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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,12,27]]},"abstract":"<jats:p>Human activity recognition (HAR) is an important research area due to its potential for building context-aware interactive systems. Though movement-based activity recognition is an established area of research, recognising sedentary activities remains an open research question. Previous works have explored eye-based activity recognition as a potential approach for this challenge, focusing on statistical measures derived from eye movement properties---low-level gaze features---or some knowledge of the Areas-of-Interest (AOI) of the stimulus---high-level gaze features. In this paper, we extend this body of work by employing the addition of mid-level gaze features; features that add a level of abstraction over low-level features with some knowledge of the activity, but not of the stimulus. We evaluated our approach on a dataset collected from 24 participants performing eight desktop computing activities. We trained a classifier extending 26 low-level features derived from existing literature with the addition of 24 novel candidate mid-level gaze features. Our results show an overall classification performance of 0.72 (F1-Score), with up to 4% increase in accuracy when adding our mid-level gaze features. Finally, we discuss the implications of combining low- and mid-level gaze features, as well as the future directions for eye-based activity recognition.<\/jats:p>","DOI":"10.1145\/3287067","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T19:28:03Z","timestamp":1545938883000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition"],"prefix":"10.1145","volume":"2","author":[{"given":"Namrata","family":"Srivastava","sequence":"first","affiliation":[{"name":"The University of Melbourne, Australia"}]},{"given":"Joshua","family":"Newn","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Australia"}]},{"given":"Eduardo","family":"Velloso","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Australia"}]}],"member":"320","published-online":{"date-parts":[[2018,12,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459466"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2015.60"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSCE.2014.7072750"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168556.2168569"},{"key":"e_1_2_2_5_1","volume-title":"Fixation Identification: The Optimum Threshold for a Dispersion Algorithm. 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