Detection of Food Intake Sensor's Wear Compliance in Free-Living

Objective detection of periods of wear and non-wear is critical for human studies that rely on information from wearable sensors, such as food intake sensors. In this paper, we present a novel method of compliance detection on the example of the Automatic Ingestion Monitor v2 (AIM-2) sensor, contain...

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Bibliographic Details
Published in:IEEE sensors journal 2021-12, Vol.21 (24), p.27728-27735
Main Authors: Ghosh, Tonmoy, Hossain, Delwar, Sazonov, Edward
Format: Article
Language:English
Subjects:
Accelerometer
Accelerometers
adherence
Cameras
Classifiers
Compliance
egocentric camera
Food intake
Ingestion
Monitoring
Particle measurements
Pitch (inclination)
Rolling motion
Sensor systems
Sensors
Standard deviation
Wear
wear compliance
Wearable sensors
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Summary:Objective detection of periods of wear and non-wear is critical for human studies that rely on information from wearable sensors, such as food intake sensors. In this paper, we present a novel method of compliance detection on the example of the Automatic Ingestion Monitor v2 (AIM-2) sensor, containing a tri-axial accelerometer, a still camera, and a chewing sensor. The method was developed and validated using data from a study of 30 participants aged 18-39, each wearing the AIM-2 for two days (a day in pseudo-free-living and a day in free-living). Four types of wear compliance were analyzed: 'normal-wear', 'non-compliant-wear', 'non-wear-carried', and 'non-wear-stationary'. The ground truth of those four types of compliance was obtained by reviewing the images of the egocentric camera. The features for compliance detection were the standard deviation of acceleration, average pitch, and roll angles, and mean square error of two consecutive images. These were used to train three random forest classifiers 1) accelerometer-based, 2) image-based, and 3) combined accelerometer and image-based. Satisfactory wear compliance measurement accuracy was obtained using the combined classifier (89.24%) on leave one subject out cross-validation. The average duration of compliant wear in the study was 9h with a standard deviation of 2h or 70.96% of total on-time. This method can be used to calculate the wear and non-wear time of AIM-2, and potentially be extended to other devices. The study also included assessments of sensor burden and privacy concerns. The survey results suggest recommendations that may be used to increase wear compliance.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3124203