MotionSense: Smartphone IMU Dataset for HAR and Attribute Recognition [24 Subjects, 6 Activities]
Abstract
"iPhone 6s accelerometer and gyroscope dataset from 24 subjects performing 6 activities (walking, jogging, stairs up/down, sitting, standing) at 50 Hz. CSV. Kaggle and GitHub. Used for human activity recognition (HAR) and mobile sensor privacy research. Imperial College London, 2018."
Description
Overview
MotionSense was created by Mohammad Malekzadeh and colleagues at Imperial College London. It contains IMU data collected from Apple iPhone 6s smartphones placed in subjects' front pants pockets via the SensingKit framework (CoreMotion API on iOS). Twenty-four subjects of different genders, heights, weights, and ages performed 6 activities in 15 trials under the same environmental conditions: downstairs (dws), upstairs (ups), walking (wlk), jogging (jog), sitting (sit), and standing (std). Data was sampled at 50 Hz.
The sensor data includes: attitude (roll, pitch, yaw), gravity vector (x, y, z), user acceleration (x, y, z — gravity-removed), and rotation rate (x, y, z from gyroscope). The dataset targets two research questions simultaneously: activity recognition from inertial data, and the inadvertent leakage of sensitive personal attributes (gender, weight) from smartphone sensor signals — a mobile privacy concern.
Column Schema
| Column | Description |
|---|---|
| attitude.roll | Roll angle of the device (radians). |
| attitude.pitch | Pitch angle of the device (radians). |
| attitude.yaw | Yaw angle of the device (radians). |
| gravity.x / .y / .z | Gravity vector components (g). |
| userAcceleration.x / .y / .z | Body (gravity-removed) acceleration (g). |
| rotationRate.x / .y / .z | Gyroscope rotation rate (rad/s). |
| subject | Anonymised subject identifier (1–24). |
Key Statistics
- Subjects: 24 (13 male, 11 female)
- Activities: 6 (downstairs, upstairs, walking, jogging, sitting, standing)
- Sensors: Accelerometer + Gyroscope via iPhone 6s CoreMotion (iOS)
- Sampling Rate: 50 Hz
- File Format: CSV (one file per activity per subject)
- Published: 2018 — GitHub (github.com/mmalekzadeh/motion-sense) and Kaggle
Use Cases
- Human activity recognition (HAR) from smartphone IMU data
- User attribute inference (gender, weight) and mobile privacy research
- Federated learning for HAR across multiple users
- Gait and posture analysis from inertial sensors in everyday settings
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleCite This Dataset
Malekzadeh, Mohammad, Clegg, Richard G., Cavallaro, Andrea, & Haddadi, Hamed (2018). MotionSense: Smartphone IMU Dataset for HAR and Attribute Recognition [24 Subjects, 6 Activities]. Proceedings of the 2nd Workshop on Privacy by Design in Distributed Systems (EuroSys 2018). [Dataset]. Kaggle. https://www.kaggle.com/datasets/malekzadeh/motionsense-dataset
Source: Kaggle (2018)
Indexed by IoTDataset.com on May 10, 2026
Ready to Start Your Research?
Download this dataset directly from the official repository and start building your next breakthrough project.