Unintrusive eating recognition using Google Glass

By Shah Atiqur Rahman, Christopher A. Merck, Yuxiao Huang, Samantha Kleinberg. 2015

77 citations recorded by the source corpus.

Activity recognition has many health applications, from helping individuals track meals and exercise to providing treatment reminders to people with chronic illness and improving closed-loop control of diabetes. While eating is one of the most fundamental health-related activities, it has proven difficult to recognize accurately and unobtrusively. Body-worn and environmental sensors lack the needed specificity, while acoustic and accelerometer sensors worn around the neck may be intrusive and uncomfortable. We propose a new approach to identifying eating based on head movement data from Google Glass. We develop the Glass Eating and Motion (GLEAM) dataset using sensor data collected from 38 participants conducting a series of activities including eating. We demonstrate that head movement data are sufficient to allow recognition of eating with high precision and minimal impact on privacy and comfort.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Public Health, Environmental and Occupational Health

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In Computer Vision and Pattern Recognition

1. Ameet Talwalkar
2. Virginia Smith
3. Maziar Sanjabi

Citations in the corpus

• Federated Multi-Task Learning (2017, methodology): “Following [39], we featurize the raw accelerometer, gyroscope, and magnetometer data into 180 statistical, spectral, and temporal features.”