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Human action recognition has been widely used in many fields of life, andmany human action datasets have been published at the same time. However, mostof the multi-modal databases have some shortcomings in the layout and number ofsensors, which cannot fully represent the action features. Regarding theproblems, this paper proposes a freely available dataset, named CZU-MHAD(Changzhou University: a comprehensive multi-modal human action dataset). Itconsists of 22 actions and three modals temporal synchronized data. Thesemodals include depth videos and skeleton positions from a kinect v2 camera, andinertial signals from 10 wearable sensors. Compared with single modal sensors,multi-modal sensors can collect different modal data, so the use of multi-modalsensors can describe actions more accurately. Moreover, CZU-MHAD obtains the3-axis acceleration and 3-axis angular velocity of 10 main motion joints bybinding inertial sensors to them, and these data were captured at the sametime. Experimental results are provided to show that this dataset can be usedto study structural relationships between different parts of the human bodywhen performing actions and fusion approaches that involve multi-modal sensordata.

CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors