Monitoring and evaluation of ingestive activities

Hassan, Eman A.; Elbially, Mohamed S.; Morsy, Ahmed

doi:10.1109/bhi.2018.8333360

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…As prevention, rather than treatment, has become emphasized, wearable sensors are increasingly being utilized in modern medicine [9]. However, most previous wearable sensors have critical drawbacks in terms of user convenience because of significant power consumption with various functionalities, large battery volume, rigid/bulky form factors, and frequent charging cycles [10][11][12]. In previous studies, people sustained efforts to develop self-powered wearable sensors using piezoelectric/triboelectric materials and solar cells [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low Power Wearable Infant Sleep Position Sensor

Yun

Jeung

Kim

et al. 2019

Sensors

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Numerous wearable sensors have been developed for a variety of needs in medical/healthcare/wellness/sports applications, but there are still doubts about their usefulness due to uncomfortable fit or frequent battery charging. Because the size or capacity of battery is the major factor affecting the convenience of wearable sensors, power consumption must be reduced. We developed a method that can significantly reduce the power consumption by introducing a signal repeater and a special switch that provides power only when needed. Antenna radiation characteristics are an important factor in wireless wearable sensors, but soft material encapsulation for comfortable fit results in poor wireless performance. We improved the antenna radiation characteristics by a local encapsulation patterning. In particular, ultra-low power operation enables the use of paper battery to achieve a very thin and flexible form factor. Also, we verified the human body safety through specific absorption rate simulations. With these methods, we demonstrated a wearable infant sleep position sensor. Infants are unable to call for help in unsafe situations, and it is not easy for caregivers to observe them all the time. Our wearable sensor detects infants' sleep positions in real time and automatically alerts the caregivers when needed.

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Section: Introductionmentioning

confidence: 99%

Ultra-Low Power Wearable Infant Sleep Position Sensor

Yun

Jeung

Kim

et al. 2019

Sensors

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sEMG-based automatic characterization of swallowed materials

Hassan,

Khalifa,

Morsy

2024

BioMed Eng OnLine

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Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.

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Volume estimation of fluid intake using regression models

Hassan,

Morsy

2023

J. Eng. Appl. Sci.

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Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different regression methods to estimate water intake using wireless surface electromyography (sEMG). The advantage of using regression is that it can provide more consistent values for different swallow volumes. In addition, the setup reported in this research employs a less controlled environment, providing stronger evidence of the practical feasibility of the used setup. Neural networks-based regression achieved an R2 of 0.99 and a root-mean-squared error of 0.14 and 0.08 after feature selection. The relative immunity of sEMG as a sensing technique and the accuracy levels achieved with the used mobile sEMG device can provide a robust system for volume estimation of fluid intake in real-world situations.

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Monitoring and evaluation of ingestive activities

Cited by 3 publications

References 10 publications

Ultra-Low Power Wearable Infant Sleep Position Sensor

Ultra-Low Power Wearable Infant Sleep Position Sensor

sEMG-based automatic characterization of swallowed materials

Volume estimation of fluid intake using regression models

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