Work

Human Daily Indoor Action (HDIA) Dataset: Privacy-Preserving Human Action Recognition Using Infrared Camera and Wearable Armband Sensors

Year: 2025

Type: article

Abstract: Human Activity Recognition (HAR) plays a vital role in applications such as healthcare, smart homes, and robotics, particularly in supporting the elderly. However, most existing HAR datasets focus on general human activities and are typically collected using RGB cameras in controlled environments with fixed angles—conditions that limit their real-world applicability. In this study, we introduce the Human Daily Indoor Actions (HDIA) dataset, specifically designed to capture natural indoor activities performed by elderly individuals. The dataset includes 48 daily actions, recorded using non-invasive infrared (IR) cameras and wearable armband sensors positioned at various angles to ensure diverse and realistic activity representation. The use of IR sensors enhances privacy, making the dataset ethically suitable for long-term monitoring. To demonstrate its utility, we implemented a fusion-based HAR model that integrates data from both IR and Inertial Measurement Unit (IMU) sensors. This model achieves strong activity recognition performance while minimizing the risk of identity exposure. By focusing on privacy-aware data collection and the daily routines of elderly individuals, the HDIA dataset offers a valuable resource for advancing real-world HAR research.

Source: IEEE Access

DOI: 10.1109/access.2025.3556001

Authors: Jongbum Park, Kyoung Ok Yang, Sunme Park, Jun Won Choi

Institutions: Hanyang University, Korea Electronics Technology Institute, Seoul National University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Ministry of Trade, Industry and Energy, Ministry of Science and ICT, South Korea