AI 中文总结
该研究发布了面向智能家居独立生活支持的HAR-IMU-IL IMU数据集，含50人17项日常活动数据，可用于开发HAR模型以支撑相关健康与智能应用。
AI 中文摘要
本文介绍了HAR-IMU-IL，这是一个为人体活动识别（HAR）开发的数据集，利用惯性测量单元（IMU）传感器在智能家居环境中开展研究，重点在于支持对老年人独立生活（IL）的客观功能评估。具体而言，HAR-IMU-IL包含50名参与者的记录，他们完成了17项与临床相关的日常生活活动，涵盖了独立生活所需的4个功能领域：移动性、卫生、营养与补水、服药。该数据集使用30个IMU传感器采集，包括集成在真实住宅环境中的可穿戴设备和物体安装式设备。数据集包含在现实、无约束条件下采集的多传感器惯性数据，以及确保跨传感器时间精度和一致性的详细标注。研究人员实施了全面的数据采集方案，以保留生态效度并实现可靠的多传感器同步。HAR-IMU-IL提供了一个大规模、基于功能的资源，用于推进和基准测试家庭环境中HAR的机器学习与人工智能方法。研究人员还通过开发模型展示了其实用性，这些模型能够利用可穿戴和物体安装式传感器准确识别活动及更广泛的功能领域。这些能力凸显了该数据集的潜力，可用于连续活动监测、功能健康评估、智能家居自动化及支持独立生活的辅助技术等应用场景。
英文摘要
This document introduces HAR-IMU-IL, a dataset developed for human activity recognition (HAR) using inertial measurement unit (IMU) sensors within a smart home environment with a focus to support objective functional assessment of older adults' independent living (IL). In particular, HAR-IMU-IL includes recordings of 50 participants performing 17 clinically relevant activities of daily living, spanning 4 functional domains essential for independent living: mobility, hygiene, nutrition and hydration, and medication intake. The dataset was collected using 30 IMU sensors, comprising both wearable and object-mounted devices integrated within a real-world residential setting. The dataset includes multi-sensor inertial data captured under realistic, unconstrained conditions, together with detailed annotations ensuring high temporal accuracy and consistency across sensors. A comprehensive data collection protocol was implemented to preserve ecological validity and enable reliable multi-sensor synchronisation. HAR-IMU-IL provides a large-scale, functionally grounded resource for advancing and benchmarking machine learning and artificial intelligence approaches for HAR in home settings. We further demonstrate its utility by developing models capable of accurately recognising both activities and broader functional domains using wearable and object-mounted sensors. These capabilities highlight the dataset's potential to enable applications in continuous activity monitoring, functional health assessment, smart home automation, and assistive technologies that support independent living.
Comments20 pages, 9 figures, 5 tables
