Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 6; pp. 1772 - 1779
• Main Authors: Fontana, Juan M., Farooq, Muhammad, Sazonov, Edward
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometry - instrumentation; Accelerometry - methods; Adult; Algorithms; Automatic ingestion monitor (AIM); Biomedical monitoring; chewing; Devices; Diet Records; Eating - physiology; Eating disorders; Environmental monitoring; Feature extraction; Feeding Behavior - physiology; Female; Food; Food intake; food intake (FI) detection; Hand - physiology; Humans; Ingestion; Male; Mastication - physiology; Monitoring; Monitoring instruments; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Monitors; Neural Networks (Computer); Obesity; Pattern recognition; Pattern Recognition, Automated - methods; Sensors; Smart phones; Wearable; Wearable sensors; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2014.2306773

Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer. A novel sensor fusion and pattern recognition method was developed for subject-independent food intake recognition. The device and the methodology were validated with data collected from 12 subjects wearing AIM during the course of 24 h in which both the daily activities and the food intake of the subjects were not restricted in any way. Results showed that the system was able to detect food intake with an average accuracy of 89.8%, which suggests that AIM can potentially be used as an instrument to monitor ingestive behavior in free-living individuals.