Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition
AU - Srivastava, Namrata
AU - Newn, Joshua
AU - Velloso, Eduardo
PY - 2018/12/31
Y1 - 2018/12/31
N2 - 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.
AB - 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.
U2 - 10.1145/3287067
DO - 10.1145/3287067
M3 - Journal article
SP - 1
EP - 27
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
SN - 2474-9567
M1 - 189
ER -