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We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. 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.<\/jats:p>","DOI":"10.1145\/3130936","type":"journal-article","created":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T12:12:26Z","timestamp":1505131946000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Progress Estimation and Phase Detection for Sequential Processes"],"prefix":"10.1145","volume":"1","author":[{"given":"Xinyu","family":"Li","sequence":"first","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Gu","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Marsic","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard A.","family":"Farneth","sequence":"additional","affiliation":[{"name":"Children\u2019s National Medical Center, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Randall S.","family":"Burd","sequence":"additional","affiliation":[{"name":"Children\u2019s National Medical Center, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,9,11]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2011.5767594"},{"key":"e_1_2_2_3_1","first-page":"126","article-title":"A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models","volume":"4","author":"Bilmes Jeff A","year":"1998","journal-title":"International Computer Science Institute"},{"key":"e_1_2_2_4_1","volume-title":"Modeling and segmentation of surgical workflow from laparoscopic video. 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