Falls are the first cause of injury-related deaths in people over the age of 65. A gait feature extraction method based on Kinect 3D skeleton data is proposed. This method can assess and predict the falls risks of the elderly according to the personalized features of the individual information of the subjects. The falls risks are divided into two classes: high falls risks and low falls risks. Considering the cost of data collection, the novelty detection model is used to train and access the feature data on an unbalanced data set. The experimental results show that the accuracy of one-class support vector machine (OC-SVM) detection is 86.96% and the F1-score is 88.55%, which means the proposed method can effectively distinguish subjects with low falls risks from those with high falls risks. These results also demonstrate the potential of predicting the falls risks of the elderly with Kinect 3D skeleton data.