  To train deep learning models for vision-based action recognition of elders'
daily activities, we need large-scale activity datasets acquired under various
daily living environments and conditions. However, most public datasets used in
human action recognition either differ from or have limited coverage of elders'
activities in many aspects, making it challenging to recognize elders' daily
activities well by only utilizing existing datasets. Recently, such limitations
of available datasets have actively been compensated by generating synthetic
data from realistic simulation environments and using those data to train deep
learning models. In this paper, based on these ideas we develop ElderSim, an
action simulation platform that can generate synthetic data on elders' daily
activities. For 55 kinds of frequent daily activities of the elders, ElderSim
generates realistic motions of synthetic characters with various adjustable
data-generating options, and provides different output modalities including RGB
videos, two- and three-dimensional skeleton trajectories. We then generate KIST
SynADL, a large-scale synthetic dataset of elders' activities of daily living,
from ElderSim and use the data in addition to real datasets to train three
state-of the-art human action recognition models. From the experiments
following several newly proposed scenarios that assume different real and
synthetic dataset configurations for training, we observe a noticeable
performance improvement by augmenting our synthetic data. We also offer
guidance with insights for the effective utilization of synthetic data to help
recognize elders' daily activities.
