Assisting Food Journaling with Automatic Eating Detection

The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user’s behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user’s diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.

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“…In the future, technical solutions aimed at facilitating and automating food logging might overcome this limitation [60]. …”

Section: Discussionmentioning

confidence: 99%

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

Choudhury

Kumar

Weber

2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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“…Two studies sent participants real-time messages to confirm whether or not they were eating. In Ye et al 62 , participants were sent a short message on their smartwatch if an eating gesture was detected; participants were able to confirm or reject if they were eating in real-time. Similarly, in Gomes and Sousa 48 , when drinking activity was detected, participants were sent an alert on their smartphone and could then confirm or reject if they were drinking in real-time.…”

Section: Ground-truth Methodsmentioning

confidence: 99%

“…On average, 2.10 wearable sensors (SD = 0.96) were used in the studies, with a range of 1-4 sensors ( Table 1). Approximately 63% (N = 25) of the 40 studies utilized an accelerometer (device that determines acceleration) either by itself (N = 4) or incorporated into a sensor system (N = 21) to detect eating activity 18,[37][38][39][40][41]43,45,46,48,49,51,53,[56][57][58][59]62 (Table 2). The second most frequently utilized wearable sensor was a gyroscope (device that determines orientation) (N = 15) 33,38,39,46,48,49,51,53,[56][57][58] , followed by a microphone (N = 8) 34,35,47,52,54,60,61 , a piezoelectric sensor (N = 7) 18,40-42,44,45 , a RF transmitter and receiver (N = 6) 18,40,41,44,45 , and a smartwatch camera (N = 5) 56,57 (Table 2).…”

Section: Wearable Sensorsmentioning

confidence: 99%

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

et al. 2020

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Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included N = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors' ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.npj Digital Medicine (2020) 3:38 ; https://doi.

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“…-Acoustic: [72] -Inertial: [103], [113] -Acoustic: [49] Overview of food intake mechanism classification with corresponded technologies and studies. These will be examined further in Section 5.…”

Section: Food Intake Mechanismsmentioning

confidence: 99%

“…A similar approach, developed by Mendi et al [112], is even adept at sending data from the accelerometer to the smart phone via Bluetooth. In another advance in the most recent study, Ye et al [113,114] proposed an automatic structure using two accelerometers simultaneously: wrist-worn and head-mounted accelerometers on such devices as the Pebble Watch and Google Glass to detect chewing events and eating duration with high accuracy: 89.5% in a dataset of 12,003 epochs in which 3325 epochs are chewing-related under the laboratory condition. Another similar inertial system developed by Amft et al [115] employed four motion sensors at two positions on each arm (upper and lower) to detect drinking and eating gestures and further to detect chewing and swallowing sounds.…”

Section: Inertial Approachmentioning

confidence: 99%

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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Wearable devices monitoring food intake through passive sensing is slowly emerging to complement self-reporting of users' caloric intake and eating behaviors. Though the ultimate goal for the passive sensing of eating is to become a reliable gold standard in dietary assessment, it is currently showing promise as a means of validating self-report measures. Continuous food-intake monitoring allows for the validation and refusal of users' reported data in order to obtain more reliable user information, resulting in more effective health intervention services. Recognizing the importance and strength of wearable sensors in food intake monitoring, there has been a variety of approaches proposed and studied in recent years. While existing technologies show promise, many challenges and opportunities discussed in this survey, still remain. This paper presents a meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques. This paper focuses on the comparison of different technologies and approaches that relate to user comfort, body location, and applications for medical research. We identify and summarize the forthcoming opportunities and challenges in wearable food intake monitoring technologies.

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Assisting Food Journaling with Automatic Eating Detection

Cited by 33 publications

References 14 publications

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

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