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Physical Sciences and Mathematics Commons

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Full-Text Articles in Physical Sciences and Mathematics

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz Dec 2021

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz

Computer Science Technical Reports

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or …


Detection Of Health-Related Behaviours Using Head-Mounted Devices, Shengjie Bi May 2021

Detection Of Health-Related Behaviours Using Head-Mounted Devices, Shengjie Bi

Dartmouth College Ph.D Dissertations

The detection of health-related behaviors is the basis of many mobile-sensing applications for healthcare and can trigger other inquiries or interventions. Wearable sensors have been widely used for mobile sensing due to their ever-decreasing cost, ease of deployment, and ability to provide continuous monitoring. In this dissertation, we develop a generalizable approach to sensing eating-related behavior.

First, we developed Auracle, a wearable earpiece that can automatically detect eating episodes. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the head. This audio data is then processed by a …


A Multi-Resolution Graph Convolution Network For Contiguous Epitope Prediction, Lisa Oh Jan 2021

A Multi-Resolution Graph Convolution Network For Contiguous Epitope Prediction, Lisa Oh

Dartmouth College Master’s Theses

Computational methods for predicting binding interfaces between antigens and antibodies (epitopes and paratopes) are faster and cheaper than traditional experimental structure determination methods. A sufficiently reliable computational predictor that could scale to large sets of available antibody sequence data could thus inform and expedite many biomedical pursuits, such as better understanding immune responses to vaccination and natural infection and developing better drugs and vaccines. However, current state-of-the-art predictors produce discontiguous predictions, e.g., predicting the epitope in many different spots on an antigen, even though in reality they typically comprise a single localized region. We seek to produce contiguous predicted epitopes, …