Synthetic Action Simulation Platform for Elderly Action Data Generation

The world’s elderly population growth emphasizes the necessity of eldercare technologies and underlines the role of action recognition tasks to comprehend elders’ activities of daily living. However, most public datasets used in human action recognition either differ from or have limited coverage of elders’ activities in many aspects. Moreover, data acquisition of elders’ ADL is challenging due to the privacy and physical limitations of the elderly.

We introduce ElderSim, a synthetic 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 several customizable data-generating options and provides several output modalities. We also provide KIST SynADL dataset which is generated from our simulation platform.



Unreal Engine 4

We chose the Unreal Engine 4 (UE4) as our main rendering engine for real-time photorealistic rendering.


We have modeled four residential houses based on their indoor measurements using Maya. The household background has become visually more realistic by using physics-based materials and the Post-Process Volume function of UE4.



Fifteen subjects, including thirteen elders, have been recruited to sufficiently represent a variety of body shapes and appearances. Their body shapes have been captured from Kinect and utilized to design the body shape of synthetic characters in Maya.



In ElderSim, we provide motions for 55 activity classes. Based on the Motion Capture data obtained from the subjects, motion data have been rigged in Maya. The motions for synthetic characters are then generated by adjusting the template’s kinematic parameters to those of each character and playing the constructed movements.

ID Action descripction ID Action descripction
1 eating food with a fork 29 hanging out laundry
2 pouring water into a cup 30 looking around for something
3 taking medicine 31 using a remote control
4 drinking water 32 reading a book
5   putting food in the fridge/taking food from the fridge    33 reading a newspaper
6 trimming vegetables 34 handwriting
7 peeling fruit 35 talking on the phone
8 using a gas stove 36 playing with a mobile phone
9 cutting vegetable on the cutting board 37 using a computer
10 brushing teeth 38 smoking
11 washing hands 39 clapping
12 washing face 40 rubbing face with hands
13 wiping face with a towel 41 doing freehand exercise
14 putting on cosmetics 42 doing neck roll exercise
15 putting on lipstick 43 massaging a shoulder oneself
16 brushing hair 44 taking a bow
17 blow drying hair 45 talking to each other
18 putting on a jacket 46 handshaking
19 taking off a jacket 47 hugging each other
20 putting on/taking off shoes 48 fighting each other
21 putting on/taking off glasses 49 waving a hand
22 washing the dishes 50      flapping a hand up and down (beckoning)     
23 vacuumming the floor 51 pointing with a finger
24 scrubbing the floor with a rag 52 opening the door and walking in
25 wipping off the dinning table 53 fallen on the floor
26 rubbing up furniture 54 sitting up/standing up
27 spreading bedding/folding bedding 55 lying down
28 washing a towel by hands    


ElderSim contains viewpoints reflecting eldercare applications. To implement the various viewpoints, we define UE4 splines that contain several vertical cameras and position these splines based on the user parameters.



To simulate the effect of sunlight over time, we utilize the SkySphere Blueprint function of UE4 and provide an adjustable time parameter in 100 levels to vary sunlight. Indoor light sources are placed according to lighting layouts of actual houses. Finer rendering effects are applied by the Post-Process Volume effect of UE4.



  • Customizable parameters
    ElderSim is provided with an intuitive graphical user interface (GUI) to select data-generating options as needed.
  • Large-scale labeled data generation
    The data are generated and recorded according to all possible combinations of options in ElderSim. The total number of samples can be calculated with a simple multiplication.
    Total Samples=actions×subjects×backgrounds×viewpoints×light conditions×objects
  • Various modality outputs
    ElderSim provides video resolutions and frame rates of up to 1920x1080 and 60 FPS, respectively. Modalities of RGB video, 2D, and 3D skeleton are provided.
  • KIST SynADL Dataset
    Based on the above developmental features of ElderSim, we generate KIST SynADL, a large-scale synthetic dataset of elders’ activities considering eldercare applications. KIST SynADL provides 462k RGB videos and 2D, 3D skeleton data, covering 55 action classes, 28 camera viewpoints, 15 characters, five lighting conditions, four backgrounds, and a single object.
Feature Number
Samples 462,200
Classes 55
Subjects 15
Viewpoints 28
Backgrounds 4
Objects 1


If you use either the platform or the dataset, please refer to the following technical report: PUBLICATION (arXiv)


● ElderSim

The ElderSim platform can be downloaded by clicking here.


The KIST SynADL dataset can be downloaded by clicking here.

Modality Details Format Size
RGB Video 640x360 resolution AVI 433 GB
2D Skeleton 25 joints (OpenPose) JSON 18.4 GB
2D Skeleton 25 joints (Kinect) JSON 24.4 GB
3D Skeleton 25 joints (OpenPose) JSON 18.5 GB
3D Skeleton 25 joints (Kinect) JSON 24.4 GB
    Total 518.7 GB


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  • This work was supported by the Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00162, Development of Human-care Robot Technology for Aging Society)