Toward on-device weight monitoring from selfie face images using smartphones
Woods, Nikki Keene
Lewis, Rhonda K.
Twomey, Janet M.
Hill, Twyla J.
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Siddiqui 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. https://doi.org/10.1007/978-3-030-91181-2_4
Obesity 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.
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