Abstract
"Multimodal body motion and vital sign recordings from 10 volunteers performing 12 physical activities, collected with three body-worn sensor units (chest, wrist, ankle) including 2‑lead ECG.[web:124][web:130][web:148][web:153]"
Description
Overview
The MHEALTH (Mobile Health) dataset was designed to benchmark human behavior analysis techniques using multimodal wearable sensing for mobile health applications.[web:124][web:130][web:153]
Data Collection
- Data were collected from 10 volunteers with diverse profiles while they performed 12 annotated physical activities, such as walking, jogging, running, cycling, standing, sitting, and jumping.[web:124][web:130][web:153]
- Three synchronized sensor units were placed on the chest, right wrist, and left ankle of each subject.[web:124][web:130][web:153]
- All sensing modalities were recorded at 50 Hz, which is sufficient to capture typical human movements.[web:124][web:130][web:153]
Signals and Variables
- Each unit records tri-axial acceleration, angular velocity (gyroscope), and magnetic field orientation, providing rich kinematic information for three body parts.[web:124][web:130][web:153]
- The chest unit additionally provides 2‑lead ECG, enabling basic heart monitoring and analysis of exercise effects on cardiac activity.[web:124][web:130][web:153]
- Data for each subject are stored in a dedicated log file (e.g.,
mHealth_subject<ID>.log) with rows corresponding to time samples and columns to sensor channels plus activity labels.[web:124][web:130][web:148]
Use Cases
- Wearable-based human activity recognition and behavior analysis with multiple body locations.[web:124][web:127][web:151]
- Mobile health applications combining motion and ECG for exercise monitoring and basic arrhythmia screening research.[web:124][web:148][web:150]
- Evaluation of multimodal sensor fusion methods for robust HAR on low-power IoT wearables.[web:124][web:149][web:151]
License and Terms
The UCI Machine Learning Repository notes that its datasets, including MHEALTH, are made available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, allowing sharing and adaptation with proper credit.[web:70][web:136][web:154]
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on Kaggle
Cite This Dataset
Banos, Oresti, Garcia, Rafael, & Saez, Alejandro (2014). MHEALTH (Mobile Health) Wearable Sensor Dataset. [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5TW22
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Original source: UCI Machine Learning Repository (2014). Visit official page for more details.
Indexed by IoTDataset.com on Jan 28, 2026
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