In this paper, we present results of a food type classification study based on a sub-centimeter scale wireless intraoral sensor that continuously measures temperature and jawbone movement. We explored the feasibility of classifying nine different types of foods into five classes based on their water-content and typical serving temperature in a controlled environment (n=4). We demonstrated that the system can classify foods into five classes with a weighted accuracy of 77.5% using temperature-derived features only and with a weighted accuracy of 85.0% using both temperature- and acceleration-derived features. Despite the limitations of our study, these results are encouraging and suggest that intraoral computing might be a viable direction for ADM in the future.
Keum San Chun, Sarnab Bhattacharya, Caroline Dolbear, Jordon Kashanchi, Edison Thomaz
Recent work in Automated Dietary Monitoring (ADM) has shown promising results in eating detection by tracking jawbone movements with a proximity sensor mounted on a necklace. A significant challenge with this approach, however, is that motion artifacts introduced by natural body movements cause the necklace to move freely and the sensor to become misaligned. In this paper, we propose a different but related approach: we developed a small wireless inertial sensing platform and perform eating detection by mounting the sensor directly on the underside of the jawbone.
Keum San Chun, Hyoyoung Jeong, Rebecca Adaimi, Edison Thomaz
In this work, two algorithms are proposed for passive assessment of pulmonary condition: one for detection of obstructive pulmonary disease and the other for estimation of the pulmonary function in terms of FEV_1/FVC ratio, which is an established clinical metric. This work presents a meaningful milestone towards the passive assessment of pulmonary functions from spontaneous speech collected from a mobile phone.
Keum San Chun, Viswam Nathan, Korosh Vatanparvar, Ebrahim Nemati, Md Mahbubur Rahman, Erin Blackstock, Jilong Kuang
We propose an approach that leverages off-the-shelf activity tracker to detect drinking events. Here, we also apply an adaptive windowing approach in the analysis to capture the wide distribution of drinking durations.
Keum San Chun, Ashley B. Sanders, Rebecca Adaimi, Necole Streeper, David E. Conroy, Edison Thomaz
FlashBite is a wearable necklace that continuously tracks the jaw motion using an infrared proximity sensor. In this paper, we talk about the application of FlashBite for dietary monitoring.
Keum San Chun, Sarnab Bhattacharya, Edison Thomaz