{"id":28310,"date":"2022-07-05T07:45:25","date_gmt":"2022-07-05T12:45:25","guid":{"rendered":"https:\/\/saluddigital.com\/?p=28310"},"modified":"2025-10-20T11:35:58","modified_gmt":"2025-10-20T17:35:58","slug":"estudio-utiliza-camara-de-telefono-movil-para-estimar-el-porcentaje-de-grasa-corporal","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/noticias\/estudio-utiliza-camara-de-telefono-movil-para-estimar-el-porcentaje-de-grasa-corporal\/","title":{"rendered":"Study uses mobile phone camera to estimate body fat percentage"},"content":{"rendered":"
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Study uses mobile phone camera to estimate body fat percentage<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The measurement of body composition is an important aspect for the prevention and control of diseases. For example, excess fat in the body, or adiposity, is a risk factor in the development of chronic diseases. Also, tools for body measurements, such as body mass index (BMI), can be inaccurate.<\/p>

In this sense, the study in question aimed to evaluate the performance of a new automated computer vision method to obtain visual body composition (VBC). VBC uses two-dimensional photographs captured through a smartphone. conventional and according to the information evaluated can estimate the percentage of total body fat. This method is based on a state-of-the-art convolutional neural network.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The study hypothesis holds that the VBC achieves greater accuracy than other body fat measurement devices. Therefore, tests were conducted at two clinical sites, in which participants had their total body fat percentage measured with VBC, three consumers, and two professional bioimpedance analysis systems.<\/p>

Thus, CBV had the highest overall agreement and the tightest limits of agreement among the methods used for this evaluation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\u201cWhile many other devices are available to capture a person's image and transform the quantified information into an estimate of body composition, VBC requires only two conventional photographs of the participant captured via their personal smartphone camera. These two images are sent securely to the cloud where they are: (1) segmented into person and background, and (2) passed to a CNN model that automatically analyzes the images, extracts visual features relevant to body composition, and generates an estimate of the percentage of total body fat\u201d, explain the authors in the study<\/p>

Learn more about this advance and the full results of the study at the following link: https:\/\/www.nature.com\/articles\/s41746-022-00628-3<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-022-00628-3<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Estudio utiliza c\u00e1mara de tel\u00e9fono m\u00f3vil para estimar el porcentaje de grasa corporal La medici\u00f3n de la composici\u00f3n corporal es un aspecto importante para la prevenci\u00f3n y control de enfermedades. Por ejemplo, el exceso de grasa en el cuerpo, o adiposidad, es un factor de riesgo en el desarrollo de enfermedades cr\u00f3nicas. Asimismo, herramientas para realizar mediciones corporales, como el \u00edndice de masa corporal (IMC), pueden ser imprecisas. En este sentido, el estudio en cuesti\u00f3n, tuvo como objetivo evaluar el rendimiento de un nuevo m\u00e9todo de visi\u00f3n automatizada por computadora para obtener la composici\u00f3n visual del cuerpo (VBC, siglas en ingl\u00e9s) La VBC utiliza fotograf\u00edas bidimensionales capturadas a trav\u00e9s de un tel\u00e9fono inteligente convencional y de acuerdo a la informaci\u00f3n evaluada puede estimar el porcentaje de grasa corporal total.  Este m\u00e9todo est\u00e1 basado en una red neuronal convolucional de \u00faltima generaci\u00f3n. La hip\u00f3tesis del estudio sostiene que la VBC logra una precisi\u00f3n mayor a la de otros dispositivos de medici\u00f3n de grasa corporal. Por ello a se realizaron pruebas en dos sitios cl\u00ednicos, en los cuales a los participantes se les midi\u00f3 el porcentaje de grasa corporal total con VBC, tres consumidores y dos sistemas de an\u00e1lisis de bioimpedancia profesionales. De esta forma la VBC tuvo la concordancia general m\u00e1s alta y los l\u00edmites de concordancia m\u00e1s ajustados entre los m\u00e9todos utilizados para esta evaluaci\u00f3n. \u201cMientras que muchos otros dispositivos est\u00e1n disponibles para capturar la imagen de una persona y transformar la informaci\u00f3n cuantificada en una estimaci\u00f3n de la composici\u00f3n corporal VBC solo necesita dos fotograf\u00edas convencionales del participante capturadas a trav\u00e9s de la c\u00e1mara de su tel\u00e9fono inteligente personal. Estas dos im\u00e1genes se env\u00edan de forma segura a la nube donde: (1) se segmentan en persona y fondo, y (2) se pasan a un modelo CNN que analiza autom\u00e1ticamente las im\u00e1genes, extrae las caracter\u00edsticas visuales relevantes para la composici\u00f3n corporal y genera una estimaci\u00f3n del porcentaje de grasa corporal total\u201d, explican los autores en el estudio Conoce m\u00e1s sobre este avance y los resultados completos del estudio en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-022-00628-3 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-022-00628-3<\/p>","protected":false},"author":1,"featured_media":28311,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3395,3401,160],"tags":[145],"class_list":["post-28310","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps-moviles-e-internet-de-las-cosas","category-iotapps","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=28310"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28310\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/28311"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=28310"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=28310"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=28310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}