# ETRI-Activity3D: A Large-Scale RGB-D Dataset for Robots to Recognize Daily Activities of the Elderly

arXiv: https://arxiv.org/abs/2003.01920 · HTML: https://mlsanity.com/paper/2003.01920/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 26 of 26)

- [Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition](https://mlsanity.com/paper/2112.10992/) (2021)
- [Hypergraph-based Multi-View Action Recognition using Event Cameras](https://mlsanity.com/paper/2403.19316/) (2024)
- [Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model](https://mlsanity.com/paper/2406.13987/) (2024)
- [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games](https://mlsanity.com/paper/2107.05617/) (2021)
- [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)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Anonymization for Skeleton Action Recognition](https://mlsanity.com/paper/2111.15129/) (2021)
- [ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization](https://mlsanity.com/paper/2208.09414/) (2022)
- [Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context](https://mlsanity.com/paper/2410.21275/) (2024)
- [From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision](https://mlsanity.com/paper/2501.05711/) (2025)
- [Action Anticipation at a Glimpse: To What Extent Can Multimodal Cues Replace Video?](https://mlsanity.com/paper/2512.02846/) (2025)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living](https://mlsanity.com/paper/2208.01910/) (2022)
- [ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition](https://mlsanity.com/paper/2310.14416/) (2023)
- [Recovering Complete Actions for Cross-dataset Skeleton Action Recognition](https://mlsanity.com/paper/2410.23641/) (2024)
- [Understanding Multimodal Complementarity for Single-Frame Action Anticipation](https://mlsanity.com/paper/2601.22039/) (2026)
- [Identifying Ethical Biases in Action Recognition Models](https://mlsanity.com/paper/2604.17971/) (2026)
- [Gesture Recognition with a Skeleton-Based Keyframe Selection Module](https://mlsanity.com/paper/2112.01736/) (2021)
- [REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in Video](https://mlsanity.com/paper/2401.11796/) (2024)
- [In-Bed Pose Estimation: A Review](https://mlsanity.com/paper/2402.00700/) (2024)
- [Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)](https://mlsanity.com/paper/2406.01662/) (2024)
- [Elderly Activity Recognition in the Wild: Results from the EAR Challenge](https://mlsanity.com/paper/2503.07821/) (2025)
- [KRAST: Knowledge-Augmented Robotic Action Recognition with Structured Text for Vision-Language Models](https://mlsanity.com/paper/2509.16452/) (2025)
- [PovNet+: A Deep Learning Architecture for Socially Assistive Robots to Learn and Assist with Multiple Activities of Daily Living](https://mlsanity.com/paper/2602.00131/) (2026)
- [One for All: Generalist Foundation Model for Cross-Sensor Skeleton Representation Learning](https://mlsanity.com/paper/2609.07078/) (2026)

## Abstract (arXiv)

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.