# ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications

arXiv: https://arxiv.org/abs/2010.14742 · HTML: https://mlsanity.com/paper/2010.14742/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games](https://mlsanity.com/paper/2107.05617/) (2021)
- [PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data](https://mlsanity.com/paper/2212.04821/) (2022)
- [Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario](https://mlsanity.com/paper/2306.12152/) (2023)
- [A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval](https://mlsanity.com/paper/2208.02080/) (2022)
- [Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data](https://mlsanity.com/paper/2412.01383/) (2024)
- [Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?](https://mlsanity.com/paper/2312.02672/) (2023)
- [FFPDG: Fast, Fair and Private Data Generation](https://mlsanity.com/paper/2307.00161/) (2023)
- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [MV2MAE: Multi-View Video Masked Autoencoders](https://mlsanity.com/paper/2401.15900/) (2024)
- [M3Act: Learning from Synthetic Human Group Activities](https://mlsanity.com/paper/2306.16772/) (2023)
- [Synthesizing Event-centric Knowledge Graphs of Daily Activities Using Virtual Space](https://mlsanity.com/paper/2307.16206/) (2023)
- [Learning Human Action Recognition Representations Without Real Humans](https://mlsanity.com/paper/2311.06231/) (2023)
- [VIGFace: Virtual Identity Generation for Privacy-Free Face Recognition](https://mlsanity.com/paper/2403.08277/) (2024)
- [SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation](https://mlsanity.com/paper/2504.05925/) (2025)
- [Action Anticipation at a Glimpse: To What Extent Can Multimodal Cues Replace Video?](https://mlsanity.com/paper/2512.02846/) (2025)
- [UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments](https://mlsanity.com/paper/2104.11776/) (2021)
- [Human Pose Estimation in Monocular Omnidirectional Top-View Images](https://mlsanity.com/paper/2304.08186/) (2023)
- [Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy](https://mlsanity.com/paper/2410.17098/) (2024)
- [H-GAN: the power of GANs in your Hands](https://mlsanity.com/paper/2103.15017/) (2021)
- [Learning from scarce information: using synthetic data to classify Roman fine ware pottery](https://mlsanity.com/paper/2107.01401/) (2021)
- [Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living](https://mlsanity.com/paper/2208.01910/) (2022)
- [Data Collection-free Masked Video Modeling](https://mlsanity.com/paper/2409.06665/) (2024)
- [Synthetic Human Action Video Data Generation with Pose Transfer](https://mlsanity.com/paper/2506.09411/) (2025)
- [Understanding Multimodal Complementarity for Single-Frame Action Anticipation](https://mlsanity.com/paper/2601.22039/) (2026)
- [GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition](https://mlsanity.com/paper/2401.13414/) (2024)
- [VR-based generation of photorealistic synthetic data for training hand-object tracking models](https://mlsanity.com/paper/2401.17874/) (2024)
- [AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection](https://mlsanity.com/paper/2505.04660/) (2025)
- [ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling](https://mlsanity.com/paper/2505.17987/) (2025)

## Abstract (arXiv)

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.