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.
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.
|Sun, Wei; Wang, Hao; Sun, Changhao et al. (2015) Fast single image haze removal via local atmospheric light veil estimation. Comput Electr Eng 46:371-383|
|Li, Zhaoxin; Wang, Kuanquan; Jia, Wenyan et al. (2015) Multiview stereo and silhouette fusion via minimizing generalized reprojection error. Image Vis Comput 33:1-14|
|Cheng, Feiyang; Zhang, Hong; Sun, Mingui et al. (2015) Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching. Pattern Recognit 48:2269-2278|
|Li, Yuecheng; Jia, Wenyan; Luan, Bo et al. (2015) A FPGA Implementation of JPEG Baseline Encoder for Wearable Devices. Proc IEEE Annu Northeast Bioeng Conf 2015:|
|Sun, Mingui; Burke, Lora E; Baranowski, Thomas et al. (2015) An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle. J Healthc Eng 6:1-22|
|Zhang, Boxue; Zhao, Qi; Feng, Wenquan et al. (2015) SIFT-Based Indoor Localization for Older Adults Using Wearable Camera. Proc IEEE Annu Northeast Bioeng Conf 2015:|
|Li, Yuecheng; Jia, Wenyan; Yu, Tianjian et al. (2015) A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation. Proc IEEE Annu Northeast Bioeng Conf 2015:|
|Zhao, Qi; Wang, Jingjing; Feng, Wenquan et al. (2015) Assessing Physical Performance in Free-Living Older Adults with a Wearable Computer. Proc IEEE Annu Northeast Bioeng Conf 2015:|
|Chen, Hsin-Chen; Jia, Wenyan; Sun, Xin et al. (2015) Saliency-aware food image segmentation for personal dietary assessment using a wearable computer. Meas Sci Technol 26:|
|Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng et al. (2015) An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device. J Med Syst 39:57|
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