Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.