no code implementations • 6 Jul 2022 • Keyi Li, Sen yang, Travis M. Sullivan, Randall S. Burd, Ivan Marsic
The best model achieved an average F1-score of 0. 67 for 61 activity types.
no code implementations • 15 Mar 2022 • Keyi Li, Sen yang, Travis M. Sullivan, Randall S. Burd, Ivan Marsic
We experimented with different models of representation learning and used the learned model to generate synthetic process data.
no code implementations • 16 Sep 2017 • Shuhong Chen, Sen yang, Moliang Zhou, Randall S. Burd, Ivan Marsic
We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.
no code implementations • 28 Feb 2017 • Xinyu Li, Yanyi Zhang, Jianyu Zhang, Yueyang Chen, Shuhong Chen, Yue Gu, Moliang Zhou, Richard A. Farneth, Ivan Marsic, Randall S. Burd
For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0. 58, a completeness estimation error of 6. 3% and a remaining-time estimation error of 2. 9 minutes.
no code implementations • 10 Feb 2017 • Xinyu Li, Yanyi Zhang, Ivan Marsic, Randall S. Burd
We introduce a novel, accurate and practical system for real-time people tracking and identification.
no code implementations • 6 Feb 2017 • Xinyu Li, Yanyi Zhang, Jianyu Zhang, Shuhong Chen, Ivan Marsic, Richard A. Farneth, Randall S. Burd
Our system is the first to address the concurrent activity recognition with multisensory data using a single model, which is scalable, simple to train and easy to deploy.