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 ExternalCite 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
Source: External (2026)
Indexed by IoTDataset.com on Jan 06, 2026
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