IoMT: Human Activity Recognition (HAR) via Wearables
Abstract
"Inertial sensor data (Accelerometer/Gyroscope) for detecting human activities like walking, sitting, and standing for health monitoring."
Description
This dataset tracks the movement of 30 volunteers wearing smartphones with embedded inertial sensors. It is a benchmark for Internet of Medical Things (IoMT) device development.
Sensor Data:
- Triaxial Acceleration: Total acceleration from the accelerometer.
- Triaxial Angular Velocity: Readings from the gyroscope.
- Feature Vector: 561 features with time and frequency domain variables.
ML Applications:
Ideal for Deep Learning (CNN/LSTM) classification tasks to identify physical health patterns and elderly fall detection.
Source: Smartlab - Non-Conventional Robotics and Artificial Intelligence.
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on External.
Preview on External
Cite This Dataset
External (2026). IoMT: Human Activity Recognition (HAR) via Wearables. [Dataset]. External. https://archive.ics.uci.edu/static/public/240/human+activity+recognition+using+smartphones.zip
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Original source: External (2026). Visit official page for more details.
Indexed by IoTDataset.com on Jan 06, 2026
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