This project explores the possibility of human identification for the Care and Alarm System by using human skeleton information captured by the Xbox One Kinect sensor. This project experiment three classifiers and three main different kinds of skeleton information. These three classifiers are artificial neural networks (ANNS), K nearest neighbours (KNNs) and support vector machine (SVM); three main kinds of skeleton information include absolute full-body skeleton information, absolute partly body skeleton information and proportional full-body skeleton information. According to the result, ANNs perform the best classification with an accuracy rate of 99.50% by using absolute full-body skeleton information. KNN generally has a high classification accuracy higher than 90% by using all kinds of skeleton information. SVM is not a good choice since it only results in bad classification results. Regarding the skeleton information, Absolute full-body produces a better classification result than proportional full-body information and absolute partly body information. Additionally, for absolute partly body information, torso information leads to a better result than bottom part. Generally speaking, skeleton information is applicable for human identification and can help generate good classification result.