Title
Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist
Abstract
Motivated by health applications, eating detection with off-the-shelf devices has been an active area of research. A common approach has been to recognize and model individual intake gestures with wrist-mounted inertial sensors. Despite promising results, this approach is limiting as it requires the sensing device to be worn on the hand performing the intake gesture, which cannot be guaranteed in practice. Through a study with 14 participants comparing eating detection performance when gestural data is recorded with a wrist-mounted device on (1) both hands, (2) only the dominant hand, and (3) only the non-dominant hand, we provide evidence that a larger set of arm and hand movement patterns beyond food intake gestures are predictive of eating activities when L1 or L2 normalization is applied to the data. Our results are supported by the theory of asymmetric bimanual action and contribute to the field of automated dietary monitoring. In particular, it shines light on a new direction for eating activity recognition with consumer wearables in realistic settings.
Year
DOI
Venue
2017
10.1145/3089341.3089345
DigitalBioMarker@MobiSys
Keywords
DocType
Volume
Activity Recognition,Dietary Monitoring,Eating Detection,Food Logging,Food Tracking,Inertial Sensing
Conference
2017
ISBN
Citations 
PageRank 
978-1-4503-4963-5
0
0.34
References 
Authors
4
5
Name
Order
Citations
PageRank
Edison Thomaz124517.29
Abdelkareem Bedri2555.53
Temiloluwa Prioleau381.64
Irfan A. Essa44876580.85
Gregory D. Abowd5119791503.13