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Author

Wenyan Jia

Other affiliations: Tsinghua University
Bio: Wenyan Jia is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Wearable computer & Image segmentation. The author has an hindex of 23, co-authored 121 publications receiving 1642 citations. Previous affiliations of Wenyan Jia include Tsinghua University.


Papers
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Journal ArticleDOI
TL;DR: A research program has been initiated to develop a small electronic device to record food intake automatically, which contains a miniature camera, a microphone, and several other sensors that can be worn on a lanyard around the neck.
Abstract: Dietary reporting by individuals is subject to error (1–3). Therefore, a research program has been initiated to develop a small electronic device to record food intake automatically. This device, which contains a miniature camera, a microphone, and several other sensors, can be worn on a lanyard around the neck. It collects visual data immediately in front of the participant and stores them on a memory card in the device. The data are transferred regularly to the dietitian’s computer for further processing and analysis. The device is designed to be almost completely passive to the participant, and thus hopefully will not intrude on or alter the participant’s eating activities. In addition to this function, in the future the device will have other functions, such as the measurement of physical activity, human behavior, and environmental exposure (e.g., pollutants).

170 citations

Proceedings ArticleDOI
01 Jun 2014
TL;DR: An overview of the research on a new wearable computer called eButton is presented, and several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.
Abstract: Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.

137 citations

Proceedings ArticleDOI
01 Oct 2014
TL;DR: Considering the negative feedback mechanism in the blood Pressure control, the heart rate and the blood pressure estimate are introduced in the previous step to obtain the current estimate and the results show that the PTT, HR and previous estimate reduce the estimated error significantly when compared to the conventional PTT estimation approach.
Abstract: It has been reported that the pulse transit time (PTT), the interval between the peak of the R-wave in electrocardiogram (ECG) and the fingertip photoplethysmogram (PPG), is related to arterial stiffness, and can be used to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP). This phenomenon has been used as the basis to design portable systems for continuously cuff-less blood pressure measurement, benefiting numerous people with heart conditions. However, the PTT-based blood pressure estimation may not be sufficiently accurate because the regulation of blood pressure within the human body is a complex, multivariate physiological process. Considering the negative feedback mechanism in the blood pressure control, we introduce the heart rate (HR) and the blood pressure estimate in the previous step to obtain the current estimate. We validate this method using a clinical database. Our results show that the PTT, HR and previous estimate reduce the estimated error significantly when compared to the conventional PTT estimation approach (p<0.05).

97 citations

Journal ArticleDOI
TL;DR: From the same eButton pictures, the computer-based method provides more objective and accurate estimates of food volume than the visual estimation method.
Abstract: Self-reporting (e.g. electronic or paper-and-pencil food diary, 24h dietary recall, FFQ) is the most common method of dietary assessment(1–5). Although this approach is used widely in large cohort studies, its accuracy is limited by its dependence on the willingness of the participant to report and his/her ability to estimate accurately the amount of food consumed(6–8). To improve assessment accuracy, various portion size measurement aids are employed, including pictures (two dimensions) or realistic models (three dimensions) of objects of known sizes (e.g. a life-size picture of a tennis ball or a real tennis ball)(9–12). With the help of portion size measurement aids, an individual’s ability to estimate portion size can be improved significantly, especially after training(13–16). However, the ability of portion size measurement aids to improve accuracy varies with food models, training methods, food type and study population(13–25). For example, Lanerolle and co-workers developed models specifically for Asian foods (e.g. rice, noodles)(21,22). Yuhas et al. compared estimation accuracies among solid foods, liquids and amorphous foods using portion size measurement aids. They concluded that errors were smallest in solid foods and largest in amorphous foods(23). Foster et al. showed the importance of using age-appropriate food photographs for studies in children(24,25). Regardless of these findings, the accuracy of dietary assessment methods still highly depends on the individual’s ability to estimate portion size accurately. Recently a picture-based method for dietary analysis has been reported that uses camera-enabled mobile phones or tablet computers to record pictures of consumed foods and beverages. Pictures are acquired by the participant before and after meal and snack consumption. Food volume is then estimated from the pictures, and converted to energy and nutrient values using a nutritional database(5,26–31). Compared with the method of employing portion size measurement aids, the picture-based method provides more objective estimation of portion size. However, it requires the willingness of the participant to take pictures at each eating event. Hence, the food intake record may be incomplete if the participant forgets or ignores picture taking, especially when a meal involves multiple courses of foods and when picture taking disrupts his/her normal social interaction during eating. To resolve this issue, we developed a wearable device (‘eButton’) that automatically takes pictures at a pre-set rate without interrupting the participant’s eating behaviour. eButton is convenient to use, since the wearer only needs to turn it on and off. However, an important question is whether eButton pictures (which are two-dimensional) can provide accurate food volume (i.e. three-dimensional (3D)) estimates. In the present study we therefore compared food volumes estimated from eButton pictures with actual volumes measured using a seed displacement method(32,33). A few picture-based studies have attempted to analyse volume measurement error, but the food samples used in these studies were limited to those with standard volumes or volumes that could easily be measured by water displacement (e.g. solid fruits)(31,34). In this experiment, we studied real foods prepared or purchased by study participants and consumed at lunch break in the lab (see Fig. 1). The volume of each food item was first measured using the seed displacement method (see ‘Experimental methods’ section and online supplementary material) and then calculated using a software program from eButton images acquired during lunch. Different from water displacement, seed displacement involves no liquids and thus permits volume measurements of a wide variety of foods. For example, an airtight waterproof enclosure is required for measuring hamburgers with water displacement; yet controlling the amount of sealed air appropriately is more difficult. To validate further the accuracy of our software for volume estimation, we recruited three human raters to estimate the volume of each food sample by observing the same eButton-acquired pictures. Fig. 1 (colour online) (a) eButton Prototype; (b) a person wearing an eButton during eating

91 citations

Journal ArticleDOI
TL;DR: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.
Abstract: Objective To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. Design To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. Results A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. Conclusions The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

69 citations


Cited by
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Journal ArticleDOI
TL;DR: This article reviews common dietary assessment methods and their feasibility in epidemiological studies and concludes that open-ended surveys using food frequency questionnaires are the most suitable for dietary intake assessment.
Abstract: Diet is a major lifestyle-related risk factor of various chronic diseases. Dietary intake can be assessed by subjective report and objective observation. Subjective assessment is possible using open-ended surveys such as dietary recalls or records, or using closed-ended surveys including food frequency questionnaires. Each method has inherent strengths and limitations. Continued efforts to improve the accuracy of dietary intake assessment and enhance its feasibility in epidemiological studies have been made. This article reviews common dietary assessment methods and their feasibility in epidemiological studies.

1,010 citations

Book ChapterDOI
01 Jan 2017
TL;DR: This chapter reviews major dietary assessment methods, their advantages and disadvantages, and validity; describes which dietary Assessment methods are appropriate for different types of studies and populations; and discusses specific issues that relate to all methods.
Abstract: The intent of this chapter is to enhance understanding of various dietary assessment methods so that the most appropriate method for a particular need is chosen. This review focuses only on individual level food intake assessment. It is intended to serve as a resource for those who wish to assess diet in a research study using individual measurements for group level analysis. The chapter reviews major dietary assessment methods, their advantages and disadvantages, and validity; describes which dietary assessment methods are appropriate for different types of studies and populations; and discusses specific issues that relate to all methods.

873 citations

Journal ArticleDOI
TL;DR: This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006, and asks what are the key signal processing components of a BCI, and what signal processing algorithms have been used in BCIs.
Abstract: Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files

844 citations

Journal ArticleDOI
TL;DR: The state-of-the-art research efforts directed toward big IoT data analytics are investigated, the relationship between big data analytics and IoT is explained, and several opportunities brought by data analytics in IoT paradigm are discussed.
Abstract: Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

697 citations