Spaces," Submitted to the IEEE Symposium on Computers and Communications (ISCC'09) to be held July 5-8, 2009, Sousse, Tunisia.
  • M. Weitzel, A. Smith, D. Lee, S. de Deugd1, and A. Helal, "Participatory Medicine: Leveraging Social Networks in Telehealth Solutions," in Proceedings of the 7th International Conference on Smart Homes and Health Telematics (ICOST), July 1-3, 2009, Tours, France.
  • C. Chen and A. Helal, "Device Integration in SODA using the Device Description Language," in Proceedings of the IEEE/IPSJ Symposium on Applications and the Internet, July 2009, Seattle, Washington, USA.
  • R. Bose and A. Helal, "Localized In-network Detection and Tracking of Phenomena Clouds using Wireless Sensor Networks," in Proceedings of the International Conference on Intelligent Environments (IE) to be held July 20-21, 2009, Barcelona, Spain
  • A. Mendez-Vazquez and A. Helal, "Programmatic Sensor Fusion in Intelligent Environments - Preliniary Studies," Technical Report MPCL-09-05, May 2009. Available online: pdf
  • A. Mendez-Vasquez, M. Schmalz, A. Helal, "An Enhanced, Multi Context Chewing Large Data Set," Technical Report MPCL-09-03, June 2009. pdf
  • H. Lee J. Park and A. Helal, ¡°Estimation of Indoor Physical Activity Level Based on Footstep Vibration Signal Measured by MEMS Accelerometer for Personal Health Care Under Smart Home Environments,¡± Technical Report MPCL-09-06, June 2009. pdf
  • A. Mendez-Vazquez, A. Helal, and D. Cook. "Simulating events to generate synthetic data for pervasive spaces," Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, 2009.
  • A. Mendez-Vazquez, A. Helal and D. Cook, "Synthesizing Datasets for Pervasive Spaces," in Proceedings of the International Conference on Intelligent Environments (IE) to be held July 20-21, 2009, Barcelona, Spain.
  • G. Singla, D. Cook, and M. Schmitter-Edgecombe, "Recognizing independent and joint activities among multiple residents in smart environments," Ambient Intelligence and Humanized Computing Journal, to appear.
  • G. Singla, D. Cook, and M. Schmitter-Edgecombe, "Tracking activities in complex settings using smart environment technologies," International Journal of BioSciences, Psychiatry and Technology, 1(1):25-35, 2009.
  • S. Szewcyzk, K. Dwan, B. Minor, B. Swedlove, and D. Cook, "Annotating smart environment sensor data for activity learning. Technology and Health Care," special issue on Smart Environments: Technology to support health care, 17:1-9, 2009.
  • D. Cook and M. Schmitter-Edgecombe, "Assessing the quality of activities in a smart environment," Methods of Information in Medicine, 2009.
  • D. Cook and W. Song, "Ambient intelligence and wearable computing: Sensors on the body, in the home, and beyond," Journal of Ambient Intelligence and Smart Environments, 3:1-4, 2009.
  • D. Cook, M. Schmitter-Edgecombe, A. Crandall, C. Sanders, and B. Thomas. "Collecting and disseminating smart home sensor data in the CASAS project," Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, 2009.
  • R. Bose, A. Helal, "Observing Walking Behavior of Humans using Distributed Phenomenon Detection and Tracking Mechanisms," Proceedings of 2nd International Workshop on Practical Applications of Sensor Networks, held in conjunction with the International Symposium on Applications and the Internet (SAINT), Turku (Finland), July 2008
  • M. Schmalz, A. Mendez-Vasquez, and A. Helal, "Dietary Monitoring for Diabetes and Obesity: Early Algorithm for Detection and Quantification of Chewing Behavior," Technical Report MPCL-08-08, May 2008. Available online: pdf
  • M. Schmalz, "Dietary Monitoring for Diabetes & Obesity: Detection and Quantification of Chewing Behavior -Background and Phenomenology," Technical Report MPCL-08-07, May 2008. Available online: pdf
  • C. Chen, S. Anton, and A. Helal, "A Brief Survey of Physical Activity Monitoring Devices," Technical Report MPCL-08-09, May 2008. Available online: pdf
  • C. Chen, "A Multi Context Chewing Data Set," Technical Report MPCL-08-10, June 2008. Available online: pdf
  • H. Yang and A. Helal, "Safety Enhancing Mechanisms for Pervasive Computing Systems in Intelligent Environment", In Proceedings of the Middleware Support for Pervasive Computing Workshop, held in conjunction with IEEE PerCom 2008, Hong Kong, March 2008
  • A. Helal, M. Mokhtari and B. Abdulrazak, "The Engineering Handbook on Smart Technology for Aging, Disability and Independence," John Wiley & Sons. ISBN 0471711551, Computer Engineering Series, Copyright 2008.
  • V. Jakkula and D. Cook, Anomaly detection using temporal data mining in a smart home environment, Methods of Information in Medicine, 47:70-75, 2008.
  • V. Jakkula and D. Cook, Mining temporal relations in smart environment data using TempAl, in Knowledge Discovery from Sensor Data, Taylor and Francis, 2008.
  • G. Singla, D. Cook, and M. Schmitter-Edgecombe, Incorporating temporal reasoning into activity recognition for smart home residents, Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, 2008.

Research Community Resources

Project Management Page (Internal Link)