Wearable eButton for Evaluation of Energy Balance with Environmental Context and Behavior Overweight and obesity have become a wide spread epidemic affecting more than 60% of the population in the United States. A recent study indicates that the estimated direct and indirect costs of obesity to the U.S. economy are at least $215 billion annually. In the battle against obesity and related diseases, both the research and clinical communities require an accurate tool to measure people's energy intake and expenditure in real life. However, presently, the most utilized tool is still a questionnaire which is subjective and often inaccurate. The current state-of-the-art in diet and physical activity measurement has fallen far behind what the modern technology is able to provide. The field is mature for a substantial innovation. In this application, we propose the development of a new electronic device which has the potential to produce a technological quantum leap in the measurement of diet and physical activity. This button- like device, eButton, will be worn naturally on the chest using a pair of magnets or a pin. A new measurement concept based on the use of the wearable computer will be utilized in our device design. The eButton will contain a low-power, high-performance microprocessor running a simplified version of the LINUX operating system. It will contain numerous innovative designs, including an optical eating detector to monitor eating/drinking/smoking, two miniature cameras that produce a stereo vision to measure food portion size without depending on a reference card, an ear-based oximeter for measurement of heart rate and oxygen saturation, and an extrapolation formula to measure outdoor environment using the US environmental protection agency (EPA) database. eButton will store the multimedia data acquired by a variety of advanced miniature sensors in a flash memory within the device. It will also have a wireless link to a smart phone which will allow researchers to monitor the operating status of eButton remotely in real time. All sensors and function modules will be individually controlled by software to allow researchers to select among the available system resources for their particular needs. Despite the wide functionality of the device, eButton requires an extremely low user respondent burden. The research participant is required to do nothing more than turning on/off the device and recharging its battery at night. During our research, eButton and associated algorithms/software will be designed and constructed in our laboratory by an experienced team of electronic/software engineers based on an early version of the device developed under the NIH GEI diet and physical activity research program. Once eButton is constructed, we will implement a thorough validation process using human subjects to evaluate its accuracy in diet and physical activity assessment by comparing against the doubly-labeled water method as the gold standard.

Public Health Relevance

The primary goal of this research is to develop an advanced, button-like electronic device (eButton) that can be worn naturally on the chest. This device will contain a powerful microprocessor, a novel eating detector, a pair of cameras, and a variety of electronic sensors to automatically, jointly and objectively measure energy intake and expenditure, as well as environment and behavior related to diet and physical activity. All these measurements will be conducted with an extremely low respondent burden to research participants.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-PSE-C (04))
Program Officer
Reedy, Jill
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Schools of Medicine
United States
Zip Code
Jia, Wenyan; Chen, Hsin-Chen; Yue, Yaofeng et al. (2014) Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr 17:1671-81
Sun, Mingui; Burke, Lora E; Mao, Zhi-Hong et al. (2014) eButton: A Wearable Computer for Health Monitoring and Personal Assistance. Proc Des Autom Conf 2014:1-6
Zhai, Haitian; Li, Hui; Bai, Yicheng et al. (2014) A Novel Binocular Vision System for Wearable Devices. Proc IEEE Annu Northeast Bioeng Conf 2014:1-3
Yu, Hongshan; Zhu, Jiang; Wang, Yaonan et al. (2014) Obstacle classification and 3D measurement in unstructured environments based on ToF cameras. Sensors (Basel) 14:10753-82
Sun, Wei; Han, Long; Guo, Baolong et al. (2014) A fast color image enhancement algorithm based on Max Intensity Channel. J Mod Opt 61:466-477
Chen, Hsin-Chen; Jia, Wenyan; Yue, Yaofeng et al. (2013) Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration. Meas Sci Technol 24:
Bai, Yicheng; Jia, Wenyan; Zhang, Hong et al. (2013) Helping the blind to find the floor of destination in multistory buildings using a barometer. Conf Proc IEEE Eng Med Biol Soc 2013:4738-41
Li, Zhen; Wei, Zhiqiang; Jia, Wenyan et al. (2013) Daily life event segmentation for lifestyle evaluation based on multi-sensor data recorded by a wearable device. Conf Proc IEEE Eng Med Biol Soc 2013:2858-61
Li, Zhaoxin; Jia, Wenyan; Mao, Zhi-Hong et al. (2013) Anthropometric body measurements based on multi-view stereo image reconstruction. Conf Proc IEEE Eng Med Biol Soc 2013:366-9
Li, Jie; Shi, Wenxuan; Deng, Dexiang et al. (2013) Dense Stereo Matching Method Based on Local Affine Model. J Comput (Taipei) 8:1696-1703