DisabledHAR: A Wearable Sensor Dataset for Human Activity Recognition of Disabled and Non-Disabled Individuals
Description
DisabledHAR is a multivariate time-series dataset designed for human activity recognition using wearable smartphone sensors, with a focus on individuals with physical disabilities. The dataset includes data collected from 20 participants, consisting of 10 non-disabled individuals and 10 individuals with various physical disabilities. Data were recorded using an iPhone 14 Pro smartphone worn vertically in a waist pouch. Motion and location signals were captured from the device’s internal sensors, including accelerometer, gyroscope, magnetometer, motion sensors, and GPS-derived data, at a sampling rate of 50 Hz. Participants performed six daily activities: walking, standing, sitting, jogging, upstairs, and downstairs. For safety reasons, the upstairs and downstairs activities were conducted on an inclined ramp with an 8% slope. Each activity was repeated multiple times to ensure consistent recordings. The dataset is organized as a time-series table in which each row corresponds to a single time-step. It contains 30 feature columns representing sensor signals, along with an activity label column, a binary disabled indicator where 0 denotes non-disabled participants and 1 denotes participants with disabilities, and a UserID column identifying each participant. DisabledHAR is intended to support research on machine learning and deep learning methods for human activity recognition, particularly in healthcare, rehabilitation, and assistive technology applications. All participants provided informed consent, and data collection was conducted under professional supervision with strict attention to safety and privacy.
Files
Institutions
- Islamic Azad University
- Islamic Azad University Mashhad Branch