UniMTS is a new pre-training method for motion time series data. This technique aims to solve the problem of varied data sources and activities in motion data by using a unified approach.

UniMTS uses contrastive learning and masked reconstruction to capture both broad and specific patterns in the motion data.

This approach has been proven to improve performance on various tasks, demonstrating its effectiveness in managing diverse motion time series data.