title: Audio-based detection and evaluation of eating behavior using the smartwatch platform.
abstract: In recent years, smartwatches have emerged as a viable platform for a variety of medical and health-related applications. In addition to the benefits of a stable hardware platform, these devices have a significant advantage over other wrist-worn devices, in that user acceptance of watches is higher than other custom hardware solutions. In this paper, we describe signal-processing techniques for identification of chews and swallows using a smartwatch device׳s built-in microphone. Moreover, we conduct a survey to evaluate the potential of the smartwatch as a platform for monitoring nutrition. The focus of this paper is to analyze the overall applicability of a smartwatch-based system for food-intake monitoring. Evaluation results confirm the efficacy of our technique; classification was performed between apple and potato chip bites, water swallows, talking, and ambient noise, with an F-measure of 94.5% based on 250 collected samples.
authors.affiliation: Wireless Health Institute, Department of Computer Science, University of California, Los Angeles, United States. Electronic address: kalantarian@cs.ucla.edu.
authors.forename: Haik
authors.initials: H
authors.lastname: Kalantarian
authors.affiliation: Wireless Health Institute, Department of Computer Science, University of California, Los Angeles, United States. Electronic address: majid@cs.ucla.edu.
authors.forename: Majid
authors.initials: M
authors.lastname: Sarrafzadeh
mesh_terms: D000162:Acoustics / Q000295:instrumentation; D034201:Computers, Handheld; D004435:Eating; D005247:Feeding Behavior; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods
keywords: Machine learning; Nutrition; Pervasive computing; Signal processing; Smartwatch; Wireless Health