AAI_2025_Capstone_Chronicles_Combined

15

Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv . https://arxiv.org/abs/1803.01271 Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C. L., Yong, M. G., Lee, J., Chang, W. T., Hua, W., Georg, M., Grundmann, M., & Google AI. (2019). MediaPipe: A framework for building perception pipelines . arXiv. https://arxiv.org/abs/1906.08172 M. A. Raza, L. Chen, L. Nanbo, R. B. Fisher (2023). EatSense: Human Centric, Action Recognition and Localization Dataset for Understanding Eating Behaviors and Quality of Motion Assessment, Image and Vision Computing . https://groups.inf.ed.ac.uk/vision/DATASETS /EATSENSE/ Rouast, P. V., Heydarian, H., Adam, M. T. P., & Rollo, M. E. (2020). OREBA: A dataset for objectively recognizing eating behaviour and associated intake. arXiv . https://arxiv.org/abs/2007.15831 Su, Y. C., Wang, W. M., Wang, S. Y., Lu, S. N., Chen, L. T., Wu, D. C., Chen, C. Y., Jan, C. M., & Horowitz, M. (2000). The association between Helicobacter pylori infection and functional dyspepsia in patients with irritable bowel syndrome. American Journal of Gastroenterology, 95 (8), 1900 – 1905. https://pubmed.ncbi.nlm.nih.gov/10950033/ Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3D convolutional networks. In 2015 IEEE International Conference on Computer Vision (ICCV) (pp. 4489 – 4497). IEEE. https://arxiv.org/abs/1412.0767 Zhu, B., Haruyama, Y., Muto, T., Yamazaki, T., Sobue, T., & Koyama, W. (2015). Association between eating speed and metabolic syndrome

in a three-year population-based cohort study. Journal of Epidemiology, 25 (5), 332 – 336. https://pubmed.ncbi.nlm.nih.gov/25787239/

397