9th International Conference on Pervasive Computing Technologies for Healthcare

Research Article

Unintrusive Eating Recognition using Google Glass

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2015.259044,
        author={Shah Atiqur Rahman and Christopher Merck and Yuxiao Huang and Samantha Kleinberg},
        title={Unintrusive Eating Recognition using Google Glass},
        proceedings={9th International Conference on Pervasive Computing Technologies for Healthcare},
        publisher={IEEE},
        proceedings_a={PERVASIVEHEALTH},
        year={2015},
        month={8},
        keywords={eating activity recognition sensor data},
        doi={10.4108/icst.pervasivehealth.2015.259044}
    }
    
  • Shah Atiqur Rahman
    Christopher Merck
    Yuxiao Huang
    Samantha Kleinberg
    Year: 2015
    Unintrusive Eating Recognition using Google Glass
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2015.259044
Shah Atiqur Rahman1, Christopher Merck1, Yuxiao Huang1, Samantha Kleinberg1,*
  • 1: Stevens Institute of Technology
*Contact email: samantha.kleinberg@stevens.edu

Abstract

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.