Key-frame extraction for first-person vision (FPV) videos is a core technology for selecting important scenes and memorizing impressive life experiences in our daily activities. The difficulty of selecting key frames is the scene instability caused by head-mounted cameras used for capturing FPV videos. Because head-mounted cameras tend to frequently shake, the frames in an FPV video are noisier than those in a third-person vision (TPV) video. However, most existing algorithms for key-frame extraction mainly focus on handling the stable scenes in TPV videos. The technical development of key-frame extraction techniques for noisy FPV videos is currently immature. Moreover, most key-frame extraction algorithms mainly use visual information from FPV videos, even though our visual experience in daily activities is associated with human motions. To incorporate the features of dynamically changing scenes in FPV videos into our methods, integrating motions with visual scenes is essential. In this paper, we propose a novel key-frame extraction method for FPV videos that uses multi-modal sensor signals to reduce noise and detect salient activities via projecting multi-modal sensor signals onto a common space by canonical correlation analysis (CCA). We show that the two proposed multi-sensor integration models for key-frame extraction (a sparse-based model and a graph-based model) work well on the common space. The experimental results obtained using various datasets suggest that the proposed key-frame extraction techniques improve the precision of extraction and the coverage of entire video sequences.