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ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications by Hochul Hwang; Cheongjae Jang; Geonwoo Park; Junghyun Cho; Ig-Jae Kim is a Engineering article available to read on EtoBox.

What is ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications about?

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

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Author

Hochul Hwang; Cheongjae Jang; Geonwoo Park; Junghyun Cho; Ig-Jae Kim

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)