Meal-time and duration monitoring using wearable sensors

Dong, Bo; Biswas, Subir

doi:10.1016/j.bspc.2016.09.018

Cited by 24 publications

(27 citation statements)

References 27 publications

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“…Surprisingly, the Cardiovascular diseases [20,23] were conducted solely with healthy participants while another [21] and three of the sub-studies in [21,22] lack information on whether the participants were healthy or patients. Regarding Diabetes and Nutrition, two works [24,28] were conducted with patients, one study [27] was conducted with healthy participants while two articles [25,26] lack this information. Finally, regarding Stress and sleep, none of the studies report on studies with patients.…”

Section: Tests On Patients and Healthy Usersmentioning

confidence: 99%

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

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Section: Tests On Patients and Healthy Usersmentioning

confidence: 99%

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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“…Many previous studies considered food intake detection when participants were sedentary [4]- [9], [13], [19]- [23], [29]- [31], [36]- [40], [42], [43], [47]- [51]. Our method is intended to monitor participants' ingestive activity while they were moderately active (i.e., ingesting on the go).…”

Section: Resultsmentioning

confidence: 99%

“…It is important to process the data before extracting features and developing any statistical model. Prior researchers [29]- [31] have divided the process of swallowing into three phases: the oral preparation phase (food is chewed to form the viscous bolus), the pharyngeal phase (the bolus travels through the pharynx and passes the upper esophageal sphincter), and the esophageal phase (the bolus moves to the stomach from the esophagus). We have combined the pharyngeal and esophageal phases to represent swallowing, and have used the oral preparation phase to represent chewing.…”

Section: Data Processingmentioning

confidence: 99%

“…There are various methods of measuring and recording daily dietary information. Different researchers have developed automated non-invasive food monitoring methods using different sensors such as accelerometers [29], microphones [4], [5], [7]- [9], [30], [31], cameras [35], gyroscopes [36]- [39], proximity sensor [17], textile pressure sensors [21], strain gauges [40], piezoelectric sensors [6], [13], [15], [16], [19], [22], [23], [26], [41], orientation sensors [10], [42], [43], electromyography [18], electroglottography [44], and wrist band [45].…”

Section: Related Workmentioning

confidence: 99%

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Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System

Hussain

Javed

Cho

et al. 2018

IEICE Trans. Inf. & Syst.

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Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklacetype wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous stateof-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.

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“…Within only a few years, the price of these sensors dropped to less than US $200. At the same time, sensors have now substantially reduced in size, operate wirelessly, and are powered by rechargeable batteries (e.g., [9,14]). High-quality motion tracking sensors are available within off-the-shelf, commercial smart watches that can be purchased at a fraction of the price of earlier devices and by the general population, not just researchers.…”

Section: Introductionmentioning

confidence: 99%

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

et al. 2019

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Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM, n = 21), Random Forest (n = 19), Decision Tree (n = 16), Hidden Markov Model (HMM, n = 10) algorithms, and from 2017 Deep Learning (n = 5). While comparisons of the detection models are not valid due to the use of different datasets, the models that consider the sequential context of data across time, such as HMM and Deep Learning, show promising results for eating activity detection. We discuss opportunities for future research and emerging applications in the context of dietary assessment and monitoring.

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Meal-time and duration monitoring using wearable sensors

Cited by 24 publications

References 27 publications

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

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