A practical approach for recognizing eating moments with wrist-mounted inertial sensing

Thomaz, Edison; Essa, Irfan; Abowd, Gregory D.

doi:10.1145/2750858.2807545

Cited by 262 publications

(244 citation statements)

References 33 publications

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“…Since motions and the types of this sensor vary, different research in this field is performed. For example, Thomaz et al [111] described a method using a three-axis accelerometer embedded in a smart watch, which attempts to recognize the eating process, food types and food intake amount. A similar approach, developed by Mendi et al [112], is even adept at sending data from the accelerometer to the smart phone via Bluetooth.…”

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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Section: Inertial Approachmentioning

confidence: 99%

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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“…Therefore, most smartwatches are designed with functions for monitoring users' activities and fitness. Thomaz et al proposed the use of a smartwatch to detect eating episodes as a step towards monitoring food intake [13]. Random forest was proposed to classify the eating episodes based on features, such as mean, variance, skewness, kurtosis and RMS extracted from the 3 axes accelerometer.…”

Section: Previous Workmentioning

confidence: 99%

A wearable sensing framework for improving personal and oral hygiene for people with developmental disabilities

Wijayasingha

2016

2016 IEEE Wireless Health (WH)

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Abstract-People with developmental disabilities often face difficulties in coping with daily activities and many require constant support. One of the major health issues for people with developmental disabilities is personal hygiene. Many lack the ability, poor memory or lack of attention to carry out normal daily activities like brushing teeth and washing hands. Poor personal hygiene may result in increased susceptibility to infection and other health issues. To enable independent living and improve the quality of care for people with developmental abilities, this paper proposes a new wearable sensing framework to monitoring personal hygiene. Based on a smartwatch, this framework is designed as a pervasive monitoring and learning tool to provide detailed evaluation and feedback to the user on hand washing and tooth brushing. A preliminary study was conducted to assess the performance of the approach, and the results showed the reliability and robustness of the framework in quantifying and assessing hand washing and tooth brushing activities.

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“…If these devices are connected, some analytical value may be realized by inferring eating moments from the time-stamps of insulin bolus data, though not every bolus is administered to cover food intake, likely leading to false positive calls of meal events and diminishing confidence in any therapeutic insights gained from these data. Recognizing the usefulness of passively tracking eating moments (ie, with little or no burden on the user), ongoing research is developing methods to detect an individual's consumption of meals and snacks via data from sensors embedded in wearable technologies, most recently including a smartwatch, 55 smartglasses, 56 or an earpiece. 57 Using machine learning techniques to analyze the continuous timeseries sensor data, these approaches are increasingly accurate in distinguishing true eating moments throughout an individual's daily activity, and hold great potential for passively tracking meal and snack events, as the technologies mature toward commercialization.…”

Section: The Meal Challengementioning

confidence: 99%

A Digital Ecosystem of Diabetes Data and Technology

Heintzman

2015

J Diabetes Sci Technol

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The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies-such as smartphone apps, and wearable devices and sensors-which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.

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A practical approach for recognizing eating moments with wrist-mounted inertial sensing

Cited by 262 publications

References 33 publications

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

A wearable sensing framework for improving personal and oral hygiene for people with developmental disabilities

A Digital Ecosystem of Diabetes Data and Technology

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