  Deep learning, based on which many modern algorithms operate, is well known
to be data-hungry. In particular, the datasets appropriate for the intended
application are difficult to obtain. To cope with this situation, we introduce
a new dataset called ETRI-Activity3D, focusing on the daily activities of the
elderly in robot-view. The major characteristics of the new dataset are as
follows: 1) practical action categories that are selected from the close
observation of the daily lives of the elderly; 2) realistic data collection,
which reflects the robot's working environment and service situations; and 3) a
large-scale dataset that overcomes the limitations of the current 3D activity
analysis benchmark datasets. The proposed dataset contains 112,620 samples
including RGB videos, depth maps, and skeleton sequences. During the data
acquisition, 100 subjects were asked to perform 55 daily activities.
Additionally, we propose a novel network called four-stream adaptive CNN
(FSA-CNN). The proposed FSA-CNN has three main properties: robustness to
spatio-temporal variations, input-adaptive activation function, and extension
of the conventional two-stream approach. In the experiment section, we
confirmed the superiority of the proposed FSA-CNN using NTU RGB+D and
ETRI-Activity3D. Further, the domain difference between both groups of age was
verified experimentally. Finally, the extension of FSA-CNN to deal with the
multimodal data was investigated.
