We have developed a method to visualize search and rescue (SAR) dogs' activities from sensor data recorded by the SAR dogs' sensor vests. This paper proposes two methods for detecting continuous barking actions of SAR dogs, which locate victims by smell and then bark continuously to tell handlers where victims are located. Continuous barking action is detected from audio information and a dog's body motions. This detection method is based on dynamic time warping (DTW), which has been used successfully to analyze human audio information. Cyclic body motion was observed during dogs' barking motions. This cyclic motion can be detected by an inertial measurement unit (IMU) attached to the vest. A fast Fourier transform (FFT) is used to analyze a dog's barking motion. The proposed detection methods were evaluated using audio and IMU data recorded during actual SAR dog training sessions. The F-scores of the audio and motion-based barking detection methods were 0.95 and 0.90, respectively. As a trial, we marked victim locations on a map based on the body motion.