An apparatus for generating the weight estimation model includes a training data collection unit that collects training data, the training data including skin spectrum information and weight information of a plurality of objects, and a model generation unit that generates the weight estimation model, used for a spectrum-based weight estimation, through machine learning based on the collected training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for generating a weight estimation model, the apparatus comprising: a training data collection unit configured to collect training data, the training data comprising skin spectrum information and weight information of a plurality of objects; and a model generation unit configured to generate the weight estimation model, used for spectrum-based weight estimation, through machine learning based on the collected training data. 2. The apparatus of claim 1, wherein the model generation unit generates the weight estimation model through the machine learning, in which the skin spectrum information is used as an input and the weight information is used as an output. 3. The apparatus of claim 1, wherein the training data further comprises at least one of age information, height information, gender information, and bio impedance information of an object. 4. The apparatus of claim 3, wherein the model generation unit generates the weight estimation model through the machine learning in which the skin spectrum information and the at least one of the age information, the height information, the gender information, and the bio impedance information are used as inputs and the weight information is used as an output. 5. The apparatus of claim 1, wherein an algorithm used to perform the machine learning comprises at least one of partial least squares regression, linear regression, neural network, decision tree, genetic algorithm, genetic programming, K-nearest neighbor, radial basis function network, random forest, support vector machine, and deep-learning. 6. A method for generating a weight estimation model, the method comprising: collecting training data, the training data comprising skin spectrum information and weight information of a plurality of objects; and generating the weight estimation model, used for spectrum-based weight estimation, through machine learning based on the collected training data. 7. The method of claim 6, wherein the generating of the weight estimation model comprises generating the weight estimation model through the machine learning, in which the skin spectrum information is used as an input and the weight information is used as an output. 8. The method for generating the weight estimation model of claim 6, wherein the training data further comprises at least one of age information, height information, gender information, and bio impedance information of an object. 9. The method of claim 8, wherein the generating of the weight estimation model comprises generating the weight estimation model through the machine learning in which the skin spectrum information and the at least one of the age information, the height information, the gender information, and the bio impedance information are used as inputs and the weight information is used as an output. 10. The method of claim 6, wherein an algorithm used to perform the machine learning comprises at least one of partial least squares regression, linear regression, neural network, decision tree, genetic algorithm, genetic programming, K-nearest neighbor, radial basis function network, random forest, support vector machine, and deep-learning. 11. An apparatus for estimating a weight, the apparatus comprising: a spectrum measurement unit configured to measure a skin spectrum of a user; and a weight estimation unit configured to estimate a weight of the user using a weight estimation model based on the measured skin spectrum. 12. The apparatus of claim 11, wherein the weight estimation model is generated through machine learning based on training data, the training data comprising skin spectrum information and weight information of a plurality of objects. 13. The apparatus of claim 12, wherein the training data further comprises at least one of age information, height information, gender information, and bio impedance information of the plurality objects. 14. The apparatus of claim 13, wherein the weight estimation unit estimates the weight of the user further based on at least one of age information, height information, gender information, and bio impedance information of the user. 15. The apparatus of claim 11, wherein an algorithm used to perform the machine learning comprises at least one of partial least squares regression, linear regression, neural network, decision tree, genetic algorithm, genetic programming, K-nearest neighbor, radial basis function network, random forest, support vector machine, and deep-learning.