Comparison of Wearable Sensors for Estimation of Chewing Strength

Hossain, Delwar; Imtiaz, Masudul H.; Sazonov, Edward

doi:10.1109/jsen.2020.2968009

Cited by 24 publications

(22 citation statements)

References 42 publications

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“…Therefore, it can be used in various soft applications, such as actuators or strain sensors, etc. [ 55 ]. Virgin SR is easy to process, soft, has good flexibility and good tensile strength, and is easy to vulcanize.…”

Section: Introductionmentioning

confidence: 99%

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Silicone Rubber Composites Reinforced by Carbon Nanofillers and Their Hybrids for Various Applications: A Review

et al. 2021

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Without fillers, rubber types such as silicone rubber exhibit poor mechanical, thermal, and electrical properties. Carbon black (CB) is traditionally used as a filler in the rubber matrix to improve its properties, but a high content (nearly 60 per hundred parts of rubber (phr)) is required. However, this high content of CB often alters the viscoelastic properties of the rubber composite. Thus, nowadays, nanofillers such as graphene (GE) and carbon nanotubes (CNTs) are used, which provide significant improvements to the properties of composites at as low as 2–3 phr. Nanofillers are classified as those fillers consisting of at least one dimension below 100 nanometers (nm). In the present review paper, nanofillers based on carbon nanomaterials such as GE, CNT, and CB are explored in terms of how they improve the properties of rubber composites. These nanofillers can significantly improve the properties of silicone rubber (SR) nanocomposites and have been useful for a wide range of applications, such as strain sensing. Therefore, carbon-nanofiller-reinforced SRs are reviewed here, along with advancements in this research area. The microstructures, defect densities, and crystal structures of different carbon nanofillers for SR nanocomposites are characterized, and their processing and dispersion are described. The dispersion of the rubber composites was reported through atomic force microscopy (AFM), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). The effect of these nanofillers on the mechanical (compressive modulus, tensile strength, fracture strain, Young’s modulus, glass transition), thermal (thermal conductivity), and electrical properties (electrical conductivity) of SR nanocomposites is also discussed. Finally, the application of the improved SR nanocomposites as strain sensors according to their filler structure and concentration is discussed. This detailed review clearly shows the dependency of SR nanocomposite properties on the characteristics of the carbon nanofillers.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, it can be used in various soft applications, such as actuators or strain sensors, etc. [ 55 ].…”

Section: Introductionmentioning

confidence: 99%

Silicone Rubber Composites Reinforced by Carbon Nanofillers and Their Hybrids for Various Applications: A Review

et al. 2021

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“…Photoplethysmography (PPG) was used for monitoring in 8% (4/51) of the studies [ 32 , 48 , 60 , 69 ], acceleration stress was monitored in 8% (4/51) of the studies [ 55 - 58 ], and ECG was used for monitoring in 6% (3/51) of the studies [ 49 , 50 , 59 ]. A total of 25% (13/51) of the articles reported the monitoring of multiple exposure- and disease-related biomarkers (ie, EEG outputs, breathing signals, and mechanical plethysmography [MPG] outputs [ 65 ]; ECG outputs, lactate levels, and head acceleration [ 66 ]; ECG, ballistocardiography, and PPG outputs [ 46 ]; PPG and bioacoustics outputs and vibrations [ 36 ]; PPG and bioacoustics outputs [ 34 ]; ear canal shape, electromyography [EMG] outputs, and occlusal force [ 39 ]; acceleration stress, body temperature, and HR variability [HRV] [ 68 ]; EEG, electro-oculography, and EMG outputs [ 42 ]; EEG and ECG outputs [ 70 ]; ear canal shape, muscle movement, and acoustic signals [ 35 ]; EMG outputs, ear canal pressure, and muscle movement [ 38 ]; PPG outputs and air pressure [ 61 ]; and body potential, EMG outputs, and capacitance [ 74 ]). Other biomarkers measured in the studies included oxygen saturation [ 62 ], caloric vestibular stimulation [ 79 ], breathing signals [ 67 ], ear pulse waves (EPWs) [ 47 ], and EMG outputs [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Health-Related Indicators Measured Using Earable Devices: Systematic Review

Choi¹,

Jeon²,

Kim³

et al. 2022

JMIR Mhealth Uhealth

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Background Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. Objective Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. Methods A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. Results A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). Conclusions Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.

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“…Recently, many health researchers are exploring several wearable devices to monitor food intake behavior and record meal microstructure in eating episodes [ 12 ]. Different sensor modalities such as acoustic sensors [ 11 ], [ 13 ], accelerometers [ 14 ], [ 15 ], surveillance video cameras [ 16 ], smartphone cameras [ 17 ], piezoelectric sensors [ 18 ], [ 19 ], motion sensors [ 5 ], hand gesture sensors [ 20 ] are being explored in development of these wearable devices.…”

Section: Introductionmentioning

confidence: 99%

Automatic Count of Bites and Chews From Videos of Eating Episodes

2020

Self Cite

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Methods for measuring of eating behavior (known as meal microstructure) often rely on manual annotation of bites, chews, and swallows on meal videos or wearable sensor signals. The manual annotation may be time consuming and erroneous, while wearable sensors may not capture every aspect of eating (e.g. chews only). The aim of this study is to develop a method to detect and count bites and chews automatically from meal videos. The method was developed on a dataset of 28 volunteers consuming unrestricted meals in the laboratory under video observation. First, the faces in the video (regions of interest, ROI) were detected using Faster R-CNN. Second, a pre-trained AlexNet was trained on the detected faces to classify images as a bite/no bite image. Third, the affine optical flow was applied in consecutively detected faces to find the rotational movement of the pixels in the ROIs. The number of chews in a meal video was counted by converting the 2-D images to a 1-D optical flow parameter and finding peaks. The developed bite and chew count algorithm was applied to 84 meal videos collected from 28 volunteers. A mean accuracy (±STD) of 85.4% (±6.3%) with respect to manual annotation was obtained for the number of bites and 88.9% (±7.4%) for the number of chews. The proposed method for an automatic bite and chew counting shows promising results that can be used as an alternative solution to manual annotation.

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Comparison of Wearable Sensors for Estimation of Chewing Strength

Cited by 24 publications

References 42 publications

Silicone Rubber Composites Reinforced by Carbon Nanofillers and Their Hybrids for Various Applications: A Review

Silicone Rubber Composites Reinforced by Carbon Nanofillers and Their Hybrids for Various Applications: A Review

Health-Related Indicators Measured Using Earable Devices: Systematic Review

Automatic Count of Bites and Chews From Videos of Eating Episodes

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