Detecting Periods of Eating During Free-Living by Tracking Wrist Motion

Abstract

This paper is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people. Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require a considerable manual effort, leading to underreporting of consumption, noncompliance, and discontinued use over the long term. The purpose of this paper is to describe a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Our method uses the novel idea that meals tend to be preceded and succeeded by the periods of vigorous wrist motion. We describe an algorithm that segments and classifies such periods as eating or noneating activities. We also evaluate our method on a large dataset (43 subjects, 449 total h of data, containing 116 periods of eating) collected during free-living. Our results show an accuracy of 81% for detecting eating at 1-s resolution in comparison to manually marked event logs of periods eating. These results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that our method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.

Quantifying Sources and Types of Smartwatch Usage Sessions

“…Researchers also studied detection of eating [7,26,33], smoking [31,33], drinking coffee [33], and giving a talk [33]. Findings suggest that being able to detect human activities might help in mitigating bad habits, such as drinking too much coffee, smoking, or skipping a meal [33], and argue 1 http://insight4wear.com that detection of user activities might be useful in determining the optimal time for interrupting the user.…”

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

“…One of the most pioneering method in this approach is developed by Dong et al [51,[106][107][108][109]. The device in this method, which is illustrated in Figure 4c, applies the MEMS gyroscope to automatically track wrist motion every time the users take a bite of food from the radical velocity [51].…”

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

“…The Gaussian method was used as a classifier. Dong et al [10] described a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Accelerometer and gyroscope sensor data were used in this study.…”