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dc.contributor.authorSiddiqui, Hera
dc.contributor.authorRattani, Ajita
dc.contributor.authorCure, Laila
dc.contributor.authorWoods, Nikki Keene
dc.contributor.authorLewis, Rhonda K.
dc.contributor.authorTwomey, Janet M.
dc.contributor.authorSmith-Campbell, Betty
dc.contributor.authorHill, Twyla J.
dc.identifier.citationSiddiqui H. et al. (2022) Toward On-Device Weight Monitoring from Selfie Face Images Using Smartphones. In: Comito C., Forestiero A., Zumpano E. (eds) Integrating Artificial Intelligence and IoT for Advanced Health Informatics. Internet of Things (Technology, Communications and Computing). Springer, Cham.
dc.descriptionClick on the DOI link to access this book chapter (may not be free).en_US
dc.description.abstractObesity is a serious health problem that is on the rise at the global level. Recent studies suggest that BMI can be inferred from facial images using deep learning-based convolutional neural networks (CNNs) for obesity classification with about 85–90% accuracy. However, training and testing these deep learning models involves high computation and storage due to the involvement of millions of parameters. A recent trend is the use of lightweight CNN models to facilitate on-device computation in resource-constrained mobile and wearable devices. In this study, we evaluate several lightweight CNNs such as MobileNet-V2, ShuffleNet-V2, and lightCNN-29 for BMI prediction and obesity classification from facial images captured using smartphones. The comparative analysis is done with heavyweight VGG-16 and ResNet-50-based CNN models. These lightweight models when deployed on smartphones can act as self-diagnostic tool in weight changes and obesity monitoring. These tools can facilitate remote monitoring of patients, obtaining patients’ vital signs, and in improving the quality of care provided. Self-diagnostic tools would also help in keeping users’ health data private, safe, and secure.en_US
dc.subjectSelf-diagnostic toolsen_US
dc.subjectObesity classificationen_US
dc.subjectFace biometricsen_US
dc.titleToward on-device weight monitoring from selfie face images using smartphonesen_US
dc.typeBook chapteren_US
dc.rights.holder© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022en_US

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