A Framework for Internet of Things-Based Body Mass Index Estimation and Obesity Prediction

Obasi, Chukwuemeka; Ndu, Ijeoma; Iloanusi, Ogechukwu N.

doi:10.1109/ehb50910.2020.9280202

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…This means that he or she will be able to quickly determine whether or not they need to control or monitor their health. Future studies will benefit from the findings, which show that by using certain techniques, people can reduce their risk of developing weight problems [14].…”

Section: Related Workmentioning

confidence: 95%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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People's health is important. It must be preserved. There are a variety of issues that might contribute to a person's health, including their lifestyle. One of the most prominent concerns during this pandemic is a person's body weight, which is particularly significant these days. Obesity is one of those body diseases about which it is important to be aware of the reasons. This study intends to create different machine learning models to define what causes obesity, select the most appropriate model that did the best, and discuss how accurate it performed. It can also be used in determining the how the obesity impact in our daily lives. Through the use of different machine learning models such as KNN, Random Forest, Gradient Boosting and Ada Boost, the study be able to obtain the appropriate model. Despite being trained on unbalanced data, the classifiers utilized were able to predict the properties of the presented datasets that Random Forest has an accuracy of 83.6%.

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Section: Related Workmentioning

confidence: 95%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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“…Anthropometric measurements contained weight, height, Hip Circumference (HC), and Waist Circumference (WC) were assessed by the same health-care expert. Body Mass Index (BMI) was calculated based on dividing weight in kilogram by square of height in meter for assessing the obesity (17). Additionally, WC was measured at the narrowest point between the lower rib and iliac crest in the standing position and HC at widest part of the buttocks (18).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Sexual Function in Fertile and Infertile Women with Polycystic Ovary Syndrome (PCOS): A Comparative Cross-Sectional Study

Azarbayjani

Sadatmahalleh

mirzaie

et al. 2021

Preprint

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Background: Polycystic Ovarian Syndrome (PCOS) is a common hormonal disorder that affects approximately 5-20% of women of childbearing age. Physical and psychological changes caused by PCOS might impair women’s sexual function. Infertility is one of the most important complications that may be seen in women with PCOS. This study aimed to compare sexual function in fertile and infertile women with PCOS diagnosis.Method: This cross-sectional study was conducted on 364 fertile and 239 infertile women that diagnosed with PCOS according to the Rotterdam criteria from May 2018 to February 2019. All participants were asked to fill out included demographic survey and Female Sexual Function Index (FSFI) questionnaires.Result: The mean total FSFI score in fertile and infertile women was 23.70±6.26 and 24.65±4.04, respectively. With regard to FSFI, the differences between the two groups were not statistically significant in terms of total FSFI score, desire, lubrication, satisfaction, and arousal score (P>0.05). However, the pain score, was significantly higher in the fertile group. (3.51±1.27, 3.10±1.11, P<0.001). Conclusion: Impaired sexual function in infertile women diagnosed with PCOS can be more associated with PCOS-related complications and other concurrent factors that affects sexual function rather than infertility. It seems that the effects of infertility on sexual function can be different in infertile women according to each person's condition. Hence, screening for sexual dysfunction in women with PCOS and infertility should be deemed as a part of clinical assessment, leading to early diagnosing and improving their quality of life.

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“…It is advisable for these people to take extra precautions and prevention from the COVID-19 in this pandemic time [29][30][31][32][33][34][35][36]. The developed system measures BMI with high precision and accuracy as compared to existing systems [37][38][39][40]. In case of India, the under mass issue is normally applicable for the people below the poverty line where the nutrient and healthy food availability is not always adequate or enough.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Temperature-Compensated Body Mass Index Measuring System

Kumar

Dubey

Zafer

et al. 2021

MAPAN

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The measurement of body mass index (BMI) is an important indicator of preliminary assessment of human health. A higher BMI value increases the risk factor for critical diseases, e.g., high blood pressure (hypertension), cardiovascular disease, gallbladder disease, type 2 diabetes, etc. Obesity also increases the risk factor of COVID-19 in patients. BMI, the ratio of mass (in kilogram) to the square of height (in meter), is an estimation of an individual's physique. It depends upon the adult's height and mass and is intended to evaluate the tissue mass. In this article, we have described the design and developmental aspects of a temperature-compensated BMI measuring system. The developed BMI measuring system is comprised of an ultrasonic sensor for height measurement, strain gauge-based load cell for precise and accurate mass measurement, a temperature sensor to eliminate the effect of temperature, and a microcontroller with related electronics, software, and display system. The developed BMI system is capable to measure mass precisely in the range (0-200) kg with a resolution of 0.01 kg, and height in the range of (0-4) m with resolution of 0.01 m. The expanded uncertainty in height measurement is estimated as 2.8 mm. The BMI value is determined with the help of these parameters and displayed with precision of 0.01. The novelty of the developed system is temperature compensation in the height measurement using an ultrasonic sensor, which has been introduced first time. Also, system is improved for its highly precise and accurate measurements at a comparatively lower cost than the commercially available existing systems.

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A Framework for Internet of Things-Based Body Mass Index Estimation and Obesity Prediction

Cited by 6 publications

References 18 publications

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Comparison of Sexual Function in Fertile and Infertile Women with Polycystic Ovary Syndrome (PCOS): A Comparative Cross-Sectional Study

Design and Development of a Temperature-Compensated Body Mass Index Measuring System

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