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In this paper, we propose an unsupervised method for multi-level segmentation, which could be used for a pre-process of non-sequential activity recognition, and could construct a high-level activity recognition using accelerometers on mobile phones. We extend single-level segmentation to multi-level by sweeping the temporal parameter. To confirm the validity of our approach. we pursued the experiment of gathering accelerometer data of real nursing in a hospital. After the experiment and multi-level segmentation, we confirmed several phenomena to imply the validity of multi-level segmentation such that sequence seems to be properly segmented fitting to the annotations transcribed from the voice, that there are peaks of lower-level segment boundaries without higher-level boundaries, and that higher-level boundaries are not lower-level boundaries.