A dog which assists rescue activity in the scene of disasters such as earthquakes and landslides is called a “disaster rescue dog” or just a “rescue dog”. In Japan where earthquakes happen frequently, a research project on “Cyber-Rescue” is being organized for more efficient rescue activities. In the project, to analyze the activities of rescue dogs in the scene of disasters, “Cyber Dog Suits” equipped with sensors, a camera and a GPS were developed. In this work, we recognize dog activities in the ego-centric dog videos taken by the camera mounted on the cyber-dog suits. To do that, we propose an image/sound/sensor-based four-stream CNN for dog activity recognition which integrates sound and sensor signals as well as motion and appearance. We conducted some experiments for multi-class activity categorization using the proposed method. As a result, the proposed method which integrates appearance, motion, sound and sensor information achieved the highest accuracy, 48.05%. This result is relatively high as a recognition result of ego-centric videos.