Gait is an important indicator for specific diseases. Abnormal gait patterns are caused by various factors such as physical, neurological, and sensory problems. If it is possible to recognize abnormal gait patterns in the early stage of the related disease, patients can receive proper treatment early and prevent secondary accidents such as falls caused by unbalanced gait. In this paper, we propose a gait recognition system that can recognize 5 abnormal gait patterns. Our system using 3D joint information obtained by using multiple Kinect v2 sensors and RNN-LSTM. In particular, abnormal gaits caused by physical problems such as injury, weakness of muscle, and joint problems are targeted for recognition. The purpose of this paper is to find optimal condition for gait recognition when using the multiple Kinect v2 sensors. Experiments were conducted by comparing the test accuracies on 14 combinations of human joint. Through this experiment, we selected optimal joints to show outstanding results so that our gait recognition model performs optimally. Results show that Ankles, Wrists, and the Head are the most influential joints on RNN-LSTM model. We applied 25-joint information of the human body to recognize gait patterns and achieved an accuracy over 97%.