Osteoporosis is a worldwide health issue marked by decreased bone density and degradation of bone tissue, which raises the risk of fractures. Early diagnosis of low bone mineral density (BMD) is crucial in reducing risks by providing appropriate treatment or prevention methods. However, the most common method of measuring BMD is the Dual-energy x-ray absorptiometry, which might not be affordable or accessible to many patients. This study proposes using machine learning methods to predict BMD through anthropometric measurements, anamnesis, age, and sex. A dataset containing 905 patients with their corresponding features and BMD values was also introduced. Different regression algorithms were evaluated, and the model predictions were interpreted using SHapley Additive exPlanations. The approach demonstrated good performance, with an average mean absolute error and mean absolute percentage error of 0.0771 g/cm2and 6.34%, respectively. As a result, this proposed method can potentially become a tool for healthcare professionals to predict BMD in a cost-effective and accessible manner.

Anthropometric measurements are used in several fields of study and can be used in public health as an indicator of cardiovascular risk; they can also be used as a parameter for making tailored clothes or even for reconstructing the body for nutritional monitoring purposes. Thus, the automatic estimation of these measurements can improve anthropometric processes, even more so if it is based on 2D images, as this represents a gain due to the low cost of implementing this technology. Our work presents an approach to estimating these anthropometric measurements through images using Machine Learning. Furthermore, in this work, we propose a dataset containing 913 samples with images and measurements. Finally, we performed experiments to analyze the influence of patient information on the estimation of measurements, as well as a gender analysis to get insights from the dataset. In our approach, with specific configurations, we reached the mark of 0.778 ± 0.083 cm using MAE, 1.096 ± 0.292 cm using RMSE, and 2% using MAPE, differing on the experiment, gender, and classifier.

International audience

Emerging IoT applications with stringent requirements on latency and data processing have posed many challenges to cloud-centric platforms for Smart Cities. Recently, Fog Computing has been advocated as a promising approach to support such new applications and handle the increasing volume of IoT data and devices. The Fog Computing paradigm is characterized by a horizontal system-level architecture where devices close to end-users and IoT devices are used for processing, storage, and networking functions. Fog Computing platforms aim to facilitate the development of applications and systems for Smart Cities by providing services and abstractions designed to integrate data from IoT devices and various information systems deployed in the city. Despite the potential of the Fog Computing paradigm, the literature still lacks a broad, comprehensive overview of what has been investigated on the use of such paradigm in platforms for Smart Cities and open issues to be addressed in future research and development. In this paper, a systematic mapping study was performed and we present a comprehensive understanding of the use of the Fog Computing paradigm in Smart Cities platforms, providing an overview of the current state of research on this topic, and identifying important gaps in the existing approaches and promising research directions.