An IMU Dataset for Human Activity Recognition to Support Independent Living in Smart Homes (IMU-HAR-IL)

2026-06-22

AI Summary

This study addresses the lack of high-quality, multi-sensor inertial datasets for objectively assessing older adults’ capacity to live independently in smart home research. The authors present a large-scale IMU dataset collected from 50 participants performing 17 activities of daily living in real home environments, spanning four functional domains: mobility, hygiene, nutrition and hydration, and medication management. For the first time, the dataset systematically integrates 30 wearable and object-attached inertial measurement units, enabling high-temporal-resolution, synchronized multi-source data acquisition. Designed with an ecologically valid protocol and fine-grained annotation scheme, the dataset ensures clinical relevance and cross-sensor temporal consistency. It has already facilitated the development of high-accuracy models for activity recognition and functional domain classification, establishing a function-oriented benchmark for continuous behavioral monitoring, functional health assessment, and intelligent assistive living systems.

Abstract

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.

Problem

• Human Activity Recognition
• Independent Living
• Inertial Measurement Unit
• Smart Home
• Functional Assessment

Innovation

• IMU
• Human Activity Recognition
• Independent Living
• Smart Home
• Multi-sensor Synchronization