An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented.

An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.

Recently, wearable computers have become new members in the family of mobile electronic devices, adding new functions to those provided by smart-phones and tablets. As "always-on" miniature computers in the personal space, they will play increasing roles in the field of healthcare. In this work, we present our development of eButton, a wearable computer designed as a personalized, attractive, and convenient chest pin in a circular shape.

Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera.

Objective: Accurate estimation of food portion size is of paramount importance in dietary studies. We have developed a small, chest-worn electronic device called eButton which automatically takes pictures of consumed foods for objective dietary assessment. From the acquired pictures, the food portion size can be calculated semi-automatically with the help of computer software.

Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration.

Dietary assessment is important in health maintenance and intervention in many chronic conditions, such as obesity, diabetes, and cardiovascular disease. However, there is currently a lack of convenient methods for measuring the volume of food (portion size) in real-life settings. We present a computational method to estimate food volume from a single photographical image of food contained in a typical dining plate.

3D localization of circular feature in 2D image and application to food volume estimation.

A novel method to estimate the 3D location of a circular feature from a 2D image is presented and applied to the problem of objective dietary assessment from images taken by a wearable device. Instead of using a common reference (e.g.

Measurement of food volume based on single 2-D image without conventional camera calibration.

Food portion size measurement combined with a database of calories and nutrients is important in the study of metabolic disorders such as obesity and diabetes. In this work, we present a convenient and accurate approach to the calculation of food volume by measuring several dimensions using a single 2-D image as the input. This approach does not require the conventional checkerboard based camera calibration since it is burdensome in practice.

Imaged based estimation of food volume using circular referents in dietary assessment.

Measuring food volume (portion size) is a critical component in both clinical and research dietary studies. With the wide availability of cell phones and other camera-ready mobile devices, food pictures can be taken, stored or transmitted easily to form an image based dietary record. Although this record enables a more accurate dietary recall, a digital image of food usually cannot be used to estimate portion size directly due to the lack of information about the scale and orientation of the food within the image.

Designing a Wearable Computer for Lifestyle Evaluation.

A wearable computer, called eButton, has been developed for evaluation of the human lifestyle. This ARM-based device acquires multimodal data from a camera module, a motion sensor, an orientation sensor, a light sensor and a GPS receiver. Its performance has been tested both in our laboratory and by human subjects in free-living conditions.