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New conference contribution at the "International Conference on Bioinformatics and Computational Biology (ICBCB) 2024"
The article entitled "Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges" by Birk Martin Magnussen, Frank Möckel, Maik Jessulat, Claudius Stern, and Bernhard Sick includes the following:
Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $R=0.61$ with $p<0.0001$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($R=0.44,p=0.0023$) and skeletal muscle mass index ($R=0.52,p=0.0003$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.
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New conference contribution at the "International Conference on Bioinformatics and Computational Biology (ICBCB) 2024"
The article entitled "Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges" by Birk Martin Magnussen, Frank Möckel, Maik Jessulat, Claudius Stern, and Bernhard Sick includes the following:
Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $R=0.61$ with $p<0.0001$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($R=0.44,p=0.0023$) and skeletal muscle mass index ($R=0.52,p=0.0003$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.
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New conference contribution at the "International Conference on Bioinformatics and Computational Biology (ICBCB) 2024"
The article entitled "Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges" by Birk Martin Magnussen, Frank Möckel, Maik Jessulat, Claudius Stern, and Bernhard Sick includes the following:
Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $R=0.61$ with $p<0.0001$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($R=0.44,p=0.0023$) and skeletal muscle mass index ($R=0.52,p=0.0003$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.