Many types of research focus on supporting fisheries by utilizing information and communication technology (ICT) to realize sustainable and efficient fishing industries. However, most of the previous studies focused on monitoring environmental data and fishing vessels. Our study focused on identifying the timing of when a fisherman catches an octopus in pot drift fishing for North Pacific giant octopus, in order to support fishery industries. A support vector machine (SVM) detects the timing at which a fisherman catches an octopus, using the feature values derived from acceleration data obtained with a wearable device. In this study, the author adopted three types of feature value sets for the SVM: peak frequency of fast Fourier transform (FFT), statistics, and the combination of peak frequency and statistics. From the evaluation results, the SVM was found to achieve a recall of over 90%. Future works include collecting a large amount of data and developing an application to detect the timing of catch.