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Abstract

Human activity recognition (HAR) with wearable sensors is widely applied in health monitoring, fitness tracking, and smart environments, but the choice of sensor configuration remains a critical factor for balancing recognition performance with usability and comfort. Existing datasets often lack the full-body coverage required to systematically evaluate sensor placement strategies. We present a comprehensive dataset of 12 daily activities performed by 30 participants, recorded using 17 inertial measurement units (IMUs) distributed across the entire body. Each IMU provides tri-axial acceleration and angular velocity signals at 60 Hz, aligned within a standardized global coordinate system. The dataset further includes detailed anthropometric metadata, structured annotations of activity and effort level, and processing scripts to support feature extraction, segmentation, and baseline model training. Benchmark experiments with both machine learning and deep learning models demonstrate the usability of the dataset across multiple temporal windows and sensor subsets. This resource enables systematic evaluation of sensor layout strategies and supports the development of practical, generalizable HAR systems.

Data availability

The dataset described in this study is available on Figshare29 (https://doi.org/10.6084/m9.figshare.30234940) under a non-commercial license. The repository contains CSV files for each participant’s trial and a CSV file providing anthropometric information.

Code availability

All project code including data segmentation, feature extraction, and model training process is released under a non-commercial license on the project’s repository at https://github.com/FudanBSRL/Comprehensive-IMU-Dataset.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 12532002) and the Shanghai Pilot Program for Basic Research - Fudan University, China (Grant No. 21TQ1400100- 22TQ009).

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Authors and Affiliations

College of Intelligent Robotics and Advanced Manufacturing, State Key Laboratory of Brain Function and Disorders, Fudan University, Shanghai, 200433, China

Mingfei Feng, Qiwei Zhang & Hongbin Fang 1.

Yiwu Research Institute, Fudan University, Yiwu, Zhejiang, 322000, China

Mingfei Feng, Qiwei Zhang & Hongbin Fang

Authors

1. Mingfei Feng
2. Qiwei Zhang
3. Hongbin Fang

Contributions

M.F. and H.F. designed the experimental protocol; M.F. performed data collection, annotation, technical validation, and drafted the manuscript. Q.Z. contributed to discussion of results and helped draft the manuscript; H.F. conceived of the study, supervised the study at all stages, commented on the approach and results, and critically revised the manuscript. All authors gave final approval for publication.

Corresponding author

Correspondence to Hongbin Fang.

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The authors declare no competing interests.

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Feng, M., Zhang, Q. & Fang, H. A comprehensive IMU dataset for evaluating sensor layouts in human activity and intensity recognition. Sci Data (2026). https://doi.org/10.1038/s41597-026-06710-9

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Received: 06 October 2025

Accepted: 24 January 2026

Published: 29 January 2026

DOI: https://doi.org/10.1038/s41597-026-06710-9